https://github.com/minjeekim00/Barcode
공식 코드Barcode Method for Generative Model Evaluation driven by Topological Data Analysis
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/minnesotanlp/diversity-extraction-from-llms
공식 코드How Far Can We Extract Diverse Perspectives from Large Language Models?
https://github.com/mirah-official/empathetic-chatbot-asem
공식 코드ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion Modeling
https://github.com/mirunacrt/synflownet
공식 코드SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints
https://github.com/mischad/beyondfid
공식 코드https://github.com/mismayil/creative-story-gen
공식 코드Evaluating Creative Short Story Generation in Humans and Large Language Models
https://github.com/misonuma/instopt
공식 코드Differentiable Instruction Optimization for Cross-Task Generalization
https://github.com/mit-ll/hanabi_anyplay
공식 코드Any-Play: An Intrinsic Augmentation for Zero-Shot Coordination
https://github.com/miulab/syndata-survey
공식 코드https://github.com/miulab/taiwan-llama
공식 코드Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model
https://github.com/miulab/taiwan-llm
공식 코드Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model
https://github.com/mj-storytelling/DiversityTuning
공식 코드Modifying Large Language Model Post-Training for Diverse Creative Writing
https://github.com/mj10/bioseq-gfn-al
공식 코드https://github.com/mjalali/conditional-vendi
공식 코드https://github.com/mkremins/spleenwort
공식 코드Guiding and Diversifying LLM-Based Story Generation via Answer Set Programming
https://github.com/ml-jku/Generative-Topology-Optimization
공식 코드https://github.com/ml-jku/disparate-benefits
공식 코드https://github.com/ml-jku/ginns-geometry-informed-neural-networks
공식 코드https://github.com/ml4its/timevqvae
공식 코드https://github.com/mlpgroup/e-gen
공식 코드E-Gen: Leveraging E-Graphs to Improve Continuous Representations of Symbolic Expressions
https://github.com/mmadmax/culfit
공식 코드CulFiT: A Fine-grained Cultural-aware LLM Training Paradigm via Multilingual Critique Data Synthesis
https://github.com/mmendiet/gfm
공식 코드Towards Geospatial Foundation Models via Continual Pretraining
https://github.com/mmunhappy/icassp2025-pdm
공식 코드Prototype-Driven Multi-Feature Generation for Visible-Infrared Person Re-identification
https://github.com/mnster00/poemtopainting
공식 코드Semi-supervised Chinese Poem-to-Painting Generation via Cycle-consistent Adversarial Networks
https://github.com/mo-youssef/artelingo-28
공식 코드No Culture Left Behind: ArtELingo-28, a Benchmark of WikiArt with Captions in 28 Languages
https://github.com/modelscope/3D-Speaker
공식 코드Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision
https://github.com/modelscope/data-juicer
공식 코드Data-Juicer Sandbox: A Comprehensive Suite for Multimodal Data-Model Co-development
https://github.com/modelscope/facechain
공식 코드FaceChain-FACT: Face Adapter with Decoupled Training for Identity-preserved Personalization
https://github.com/mohaEs/SignCol
공식 코드SignCol: Open-Source Software for Collecting Sign Language Gestures
https://github.com/mohmaj/DFO
공식 코드Beer Organoleptic Optimisation: Utilising Swarm Intelligence and Evolutionary Computation Methods
https://github.com/moinnadeem/characterizing-sampling-algorithms
공식 코드A Systematic Characterization of Sampling Algorithms for Open-ended Language Generation
https://github.com/molecularsets/moses
공식 코드Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
https://github.com/monisha-jega/paraphrasing_embedding_outputs
공식 코드Improving the Diversity of Unsupervised Paraphrasing with Embedding Outputs
https://github.com/mookerr/varges
공식 코드VarGes: Improving Variation in Co-Speech 3D Gesture Generation via StyleCLIPS
https://github.com/moose-eccv22/moose_eccv2022
공식 코드Probing Contextual Diversity for Dense Out-of-Distribution Detection
https://github.com/moranmiz/Cooking-Up-Creativity
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.