https://github.com/konsfik/Monte-Carlo-Elites
공식 코드Monte Carlo Elites: Quality-Diversity Selection as a Multi-Armed Bandit Problem
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/koudounasalkis/AI4Voice
공식 코드https://github.com/koziarskilab/scent
공식 코드Scalable and Cost-Efficient de Novo Template-Based Molecular Generation
https://github.com/kpkqwq/lspg
공식 코드Multilingual Lexical Simplification via Paraphrase Generation
https://github.com/krafton-ai/LaPael
공식 코드Latent Paraphrasing: Perturbation on Layers Improves Knowledge Injection in Language Models
https://github.com/krafton-ai/das
공식 코드Test-time Alignment of Diffusion Models without Reward Over-optimization
https://github.com/krennic999/STAR
공식 코드STAR: Scale-wise Text-to-image generation via Auto-Regressive representations
https://github.com/ksandeshk/lens
공식 코드https://github.com/kumarmanas/SHIFT
공식 코드Uncertainty-Aware Trajectory Prediction via Rule-Regularized Heteroscedastic Deep Classification
https://github.com/kuofenggao/verbose_images
공식 코드Inducing High Energy-Latency of Large Vision-Language Models with Verbose Images
https://github.com/kvfrans/fre
공식 코드Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings
https://github.com/kvignesh1420/cot-icl-lab
공식 코드CoT-ICL Lab: A Petri Dish for Studying Chain-of-Thought Learning from In-Context Demonstrations
https://github.com/kwanwaichung/jotr
공식 코드JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialog Policy Learning
https://github.com/kwatcharasupat/source-separation-landing
공식 코드https://github.com/kylesargent/zeronvs
공식 코드ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single Image
https://github.com/l-lyl/dpplikelihoods4seqrec
공식 코드Determinantal Point Process Likelihoods for Sequential Recommendation
https://github.com/l-zhe/btmpg
공식 코드Pushing Paraphrase Away from Original Sentence: A Multi-Round Paraphrase Generation Approach
https://github.com/l-zhe/corpg
공식 코드Towards Document-Level Paraphrase Generation with Sentence Rewriting and Reordering
https://github.com/l-zhe/vipg
공식 코드https://github.com/l1346792580123/sdfgcn
공식 코드https://github.com/lab-cosmo/pet-mad
공식 코드PET-MAD, a universal interatomic potential for advanced materials modeling
https://github.com/lab-v2/diversity_measures
공식 코드Diversity Measures: Domain-Independent Proxies for Failure in Language Model Queries
https://github.com/lagrant/smartstart
공식 코드Smart Starts: Accelerating Convergence through Uncommon Region Exploration
https://github.com/lamda-bbo/refqd
공식 코드https://github.com/lamda-rl/gentle
공식 코드https://github.com/lancopku/DPGAN
공식 코드https://github.com/lancopku/mr-vpc
공식 코드Towards Multimodal Video Paragraph Captioning Models Robust to Missing Modality
https://github.com/lancopku/text-autoaugment
공식 코드Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification
https://github.com/langliu95/divergence-frontier-bounds
공식 코드https://github.com/lanqingl/scs
공식 코드Visual Prompt Selection for In-Context Learning Segmentation
https://github.com/laprf/dstc
공식 코드Dual-stage Hyperspectral Image Classification Model with Spectral Supertoken
https://github.com/laurieburchell/exploring-diversity-bt
공식 코드Exploring Diversity in Back Translation for Low-Resource Machine Translation
https://github.com/layer6ai-labs/DRESS
공식 코드DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse Tasks
https://github.com/layer6ai-labs/dgm-eval
공식 코드Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
https://github.com/lazychih114/AgentGen-Reproduction
공식 코드https://github.com/lcalem/diva
공식 코드Diverse Probabilistic Trajectory Forecasting with Admissibility Constraints
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.