Papers
What Do Neural Networks Learn When Trained With Random Labels?
Hartmut Maennel, Ibrahim M Alabdulmohsin, Ilya O Tolstikhin et al.
What if Neural Networks had SVDs?
Alexander Mathiasen, Frederik Hvilshøj, Jakob Rødsgaard Jørgensen et al.
What is being transferred in transfer learning?
Behnam Neyshabur, Hanie Sedghi, Chiyuan Zhang
What Makes for Good Views for Contrastive Learning?
Yonglong Tian, Chen Sun, Ben Poole et al.
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation
Vitaly Feldman, Chiyuan Zhang
What shapes feature representations? Exploring datasets, architectures, and training
Katherine Hermann, Andrew Lampinen
What went wrong and when? Instance-wise feature importance for time-series black-box models
Sana Tonekaboni, Shalmali Joshi, Kieran Campbell et al.
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes
Zhaozhi Qian, Ahmed M. Alaa, Mihaela van der Schaar
When Counterpoint Meets Chinese Folk Melodies
Nan Jiang, Sheng Jin, Zhiyao Duan et al.
When Do Neural Networks Outperform Kernel Methods?
Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz et al.
Why are Adaptive Methods Good for Attention Models?
Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit et al.
Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? --- A Neural Tangent Kernel Perspective
Kaixuan Huang, Yuqing Wang, Molei Tao et al.
Why Normalizing Flows Fail to Detect Out-of-Distribution Data
Polina Kirichenko, Pavel Izmailov, Andrew G Wilson
Winning the Lottery with Continuous Sparsification
Pedro Savarese, Hugo Silva, Michael Maire
Wisdom of the Ensemble: Improving Consistency of Deep Learning Models
Lijing Wang, Dipanjan Ghosh, Maria Gonzalez Diaz et al.
Woodbury Transformations for Deep Generative Flows
You Lu, Bert Huang
WoodFisher: Efficient Second-Order Approximation for Neural Network Compression
Sidak Pal Singh, Dan Alistarh
WOR and $p$'s: Sketches for $\ell_p$-Sampling Without Replacement
Edith Cohen, Rasmus Pagh, David Woodruff
Worst-Case Analysis for Randomly Collected Data
Justin Chen, Gregory Valiant, Paul Valiant
X-CAL: Explicit Calibration for Survival Analysis
Mark Goldstein, Xintian Han, Aahlad Puli et al.
Your Classifier can Secretly Suffice Multi-Source Domain Adaptation
Naveen Venkat, Jogendra Nath Kundu, Durgesh Singh et al.
Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling
Tong Che, Ruixiang ZHANG, Jascha Sohl-Dickstein et al.
Zap Q-Learning With Nonlinear Function Approximation
Shuhang Chen, Adithya M Devraj, Fan Lu et al.
Zero-Resource Knowledge-Grounded Dialogue Generation
Linxiao Li, Can Xu, Wei Wu et al.
A Bayesian Theory of Conformity in Collective Decision Making
Koosha Khalvati, Saghar Mirbagheri, Seongmin A. Park et al.