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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/sensilab/creativediscoveryvae
공식 코드https://github.com/seon82/musedetect
공식 코드A Hierarchical Deep Learning Approach for Minority Instrument Detection
https://github.com/seraphinatarrant/plan-write-revise
공식 코드Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation
https://github.com/serre-lab/diffusion_as_artist
공식 코드Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines?
https://github.com/serre-lab/diversity_vs_recognizability
공식 코드Diversity vs. Recognizability: Human-like generalization in one-shot generative models
https://github.com/seungyoon-choi/dslr_official
공식 코드DSLR: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual Learning
https://github.com/seungyulhan/dac
공식 코드Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration
https://github.com/sf-wa-326/phrase-bert-topic-model
공식 코드Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration
https://github.com/sferes2/modular_QD
공식 코드Quality and Diversity Optimization: A Unifying Modular Framework
https://github.com/sfofgalaxy/jalmbench
공식 코드JALMBench: Benchmarking Jailbreak Vulnerabilities in Audio Language Models
https://github.com/sftekin/rl-focal
공식 코드Multi-Agent Reinforcement Learning with Focal Diversity Optimization
https://github.com/shaharharel/CDN_Molecule
공식 코드Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks
https://github.com/shahineb/deconditional-downscaling
공식 코드https://github.com/shantanu95/exposure-bias-link-rec
공식 코드https://github.com/shaoshitong/DiffuseExpand
공식 코드DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion models
https://github.com/shaoshitong/EDC
공식 코드https://github.com/sharat29ag/CDAL
공식 코드https://github.com/sharif-apu/EdgeNet
공식 코드https://github.com/sharonlevy/open_domain_covidqa
공식 코드Open-Domain Question-Answering for COVID-19 and Other Emergent Domains
https://github.com/sharpiless/rgal
공식 코드Relation-Guided Adversarial Learning for Data-free Knowledge Transfer
https://github.com/shawntanzk/cl-manuscript
공식 코드https://github.com/sheadan/NFMD-ExtractionInstantaneous
공식 코드Extraction of instantaneous frequencies and amplitudes in nonstationary time-series data
https://github.com/sheikhshafayat/multi-fact
공식 코드Multi-FAct: Assessing Factuality of Multilingual LLMs using FActScore
https://github.com/shendu-sw/statistical-loss
공식 코드Statistical Loss and Analysis for Deep Learning in Hyperspectral Image Classification
https://github.com/shesterg/jwsign-machine-translation
공식 코드https://github.com/shgaurav1/DVG
공식 코드https://github.com/shidani/rankingcontextualbandits
공식 코드https://github.com/shijun18/paal-medseg
공식 코드Predictive Accuracy-Based Active Learning for Medical Image Segmentation
https://github.com/shijx12/kqapro_baselines
공식 코드https://github.com/shikiw/dam-vp
공식 코드https://github.com/shiming-chen/genzsl
공식 코드GenZSL: Generative Zero-Shot Learning Via Inductive Variational Autoencoder
https://github.com/shinanzou/ccgr
공식 코드https://github.com/shiningsunnyday/induction
공식 코드https://github.com/shinshoji01/Style-Restricted_GAN
공식 코드https://github.com/shirgur/hp-vae-gan
공식 코드Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.