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Diversity
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Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/fakenewsresearch/dataset
공식 코드https://github.com/fangfang11-plog/adasr
공식 코드ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data Fusion
https://github.com/fanghenshaometeor/Orthogonal-Multi-Path
공식 코드https://github.com/fanghenshaometeor/diversity-via-orthogonality
공식 코드https://github.com/fangru-lin/llm_scalar_adj
공식 코드Probing Large Language Models for Scalar Adjective Lexical Semantics and Scalar Diversity Pragmatics
https://github.com/fangyuan-ksgk/ruleeval
공식 코드https://github.com/fanq15/FewX
공식 코드https://github.com/fanqiwan/explore-instruct
공식 코드Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration
https://github.com/fanyunfeng-bit/balanced-modality-selection-in-mfl
공식 코드Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality Selection
https://github.com/fartashf/vsepp
공식 코드Target-Oriented Deformation of Visual-Semantic Embedding Space
https://github.com/fau-lms/hyvid
공식 코드https://github.com/fazhan-cs/dcp
공식 코드https://github.com/fcarichon/Cultural_Novelty
공식 코드Crossing Boundaries: Leveraging Semantic Divergences to Explore Cultural Novelty in Cooking Recipes
https://github.com/felixbmuller/sast
공식 코드Massively Multi-Person 3D Human Motion Forecasting with Scene Context
https://github.com/fenglinglwb/mat
공식 코드https://github.com/ffdd-diffusion/defectdiffu
공식 코드Few-shot Defect Image Generation based on Consistency Modeling
https://github.com/fieldsoftheworld/ftw-baselines
공식 코드https://github.com/findalexli/mllm-dpo
공식 코드https://github.com/findalexli/mllm-dpo/blob/main/README.MD
공식 코드https://github.com/finnbehrendt/patched-diffusion-models-uad
공식 코드Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI
https://github.com/fiora6/divtrackee
공식 코드https://github.com/fisht0ucher/trident
공식 코드https://github.com/fistyee/mdcs
공식 코드MDCS: More Diverse Experts with Consistency Self-distillation for Long-tailed Recognition
https://github.com/fjxmlzn/PacGAN
공식 코드PacGAN: The power of two samples in generative adversarial networks
https://github.com/fjxmlzn/raregan
공식 코드https://github.com/fjzzq2002/pizza
공식 코드The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
https://github.com/flagopen/flagembedding
공식 코드Towards A Generalist Code Embedding Model Based On Massive Data Synthesis
https://github.com/flagshippioneering/flow-of-options
공식 코드Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through Options
https://github.com/flagshippioneering/pi-rldif
공식 코드Reinforcement learning on structure-conditioned categorical diffusion for protein inverse folding
https://github.com/flairox/ifg
공식 코드Intent Factored Generation: Unleashing the Diversity in Your Language Model
https://github.com/florinshen/vista3d
공식 코드https://github.com/flowersteam/Curiosity_Driven_Goal_Exploration
공식 코드Curiosity Driven Exploration of Learned Disentangled Goal Spaces
https://github.com/flowersteam/Unsupervised_Goal_Space_Learning
공식 코드Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration
https://github.com/flowersteam/curious
공식 코드CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
https://github.com/flowersteam/geppg
공식 코드GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.