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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/alpha-vllm/llama2-accessory
공식 코드SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models
https://github.com/alpv95/MemeProject
공식 코드https://github.com/aluo-x/3D_SLN
공식 코드https://github.com/alyd/humans_in_crafter_data
공식 코드Intrinsically-Motivated Humans and Agents in Open-World Exploration
https://github.com/amazon-research/wqa-cerberus
공식 코드https://github.com/amazon-science/MAGID
공식 코드MAGID: An Automated Pipeline for Generating Synthetic Multi-modal Datasets
https://github.com/amazon-science/adaptive-in-context-learning
공식 코드Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selection
https://github.com/amazon-science/auto-cot
공식 코드Automatic Chain of Thought Prompting in Large Language Models
https://github.com/amazon-science/madisse
공식 코드Faithful, Unfaithful or Ambiguous? Multi-Agent Debate with Initial Stance for Summary Evaluation
https://github.com/amazon-science/synthesizrr
공식 코드SynthesizRR: Generating Diverse Datasets with Retrieval Augmentation
https://github.com/ambroiseodt/tsim
공식 코드Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias
https://github.com/amidos2006/benchmark_experiments
공식 코드https://github.com/amidos2006/pcg_benchmark
공식 코드https://github.com/amine179/curiosity-rl-for-drug-design
공식 코드Curiosity as a Self-Supervised Method to Improve Exploration in De novo Drug Design
https://github.com/amiroor/d3questiongenerationclevr
공식 코드D3: Data Diversity Design for Systematic Generalization in Visual Question Answering
https://github.com/amitkumarj441/cikm2023_subspaceembedding
공식 코드Lightweight Adaptation of Neural Language Models via Subspace Embedding
https://github.com/amjadseyedi/SPMLD
공식 코드https://github.com/anandkamat05/TDEOC
공식 코드https://github.com/anastasia-zhukova/diverse_cdcr_datasets
공식 코드https://github.com/andim/pydiver
공식 코드Unbiased estimation of sampling variance for Simpson's diversity index
https://github.com/andreas128/SRFlow
공식 코드SRFlow: Learning the Super-Resolution Space with Normalizing Flow
https://github.com/andrewevag/SemEval2025-Task8-TQA
공식 코드https://github.com/andvg3/Grasp-Anything
공식 코드Grasp-Anything: Large-scale Grasp Dataset from Foundation Models
https://github.com/andyfrancesco29/audio-visual-figure-skating
공식 코드Skating-Mixer: Long-Term Sport Audio-Visual Modeling with MLPs
https://github.com/andyzoujm/autocast
공식 코드https://github.com/angusdujw/diversity-driven-synthesis
공식 코드Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight Adjustment
https://github.com/anindex/gtmp
공식 코드https://github.com/ankanbhunia/PIDM
공식 코드https://github.com/ankilab/spoken-100
공식 코드https://github.com/anon23423589675234/zero-shot-reward-models
공식 코드ZYN: Zero-Shot Reward Models with Yes-No Questions for RLAIF
https://github.com/anonym844/kg-diverse
공식 코드https://github.com/anonymous1100/Distributional-Discrepancy
공식 코드Distributional Discrepancy: A Metric for Unconditional Text Generation
https://github.com/anonymous2nips2023/grug
공식 코드Unifying gradient regularization for Heterogeneous Graph Neural Networks
https://github.com/anthonysicilia/leather-aacl2022
공식 코드LEATHER: A Framework for Learning to Generate Human-like Text in Dialogue
https://github.com/antoinedemathelin/maxwent-expe
공식 코드Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.