Papers
Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL
Taku Yamagata, Ahmed Khalil, Raul Santos-Rodriguez
Quantifying Human Priors over Social and Navigation Networks
Gecia Bravo-Hermsdorff
Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs
Lirong Wu, Haitao Lin, Yufei Huang et al.
Quantifying the Variability Collapse of Neural Networks
Jing Xu, Haoxiong Liu
Quantile Credit Assignment
Thomas Mesnard, Wenqi Chen, Alaa Saade et al.
Quantitative Universal Approximation Bounds for Deep Belief Networks
Julian Sieber, Johann Gehringer
Quantized Distributed Training of Large Models with Convergence Guarantees
Ilia Markov, Adrian Vladu, Qi Guo et al.
Quantum 3D Graph Learning with Applications to Molecule Embedding
Ge Yan, Huaijin Wu, Junchi Yan
QuantumDARTS: Differentiable Quantum Architecture Search for Variational Quantum Algorithms
Wenjie Wu, Ge Yan, Xudong Lu et al.
Quantum Lower Bounds for Finding Stationary Points of Nonconvex Functions
Chenyi Zhang, Tongyang Li
Quantum Policy Gradient Algorithm with Optimized Action Decoding
Nico Meyer, Daniel Scherer, Axel Plinge et al.
Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation
Hayata Yamasaki, Sathyawageeswar Subramanian, Satoshi Hayakawa et al.
Quantum Speedups for Zero-Sum Games via Improved Dynamic Gibbs Sampling
Adam Bouland, Yosheb M Getachew, Yujia Jin et al.
RACE: Improve Multi-Agent Reinforcement Learning with Representation Asymmetry and Collaborative Evolution
Pengyi Li, Jianye Hao, Hongyao Tang et al.
Raising the Cost of Malicious AI-Powered Image Editing
Hadi Salman, Alaa Khaddaj, Guillaume Leclerc et al.
Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice
Yishay Mansour, Richard Nock, Robert Williamson
Random Grid Neural Processes for Parametric Partial Differential Equations
Arnaud Vadeboncoeur, Ieva Kazlauskaite, Yanni Papandreou et al.
Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds
Shion Takeno, Yu Inatsu, Masayuki Karasuyama
Randomized Schur Complement Views for Graph Contrastive Learning
Vignesh Kothapalli
Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption
Vasilii Feofanov, Malik Tiomoko, Aladin Virmaux
Random Shuffle Transformer for Image Restoration
Jie Xiao, Xueyang Fu, Man Zhou et al.
Random Teachers are Good Teachers
Felix Sarnthein, Gregor Bachmann, Sotiris Anagnostidis et al.
RankMe: Assessing the Downstream Performance of Pretrained Self-Supervised Representations by Their Rank
Quentin Garrido, Randall Balestriero, Laurent Najman et al.
Reachability-Aware Laplacian Representation in Reinforcement Learning
Kaixin Wang, Kuangqi Zhou, Jiashi Feng et al.
Reasons for the Superiority of Stochastic Estimators over Deterministic Ones: Robustness, Consistency and Perceptual Quality
Guy Ohayon, Theo Joseph Adrai, Michael Elad et al.