Papers with Code 아카이브
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/afruehstueck/VIVE3D
https://github.com/agaier/matNEAT
Data-efficient Neuroevolution with Kernel-Based Surrogate Models
https://github.com/agentica-project/verl-pipeline
Flaming-hot Initiation with Regular Execution Sampling for Large Language Models
https://github.com/aharley/pips2
PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking
https://github.com/ahmednull/l2cs-net
L2CS-Net: Fine-Grained Gaze Estimation in Unconstrained Environments
https://github.com/ahmetustun/fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/ai21labs/lm-evaluation
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
https://github.com/ai4bharat/indic-tts
Towards Building Text-To-Speech Systems for the Next Billion Users
https://github.com/aibeedetect/bfjdet
https://github.com/aimagelab/mammoth
Gradient based sample selection for online continual learning
https://github.com/airc-keti/ke-t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/airi-institute/al_toolbox
https://github.com/airi-institute/styledomain
https://github.com/ajaysub110/A-Neural-Compositional-Paradigm-for-Image-Captioning
https://github.com/ajenningsfrankston/lungmask-master
https://github.com/akoepke/audio-retrieval-benchmark
https://github.com/alanjeffares/joint-ensembles
Joint Training of Deep Ensembles Fails Due to Learner Collusion
https://github.com/alankarj/robust_nlp
https://github.com/albertfgu/diffwave-sashimi
https://github.com/alchemi5t/MNADrc
https://github.com/ale9806/open_clip_with_biomedica
https://github.com/alex98chen/testGAN
https://github.com/alexandra-chron/ntua-slp-wassa-iest2018
https://github.com/alexandra-chron/wassa-2018
https://github.com/alexmaols/elucd
https://github.com/aliciachenw/PIPsUS
PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking
https://github.com/alignmentresearch/go_attack
https://github.com/alirezakazemipour/DIAYN-PyTorch
Diversity is All You Need: Learning Skills without a Reward Function
https://github.com/alirezasalemi7/pr-rag
https://github.com/aliyoussef97/silk-prp
https://github.com/allenai/beacon
BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing
https://github.com/allenai/c4-documentation
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/allenai/dolma
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/allenai/objaverse-xl
https://github.com/allenbeau/pfedbayes
Personalized Federated Learning via Variational Bayesian Inference
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.