Papers
What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?
Weijian Deng, Stephen Gould, Liang Zheng
What Makes for End-to-End Object Detection?
Peize Sun, Yi Jiang, Enze Xie et al.
What’s in the Box? Exploring the Inner Life of Neural Networks with Robust Rules
Jonas Fischer, Anna Olah, Jilles Vreeken
When All We Need is a Piece of the Pie: A Generic Framework for Optimizing Two-way Partial AUC
Zhiyong Yang, Qianqian Xu, Shilong Bao et al.
When Does Data Augmentation Help With Membership Inference Attacks?
Yigitcan Kaya, Tudor Dumitras
Which transformer architecture fits my data? A vocabulary bottleneck in self-attention
Noam Wies, Yoav Levine, Daniel Jannai et al.
Whitening and Second Order Optimization Both Make Information in the Dataset Unusable During Training, and Can Reduce or Prevent Generalization
Neha Wadia, Daniel Duckworth, Samuel S Schoenholz et al.
Whitening for Self-Supervised Representation Learning
Aleksandr Ermolov, Aliaksandr Siarohin, Enver Sangineto et al.
Whittle Networks: A Deep Likelihood Model for Time Series
Zhongjie Yu, Fabrizio G Ventola, Kristian Kersting
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund et al.
Winograd Algorithm for AdderNet
Wenshuo Li, Hanting Chen, Mingqiang Huang et al.
World Model as a Graph: Learning Latent Landmarks for Planning
Lunjun Zhang, Ge Yang, Bradly C Stadie
XOR-CD: Linearly Convergent Constrained Structure Generation
Fan Ding, Jianzhu Ma, Jinbo Xu et al.
You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling
Zhanpeng Zeng, Yunyang Xiong, Sathya Ravi et al.
Zero-Shot Text-to-Image Generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh et al.
Zeroth-Order Non-Convex Learning via Hierarchical Dual Averaging
Amélie Héliou, Matthieu Martin, Panayotis Mertikopoulos et al.
Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting
Yuzhou Chen, Ignacio Segovia, Yulia R. Gel
Zoo-Tuning: Adaptive Transfer from A Zoo of Models
Yang Shu, Zhi Kou, Zhangjie Cao et al.
Abstraction Mechanisms Predict Generalization in Deep Neural Networks
Alex Gain, Hava Siegelmann
Accelerated Message Passing for Entropy-Regularized MAP Inference
Jonathan Lee, Aldo Pacchiano, Peter Bartlett et al.
Accelerated Stochastic Gradient-free and Projection-free Methods
Feihu Huang, Lue Tao, Songcan Chen
Accelerating Large-Scale Inference with Anisotropic Vector Quantization
Ruiqi Guo, Philip Sun, Erik Lindgren et al.
Accelerating the diffusion-based ensemble sampling by non-reversible dynamics
Futoshi Futami, Issei Sato, Masashi Sugiyama
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization
Zhize Li, Dmitry Kovalev, Xun Qian et al.