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Search All 2021 Events
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Algorithms
Algorithms; Algorithms
Algorithms -> Bandit Algorithms; Reinforcement Learning and Planning
Algorithms -> Bandit Algorithms; Theory
Algorithms -> Classification; Algorithms -> Meta-Learning; Applications
Algorithms -> Classification; Applications -> Computational Biology and Bioinformatics; Applications
Algorithms -> Classification; Deep Learning -> CNN Architectures; Reinforcement Learning and Planning
Algorithms -> Classification; Deep Learning -> Predictive Models; Neuroscience and Cognitive Science
Algorithms -> Clustering; Theory
Algorithms -> Large Scale Learning; Algorithms -> Sparsity and Compressed Sensing; Algorithms
Algorithms -> Large Scale Learning; Optimization -> Convex Optimization; Optimization
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Algorithms -> Online Learning; Optimization
Algorithms, Optimization and Computation Methods
Algorithms -> Regression; Algorithms -> Uncertainty Estimation; Probabilistic Methods; Probabilistic Methods
Algorithms -> Regression; Optimization -> Convex Optimization; Theory -> Learning Theory; Theory
Algorithms -> Regression; Probabilistic Methods; Probabilistic Methods
Algorithms -> Representation Learning; Applications -> Computer Vision; Applications
Algorithms -> Semi-Supervised Learning; Applications -> Computer Vision; Applications
Algorithms -> Semi-Supervised Learning; Deep Learning -> Optimization for Deep Networks; Optimization
Algorithms -> Unsupervised Learning; Applications -> Computational Photography; Deep Learning
Algorithms -> Unsupervised Learning; Applications -> Computer Vision; Deep Learning
Applications
Applications -> Computer Vision; Applications -> Object Recognition; Deep Learning
Applications -> Computer Vision; Deep Learning -> CNN Architectures; Deep Learning -> Deep Autoencoders; Deep Learning
Applications -> Denoising; Applications -> Signal Processing; Deep Learning
Applications -> Denoising; Theory
Data, Challenges, Implementations, and Software
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Neuroscience and Cognitive Science -> Human or Animal Learning; Probabilistic Methods
Optimization
Probabilistic Methods
Reinforcement Learning and Planning
Theory
Results
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Oral Session
Tue 10:30
Theory of Statistical and Deep Learning Methods
Poster
Wed 12:45
DebiNet: Debiasing Linear Models with Nonlinear Overparameterized Neural Networks
Shiyun Xu · Zhiqi Bu
Poster
Thu 7:30
Variable Selection with Rigorous Uncertainty Quantification using Deep Bayesian Neural Networks: Posterior Concentration and Bernstein-von Mises Phenomenon
Jeremiah Liu
Poster
Tue 14:00
A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks
Zhiqi Bu · Shiyun Xu · Kan Chen
Poster
Tue 14:00
Sketch based Memory for Neural Networks
Rina Panigrahy · Xin Wang · Manzil Zaheer
Poster
Wed 12:45
Curriculum Learning by Optimizing Learning Dynamics
Tianyi Zhou · Shengjie Wang · Jeff Bilmes
Poster
Tue 14:00
The Base Measure Problem and its Solution
Alexey Radul · Boris Alexeev
Poster
Wed 6:00
Adaptive wavelet pooling for convolutional neural networks
Moritz Wolter · Jochen Garcke
Poster
Thu 7:30
Fourier Bases for Solving Permutation Puzzles
Horace Pan · Risi Kondor
Poster
Thu 7:30
Approximating Lipschitz continuous functions with GroupSort neural networks
Ugo Tanielian · Gerard Biau
Poster
Wed 12:45
Quick Streaming Algorithms for Maximization of Monotone Submodular Functions in Linear Time
Alan Kuhnle
Oral
Thu 13:45
Quick Streaming Algorithms for Maximization of Monotone Submodular Functions in Linear Time
Alan Kuhnle
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