https://github.com/daod/inters
공식 코드INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning
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/dariakryvosheieva/syntactic_generalization_multilingual
공식 코드Controlled Evaluation of Syntactic Knowledge in Multilingual Language Models
https://github.com/dariant/id-booth
공식 코드ID-Booth: Identity-consistent Face Generation with Diffusion Models
https://github.com/dasec/ladimo
공식 코드LADIMO: Face Morph Generation through Biometric Template Inversion with Latent Diffusion
https://github.com/data-and-decision-lab/self-improving-RL
공식 코드https://github.com/datphamvn/hsevo
공식 코드https://github.com/david-li0406/smoa
공식 코드SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents
https://github.com/davidberend/FairnessMatters
공식 코드Fair and accurate age prediction using distribution aware data curation and augmentation
https://github.com/davidhofmeyr/bopnn
공식 코드Bags of Projected Nearest Neighbours: Competitors to Random Forests?
https://github.com/davidneson/dpan
공식 코드DPAN: Dynamic Preference-based and Attribute-aware Network for Relevant Recommendations
https://github.com/davidtw999/bemps
공식 코드Diversity Enhanced Active Learning with Strictly Proper Scoring Rules
https://github.com/dayihengliu/KeyMultiHeadline
공식 코드Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline Generation
https://github.com/dazhangyu123/adr
공식 코드https://github.com/dazhangyu123/aem
공식 코드https://github.com/dbash/visda2022-org
공식 코드VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting
https://github.com/dbolya/parc
공식 코드Scalable Diverse Model Selection for Accessible Transfer Learning
https://github.com/dccastro/Morpho-MNIST
공식 코드Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning
https://github.com/dcsaunders/nmt-gender-rerank
공식 코드First the worst: Finding better gender translations during beam search
https://github.com/deanbrand/QSNN
공식 코드A Quantum Leaky Integrate-and-Fire Spiking Neuron and Network
https://github.com/deborapr/mito-nuclear-speciation
공식 코드https://github.com/deborapr/two-islands
공식 코드Diversity patterns and speciation processes in a two-island system with continuous migration
https://github.com/decile-team/submodlib
공식 코드https://github.com/decisionforce/mmTransformer
공식 코드https://github.com/declare-lab/ferret
공식 코드Ferret: Faster and Effective Automated Red Teaming with Reward-Based Scoring Technique
https://github.com/deep-over/film
공식 코드Exploring the Impact of Corpus Diversity on Financial Pretrained Language Models
https://github.com/deep-spin/entmax
공식 코드https://github.com/deep-spin/sparse_text_generation
공식 코드https://github.com/deep-spin/translation-hypothesis-ensembling
공식 코드An Empirical Study of Translation Hypothesis Ensembling with Large Language Models
https://github.com/deephyper/benchmark
공식 코드Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization?
https://github.com/deepinsight/insightface/tree/master/challenges/cvpr23-fas-wild
공식 코드Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results
https://github.com/deeplearning-wisc/args
공식 코드https://github.com/deepmicroscopy/prototype-sampling
공식 코드Leveraging image captions for selective whole slide image annotation
https://github.com/deepmind/deepmind-research
공식 코드https://github.com/deepmind/dm_nevis
공식 코드NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research
https://github.com/deepmind/sonnet
공식 코드https://github.com/deepmind/understanding_deep_generative_models_with_generalized_empirical_likelihood
공식 코드Understanding Deep Generative Models with Generalized Empirical Likelihoods
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.