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PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning
Bregman divergence for greater adaptability in personalized scenarios.
Demystifying Black-box Models with Symbolic Metamodels
Ahmed M. Alaa, Mihaela van der Schaar
Typically,G would be set as the class of all arithmetic, polynomial, algebraic, closed-form, or analytic expressions.
Mutually Regressive Point Processes
Ifigeneia Apostolopoulou, Scott Linderman, Kyle Miller, Artur Dubrawski
Neural Information Processing Systems http://nips.cc/
Harnessing small projectors and multiple views for efficient vision pretraining
We show how this idealized loss can be reformulated to a functionally equivalent loss that is more efficient to compute.
Discriminative Topic Modeling with Logistic LDA
Iryna Korshunova, Hanchen Xiong, Mateusz Fedoryszak, Lucas Theis
Neural Information Processing Systems http://nips.cc/
Real-Time Reinforcement Learning
While it is well suited to describe turn-based decision problems such as board games, this framework is ill suited for real-time applications in which the environment's state continues to evolve while the agent selects an action (Travnik et al., 2018). Nevertheless, this framework hasbeen used forreal-time problems using what areessentially tricks, e.g.
MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learning
However, visual representations are inherently independent of language.
