Papers
Towards Understanding Knowledge Distillation
Mary Phuong, Christoph Lampert
Toward Understanding the Importance of Noise in Training Neural Networks
Mo Zhou, Tianyi Liu, Yan Li et al.
Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization
Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi et al.
Traditional and Heavy Tailed Self Regularization in Neural Network Models
Michael Mahoney, Charles Martin
Trainable Decoding of Sets of Sequences for Neural Sequence Models
Ashwin Kalyan, Peter Anderson, Stefan Lee et al.
Training CNNs with Selective Allocation of Channels
Jongheon Jeong, Jinwoo Shin
Training Neural Networks with Local Error Signals
Arild Nøkland, Lars Hiller Eidnes
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
Andrew Cotter, Maya Gupta, Heinrich Jiang et al.
Trajectory-Based Off-Policy Deep Reinforcement Learning
Andreas Doerr, Michael Volpp, Marc Toussaint et al.
Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation
Xinyang Chen, Sinan Wang, Mingsheng Long et al.
Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers
Hong Liu, Mingsheng Long, Jianmin Wang et al.
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets
Chen Zhu, W. Ronny Huang, Hengduo Li et al.
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation
Shani Gamrian, Yoav Goldberg
Transfer of Samples in Policy Search via Multiple Importance Sampling
Andrea Tirinzoni, Mattia Salvini, Marcello Restelli
Trimming the $\ell_1$ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning
Jihun Yun, Peng Zheng, Eunho Yang et al.
Understanding and Accelerating Particle-Based Variational Inference
Chang Liu, Jingwei Zhuo, Pengyu Cheng et al.
Understanding and Controlling Memory in Recurrent Neural Networks
Doron Haviv, Alexander Rivkind, Omri Barak
Understanding and correcting pathologies in the training of learned optimizers
Luke Metz, Niru Maheswaranathan, Jeremy Nixon et al.
Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
Pengfei Chen, Ben Ben Liao, Guangyong Chen et al.
Understanding Geometry of Encoder-Decoder CNNs
Jong Chul Ye, Woon Kyoung Sung
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation
Sahil Singla, Eric Wallace, Shi Feng et al.
Understanding MCMC Dynamics as Flows on the Wasserstein Space
Chang Liu, Jingwei Zhuo, Jun Zhu
Understanding Priors in Bayesian Neural Networks at the Unit Level
Mariia Vladimirova, Jakob Verbeek, Pablo Mesejo et al.
Understanding the Impact of Entropy on Policy Optimization
Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi et al.
Understanding the Origins of Bias in Word Embeddings
Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson et al.