https://github.com/Prasoon1207/llm-science-miscommunication
공식 코드Can LLMs replace Neil deGrasse Tyson? Evaluating the Reliability of LLMs as Science Communicators
Papers with Code 아카이브
Knowledge Distillation
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Knowledge distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have higher knowledge capacity than small models, this capacity might not be fully utilized.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/PratikGarai/Semantic-Segmentation-UNet-Federated
공식 코드https://github.com/Precision-Medical-Imaging-Group/BraTS2024-PEDS
공식 코드https://github.com/ProsusAI/stack-eval
공식 코드https://github.com/QData/TextAttack
공식 코드https://github.com/QData/TextAttack-Search-Benchmark
공식 코드https://github.com/QDucasse/nn_benchmark
공식 코드https://github.com/QIU023/SATS_Continual_Semantic_Seg
공식 코드SATS: Self-Attention Transfer for Continual Semantic Segmentation
https://github.com/QizaoWang/DASA-LReID
공식 코드Distribution Aligned Semantics Adaption for Lifelong Person Re-Identification
https://github.com/Qrange-group/SUR-adapter
공식 코드SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language Models
https://github.com/Qualcomm-AI-research/delta-distillation
공식 코드https://github.com/RLGC-Project/RLGC
공식 코드Adaptive Power System Emergency Control using Deep Reinforcement Learning
https://github.com/RSC-Toolkit/rsc-ensemble-kd
공식 코드https://github.com/Raman1121/diffusion_memorization
공식 코드https://github.com/RayeRen/multilingual-kd-pytorch
공식 코드Multilingual Neural Machine Translation with Knowledge Distillation
https://github.com/RelationRx/pyrelational
공식 코드PyRelationAL: a python library for active learning research and development
https://github.com/Reliable-Information-Lab-HEVS/llm_detectors_fall_short
공식 코드LLM Detectors Still Fall Short of Real World: Case of LLM-Generated Short News-Like Posts
https://github.com/RetailMarketingAI/retailsynth-agentsim
공식 코드Simulation-Based Benchmarking of Reinforcement Learning Agents for Personalized Retail Promotions
https://github.com/RingBDStack/CLKD
공식 코드Transferring Knowledge Distillation for Multilingual Social Event Detection
https://github.com/Robert-Morabito/STOP
공식 코드STOP! Benchmarking Large Language Models with Sensitivity Testing on Offensive Progressions
https://github.com/RobinLu1209/Geometer
공식 코드Geometer: Graph Few-Shot Class-Incremental Learning via Prototype Representation
https://github.com/RobustBench/robustbench
공식 코드RobustBench: a standardized adversarial robustness benchmark
https://github.com/Rohit102497/Aux-Drop
공식 코드Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts
https://github.com/RolnickLab/climart
공식 코드https://github.com/Rongjiehuang/ProDiff
공식 코드ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech
https://github.com/RongrongMa/GLocalKD
공식 코드Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation
https://github.com/Rorozhl/CA-MKD
공식 코드https://github.com/Ruifeng-Tan/BatteryLife
공식 코드BatteryLife: A Comprehensive Dataset and Benchmark for Battery Life Prediction
https://github.com/SCAlabUnical/CLEAR-Bias_LLM_benchmark
공식 코드https://github.com/SIAAAAAA/MMT-PSM
공식 코드Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation
https://github.com/SIP-Lab/Deep-Learning-Mobile
공식 코드Guidelines and Benchmarks for Deployment of Deep Learning Models on Smartphones as Real-Time Apps
https://github.com/SJLeo/FFSD
공식 코드Distilling a Powerful Student Model via Online Knowledge Distillation
https://github.com/SM4DA/CheMFi
공식 코드QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules
https://github.com/SM4DA/MFDeltaML
공식 코드Benchmarking Data Efficiency in $Δ$-ML and Multifidelity Models for Quantum Chemistry
https://github.com/SUTDCV/SUTD-TrafficQA
공식 코드https://github.com/SYangDong/tse-t
공식 코드Temporal Self-Ensembling Teacher for Semi-Supervised Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.