Papers
Resource-Adaptive Federated Learning with All-In-One Neural Composition
Yiqun Mei, Pengfei Guo, Mo Zhou et al.
Respecting Transfer Gap in Knowledge Distillation
Yulei Niu, Long Chen, Chang Zhou et al.
ResQ: A Residual Q Function-based Approach for Multi-Agent Reinforcement Learning Value Factorization
Siqi Shen, Mengwei Qiu, Jun Liu et al.
ResT V2: Simpler, Faster and Stronger
Qinglong Zhang, Yu-Bin Yang
Retaining Knowledge for Learning with Dynamic Definition
Zichang Liu, Benjamin Coleman, Tianyi Zhang et al.
Rethinking Alignment in Video Super-Resolution Transformers
Shuwei Shi, Jinjin Gu, Liangbin Xie et al.
Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency Domain
Yiting Chen, Qibing Ren, Junchi Yan
Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination
YIZHEN ZHENG, Shirui Pan, Vincent CS Lee et al.
Rethinking Generalization in Few-Shot Classification
Markus Hiller, Rongkai Ma, Mehrtash Harandi et al.
Rethinking Image Restoration for Object Detection
Shangquan Sun, Wenqi Ren, Tao Wang et al.
Rethinking Individual Global Max in Cooperative Multi-Agent Reinforcement Learning
Yitian Hong, Yaochu Jin, Yang Tang
Rethinking Knowledge Graph Evaluation Under the Open-World Assumption
Haotong Yang, Zhouchen Lin, Muhan Zhang
Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective
Bohang Zhang, Du Jiang, Di He et al.
Rethinking Resolution in the Context of Efficient Video Recognition
Chuofan Ma, Qiushan Guo, Yi Jiang et al.
Rethinking the compositionality of point clouds through regularization in the hyperbolic space
Antonio Montanaro, Diego Valsesia, Enrico Magli
Rethinking the Reverse-engineering of Trojan Triggers
Zhenting Wang, Kai Mei, Hailun Ding et al.
Rethinking Value Function Learning for Generalization in Reinforcement Learning
Seungyong Moon, JunYeong Lee, Hyun Oh Song
Rethinking Variational Inference for Probabilistic Programs with Stochastic Support
Tim Reichelt, Luke Ong, Thomas Rainforth
Retrieval-Augmented Diffusion Models
Andreas Blattmann, Robin Rombach, Kaan Oktay et al.
Retrieve, Reason, and Refine: Generating Accurate and Faithful Patient Instructions
Fenglin Liu, Bang Yang, Chenyu You et al.
Retrospective Adversarial Replay for Continual Learning
Lilly Kumari, Shengjie Wang, Tianyi Zhou et al.
Revisiting Active Sets for Gaussian Process Decoders
Pablo Moreno-Muñoz, Cilie Feldager, Søren Hauberg
Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum
Nian Liu, Xiao Wang, Deyu Bo et al.
Revisiting Heterophily For Graph Neural Networks
Sitao Luan, Chenqing Hua, Qincheng Lu et al.
Revisiting Injective Attacks on Recommender Systems
Haoyang LI, Shimin DI, Lei Chen