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Rethinking the Role of Token Retrieval in Multi-Vector Retrieval
The enhanced model expressivity, however, comes at a great cost of inference complexity.
FedL2P: Federated Learning to Personalize
In this paper, we consider the federated meta-learning problem of learning personalization strategies. Specifically, we consider meta-nets that induce the batch-norm and learning rate parameters for each client given local data statistics.
A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm Qi Wang
Meta learning is a promising paradigm to enable skill transfer across tasks.
Follow-ups Also Matter: Improving Contextual Bandits via Post-serving Contexts
Standard contextual bandit problem assumes that all the relevant contexts are observed before the algorithm chooses an arm.
CODA: Generalizing to Open and Unseen Domains with Compaction and Disambiguation
To meaningfully regularize the model's decision boundary, CODA introduces virtual unknown classes
