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Shijian Lu
Assistant Professor at Nanyang Technological University
accuracy and precision, actor, attention, calgary, cosine similarity, data, experiment, few-shot detection, future, heat, heat map, internet, map, novel, object detection, paper, plug and play, prayer, prototype, regression analysis, research, scale factor (cosmology), training, validation, and test sets, weight
Lingxi Xie
Senior Researcher at Huawei
alabaster, allstate, angle, bad romance, bolton, calculator, cattle, computer, conceptual model, correlation and dependence, cosine similarity, europe, factory, fade (audio engineering), food, france, invoice, jakarta, mechanics, perception, price, space, suicide, text messaging, trigonometric functions, universal video attack
Ash Ash
Assistant Professor of Law, Economics, and Data Science at ETH Zürich
computational social science , adjective, artificial intelligence, cluster analysis, cognition, computer vision, cosine similarity, digital humanities, document classification, emotion, encoding (memory), euclidean vector, hashtag, iraq war, k-means clustering, k-nearest neighbors algorithm, motivation, neuroscience, observation, qualitative research, question, rationale for the iraq war, redistribution of income and wealth, research, science, science research, scientific computing, scipy2021, software engineering, statistical classification, topic model, unsupervised learning, vector space, word embedding
George Yu
Software Engineer at Google
artificial neural network, attention, autoencoder, binary classification, computer science, convolutional neural network, cosine similarity, cross entropy, data compression, deeep learning, directory (computing), floating-point arithmetic, hash function, hyperparameter (machine learning), locality-sensitive hashing, neural structured learning, regularization (mathematics), semi-supervised learning, social media, softmax function, statistical classification, string (computer science), tensorflow, test (assessment), training, validation, and test sets, two-dimensional space, vertex (graph theory)
Chun-Ta Lu
Software Engineer at Google AI
artificial neural network, attention, autoencoder, binary classification, computer science, convolutional neural network, cosine similarity, cross entropy, data compression, deeep learning, directory (computing), floating-point arithmetic, hash function, hyperparameter (machine learning), locality-sensitive hashing, neural structured learning, regularization (mathematics), semi-supervised learning, social media, softmax function, statistical classification, string (computer science), tensorflow, test (assessment), training, validation, and test sets, two-dimensional space, vertex (graph theory)
Chun-Sung Ferng
Software Engineer at Google
artificial neural network, attention, autoencoder, binary classification, computer science, convolutional neural network, cosine similarity, cross entropy, data compression, deeep learning, directory (computing), floating-point arithmetic, hash function, hyperparameter (machine learning), locality-sensitive hashing, neural structured learning, regularization (mathematics), semi-supervised learning, social media, softmax function, statistical classification, string (computer science), tensorflow, test (assessment), training, validation, and test sets, two-dimensional space, vertex (graph theory)
Philip Pham
Senior Software Engineer at Google
artificial neural network, attention, autoencoder, binary classification, computer science, convolutional neural network, cosine similarity, cross entropy, data compression, deeep learning, directory (computing), floating-point arithmetic, hash function, hyperparameter (machine learning), locality-sensitive hashing, neural structured learning, regularization (mathematics), semi-supervised learning, social media, softmax function, statistical classification, string (computer science), tensorflow, test (assessment), training, validation, and test sets, two-dimensional space, vertex (graph theory)
Allan Heydon
Google at Staff Software Engineer
artificial neural network, attention, autoencoder, binary classification, computer science, convolutional neural network, cosine similarity, cross entropy, data compression, deeep learning, directory (computing), floating-point arithmetic, hash function, hyperparameter (machine learning), locality-sensitive hashing, neural structured learning, regularization (mathematics), semi-supervised learning, social media, softmax function, statistical classification, string (computer science), tensorflow, test (assessment), training, validation, and test sets, two-dimensional space, vertex (graph theory)
Arjun Gopalan
Senior Software Engineer at Google AI
artificial neural network, attention, autoencoder, binary classification, computer science, convolutional neural network, cosine similarity, cross entropy, data compression, deeep learning, directory (computing), floating-point arithmetic, hash function, hyperparameter (machine learning), locality-sensitive hashing, neural structured learning, regularization (mathematics), semi-supervised learning, social media, softmax function, statistical classification, string (computer science), tensorflow, test (assessment), training, validation, and test sets, two-dimensional space, vertex (graph theory)
Da-Cheng Juan
Senior Software Engineer at Google
#tfworld, ai, api, artificial intelligence, artificial neural network, attention, autoencoder, backpropagation, binary classification, book, color, computer, computer science, computer vision, convolutional neural network, cosine similarity, cross entropy, da-cheng juan, data compression, deeep learning, deep learning, directory (computing), document classification, experiment, floating-point arithmetic, google, google ai, hash function, hyperparameter (machine learning), image segmentation, information retrieval, inheritance (object-oriented programming), knowledge graph, library (computing), locality-sensitive hashing, machine learning, mathematical optimization, ml, nearest neighbor search, neural structured learning, neural structured learning in tensorflow, neural structured learning in tf, o'reilly, o'reilly tensorflow world, prediction, regularization (mathematics), santa clara convention center, semi-supervised learning, social media, softmax function, software framework, statistical classification, string (computer science), sujith ravi, tensorflow, tensorflow 2.0, tensorflow world, tensorflow world 19, tensorflow world 2019, test (assessment), tf world, tf world 19, tf world 2019, tfworld, tfworld 19, tfworld 2019, training, validation, and test sets, two-dimensional space, vertex (graph theory), visual perception
Cesar Ilharco Magalhaes
Research Engineer at Google
artificial neural network, attention, autoencoder, binary classification, computer science, convolutional neural network, cosine similarity, cross entropy, data compression, deeep learning, directory (computing), floating-point arithmetic, hash function, hyperparameter (machine learning), locality-sensitive hashing, neural structured learning, regularization (mathematics), semi-supervised learning, social media, softmax function, statistical classification, string (computer science), tensorflow, test (assessment), training, validation, and test sets, two-dimensional space, vertex (graph theory)
Yijia Wang
Analyst at Parsons School of Design
ads recommendation, advertising, algorithm, application software, attention, bit, censorship, china, christianity, click-through rate, common technologies for feed and ads recommendation, computer, computer data storage, computer network, cosine similarity, decentralised system, encryption, equation, feed recommendation , finite set, freedom of the press, function (mathematics), image understanding, infrastructure, introduction - feed, ads, search and spam, javascript, letter case, linear combination, logic, mixture model, multimedia, multimedia infrastructure, multimedia search, multivariate random variable, nicotine, online and offline, prediction, risk, safeway inc., scraper (archaeology), simulation, social media, softmax function, software , source lines of code, speech recognition, summation, telegram (software), tool, trust (social science), url, video, video representations used in production, video search engine, video understanding, virtual private network
Liang Zhang
Global Sales Strategy & Operations at LinkedIn
before deep learning , artificial intelligence , artificial intelligence tutorial, computer science conference , feed and ads modeling, introduction to multimedia, visio-lingual representations, 3d cnns, accent (sociolinguistics), accuracy and precision, acm 2020 tutorial, acoustic model, action recognition, ads recommendation, advertising, algorithm, angle, apache hadoop, apache spark, api, application software, applications at linkedin, array data structure, array data type, art, artificial intelligence tutorial, artificial neural network, assembly language, association for computing machinery, attention, attention (machine learning), average, baby talk, bag-of-words model, bangalore, beef, before deep learning , bible, bichon frise, cartesian coordinate system, castle, central processing unit, click-through rate, closed captioning, code-division multiple access, codec, coherence (physics), color, common technologies for feed and ads recommendation, compass, computer, computer graphics, computer performance, computer science tutorial, computer science tutorials, computer vision, computer vision 2020, computing, concept, convolution, convolutional neural network, cosine similarity, data, data compression, data conversion, data science tutorial, data type, debugging, deconvolution, deep learning, deep learning and cnns, deep learnning, deep learnning tutorial, definition, depiction, design, desk, diagram, diamond, digital image processing, dimension, dsgmm and deep cluster-and-aggregate method, dynamic programming, education, email spam, energy, engine, enzyme kinetics, equation, essay, euclidean vector, experiment, extract, transform, load, feature (machine learning), feature extraction, feature learning, feed recommendation , file system, film frame, finite set, function (mathematics), gas, gender, gradient, grayscale, hidden markov model, histogram, history, hyperparameter optimization, image embeddings, image representations , image segmentation, image understanding, imagenet, improvements on 3d cnn, improvements on 3d cnns and two-stream, infection, information, information retrieval, input/output, inspection, instagram, intelligence, interface (computing), internet, introduction - feed, ads, search and spam, inverted index, k-means clustering, kdd2020 tutorials, language, language model, learning, lecture-style tutorials, letter case, library (computing), likelihood function, linear combination, linearity, linkedin, literature, loader (computing), logic, long short-term memory, machine, machine learning, map, markov chain, markov model, mathematical optimization, matrix (mathematics), mean, meme, memory, metric learning for images, metric space, microsoft, mixture model, mobile app, monotonic function, motivation, moving average, mp3, multimedia, multimedia infrastructure, multimedia search, multimodality, multivariate random variable, music, nature, navigation, neural network, news, non-local networks and slowfast, nothing, number, object detection, online and offline, optical character recognition, optical flow, optimization for cnns, oracle corporation, parameter, parameter (computer programming), parity bit, phoneme, pixel, plasterwork, podcast, precision and recall, prediction, preprocessor, protein–protein interaction, python (programming language), radio, reason, recommender system, recurrent neural network, research, robust statistics, satya nadella, search algorithm, search engine, search engine indexing, self-supervised learning, self-supervised video embeddings, sense, signal, similarity learning, simulation, sms, social media, social networking service, softmax function, software , source lines of code, space, spam detection, spamming, spectral density, spectrogram, speech, speech recognition, speech technologies for video understanding, spotify, statistical classification, statistics, streaming media, string (computer science), subroutine, summation, supervised learning, support-vector machine, temporal topic localization, tensor, texas, three-dimensional space, training, validation, and test sets, transcription (linguistics), transformer (machine learning model), transmission (mechanics), truck, two-stream networks, typing, understanding, unsupervised learning, upload, use case, variance, vector graphics, vector space, video, video captioning, video classification, video embeddings and networks, video game, video game console, video game live streaming, video representations used in production, video search, video search engine, video understanding, weight, youtube
Di Wen
Staff Software Engineer at LinkedIn
before deep learning , artificial intelligence , artificial intelligence tutorial, computer science conference , feed and ads modeling, introduction to multimedia, visio-lingual representations, 3d cnns, accent (sociolinguistics), accuracy and precision, acm 2020 tutorial, acoustic model, action recognition, ads recommendation, advertising, algorithm, angle, apache hadoop, api, application software, applications at linkedin, art, artificial intelligence tutorial, artificial neural network, association for computing machinery, attention, attention (machine learning), average, baby talk, bag-of-words model, bangalore, beef, before deep learning , bible, bichon frise, cartesian coordinate system, castle, central processing unit, click-through rate, closed captioning, code-division multiple access, codec, coherence (physics), common technologies for feed and ads recommendation, compass, computer, computer graphics, computer performance, computer science tutorial, computer science tutorials, computer vision, computer vision 2020, computing, concept, convolution, convolutional neural network, cosine similarity, data, data compression, data science tutorial, deconvolution, deep learning, deep learning and cnns, deep learnning, deep learnning tutorial, definition, depiction, design, desk, diagram, diamond, digital image processing, dimension, dsgmm and deep cluster-and-aggregate method, dynamic programming, education, email spam, energy, engine, enzyme kinetics, equation, essay, euclidean vector, experiment, extract, transform, load, feature (machine learning), feature extraction, feature learning, feed recommendation , film frame, finite set, function (mathematics), gas, gender, gradient, grayscale, hidden markov model, histogram, history, hyperparameter optimization, image embeddings, image representations , image segmentation, image understanding, imagenet, improvements on 3d cnn, improvements on 3d cnns and two-stream, infection, information, information retrieval, input/output, inspection, instagram, intelligence, interface (computing), internet, introduction - feed, ads, search and spam, inverted index, k-means clustering, kdd2020 tutorials, language, language model, learning, lecture-style tutorials, letter case, likelihood function, linear combination, linearity, linkedin, literature, logic, long short-term memory, machine, machine learning, map, markov chain, markov model, mathematical optimization, matrix (mathematics), mean, meme, memory, metric learning for images, metric space, microsoft, mixture model, mobile app, monotonic function, motivation, moving average, mp3, multimedia, multimedia infrastructure, multimedia search, multimodality, multivariate random variable, music, nature, navigation, neural network, news, non-local networks and slowfast, nothing, number, object detection, online and offline, optical character recognition, optical flow, optimization for cnns, oracle corporation, parameter, parity bit, phoneme, pixel, plasterwork, podcast, precision and recall, prediction, protein–protein interaction, radio, reason, recommender system, recurrent neural network, research, robust statistics, satya nadella, search algorithm, search engine, search engine indexing, self-supervised learning, self-supervised video embeddings, sense, signal, similarity learning, simulation, sms, social media, social networking service, softmax function, software , space, spam detection, spamming, spectral density, spectrogram, speech, speech recognition, speech technologies for video understanding, spotify, statistical classification, statistics, streaming media, summation, supervised learning, support-vector machine, temporal topic localization, texas, three-dimensional space, transcription (linguistics), transmission (mechanics), truck, two-stream networks, typing, understanding, unsupervised learning, upload, use case, variance, vector graphics, vector space, video, video captioning, video classification, video embeddings and networks, video game, video game console, video game live streaming, video representations used in production, video search, video search engine, video understanding, weight, youtube
Bharat Jain
Data Scientist at LinkedIn
before deep learning , artificial intelligence , artificial intelligence tutorial, computer science conference , feed and ads modeling, introduction to multimedia, visio-lingual representations, 3d cnns, accent (sociolinguistics), accuracy and precision, acm 2020 tutorial, acoustic model, action recognition, ads recommendation, advertising, algorithm, angle, apache hadoop, api, application software, applications at linkedin, art, artificial intelligence tutorial, artificial neural network, association for computing machinery, attention, attention (machine learning), average, baby talk, bag-of-words model, bangalore, beef, before deep learning , bible, bichon frise, cartesian coordinate system, castle, central processing unit, click-through rate, closed captioning, code-division multiple access, codec, coherence (physics), common technologies for feed and ads recommendation, compass, computer, computer graphics, computer performance, computer science tutorial, computer science tutorials, computer vision, computer vision 2020, computing, concept, convolution, convolutional neural network, cosine similarity, data, data compression, data science tutorial, deconvolution, deep learning, deep learning and cnns, deep learnning, deep learnning tutorial, definition, depiction, design, desk, diagram, diamond, digital image processing, dimension, dsgmm and deep cluster-and-aggregate method, dynamic programming, education, email spam, energy, engine, enzyme kinetics, equation, essay, euclidean vector, experiment, extract, transform, load, feature (machine learning), feature extraction, feature learning, feed recommendation , film frame, finite set, function (mathematics), gas, gender, gradient, grayscale, hidden markov model, histogram, history, hyperparameter optimization, image embeddings, image representations , image segmentation, image understanding, imagenet, improvements on 3d cnn, improvements on 3d cnns and two-stream, infection, information, information retrieval, input/output, inspection, instagram, intelligence, interface (computing), internet, introduction - feed, ads, search and spam, inverted index, k-means clustering, kdd2020 tutorials, language, language model, learning, lecture-style tutorials, letter case, likelihood function, linear combination, linearity, linkedin, literature, logic, long short-term memory, machine, machine learning, map, markov chain, markov model, mathematical optimization, matrix (mathematics), mean, meme, memory, metric learning for images, metric space, microsoft, mixture model, mobile app, monotonic function, motivation, moving average, mp3, multimedia, multimedia infrastructure, multimedia search, multimodality, multivariate random variable, music, nature, navigation, neural network, news, non-local networks and slowfast, nothing, number, object detection, online and offline, optical character recognition, optical flow, optimization for cnns, oracle corporation, parameter, parity bit, phoneme, pixel, plasterwork, podcast, precision and recall, prediction, protein–protein interaction, radio, reason, recommender system, recurrent neural network, research, robust statistics, satya nadella, search algorithm, search engine, search engine indexing, self-supervised learning, self-supervised video embeddings, sense, signal, similarity learning, simulation, sms, social media, social networking service, softmax function, software , space, spam detection, spamming, spectral density, spectrogram, speech, speech recognition, speech technologies for video understanding, spotify, statistical classification, statistics, streaming media, summation, supervised learning, support-vector machine, temporal topic localization, texas, three-dimensional space, transcription (linguistics), transmission (mechanics), truck, two-stream networks, typing, understanding, unsupervised learning, upload, use case, variance, vector graphics, vector space, video, video captioning, video classification, video embeddings and networks, video game, video game console, video game live streaming, video representations used in production, video search, video search engine, video understanding, weight, youtube
Nikita Gupta
Senior Applied Research Engineer at LinkedIn
before deep learning , artificial intelligence , artificial intelligence tutorial, computer science conference , feed and ads modeling, introduction to multimedia, visio-lingual representations, 3d cnns, accent (sociolinguistics), accuracy and precision, acm 2020 tutorial, acoustic model, action recognition, ads recommendation, advertising, algorithm, angle, apache hadoop, api, application software, applications at linkedin, art, artificial intelligence tutorial, artificial neural network, association for computing machinery, attention, attention (machine learning), average, baby talk, bag-of-words model, bangalore, beef, before deep learning , bible, bichon frise, cartesian coordinate system, castle, central processing unit, click-through rate, closed captioning, code-division multiple access, codec, coherence (physics), common technologies for feed and ads recommendation, compass, computer, computer graphics, computer performance, computer science tutorial, computer science tutorials, computer vision, computer vision 2020, computing, concept, convolution, convolutional neural network, cosine similarity, data, data compression, data science tutorial, deconvolution, deep learning, deep learning and cnns, deep learnning, deep learnning tutorial, definition, depiction, design, desk, diagram, diamond, digital image processing, dimension, dsgmm and deep cluster-and-aggregate method, dynamic programming, education, email spam, energy, engine, enzyme kinetics, equation, essay, euclidean vector, experiment, extract, transform, load, feature (machine learning), feature extraction, feature learning, feed recommendation , film frame, finite set, function (mathematics), gas, gender, gradient, grayscale, hidden markov model, histogram, history, hyperparameter optimization, image embeddings, image representations , image segmentation, image understanding, imagenet, improvements on 3d cnn, improvements on 3d cnns and two-stream, infection, information, information retrieval, input/output, inspection, instagram, intelligence, interface (computing), internet, introduction - feed, ads, search and spam, inverted index, k-means clustering, kdd2020 tutorials, language, language model, learning, lecture-style tutorials, letter case, likelihood function, linear combination, linearity, linkedin, literature, logic, long short-term memory, machine, machine learning, map, markov chain, markov model, mathematical optimization, matrix (mathematics), mean, meme, memory, metric learning for images, metric space, microsoft, mixture model, mobile app, monotonic function, motivation, moving average, mp3, multimedia, multimedia infrastructure, multimedia search, multimodality, multivariate random variable, music, nature, navigation, neural network, news, non-local networks and slowfast, nothing, number, object detection, online and offline, optical character recognition, optical flow, optimization for cnns, oracle corporation, parameter, parity bit, phoneme, pixel, plasterwork, podcast, precision and recall, prediction, protein–protein interaction, radio, reason, recommender system, recurrent neural network, research, robust statistics, satya nadella, search algorithm, search engine, search engine indexing, self-supervised learning, self-supervised video embeddings, sense, signal, similarity learning, simulation, sms, social media, social networking service, softmax function, software , space, spam detection, spamming, spectral density, spectrogram, speech, speech recognition, speech technologies for video understanding, spotify, statistical classification, statistics, streaming media, summation, supervised learning, support-vector machine, temporal topic localization, texas, three-dimensional space, transcription (linguistics), transmission (mechanics), truck, two-stream networks, typing, understanding, unsupervised learning, upload, use case, variance, vector graphics, vector space, video, video captioning, video classification, video embeddings and networks, video game, video game console, video game live streaming, video representations used in production, video search, video search engine, video understanding, weight, youtube
Suhit Sinha
Senior Applied Research Engineer at LinkedIn
before deep learning , artificial intelligence , artificial intelligence tutorial, computer science conference , feed and ads modeling, introduction to multimedia, visio-lingual representations, 3d cnns, accent (sociolinguistics), accuracy and precision, acm 2020 tutorial, acoustic model, action recognition, ads recommendation, advertising, algorithm, angle, apache hadoop, api, application software, applications at linkedin, art, artificial intelligence tutorial, artificial neural network, association for computing machinery, attention, attention (machine learning), average, baby talk, bag-of-words model, bangalore, beef, before deep learning , bible, bichon frise, cartesian coordinate system, castle, central processing unit, click-through rate, closed captioning, code-division multiple access, codec, coherence (physics), common technologies for feed and ads recommendation, compass, computer, computer graphics, computer performance, computer science tutorial, computer science tutorials, computer vision, computer vision 2020, computing, concept, convolution, convolutional neural network, cosine similarity, data, data compression, data science tutorial, deconvolution, deep learning, deep learning and cnns, deep learnning, deep learnning tutorial, definition, depiction, design, desk, diagram, diamond, digital image processing, dimension, dsgmm and deep cluster-and-aggregate method, dynamic programming, education, email spam, energy, engine, enzyme kinetics, equation, essay, euclidean vector, experiment, extract, transform, load, feature (machine learning), feature extraction, feature learning, feed recommendation , film frame, finite set, function (mathematics), gas, gender, gradient, grayscale, hidden markov model, histogram, history, hyperparameter optimization, image embeddings, image representations , image segmentation, image understanding, imagenet, improvements on 3d cnn, improvements on 3d cnns and two-stream, infection, information, information retrieval, input/output, inspection, instagram, intelligence, interface (computing), internet, introduction - feed, ads, search and spam, inverted index, k-means clustering, kdd2020 tutorials, language, language model, learning, lecture-style tutorials, letter case, likelihood function, linear combination, linearity, linkedin, literature, logic, long short-term memory, machine, machine learning, map, markov chain, markov model, mathematical optimization, matrix (mathematics), mean, meme, memory, metric learning for images, metric space, microsoft, mixture model, mobile app, monotonic function, motivation, moving average, mp3, multimedia, multimedia infrastructure, multimedia search, multimodality, multivariate random variable, music, nature, navigation, neural network, news, non-local networks and slowfast, nothing, number, object detection, online and offline, optical character recognition, optical flow, optimization for cnns, oracle corporation, parameter, parity bit, phoneme, pixel, plasterwork, podcast, precision and recall, prediction, protein–protein interaction, radio, reason, recommender system, recurrent neural network, research, robust statistics, satya nadella, search algorithm, search engine, search engine indexing, self-supervised learning, self-supervised video embeddings, sense, signal, similarity learning, simulation, sms, social media, social networking service, softmax function, software , space, spam detection, spamming, spectral density, spectrogram, speech, speech recognition, speech technologies for video understanding, spotify, statistical classification, statistics, streaming media, summation, supervised learning, support-vector machine, temporal topic localization, texas, three-dimensional space, transcription (linguistics), transmission (mechanics), truck, two-stream networks, typing, understanding, unsupervised learning, upload, use case, variance, vector graphics, vector space, video, video captioning, video classification, video embeddings and networks, video game, video game console, video game live streaming, video representations used in production, video search, video search engine, video understanding, weight, youtube
Sumit Srivastava
Staff Applied Research Engineer at LinkedIn
before deep learning , artificial intelligence , artificial intelligence tutorial, computer science conference , feed and ads modeling, introduction to multimedia, visio-lingual representations, 3d cnns, accent (sociolinguistics), accuracy and precision, acm 2020 tutorial, acoustic model, action recognition, ads recommendation, advertising, algorithm, angle, apache hadoop, api, application software, applications at linkedin, art, artificial intelligence tutorial, artificial neural network, association for computing machinery, attention, attention (machine learning), average, baby talk, bag-of-words model, bangalore, beef, before deep learning , bible, bichon frise, cartesian coordinate system, castle, central processing unit, click-through rate, closed captioning, code-division multiple access, codec, coherence (physics), common technologies for feed and ads recommendation, compass, computer, computer graphics, computer performance, computer science tutorial, computer science tutorials, computer vision, computer vision 2020, computing, concept, convolution, convolutional neural network, cosine similarity, data, data compression, data science tutorial, deconvolution, deep learning, deep learning and cnns, deep learnning, deep learnning tutorial, definition, depiction, design, desk, diagram, diamond, digital image processing, dimension, dsgmm and deep cluster-and-aggregate method, dynamic programming, education, email spam, energy, engine, enzyme kinetics, equation, essay, euclidean vector, experiment, extract, transform, load, feature (machine learning), feature extraction, feature learning, feed recommendation , film frame, finite set, function (mathematics), gas, gender, gradient, grayscale, hidden markov model, histogram, history, hyperparameter optimization, image embeddings, image representations , image segmentation, image understanding, imagenet, improvements on 3d cnn, improvements on 3d cnns and two-stream, infection, information, information retrieval, input/output, inspection, instagram, intelligence, interface (computing), internet, introduction - feed, ads, search and spam, inverted index, k-means clustering, kdd2020 tutorials, language, language model, learning, lecture-style tutorials, letter case, likelihood function, linear combination, linearity, linkedin, literature, logic, long short-term memory, machine, machine learning, map, markov chain, markov model, mathematical optimization, matrix (mathematics), mean, meme, memory, metric learning for images, metric space, microsoft, mixture model, mobile app, monotonic function, motivation, moving average, mp3, multimedia, multimedia infrastructure, multimedia search, multimodality, multivariate random variable, music, nature, navigation, neural network, news, non-local networks and slowfast, nothing, number, object detection, online and offline, optical character recognition, optical flow, optimization for cnns, oracle corporation, parameter, parity bit, phoneme, pixel, plasterwork, podcast, precision and recall, prediction, protein–protein interaction, radio, reason, recommender system, recurrent neural network, research, robust statistics, satya nadella, search algorithm, search engine, search engine indexing, self-supervised learning, self-supervised video embeddings, sense, signal, similarity learning, simulation, sms, social media, social networking service, softmax function, software , space, spam detection, spamming, spectral density, spectrogram, speech, speech recognition, speech technologies for video understanding, spotify, statistical classification, statistics, streaming media, summation, supervised learning, support-vector machine, temporal topic localization, texas, three-dimensional space, transcription (linguistics), transmission (mechanics), truck, two-stream networks, typing, understanding, unsupervised learning, upload, use case, variance, vector graphics, vector space, video, video captioning, video classification, video embeddings and networks, video game, video game console, video game live streaming, video representations used in production, video search, video search engine, video understanding, weight, youtube
Sirjan Kafle
Senior Machine Learning Engineer, Multimedia AI at LinkedIn
before deep learning , artificial intelligence , artificial intelligence tutorial, computer science conference , feed and ads modeling, introduction to multimedia, visio-lingual representations, 3d cnns, accent (sociolinguistics), accuracy and precision, acm 2020 tutorial, acoustic model, action recognition, ads recommendation, advertising, algorithm, angle, apache hadoop, api, application software, applications at linkedin, art, artificial intelligence tutorial, artificial neural network, association for computing machinery, attention, attention (machine learning), average, baby talk, bag-of-words model, bangalore, beef, before deep learning , bible, bichon frise, cartesian coordinate system, castle, central processing unit, click-through rate, closed captioning, code-division multiple access, codec, coherence (physics), common technologies for feed and ads recommendation, compass, computer, computer graphics, computer performance, computer science tutorial, computer science tutorials, computer vision, computer vision 2020, computing, concept, convolution, convolutional neural network, cosine similarity, data, data compression, data science tutorial, deconvolution, deep learning, deep learning and cnns, deep learnning, deep learnning tutorial, definition, depiction, design, desk, diagram, diamond, digital image processing, dimension, dsgmm and deep cluster-and-aggregate method, dynamic programming, education, email spam, energy, engine, enzyme kinetics, equation, essay, euclidean vector, experiment, extract, transform, load, feature (machine learning), feature extraction, feature learning, feed recommendation , film frame, finite set, function (mathematics), gas, gender, gradient, grayscale, hidden markov model, histogram, history, hyperparameter optimization, image embeddings, image representations , image segmentation, image understanding, imagenet, improvements on 3d cnn, improvements on 3d cnns and two-stream, infection, information, information retrieval, input/output, inspection, instagram, intelligence, interface (computing), internet, introduction - feed, ads, search and spam, inverted index, k-means clustering, kdd2020 tutorials, language, language model, learning, lecture-style tutorials, letter case, likelihood function, linear combination, linearity, linkedin, literature, logic, long short-term memory, machine, machine learning, map, markov chain, markov model, mathematical optimization, matrix (mathematics), mean, meme, memory, metric learning for images, metric space, microsoft, mixture model, mobile app, monotonic function, motivation, moving average, mp3, multimedia, multimedia infrastructure, multimedia search, multimodality, multivariate random variable, music, nature, navigation, neural network, news, non-local networks and slowfast, nothing, number, object detection, online and offline, optical character recognition, optical flow, optimization for cnns, oracle corporation, parameter, parity bit, phoneme, pixel, plasterwork, podcast, precision and recall, prediction, protein–protein interaction, radio, reason, recommender system, recurrent neural network, research, robust statistics, satya nadella, search algorithm, search engine, search engine indexing, self-supervised learning, self-supervised video embeddings, sense, signal, similarity learning, simulation, sms, social media, social networking service, softmax function, software , space, spam detection, spamming, spectral density, spectrogram, speech, speech recognition, speech technologies for video understanding, spotify, statistical classification, statistics, streaming media, summation, supervised learning, support-vector machine, temporal topic localization, texas, three-dimensional space, transcription (linguistics), transmission (mechanics), truck, two-stream networks, typing, understanding, unsupervised learning, upload, use case, variance, vector graphics, vector space, video, video captioning, video classification, video embeddings and networks, video game, video game console, video game live streaming, video representations used in production, video search, video search engine, video understanding, weight, youtube
Aman Gupta
Senior Machine Learning Scientist at LinkedIn Multimedia AI
before deep learning , artificial intelligence , artificial intelligence tutorial, computer science conference , feed and ads modeling, introduction to multimedia, visio-lingual representations, 3d cnns, accent (sociolinguistics), accuracy and precision, acm 2020 tutorial, acoustic model, action recognition, ads recommendation, advertising, algorithm, angle, apache hadoop, api, application software, applications at linkedin, art, artificial intelligence tutorial, artificial neural network, association for computing machinery, attention, attention (machine learning), average, baby talk, bag-of-words model, bangalore, beef, before deep learning , bible, bichon frise, cartesian coordinate system, castle, central processing unit, click-through rate, closed captioning, code-division multiple access, codec, coherence (physics), common technologies for feed and ads recommendation, compass, computer, computer graphics, computer performance, computer science tutorial, computer science tutorials, computer vision, computer vision 2020, computing, concept, convolution, convolutional neural network, cosine similarity, data, data compression, data science tutorial, deconvolution, deep learning, deep learning and cnns, deep learnning, deep learnning tutorial, definition, depiction, design, desk, diagram, diamond, digital image processing, dimension, dsgmm and deep cluster-and-aggregate method, dynamic programming, education, email spam, energy, engine, enzyme kinetics, equation, essay, euclidean vector, experiment, extract, transform, load, feature (machine learning), feature extraction, feature learning, feed recommendation , film frame, finite set, function (mathematics), gas, gender, gradient, grayscale, hidden markov model, histogram, history, hyperparameter optimization, image embeddings, image representations , image segmentation, image understanding, imagenet, improvements on 3d cnn, improvements on 3d cnns and two-stream, infection, information, information retrieval, input/output, inspection, instagram, intelligence, interface (computing), internet, introduction - feed, ads, search and spam, inverted index, k-means clustering, kdd2020 tutorials, language, language model, learning, lecture-style tutorials, letter case, likelihood function, linear combination, linearity, linkedin, literature, logic, long short-term memory, machine, machine learning, map, markov chain, markov model, mathematical optimization, matrix (mathematics), mean, meme, memory, metric learning for images, metric space, microsoft, mixture model, mobile app, monotonic function, motivation, moving average, mp3, multimedia, multimedia infrastructure, multimedia search, multimodality, multivariate random variable, music, nature, navigation, neural network, news, non-local networks and slowfast, nothing, number, object detection, online and offline, optical character recognition, optical flow, optimization for cnns, oracle corporation, parameter, parity bit, phoneme, pixel, plasterwork, podcast, precision and recall, prediction, protein–protein interaction, radio, reason, recommender system, recurrent neural network, research, robust statistics, satya nadella, search algorithm, search engine, search engine indexing, self-supervised learning, self-supervised video embeddings, sense, signal, similarity learning, simulation, sms, social media, social networking service, softmax function, software , space, spam detection, spamming, spectral density, spectrogram, speech, speech recognition, speech technologies for video understanding, spotify, statistical classification, statistics, streaming media, summation, supervised learning, support-vector machine, temporal topic localization, texas, three-dimensional space, transcription (linguistics), transmission (mechanics), truck, two-stream networks, typing, understanding, unsupervised learning, upload, use case, variance, vector graphics, vector space, video, video captioning, video classification, video embeddings and networks, video game, video game console, video game live streaming, video representations used in production, video search, video search engine, video understanding, weight, youtube
Ananth Sankar
Principal Staff Engineer at LinkedIn
before deep learning , artificial intelligence , artificial intelligence tutorial, computer science conference , feed and ads modeling, introduction to multimedia, visio-lingual representations, 3d cnns, accent (sociolinguistics), accuracy and precision, acm 2020 tutorial, acoustic model, action recognition, ads recommendation, advertising, ai, algorithm, angle, apache hadoop, api, application software, applications at linkedin, art, artificial intelligence tutorial, artificial neural network, association for computing machinery, attention, attention (machine learning), automatic summarization, average, baby talk, backpropagation, bag-of-words model, bangalore, beef, before deep learning , bible, bichon frise, black box, cartesian coordinate system, castle, central processing unit, click-through rate, closed captioning, code-division multiple access, codec, coherence (physics), common technologies for feed and ads recommendation, compass, computer, computer graphics, computer performance, computer science tutorial, computer science tutorials, computer vision, computer vision 2020, computing, concept, convolution, convolutional neural network, cosine similarity, cross entropy, data, data compression, data science, data science tutorial, deconvolution, deep learning, deep learning and cnns, deep learnning, deep learnning tutorial, definition, depiction, design, desk, diagram, diamond, digital image processing, dimension, dsgmm and deep cluster-and-aggregate method, dynamic programming, education, email spam, encoding (memory), energy, engine, english language, enzyme kinetics, equation, essay, euclidean vector, experiment, extract, transform, load, feature (machine learning), feature extraction, feature learning, feed recommendation , feedforward neural network, film frame, finite set, four-dimensional space, function (mathematics), gas, gender, gradient, grayscale, hidden markov model, histogram, history, hyperparameter optimization, image embeddings, image representations , image segmentation, image understanding, imagenet, improvements on 3d cnn, improvements on 3d cnns and two-stream, infection, information, information retrieval, input/output, inspection, instagram, intelligence, interface (computing), internet, introduction - feed, ads, search and spam, inverted index, k-means clustering, kdd2020 tutorials, language, language model, learning, lecture-style tutorials, letter case, likelihood function, linear combination, linearity, linkedin, literature, logic, long short-term memory, machine, machine learning, map, markov chain, markov model, mathematical optimization, matrix (mathematics), mean, meme, memory, metric learning for images, metric space, microsoft, mind, mixture model, mobile app, monotonic function, motivation, moving average, mp3, multimedia, multimedia infrastructure, multimedia search, multimodality, multivariate random variable, music, natural language processing, nature, navigation, neural network, neural networks, news, non-local networks and slowfast, nothing, number, object detection, odsc, odsc india, online and offline, optical character recognition, optical flow, optimization for cnns, oracle corporation, parameter, parity bit, phoneme, pixel, plasterwork, podcast, precision and recall, prediction, protein–protein interaction, radio, reason, recommender system, recurrent neural network, research, robust statistics, satya nadella, search algorithm, search engine, search engine indexing, self-supervised learning, self-supervised video embeddings, semantics, sense, signal, similarity learning, simulation, sms, social media, social networking service, softmax function, software , space, spam detection, spamming, spectral density, spectrogram, speech, speech recognition, speech technologies for video understanding, spotify, statistical classification, statistics, streaming media, summation, supervised learning, support-vector machine, temporal topic localization, texas, three-dimensional space, transcription (linguistics), transmission (mechanics), truck, two-stream networks, typing, understanding, unsupervised learning, upload, use case, vanishing gradient problem, variance, vector graphics, vector space, video, video captioning, video classification, video embeddings and networks, video game, video game console, video game live streaming, video representations used in production, video search, video search engine, video understanding, vocabulary, weight, youtube
Denis Batalov
Worldwide Technical Leader, Machine Learning and Artificial Intelligence at AWS
ai, amazon, artificial intelligence, aws, aws machine learning summit, aws ml summit, boosting (machine learning), cluster analysis, cosine similarity, decision tree learning, dependent and independent variables, dimensionality reduction, distance, dot product, floating-point arithmetic, gradient boosting, hml302, how machine learning is done, image segmentation, johnson–lindenstrauss lemma, k-nearest neighbors algorithm, machine learning, mean squared error, nearest neighbor search, norm (mathematics), principal component analysis, random forest, random projection, sampling (statistics), statistical classification, time series, topic model, unsupervised learning
Jiawei Han
Professor at University of Illinois at Urbana-Champaign
acm 2020, artificial intelligence, automatic summarization, categorical distribution, cluster analysis, computer programming, computer science 2020, computer science event 2020, computer vision, convolutional neural network, cosine similarity, data analysis, data model, data science, deep learning, feature selection, hyperparameter (machine learning), hyperparameter optimization, information extraction, kdd2020, kdd2020 tutorials, latent dirichlet allocation, machine learning, monte carlo method, natural language processing, spanning tree, supervised learning, topic model, transformer (machine learning model), variational bayesian methods, word embedding, wordnet
Yu Huang
PhD Student at University of Illinois Urbana-Champaign
acm 2020, artificial intelligence, automatic summarization, categorical distribution, cluster analysis, computer programming, computer science 2020, computer science event 2020, computer vision, convolutional neural network, cosine similarity, data analysis, data model, data science, deep learning, feature selection, hyperparameter (machine learning), hyperparameter optimization, information extraction, kdd2020, kdd2020 tutorials, latent dirichlet allocation, machine learning, monte carlo method, natural language processing, spanning tree, supervised learning, topic model, transformer (machine learning model), variational bayesian methods, word embedding, wordnet
Yu Meng
Research Assistant at University of Illinois at Urbana-Champaign
acm 2020, artificial intelligence, automatic summarization, categorical distribution, cluster analysis, computer programming, computer science 2020, computer science event 2020, computer vision, convolutional neural network, cosine similarity, data analysis, data model, data science, deep learning, feature selection, hyperparameter (machine learning), hyperparameter optimization, information extraction, kdd2020, kdd2020 tutorials, latent dirichlet allocation, machine learning, monte carlo method, natural language processing, spanning tree, supervised learning, topic model, transformer (machine learning model), variational bayesian methods, word embedding, wordnet
Byron Wallace
Associate Professor at Northeastern University
anthropology, api, apixaban, attention, bias, blood, boston, brigham and women's hospital, camera, clinical trial, cosine similarity, data, data compression, diagnosis, economics, education, electronic health record, engineering, evaluation, evidence, experience, expert, future, gender, health care, health equity, health system, hospital, information, infrastructure, language, learning, machine learning, magnetic resonance imaging, medical record, medication, metadata, microsoft, modem, natural language, pandora (streaming service), patient, performance appraisal, prediction, race (human categorization), radiology, reason, reproducibility, research, risk, science, signal, social science, software , software engineering, supermodel, tf–idf, tool, trade-off, transformer, trigonometric functions, unstructured data, variance, virtual world
Jan-Willem van de Meent
Assistant Professor at Northeastern University
apixaban, attention, blood, boston, brigham and women's hospital, cosine similarity, data compression, diagnosis, evidence, expert, hospital, information, language, magnetic resonance imaging, medical record, medication, modem, natural language, patient, radiology, supermodel, tf–idf, transformer, trigonometric functions, unstructured data
Geoffrey Young
Associate Professor at Harvard Medical School Radiology
apixaban, attention, blood, boston, brigham and women's hospital, cosine similarity, data compression, diagnosis, evidence, expert, hospital, information, language, magnetic resonance imaging, medical record, medication, modem, natural language, patient, radiology, supermodel, tf–idf, transformer, trigonometric functions, unstructured data
Anne-Sophie Touret
Clinical Fellow in Radiology at Brigham and Women's Hospital
apixaban, attention, blood, boston, brigham and women's hospital, cosine similarity, data compression, diagnosis, evidence, expert, hospital, information, language, magnetic resonance imaging, medical record, medication, modem, natural language, patient, radiology, supermodel, tf–idf, transformer, trigonometric functions, unstructured data
Borna Dabiri
Fellow at Brigham and Women's Hospital
apixaban, attention, blood, boston, brigham and women's hospital, cosine similarity, data compression, diagnosis, evidence, expert, hospital, information, language, magnetic resonance imaging, medical record, medication, modem, natural language, patient, radiology, supermodel, tf–idf, transformer, trigonometric functions, unstructured data
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