Papers
11,015 papers found
The Curious Case of Neural Text Degeneration
Ari Holtzman, Jan Buys, Li Du et al.
The Early Phase of Neural Network Training
Jonathan Frankle, David J. Schwab, Ari S. Morcos
The Gambler's Problem and Beyond
Baoxiang Wang, Shuai Li, Jiajin Li et al.
The Implicit Bias of Depth: How Incremental Learning Drives Generalization
Daniel Gissin, Shai Shalev-Shwartz, Amit Daniely
The Ingredients of Real World Robotic Reinforcement Learning
Henry Zhu, Justin Yu, Abhishek Gupta et al.
The intriguing role of module criticality in the generalization of deep networks
Niladri Chatterji, Behnam Neyshabur, Hanie Sedghi
The Local Elasticity of Neural Networks
Hangfeng He, Weijie Su
The Logical Expressiveness of Graph Neural Networks
Pablo Barceló, Egor V. Kostylev, Mikael Monet et al.
Theory and Evaluation Metrics for Learning Disentangled Representations
Kien Do, Truyen Tran
The Shape of Data: Intrinsic Distance for Data Distributions
Anton Tsitsulin, Marina Munkhoeva, Davide Mottin et al.
The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget
Anirudh Goyal, Yoshua Bengio, Matthew Botvinick et al.
Thieves on Sesame Street! Model Extraction of BERT-based APIs
Kalpesh Krishna, Gaurav Singh Tomar, Ankur P. Parikh et al.
Thinking While Moving: Deep Reinforcement Learning with Concurrent Control
Ted Xiao, Eric Jang, Dmitry Kalashnikov et al.
To Relieve Your Headache of Training an MRF, Take AdVIL
Chongxuan Li, Chao Du, Kun Xu et al.
Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control
Tsui-Wei Weng, Krishnamurthy (Dj) Dvijotham*, Jonathan Uesato* et al.
Towards a Deep Network Architecture for Structured Smoothness
Haroun Habeeb, Oluwasanmi Koyejo
Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets
Mingrui Liu, Youssef Mroueh, Jerret Ross et al.
Towards Fast Adaptation of Neural Architectures with Meta Learning
Dongze Lian, Yin Zheng, Yintao Xu et al.
Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models
Xisen Jin, Zhongyu Wei, Junyi Du et al.
Towards neural networks that provably know when they don't know
Alexander Meinke, Matthias Hein
Towards Stabilizing Batch Statistics in Backward Propagation of Batch Normalization
Junjie Yan, Ruosi Wan, Xiangyu Zhang et al.
Towards Stable and Efficient Training of Verifiably Robust Neural Networks
Huan Zhang, Hongge Chen, Chaowei Xiao et al.
Towards Verified Robustness under Text Deletion Interventions
Johannes Welbl, Po-Sen Huang, Robert Stanforth et al.
Training binary neural networks with real-to-binary convolutions
Brais Martinez, Jing Yang, Adrian Bulat et al.
Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators
Daniel Stoller, Sebastian Ewert, Simon Dixon