Papers
Graph Geometry Interaction Learning
Shichao Zhu, Shirui Pan, Chuan Zhou et al.
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
Matthew Tancik, Pratul Srinivasan, Ben Mildenhall et al.
Hierarchical nucleation in deep neural networks
Diego Doimo, Aldo Glielmo, Alessio Ansuini et al.
Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment
Govinda Kamath, Tavor Baharav, Ilan Shomorony
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Jeremiah Liu, Zi Lin, Shreyas Padhy et al.
Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian
Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay
Rotated Binary Neural Network
Mingbao Lin, Rongrong Ji, Zihan Xu et al.
Implicit Neural Representations with Periodic Activation Functions
Vincent Sitzmann, Julien Martel, Alexander Bergman et al.
Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free
Haotao Wang, Tianlong Chen, Shupeng Gui et al.
Online Neural Connectivity Estimation with Noisy Group Testing
Anne Draelos, John C. Pearson
Multi-Plane Program Induction with 3D Box Priors
Yikai Li, Jiayuan Mao, Xiuming Zhang et al.
Preference learning along multiple criteria: A game-theoretic perspective
Kush Bhatia, Ashwin Pananjady, Peter L. Bartlett et al.
HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss
Yurun Tian, Axel Barroso Laguna, Tony Ng et al.
Generating Correct Answers for Progressive Matrices Intelligence Tests
Niv Pekar, Yaniv Benny, Lior Wolf
A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems
Jiawei Zhang, Peijun Xiao, Ruoyu Sun et al.
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method
Ye He, Krishnakumar Balasubramanian, Murat A Erdogdu
Strictly Batch Imitation Learning by Energy-based Distribution Matching
Daniel Jarrett, Ioana Bica, Mihaela van der Schaar
Exactly Computing the Local Lipschitz Constant of ReLU Networks
Matt Jordan, Alexandros G Dimakis
Training Stronger Baselines for Learning to Optimize
Tianlong Chen, Weiyi Zhang, Zhou Jingyang et al.
Fair regression with Wasserstein barycenters
Evgenii Chzhen, Christophe Denis, Mohamed Hebiri et al.
Understanding the Role of Training Regimes in Continual Learning
Seyed Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu et al.
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks
Roman Pogodin, Peter E. Latham
A simple normative network approximates local non-Hebbian learning in the cortex
Siavash Golkar, David Lipshutz, Yanis Bahroun et al.
Interior Point Solving for LP-based prediction+optimisation
Jayanta Mandi, Tias Guns
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning
Yu Yao, Tongliang Liu, Bo Han et al.