https://github.com/ofa-sys/diverseevol
공식 코드Self-Evolved Diverse Data Sampling for Efficient 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/ofa-sys/instag
공식 코드#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models
https://github.com/ogunlao/glowtts_stdp
공식 코드Stochastic Pitch Prediction Improves the Diversity and Naturalness of Speech in Glow-TTS
https://github.com/ohuelab/npgpt
공식 코드NPGPT: Natural Product-Like Compound Generation with GPT-based Chemical Language Models
https://github.com/olfub/rdt-uncertainty
공식 코드Combining Predictions under Uncertainty: The Case of Random Decision Trees
https://github.com/olivierjeunen/multivariate-policy-learning-recsys-2024
공식 코드Multi-Objective Recommendation via Multivariate Policy Learning
https://github.com/omron-sinicx/com_kitchens
공식 코드COM Kitchens: An Unedited Overhead-view Video Dataset as a Vision-Language Benchmark
https://github.com/oneal2000/stard
공식 코드STARD: A Chinese Statute Retrieval Dataset with Real Queries Issued by Non-professionals
https://github.com/onehour0108/mitdet
공식 코드Rethinking Mitosis Detection: Towards Diverse Data and Feature Representation
https://github.com/onion-liu/BlendGAN
공식 코드BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation
https://github.com/openai/glide-text2im
공식 코드GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
https://github.com/openai/guided-diffusion
공식 코드https://github.com/openai/sparse_attention
공식 코드https://github.com/opendatalab/OmniDocBench
공식 코드OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations
https://github.com/opendatalab/PDF-Extract-Kit
공식 코드MinerU: An Open-Source Solution for Precise Document Content Extraction
https://github.com/opendatalab/dsdl-sdk
공식 코드DSDL: Data Set Description Language for Bridging Modalities and Tasks in AI Data
https://github.com/opendatalab/mineru
공식 코드MinerU: An Open-Source Solution for Precise Document Content Extraction
https://github.com/opendatalab/unimernet
공식 코드UniMERNet: A Universal Network for Real-World Mathematical Expression Recognition
https://github.com/opendatalab/wanjuan3.0
공식 코드WanJuanSiLu: A High-Quality Open-Source Webtext Dataset for Low-Resource Languages
https://github.com/opendrivelab/agibot-world
공식 코드Is Diversity All You Need for Scalable Robotic Manipulation?
https://github.com/opengvlab/diffagent
공식 코드DiffAgent: Fast and Accurate Text-to-Image API Selection with Large Language Model
https://github.com/openlmlab/longwanjuan
공식 코드LongWanjuan: Towards Systematic Measurement for Long Text Quality
https://github.com/openrl-lab/diversepolicies
공식 코드Diverse Policies Converge in Reward-free Markov Decision Processe
https://github.com/opensafetylab/salad-bench
공식 코드SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models
https://github.com/openvinotoolkit/training_extensions
공식 코드Why You Should Try the Real Data for the Scene Text Recognition
https://github.com/openvlg/della
공식 코드https://github.com/openxrlab/xrfeitoria
공식 코드SynBody: Synthetic Dataset with Layered Human Models for 3D Human Perception and Modeling
https://github.com/optas/latent_3d_points
공식 코드Learning Representations and Generative Models for 3D Point Clouds
https://github.com/optsuite/mcpg
공식 코드https://github.com/oran-ac/lot-crs
공식 코드Alleviating the Long-Tail Problem in Conversational Recommender Systems
https://github.com/orpatashnik/BalaGAN
공식 코드BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal Transfer
https://github.com/osirislambert/chatbot-evaulator
공식 코드https://github.com/osu-nlp-group/mqa
공식 코드Multimodal Question Answering for Unified Information Extraction
https://github.com/oswaldoludwig/Adversarial-Learning-for-Generative-Conversational-Agents
공식 코드End-to-end Adversarial Learning for Generative Conversational Agents
https://github.com/oswaldoludwig/Seq2seq-Chatbot-for-Keras
공식 코드End-to-end Adversarial Learning for Generative Conversational Agents
https://github.com/oulu-imeds/greedy_ensembles_training
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.