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SIAM Conference on Mathematics of Data Science (MDS20)
Talk Videos from SIAM Conference on Mathematics of Data Science (MDS20)
May 4, 2020.
Online, Ohio, OH, USA
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22
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2000
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17
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About the Event
This conference provide a forum to present work that advances mathematical, statistical, and computational methods in the context of data and information sciences and aims to unite researchers who are building mathematical foundations for data science and making principled applications to science, engineering, technology, and society.
#big data
#data science
#data-driven modeling
#deep learning
#ds
#dynamical systems
#machine learning
#method of moments
#statistics
#topological image analysis
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Featured Speakers
Yacoub Kureh
PHD Candidate at University of California, Los Angeles
I completed my PhD in Mathematics at UCLA in 2020. My research interests lie in Network Science and Machine Learning.
I wear many hats. I will always consider myself a student as my curiosities are never sated. I enjoy programming and teaching. I've been in numerous leadership roles and have an affinity for working in groups. I constantly search for efficient and creative solutions to all kinds of problems.
I am also involved in social activism in a number of spaces. Evidence and argumentation are my guides to life.
I wear many hats. I will always consider myself a student as my curiosities are never sated. I enjoy programming and teaching. I've been in numerous leadership roles and have an affinity for working in groups. I constantly search for efficient and creative solutions to all kinds of problems.
I am also involved in social activism in a number of spaces. Evidence and argumentation are my guides to life.
Luigi Capodieci
CTO at Motivo
Electronic and Semiconductor Industry expert in the field of Design For Manufacturability and IC CAD.
Prior Positions:
+ President and Chief Technologist at KnotPrime
+ Design Enablement Fellow at GLOBALFOUNDRIES
+ Director of DFM/CAD and Engineering R&D Fellow at GLOBALFOUNDRIES (www.globalfoundries.com), coordinating DFM R&D from 45 and 32/28nm, down to the next generations of 20 and 14nm technology nodes.
+ R&D Fellow at AMD, focusing on advanced Resolution Enhancement Technologies (RET), Optical Proximity Correction (OPC) and Design For Manufacturability (DFM) for 45 nm, 32 nm and 22 nm Technology Nodes.
Technical Areas: Physical design implementation and verification, lithography simulation and process modeling, CAD and electronic design automation (EDA).
Prior Positions:
+ President and Chief Technologist at KnotPrime
+ Design Enablement Fellow at GLOBALFOUNDRIES
+ Director of DFM/CAD and Engineering R&D Fellow at GLOBALFOUNDRIES (www.globalfoundries.com), coordinating DFM R&D from 45 and 32/28nm, down to the next generations of 20 and 14nm technology nodes.
+ R&D Fellow at AMD, focusing on advanced Resolution Enhancement Technologies (RET), Optical Proximity Correction (OPC) and Design For Manufacturability (DFM) for 45 nm, 32 nm and 22 nm Technology Nodes.
Technical Areas: Physical design implementation and verification, lithography simulation and process modeling, CAD and electronic design automation (EDA).
Alexandria Volkening
NSF-Simons Fellow at NSF-Simons Center for Quantitative Biology, Northwestern University
Jacob Leygonie
Doctor of Philosophy - PhDField Of StudyMathematics/Topological Data Analysis at University of Oxford
Jennifer Chayes
Technical Fellow & Managing Director at Microsoft Research New England; Microsoft Research New York City; Microsoft Research Maluuba, Montreal
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Yann LeCun
Director at Facebook AI Research
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The Deep Learning - Applied Math Connection
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A Mathematical Data Scientist's perspective on Covid-19 Testing Scale-up
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Minitutorial: A Mathematical Perspective of Machine Learning by Weinan E
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The Deep Learning - Applied Math Connection
05/04/2020
3.35 K
0
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code, data science, deep learning, machine learning, math, prediction, problem solving, pytorch, speech recognition, wave, artificial neural network, backpropagation, courant institute of mathematical sciences, equation, executive functions, explanation, geoffrey hinton, image segmentation, likelihood function, mathematical optimization, matrix (mathematics), matrix multiplication, theory, understanding, thermodynamic free energy, chain rule, applied and computational geometry, applied mathematics, simulation and modeling
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A Mathematical Data Scientist's perspective on Covid-19 Testing Scale-up
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Minitutorial: A Mathematical Perspective of Machine Learning by Weinan E
05/04/2020
2.38 K
0
0
computer, data science, hypothesis, information, machine learning, math, mathematics, mind, prediction, reason, science, simulation, atom, attention, curse of dimensionality, degrees of freedom (statistics), equation, euclidean vector, function (mathematics), linear equation, low-pass filter, parameter, quantum mechanics, scalar (mathematics), test (assessment), variable (mathematics), applied and computational geometry, applied mathematics, simulation and modeling
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Topological Data Analysis of Complex High-Dim. Layout Configurations for IC Physical Designs
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Chris Miles
Courant Instructor (Assistant Professor) at Courant Institute of Mathematical Sciences
+ 3 speakers
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Analysis and Modeling of Chromosomal Spatiotemporal Trajectories During Mitosis
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Ann Sizemore
TitleData Scientist/Application Developer Sr. at University of Pennsylvania
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Growing Graphs and Topological Robustness to Node Order Perturbation
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Alexandria Volkening
NSF-Simons Fellow at NSF-Simons Center for Quantitative Biology, Northwestern University
+ 2 speakers
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Persistence Optimization on the Graph Spectrum for Graph Classification Neural Networks
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Analyzing Collective Motion with Machine Learning and Topology
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E Weinan
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Minitutorial: A Mathematical Perspective of Machine Learning by Weinan E
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Christoph Reisinger
Professor of Applied Mathematics at University of Oxford
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Minitutorial: Deep Neural Networks for High-Dimensional Parabolic PDEs by Christoph Reisinger
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Eldad Haber
Professor at The University of British Columbia
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Minitutorial: ODE/PDE Neural Networks by Eldad Haber
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Ozan Öktem
Associate Professor at KTH, The Royal Institute of Technology
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Minitutorial: Data-Driven Methods for Inverse Problems by Ozan Öktem
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Rebecca Willett
Professor of Statistics and Computer Science at University of Chicago
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Minitutorial: Learning to Solve Inverse Problems in Imaging by Rebecca Willett
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Lexing Ying
Professor of Mathematics at Stanford University
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Minitutorial: Solving Inverse Problems with Deep Learning by Lexing Ying
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Michael Jordan
Professor at UC Berkeley
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Machine Learning: Dynamical, Statistical, and Economic Perspectives
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Jennifer Chayes
Technical Fellow & Managing Director at Microsoft Research New England; Microsoft Research New York City; Microsoft Research Maluuba, Montreal
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Graphons and Machine Learning: Modeling and Estimation of Sparse Networks at Scale
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Cynthia Dwork
Professor of Computer Science at Harvard / Radcliffe Institute for Advanced Study
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Differential Privacy: The Mathematical Bulwark Against Reidentification and Reconstruction in Private Data Analysis
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David Donoho
Professor at Stanford University
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A Mathematical Data Scientist's perspective on Covid-19 Testing Scale-up
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Yurii Nesterov
Professor at Université catholique de Louvain
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Inexact Accelerated High-order Proximal-point Methods
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Yann LeCun
Director at Facebook AI Research
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The Deep Learning - Applied Math Connection
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John Gilbert
Professor of Computer Science at University of California at Santa Barbara
+ 2 speakers
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High Performance Linear System Solvers with Focus on Graph Laplacians
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John Gilbert
Professor of Computer Science at University of California at Santa Barbara
+ 2 speakers
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Topological Data Analysis of Complex High-Dim. Layout Configurations for IC Physical Designs
05/04/2020
1.9 K
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0
causality, data science, dementia, dimension, drawing, gradient descent, hypothesis, knowledge, math, photography, technology, tool, computer-aided design, decomposition, electronic circuit, engineering tolerance, integrated circuit, light, mathematical optimization, metaphor, moore's law, page layout, paradigm, physical design (electronics), semiconductor, topology, applied and computational geometry, applied mathematics, simulation and modeling
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Analysis and Modeling of Chromosomal Spatiotemporal Trajectories During Mitosis
05/04/2020
328
0
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biology, data science, math, question, reality, research, space, speech recognition, time, barcode, cell (biology), diffusion, euclidean space, euclidean vector, force, hope, motion, paradigm, rotation, thought, universe, applied and computational geometry, applied mathematics, simulation and modeling, trajectory, ellipsoid, mitosis, kinetochore, mathematical analysis, microtubule
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Growing Graphs and Topological Robustness to Node Order Perturbation
05/04/2020
373
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brain, concept, data science, disability, ecology, glass, history, information, knowledge, language, math, news, property, question, semantics, system, time, covid-19 pandemic, dynamical system, laptop, matrix (mathematics), matter, ring (mathematics), theory, topology, network theory, applied and computational geometry, applied mathematics, simulation and modeling, semantic network
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Modeling and Measuring Pattern Formation on Zebrafish
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370
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biodiversity, communication, data science, experiment, expert, future, genetics, math, motivation, prediction, qualitative research, time, topological data analysis, cluster analysis, gene, mathematical model, mean, sense, signal, topology, discrete time and continuous time, homology (mathematics), applied and computational geometry, applied mathematics, simulation and modeling, self-organization, collective behavior, mutant, pattern formation, zebrafish
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Persistence Optimization on the Graph Spectrum for Graph Classification Neural Networks
05/04/2020
442
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carbon, data science, ecology, experiment, gradient descent, instagram, manifold, math, mathematics, statistics, artificial neural network, cartesian coordinate system, continuous function, curvature, equation, function (mathematics), gradient, graph (discrete mathematics), hubble space telescope, linear combination, numerical analysis, polynomial, sequence, variance, vertex (graph theory), homology (mathematics), applied and computational geometry, applied mathematics, simulation and modeling, directional derivative
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Analyzing Collective Motion with Machine Learning and Topology
05/04/2020
391
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data science, dimension, machine learning, math, physics, topological data analysis, acceleration, accuracy and precision, clockwise, derivative, differential equation, euclidean vector, force, mass, momentum, motion, newton's laws of motion, polarization (waves), statistical classification, swarm behaviour, topology, unsupervised learning, angular momentum, homology (mathematics), inverse problem, support vector machine, applied and computational geometry, applied mathematics, simulation and modeling, time derivative
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Cities, Voting, and Spiders Spinning under the Influence: Spatial Appl'ns of Topological Tools
05/04/2020
240
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computer, data science, dimension, machine learning, math, mathematics, motivation, nothing, reason, velocity, caffeine, equation, function (mathematics), geographic coordinate system, guitar, lysergic acid diethylamide, map, space (mathematics), spider web, stereoscopy, text messaging, topology, toy, homology (mathematics), persistent homology, applied and computational geometry, applied mathematics, simulation and modeling, effect of psychoactive drugs on animals
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Minitutorial: A Mathematical Perspective of Machine Learning by Weinan E
05/04/2020
2.38 K
0
0
computer, data science, hypothesis, information, machine learning, math, mathematics, mind, prediction, reason, science, simulation, atom, attention, curse of dimensionality, degrees of freedom (statistics), equation, euclidean vector, function (mathematics), linear equation, low-pass filter, parameter, quantum mechanics, scalar (mathematics), test (assessment), variable (mathematics), applied and computational geometry, applied mathematics, simulation and modeling
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Minitutorial: Deep Neural Networks for High-Dimensional Parabolic PDEs by Christoph Reisinger
05/04/2020
1.34 K
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data science, deep learning, gradient descent, interest, machine learning, math, mathematics, neural network, science, bank, derivative, derivative (finance), equity (finance), exchange rate, financial crisis of 2007–2008, interest rate, least squares, linear map, linear programming, mathematical finance, mathematical optimization, matrix (mathematics), nonlinear system, option (finance), applied and computational geometry, applied mathematics, simulation and modeling, heat equation, stochastic simulation
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Minitutorial: ODE/PDE Neural Networks by Eldad Haber
05/04/2020
1.48 K
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data science, deep learning, engineering, machine learning, math, memory, mind, reason, science, self-driving car, simulation, siri, complex number, derivative, euclidean vector, facial recognition system, ground truth, image resolution, image segmentation, mathematical optimization, matrix (mathematics), nonlinear system, radiology, understanding, initial condition, applied and computational geometry, applied mathematics, simulation and modeling, skew-symmetric matrix, scale space
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Minitutorial: Data-Driven Methods for Inverse Problems by Ozan Öktem
05/04/2020
839
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algorithm, astronomy , data science, database, deep learning, internet, machine learning, math, reason, adolescence, artificial neural network, bayesian inference, deepfake, expected value, function (mathematics), generative adversarial network, loss function, mathematical optimization, parameter, prior probability, random variable, randomness, spectral density, supervised learning, unsupervised learning, inverse problem, applied and computational geometry, applied mathematics, simulation and modeling
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Minitutorial: Learning to Solve Inverse Problems in Imaging by Rebecca Willett
05/04/2020
948
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data science, gradient descent, math, optimism, statistics, artificial neural network, attention, autoencoder, generative model, least squares, prior probability, race (human categorization), regression analysis, regularization (mathematics), stochastic gradient descent, theory, training, validation, and test sets, transpose, variance, eigenvalues and eigenvectors, inverse problem, applied and computational geometry, applied mathematics, simulation and modeling, linear subspace, wavelet, kernel (linear algebra), proximal gradient methods for learning, super-resolution imaging
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Minitutorial: Solving Inverse Problems with Deep Learning by Lexing Ying
05/04/2020
1.66 K
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algorithm, computer science, computer vision, data science, database, deep learning, engineering, internet, machine learning, math, mathematics, science, tensorflow, university, computational science, medical imaging, statistical classification, stochastic gradient descent, transpose, digital image processing, inverse problem, wolfram mathematica, california institute of technology, applied and computational geometry, applied mathematics, simulation and modeling, green's function, heat equation
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Machine Learning: Dynamical, Statistical, and Economic Perspectives
05/04/2020
2.38 K
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0
computer vision, data science, deep learning, electricity, engineering, gradient descent, machine learning, math, multi-armed bandit, pattern recognition, speech recognition, statistics, chemistry, computational complexity theory, emergence, fluid dynamics, game theory, momentum, scientific law, statistical inference, applied and computational geometry, applied mathematics, simulation and modeling, hamiltonian mechanics, electromagnetism, bellman equation, calculus of variations, gale–shapley algorithm, symplectic integrator
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Graphons and Machine Learning: Modeling and Estimation of Sparse Networks at Scale
05/04/2020
573
0
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collaborative filtering, data science, differential privacy, machine learning, math, adjacency matrix, continued fraction, distance, expected value, infinity, likelihood function, logarithm, matrix (mathematics), maximum likelihood estimation, norm (mathematics), parameter, sequence, universe, university of california, berkeley, vertex (graph theory), mathematical statistics, applied and computational geometry, applied mathematics, simulation and modeling, sparse networks, maximum cut, mathematical analysis, homomorphism density, knot (mathematics), large deviations theory
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Differential Privacy: The Mathematical Bulwark Against Reidentification and Reconstruction in Private Data Analysis
05/04/2020
747
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analysis, census, computer programming, computer science, data analysis, data science, differential privacy, experiment, facebook, machine learning, math, mathematics, mind, research, standard deviation, statistics, accuracy and precision, intelligence analysis, internet privacy, logarithm, overfitting, probability distribution, sampling (statistics), statistical classification, total information awareness, random walk, applied and computational geometry, applied mathematics, simulation and modeling
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A Mathematical Data Scientist's perspective on Covid-19 Testing Scale-up
05/04/2020
2.69 K
0
0
data science, machine learning, math, compressed sensing, dna, dna sequencing, field (mathematics), generalized linear model, infection, linear algebra, matrix (mathematics), polymerase chain reaction, primer (molecular biology), real-time polymerase chain reaction, transcription (biology), vector space, hiv, underdetermined system, applied and computational geometry, applied mathematics, simulation and modeling, covid-19 testing, neutralizing antibody, group testing, amplicon, complementary dna, denaturation (biochemistry), emergency use authorization, paul romer, reverse transcriptase, thermus aquaticus
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Free
Free
Free
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Inexact Accelerated High-order Proximal-point Methods
05/04/2020
836
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behavior, causality, computer, data science, food, machine learning, math, mathematics, mind, property, reason, research, science, standard deviation, youtube, complexity, door, function (mathematics), gradient, mathematical optimization, number, polynomial, social norm, topical medication, conjecture, applied and computational geometry, applied mathematics, simulation and modeling, preconditioner
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The Deep Learning - Applied Math Connection
05/04/2020
3.35 K
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code, data science, deep learning, machine learning, math, prediction, problem solving, pytorch, speech recognition, wave, artificial neural network, backpropagation, courant institute of mathematical sciences, equation, executive functions, explanation, geoffrey hinton, image segmentation, likelihood function, mathematical optimization, matrix (mathematics), matrix multiplication, theory, understanding, thermodynamic free energy, chain rule, applied and computational geometry, applied mathematics, simulation and modeling
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High Performance Linear System Solvers with Focus on Graph Laplacians
05/04/2020
349
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data science, math, algebra, approximation, best, worst and average case, differential equation, floating-point arithmetic, image segmentation, linear algebra, linear programming, matrix (mathematics), matrix multiplication, number, numerical analysis, observation, runtime system, sparse matrix, square root, supercomputer, symmetric matrix, time complexity, vertex (graph theory), triangular matrix, quadratic equation, applied and computational geometry, applied mathematics, diagonal, laplacian matrix, multigrid method, preconditioner
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Sessions 1 & 2
05/04/2020
879
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data science, math, adjacency matrix, arithmetic, basis (linear algebra), binary search algorithm, connectome, data compression, directed graph, floating-point arithmetic, hash function, linear algebra, multiplication, nearest neighbor search, primitive data type, ring (mathematics), sparse matrix, spreadsheet, table (database), transpose, vector space, vertex (graph theory), eigenvalues and eigenvectors, applied and computational geometry, applied mathematics, trace (linear algebra), blowing up, characteristic (algebra), dual space
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