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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/iamkanghyunchoi/qimera
공식 코드Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples
https://github.com/ibm/afdistill
공식 코드https://github.com/ibm/kvp10k
공식 코드KVP10k : A Comprehensive Dataset for Key-Value Pair Extraction in Business Documents
https://github.com/ibm/quality-controlled-paraphrase-generation
공식 코드https://github.com/ibm/reprogbert
공식 코드Reprogramming Pretrained Language Models for Antibody Sequence Infilling
https://github.com/ibraheem-moosa/xlm-indic
공식 코드https://github.com/icaros-usc/MarioGAN-LSI
공식 코드Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network
https://github.com/icaros-usc/dqd
공식 코드https://github.com/icaros-usc/dqd-rl
공식 코드Approximating Gradients for Differentiable Quality Diversity in Reinforcement Learning
https://github.com/icaros-usc/evostone2
공식 코드Deep Surrogate Assisted MAP-Elites for Automated Hearthstone Deckbuilding
https://github.com/icaros-usc/pyribs
공식 코드https://github.com/iceli1007/fakeclr
공식 코드FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANs
https://github.com/icon-lab/ResViT
공식 코드ResViT: Residual vision transformers for multi-modal medical image synthesis
https://github.com/icon-lab/syndiff
공식 코드Unsupervised Medical Image Translation with Adversarial Diffusion Models
https://github.com/icoz69/styleavatar3d
공식 코드StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity 3D Avatar Generation
https://github.com/ictnlp/TLAT-NMT
공식 코드Token-level Adaptive Training for Neural Machine Translation
https://github.com/idejie/3DSyn
공식 코드3D Vision and Language Pretraining with Large-Scale Synthetic Data
https://github.com/idiap/abroad-re
공식 코드https://github.com/idiap/gme-sampler
공식 코드https://github.com/idrl-lab/supervised_layout_benchmark
공식 코드https://github.com/iesl/leopard
공식 코드Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks
https://github.com/ignf/flair-2-ai-challenge
공식 코드FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery
https://github.com/ignf/myria3d
공식 코드https://github.com/ignf/pacasam
공식 코드https://github.com/iiezyf/sesm
공식 코드Learning to Select Prototypical Parts for Interpretable Sequential Data Modeling
https://github.com/iipl-hangzhoudianziuniversity/defn-pytorch
공식 코드https://github.com/ijcruic/multi-modal-networks-reveal-patterns-of-operational-similarity-of-terrorist-organizations
공식 코드Multi-modal Networks Reveal Patterns of Operational Similarity of Terrorist Organizations
https://github.com/ikb-a/systematicity-gap-in-vqa
공식 코드https://github.com/ilyaamburg/fair-clustering-for-diverse-and-experienced-groups
공식 코드Hypergraph Clustering for Finding Diverse and Experienced Groups
https://github.com/imatge-upc/egocentric-2017-lta
공식 코드https://github.com/imop-lab/defn-pytorch
공식 코드https://github.com/import-myself/membench
공식 코드MemBench: Towards More Comprehensive Evaluation on the Memory of LLM-based Agents
https://github.com/indian-nlp/assamese-dataset
공식 코드Enhancing Assamese NLP Capabilities: Introducing a Centralized Dataset Repository
https://github.com/indobenchmark/indonlu
공식 코드IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding
https://github.com/indonlp/nusa-writes
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.