Papers
DNN-based Topology Optimisation: Spatial Invariance and Neural Tangent Kernel
Benjamin Dupuis, Arthur Jacot
DOBF: A Deobfuscation Pre-Training Objective for Programming Languages
Marie-Anne Lachaux, Baptiste Roziere, Marc Szafraniec et al.
DOCTOR: A Simple Method for Detecting Misclassification Errors
Federica Granese, Marco Romanelli, Daniele Gorla et al.
Do Different Tracking Tasks Require Different Appearance Models?
Zhongdao Wang, Hengshuang Zhao, Ya-Li Li et al.
Does enforcing fairness mitigate biases caused by subpopulation shift?
Subha Maity, Debarghya Mukherjee, Mikhail Yurochkin et al.
Does Knowledge Distillation Really Work?
Samuel Stanton, Pavel Izmailov, Polina Kirichenko et al.
Does Preprocessing Help Training Over-parameterized Neural Networks?
Zhao Song, Shuo Yang, Ruizhe Zhang
Do Input Gradients Highlight Discriminative Features?
Harshay Shah, Prateek Jain, Praneeth Netrapalli
Domain Adaptation with Invariant Representation Learning: What Transformations to Learn?
Petar Stojanov, Zijian Li, Mingming Gong et al.
Domain Invariant Representation Learning with Domain Density Transformations
A. Tuan Nguyen, Toan Tran, Yarin Gal et al.
DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks
Wei Sun, Aojun Zhou, Sander Stuijk et al.
Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark
Alexander Korotin, Lingxiao Li, Aude Genevay et al.
Don’t Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence
Tianshi Cao, Alex Bie, Arash Vahdat et al.
Do Transformers Really Perform Badly for Graph Representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo et al.
Double/Debiased Machine Learning for Dynamic Treatment Effects
Greg Lewis, Vasilis Syrgkanis
Double Machine Learning Density Estimation for Local Treatment Effects with Instruments
Yonghan Jung, Jin Tian, Elias Bareinboim
Doubly Robust Thompson Sampling with Linear Payoffs
Wonyoung Kim, Gi-Soo Kim, Myunghee Cho Paik
Do Vision Transformers See Like Convolutional Neural Networks?
Maithra Raghu, Thomas Unterthiner, Simon Kornblith et al.
Do Wider Neural Networks Really Help Adversarial Robustness?
Boxi Wu, Jinghui Chen, Deng Cai et al.
DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples
Yi Xu, Jiandong Ding, Lu Zhang et al.
Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks
Yonggan Fu, Qixuan Yu, Yang Zhang et al.
DRIVE: One-bit Distributed Mean Estimation
Shay Vargaftik, Ran Ben-Basat, Amit Portnoy et al.
Dr Jekyll & Mr Hyde: the strange case of off-policy policy updates
Romain Laroche, Remi Tachet des Combes
DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras
Zachary Teed, Jia Deng
DRONE: Data-aware Low-rank Compression for Large NLP Models
Patrick Chen, Hsiang-Fu Yu, Inderjit S. Dhillon et al.