Papers
11,015 papers found
The inductive bias of ReLU networks on orthogonally separable data
Mary Phuong, Christoph H Lampert
The Intrinsic Dimension of Images and Its Impact on Learning
Phil Pope, Chen Zhu, Ahmed Abdelkader et al.
Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data
Colin Wei, Kendrick Shen, Yining Chen et al.
Theoretical bounds on estimation error for meta-learning
James Lucas, Mengye Ren, Irene Raissa KAMENI KAMENI et al.
The Recurrent Neural Tangent Kernel
Sina Alemohammad, Zichao Wang, Randall Balestriero et al.
The Risks of Invariant Risk Minimization
Elan Rosenfeld, Pradeep Kumar Ravikumar, Andrej Risteski
The role of Disentanglement in Generalisation
Milton Llera Montero, Casimir JH Ludwig, Rui Ponte Costa et al.
The Role of Momentum Parameters in the Optimal Convergence of Adaptive Polyak's Heavy-ball Methods
Wei Tao, Sheng Long, Gaowei Wu et al.
The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings
Elliot Meyerson, Risto Miikkulainen
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods
Louis THIRY, Michael Arbel, Eugene Belilovsky et al.
Tilted Empirical Risk Minimization
Tian Li, Ahmad Beirami, Maziar Sanjabi et al.
Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data
Francesco Tonolini, Pablo Garcia Moreno, Andreas Damianou et al.
Topology-Aware Segmentation Using Discrete Morse Theory
Xiaoling Hu, Yusu Wang, Li Fuxin et al.
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis
Bingchen Liu, Yizhe Zhu, Kunpeng Song et al.
Towards Impartial Multi-task Learning
Liyang Liu, Yi Li, Zhanghui Kuang et al.
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
David A. Klindt, Lukas Schott, Yash Sharma et al.
Towards Resolving the Implicit Bias of Gradient Descent for Matrix Factorization: Greedy Low-Rank Learning
Zhiyuan Li, Yuping Luo, Kaifeng Lyu
Towards Robustness Against Natural Language Word Substitutions
Xinshuai Dong, Anh Tuan Luu, Rongrong Ji et al.
Towards Robust Neural Networks via Close-loop Control
Zhuotong Chen, Qianxiao Li, Zheng Zhang
Tradeoffs in Data Augmentation: An Empirical Study
Raphael Gontijo-Lopes, Sylvia Smullin, Ekin Dogus Cubuk et al.
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs
Jonathan Frankle, David J. Schwab, Ari S. Morcos
Training GANs with Stronger Augmentations via Contrastive Discriminator
Jongheon Jeong, Jinwoo Shin
Training independent subnetworks for robust prediction
Marton Havasi, Rodolphe Jenatton, Stanislav Fort et al.
Training with Quantization Noise for Extreme Model Compression
Pierre Stock, Angela Fan, Benjamin Graham et al.
Trajectory Prediction using Equivariant Continuous Convolution
Robin Walters, Jinxi Li, Rose Yu