Asia
Better Exploration with Optimistic Actor Critic
Kamil Ciosek, Quan Vuong, Robert Loftin, Katja Hofmann
Actor-critic methods, a type of model-free Reinforcement Learning, have beensuccessfully applied to challenging tasks in continuous control, often achievingstate-of-the artperformance.
GroundedAnswersforMulti-agentDecision-making ProblemthroughGenerativeWorldModel
Theempirical results demonstrate that this framework can improve the answers for multi-agent decision-making problems by showing superior performance on the training and unseen tasks of the StarCraft Multi-Agent Challenge benchmark.
Scalable Deep Generative Relational Model with High-Order Node Dependence
Xuhui Fan, Bin Li, Caoyuan Li, Scott SIsson, Ling Chen
Neural Information Processing Systems http://nips.cc/
Random Projections with Asymmetric Quantization
Neural Information Processing Systems http://nips.cc/
ReFIR: GroundingLargeRestorationModels withRetrievalAugmentation
Recently, diffusion models [10, 11] have emerged as a promising alternative, delivering noteworthy results in real-world image restoration [12, 13, 14].
Space and Time Efficient Kernel Density Estimation in High Dimensions
Arturs Backurs, Piotr Indyk, Tal Wagner
However, their data structure requires a significantly increased super-linear storage space, as well as super-linear preprocessing time. These limitations inhibit the practical applicability of their approach on large datasets.
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, Dawn Song
Neural Information Processing Systems http://nips.cc/