Showing 1–19 of 19 results
/ Date/ Name
Aug 20, 2020BOIL: Towards Representation Change for Few-shot LearningOct 2, 2024House of Cards: Massive Weights in LLMsJun 18, 2022Demystifying the Base and Novel Performances for Few-shot Class-incremental LearningOct 18, 2022Synergy with Translation Artifacts for Training and Inference in Multilingual TasksAug 29, 2023Cross-Modal Retrieval Meets Inference:Improving Zero-Shot Classification with Cross-Modal RetrievalFeb 1, 2022Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot DifficultySep 14, 2018TED Talk Recommender Using Speech TranscriptsOct 26, 2018Spectrogram-channels u-net: a source separation model viewing each channel as the spectrogram of each sourceAug 24, 2023FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated LearningMay 13, 2022How to Fine-tune Models with Few Samples: Update, Data Augmentation, and Test-time AugmentationMay 19, 2021Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge DistillationDec 6, 2020TornadoAggregate: Accurate and Scalable Federated Learning via the Ring-Based ArchitectureJun 4, 2021FedBABU: Towards Enhanced Representation for Federated Image ClassificationApr 24, 2020SIPA: A Simple Framework for Efficient NetworksMay 11, 2022ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot LearningJun 30, 2024BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models PersonalizationJul 5, 2025OrthoRank: Token Selection via Sink Token Orthogonality for Efficient LLM inferenceNov 22, 2023FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated LearningDec 9, 2020Accurate and Fast Federated Learning via IID and Communication-Aware Grouping