https://github.com/morningmoni/unipelt
공식 코드UniPELT: A Unified Framework for Parameter-Efficient Language Model 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/morningstarovo/emdepart
공식 코드Visual-Semantic Decomposition and Partial Alignment for Document-based Zero-Shot Learning
https://github.com/morphdiv/jmm_diversity
공식 코드A Measure for Transparent Comparison of Linguistic Diversity in Multilingual NLP Data Sets
https://github.com/mourga/contrastive-active-learning
공식 코드https://github.com/moxmiller/counterfactual-reasoning
공식 코드Counterfactual reasoning: an analysis of in-context emergence
https://github.com/mpagli/agree-to-disagree
공식 코드Agree to Disagree: Diversity through Disagreement for Better Transferability
https://github.com/mr-peach0301/flower
공식 코드https://github.com/mraghavan/llm-scattergories
공식 코드https://github.com/mraihan-gmu/mHumanEval-Benchmark
공식 코드mHumanEval -- A Multilingual Benchmark to Evaluate Large Language Models for Code Generation
https://github.com/mrinal18/MAMBA_QA
공식 코드Multilingual State Space Models for Structured Question Answering in Indic Languages
https://github.com/mrweiqk/cultour
공식 코드https://github.com/ms-synthetic-satellite-image/synthetic-satellite-imagery
공식 코드https://github.com/msadat3/mscinli
공식 코드MSciNLI: A Diverse Benchmark for Scientific Natural Language Inference
https://github.com/mueller-franzes/medfusion
공식 코드https://github.com/mulplue/mgf
공식 코드https://github.com/munia03/dermdiff
공식 코드DermDiff: Generative Diffusion Model for Mitigating Racial Biases in Dermatology Diagnosis
https://github.com/muse1998/source-free-domain-generalization
공식 코드Domain-Unified Prompt Representations for Source-Free Domain Generalization
https://github.com/mustang-gan/mustang
공식 코드https://github.com/mutiann/ccc
공식 코드On the Role of Conceptualization in Commonsense Knowledge Graph Construction
https://github.com/muzairkhattak/promptsrc
공식 코드Self-regulating Prompts: Foundational Model Adaptation without Forgetting
https://github.com/mycfhs/dreammix
공식 코드DreamMix: Decoupling Object Attributes for Enhanced Editability in Customized Image Inpainting
https://github.com/mysong7nlper/chatgpt_as_keyphrase_generator
공식 코드https://github.com/myt517/kcod
공식 코드https://github.com/nachifur/rddm
공식 코드https://github.com/nadicasm/syegg
공식 코드Data Augmentation for Generating Synthetic Electrogastrogram Time Series
https://github.com/nairoj/Probing-Simile-from-PLM
공식 코드https://github.com/nalemadi/VME_CDSI_dataset_benchmark
공식 코드VME: A Satellite Imagery Dataset and Benchmark for Detecting Vehicles in the Middle East and Beyond
https://github.com/namtuanly/MTL-TabNet
공식 코드An End-to-End Multi-Task Learning Model for Image-based Table Recognition
https://github.com/nancyquris/xdomainmix
공식 코드https://github.com/naoto0804/SynShadow
공식 코드Learning from Synthetic Shadows for Shadow Detection and Removal
https://github.com/naranjuelo/gc-gan
공식 코드https://github.com/nasaharvest/timl
공식 코드https://github.com/nasir6/zero_shot_detection
공식 코드https://github.com/nataliemaus/robot
공식 코드Discovering Many Diverse Solutions with Bayesian Optimization
https://github.com/naver/disco
공식 코드Aligning Language Models with Preferences through f-divergence Minimization
https://github.com/nccr-itmo/FEDOT
공식 코드Multi-Objective Evolutionary Design of Composite Data-Driven Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.