Papers
The promises and pitfalls of Stochastic Gradient Langevin Dynamics
Nicolas Brosse, Alain Durmus, Eric Moulines
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning
Rui Luo, Jianhong Wang, Yaodong Yang et al.
The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models
Chen Dan, Liu Leqi, Bryon Aragam et al.
The Sparse Manifold Transform
Yubei Chen, Dylan Paiton, Bruno Olshausen
The Spectrum of the Fisher Information Matrix of a Single-Hidden-Layer Neural Network
Jeffrey Pennington, Pratik Worah
The streaming rollout of deep networks - towards fully model-parallel execution
Volker Fischer, Jan Koehler, Thomas Pfeil
Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima
Yaodong Yu, Pan Xu, Quanquan Gu
Thwarting Adversarial Examples: An $L_0$-Robust Sparse Fourier Transform
Mitali Bafna, Jack Murtagh, Nikhil Vyas
Tight Bounds for Collaborative PAC Learning via Multiplicative Weights
Jiecao Chen, Qin Zhang, Yuan Zhou
Toddler-Inspired Visual Object Learning
Sven Bambach, David Crandall, Linda Smith et al.
Topkapi: Parallel and Fast Sketches for Finding Top-K Frequent Elements
Ankush Mandal, He Jiang, Anshumali Shrivastava et al.
TopRank: A practical algorithm for online stochastic ranking
Tor Lattimore, Branislav Kveton, Shuai Li et al.
To Trust Or Not To Trust A Classifier
Heinrich Jiang, Been Kim, Melody Guan et al.
Towards Deep Conversational Recommendations
Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz et al.
Towards Robust Detection of Adversarial Examples
Tianyu Pang, Chao Du, Yinpeng Dong et al.
Towards Robust Interpretability with Self-Explaining Neural Networks
David Alvarez Melis, Tommi Jaakkola
Towards Text Generation with Adversarially Learned Neural Outlines
Sandeep Subramanian, Sai Rajeswar Mudumba, Alessandro Sordoni et al.
Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization
Tianyi Liu, Shiyang Li, Jianping Shi et al.
Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation
Liwei Wang, Lunjia Hu, Jiayuan Gu et al.
Trading robust representations for sample complexity through self-supervised visual experience
Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos
Training deep learning based denoisers without ground truth data
Shakarim Soltanayev, Se Young Chun
Training Deep Models Faster with Robust, Approximate Importance Sampling
Tyler B Johnson, Carlos Guestrin
Training Deep Neural Networks with 8-bit Floating Point Numbers
Naigang Wang, Jungwook Choi, Daniel Brand et al.
Training DNNs with Hybrid Block Floating Point
Mario Drumond, Tao LIN, Martin Jaggi et al.