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Davide Venturelli

Artificial Intelligence, Quantum Computing, Physics, Robotics, Entrepreneurship at USRA

algorithm, application software, bayesian inference, brute-force search, cluster analysis, computing, database index, estimation theory, gradient descent, likelihood function, linear programming, machine learning, mathematical optimization, matrix (mathematics), maximum a posteriori estimation, maximum likelihood estimation, mean, quantum computing, quantum superposition, sampling (statistics), software , statistical inference, statistics, support-vector machine, zero-sum game

Cees Snoek

Professor in Artificial Intelligence at University of Amsterdam

algorithmic bias, artificial neural network, central processing unit, computer vision, concept, conceptual model, convolutional neural network, data sheet, deep learning, feature learning, hyperparameter optimization, image quality, image segmentation, imagenet, learning to rank, neural network, overfitting, prediction, programmer, representation (arts), statistical classification, supervised learning, support-vector machine, unsupervised learning, variance

Kuan-Chuan Peng

Research Scientist at Mitsubishi Electric Research Laboratories

algorithmic bias, artificial neural network, central processing unit, computer vision, concept, conceptual model, convolutional neural network, data sheet, deep learning, feature learning, hyperparameter optimization, image quality, image segmentation, imagenet, learning to rank, neural network, overfitting, prediction, programmer, representation (arts), statistical classification, supervised learning, support-vector machine, unsupervised learning, variance

Andrew David Bagdanov

Associate Professor at University of Florence

algorithmic bias, artificial neural network, central processing unit, computer vision, concept, conceptual model, convolutional neural network, data sheet, deep learning, feature learning, hyperparameter optimization, image quality, image segmentation, imagenet, learning to rank, neural network, overfitting, prediction, programmer, representation (arts), statistical classification, supervised learning, support-vector machine, unsupervised learning, variance

Pavan Chundi

Lead Decision Science & Analytics at Cincinnati Children's Hospital Medical Center

aggression, algorithm, analytics, bias of an estimator, biomedical engineering, clinic, dentist, elastic net regularization, electronic health record, end user, engineering, feature selection, health system, k-nearest neighbors algorithm, lasso (statistics), machine learning, mathematics, medical record, pain, pain management, patient, regression analysis, regularization (mathematics), statistical classification, support-vector machine

Duc Hieu Ho

CSE Undergraduate Student at Seoul National University

automation, cloud computing, computer vision, data mining, information, intelligence, k-nearest neighbors algorithm, language, linear regression, machine learning, natural language processing, random forest, regression analysis, robot, self-driving car, social media, software , statistical classification, supervised learning, support-vector machine, system, text messaging, unsupervised learning, vector space

Moez Ali

Data Scientist at PyCaret

amazon web services, cloud computing, cross-validation (statistics), data, fax, finance, function (mathematics), graphics processing unit, hyperparameter (machine learning), laptop, linear regression, machine learning, mind, music, neural network, parameter, pipeline transport, random forest, regression analysis, simulation, software , standard deviation, support-vector machine, time series, vector space

Jorg Schad

Head Of Engineering and Machine Learning at ArangoDB

analytics, behavior, collaborative filtering, community, convolutional neural network, database, deep learning, deepmind, facebook, feature learning, gender, graph database, inference, information, knowledge, linkedin, machine learning, political corruption, prediction, sense, sexual orientation, statistical classification, support-vector machine, system, tensor

Krishnav Dave

Teaching Assistant at Northwestern University School of Professional Studies

amazon alexa, android (operating system), bank, chatbot, cloud computing, computer vision, credit score, customer relationship management, data analysis, gradient boosting, machine learning, microservices, microsoft excel, microsoft windows, mobile app, mongodb, password, pattern recognition, prediction, relational database, requirement, support-vector machine, user (computing), user interface

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

Yijie Dylan Wang

Software Engineering Manager 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, 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, closed captioning, code-division multiple access, codec, coherence (physics), compass, computer, computer graphics, computer performance, computer science tutorial, computer science tutorials, computer vision, computer vision 2020, computing, concept, convolution, convolutional neural network, 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, film frame, function (mathematics), gas, gender, gradient, grayscale, hidden markov model, histogram, history, hyperparameter optimization, image embeddings, image representations , image segmentation, imagenet, improvements on 3d cnn, improvements on 3d cnns and two-stream, infection, information, information retrieval, input/output, inspection, instagram, intelligence, interface (computing), internet, inverted index, k-means clustering, kdd2020 tutorials, language, language model, learning, lecture-style tutorials, likelihood function, linearity, linkedin, literature, 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, multimodality, 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, sms, social networking service, softmax function, space, spam detection, spamming, spectral density, spectrogram, speech, speech recognition, speech technologies for video understanding, spotify, statistical classification, statistics, streaming media, 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 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

Sumit Srivastava

Staff Applied Research Engineer at LinkedIn

Sirjan Kafle

Senior Machine Learning Engineer, Multimedia AI at LinkedIn

Aman Gupta

Senior Machine Learning Scientist at LinkedIn Multimedia AI

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

Lee Rhodes

Distinguished Architect at Verizon Media

acm 2020, artificial intelligence, association for computing machinery, big data, computer science, computer science event 2020, computer vision 2020, confidence interval, cumulative distribution function, deep learning, differential privacy, dimensionality reduction, estimation theory, hyperloglog, kdd2020, likelihood function, loss function, machine learning, mean, mode (statistics), numerical analysis, online and offline, probability distribution, quantile, quantization (signal processing), regression analysis, robust statistics, sample size determination, sampling (statistics), selection algorithm, singular value decomposition, sorting algorithm, support-vector machine

Jonathan Malkin

Distinguished Architect at Verizon Media

acm 2020, artificial intelligence, association for computing machinery, big data, computer science, computer science event 2020, computer vision 2020, confidence interval, cumulative distribution function, deep learning, differential privacy, dimensionality reduction, estimation theory, hyperloglog, kdd2020, likelihood function, loss function, machine learning, mean, mode (statistics), numerical analysis, online and offline, probability distribution, quantile, quantization (signal processing), regression analysis, robust statistics, sample size determination, sampling (statistics), selection algorithm, singular value decomposition, sorting algorithm, support-vector machine

Daniel Ting

Staff Research Scientist at Tableau

acm 2020, artificial intelligence, association for computing machinery, big data, computer science, computer science event 2020, computer vision 2020, confidence interval, cumulative distribution function, deep learning, differential privacy, dimensionality reduction, estimation theory, hyperloglog, kdd2020, likelihood function, loss function, machine learning, mean, mode (statistics), numerical analysis, online and offline, probability distribution, quantile, quantization (signal processing), regression analysis, robust statistics, sample size determination, sampling (statistics), selection algorithm, singular value decomposition, sorting algorithm, support-vector machine

Chia-Lin Yang

Professor at National Taiwan University

3d xpoint, accuracy and precision, apple lisa, camera, central processing unit, computer data storage, computer file, computing, deep neural network, disk storage, efficient energy use, embedded system, energy, energy development, experiment, flash memory, hard disk drive, intel, intel core, machine learning, memory, memory paging, nvidia, solid-state drive, supercomputer, support vector machine, support-vector machine, vector processor

Yue Li

Assistant Professor at McGill University

accuracy and precision, algorithm, arrhythmia, automation, bias, birth defect, bit, canada, cardiovascular disease, congenital heart defect, data, database, decision tree, disease, health care, heart, learning, logistic regression, machine learning, prediction, regression analysis, research, sensitivity and specificity, support-vector machine, truth

Joelle Pineau

Associate Professor at McGill University

academic discipline, accuracy and precision, acm 2020, ai, algorithm, arrhythmia, artificial neural network, automation, backpropagation, best practice, bias, birth defect, bit, brain, canada, cardiovascular disease, communication protocol, complexity, computer network, computer science, computer science 2020, computer science event 2020, congenital heart defect, data, data science, data visualization , database, decision tree, deep learning, deep learning 2020, deep reinforcement learning, disease, epilepsy, evaluation, experiment, expert, facebook, functional electrical stimulation, health care, healthcare, heart, hyperparameter (machine learning), hypothesis, internet, joelle pineau, jöelle pineau, kdd2020, learning, logistic regression, machine learning, mathematical optimization, mathematical proof, mcgill university, motivation, neural network, neurostimulation, prediction, protocol (science), reason, regression analysis, reinforcement learning, reproducibility, research, research labs, reward system, science, scientific community, scientific method, scientist, seizure, sensitivity and specificity, simulation, standford university, stanford conference, statistics, supervised learning, support-vector machine, technology, truth, wids artificial intelligence, wids datathon, wids podcast, women in data, women in data science, women in technology, women in technology conference

Yi Yang

Associate Professor of Statistics at McGill University

accuracy and precision, algorithm, arrhythmia, automation, bias, birth defect, bit, canada, cardiovascular disease, congenital heart defect, data, database, decision tree, disease, health care, heart, learning, logistic regression, machine learning, prediction, regression analysis, research, sensitivity and specificity, support-vector machine, truth

Jian Tang

Assistant Professor at HEC Montreal

accuracy and precision, algorithm, arrhythmia, automation, bias, birth defect, bit, canada, cardiovascular disease, child, conformational isomerism, congenital heart defect, costco, data, data science, database, decision tree, diffusion, disease, drug discovery, energy, function (mathematics), grand theft auto v, health care, heart, interactive voice response, joint commission, learning, logic, logistic regression, machine learning, mass, molecular dynamics, molecular mass, moon, physics, prediction, price, protein, protein structure, regression analysis, research, sampling (statistics), sensitivity and specificity, space, support-vector machine, truth, weather, weight

David Buckeridge

Professor at McGill University

accuracy and precision, algorithm, arrhythmia, automation, bias, birth defect, bit, canada, cardiovascular disease, congenital heart defect, data, database, decision tree, disease, health care, heart, learning, logistic regression, machine learning, prediction, regression analysis, research, sensitivity and specificity, support-vector machine, truth

Liming Guo

Research at McGill University

James Brophy

Professor at McGill University

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