Europe
Multi-View Reinforcement Learning
Minne Li, Lisheng Wu, Jun WANG, Haitham Bou Ammar
First, we rewrite Eq. (3) as max
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting
Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, Xifeng Yan
Neural Information Processing Systems http://nips.cc/
Learning convex polytopes with margin
Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch
Fortheformer setting, an extensive literature has explored the statistical and computational aspects of learning Booleanfunctions[Angluin,1992,HellersteinandServedio,2007].
Embedding Dimension of Contrastive Learning and k-Nearest Neighbors
We study the embedding dimension of distance comparison data in two settings: contrastive learning and k -nearest neighbors ( k -NN).
Non-metric Similarity Graphs for Maximum Inner Product Search
Stanislav Morozov, Artem Babenko
For large-scale databases the sequential scan with theO(nd) complexity is not feasible, and the efficient approximate methods arerequired.
Anytime Model Selection in Linear Bandits
M different samples in parallel from the environment so that the reward for each agent is realized.
Implicit Probabilistic Integrators for ODEs
Onur Teymur, Han Cheng Lie, Tim Sullivan, Ben Calderhead
Neural Information Processing Systems http://nips.cc/
