https://github.com/stanfordmlgroup/medagentbench
공식 코드MedAgentBench: A Realistic Virtual EHR Environment to Benchmark Medical LLM Agents
Papers with Code 아카이브
Data-free Knowledge Distillation
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/star-center/ros_mp4
공식 코드High-Quality, ROS Compatible Video Encoding and Decoding for High-Definition Datasets
https://github.com/stavrosorf/ev2gym
공식 코드EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and Benchmarking
https://github.com/stegmaierj/CellSynthesis
공식 코드3D fluorescence microscopy data synthesis for segmentation and benchmarking
https://github.com/stepjam/ARM
공식 코드https://github.com/stepmat/ContinuousInformationGain
공식 코드A Continuous Information Gain Measure to Find the Most Discriminatory Problems for AI Benchmarking
https://github.com/stern5497/doc2docbeirir
공식 코드One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial Support
https://github.com/stogiannidis/srbench
공식 코드Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language Models
https://github.com/stonybrooknlp/appworld
공식 코드AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents
https://github.com/stream-bench/stream-bench
공식 코드StreamBench: Towards Benchmarking Continuous Improvement of Language Agents
https://github.com/sunblaze-ucb/llm-api-audit
공식 코드Are You Getting What You Pay For? Auditing Model Substitution in LLM APIs
https://github.com/sunblaze-ucb/verina
공식 코드https://github.com/sunnweiwei/co-bench
공식 코드CO-Bench: Benchmarking Language Model Agents in Algorithm Search for Combinatorial Optimization
https://github.com/sunnyhaze/iml-vit
공식 코드IML-ViT: Benchmarking Image Manipulation Localization by Vision Transformer
https://github.com/suntongtongtong/benchmark-robustness-text-image-compose-retrieval
공식 코드https://github.com/superallen13/GCondenser
공식 코드https://github.com/superjamessyx/robustness_benchmark
공식 코드Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology
https://github.com/supertext/evaluation_deepl_supertext
공식 코드A comparison of translation performance between DeepL and Supertext
https://github.com/suscom-lab/wattsonai
공식 코드WattsOnAI: Measuring, Analyzing, and Visualizing Energy and Carbon Footprint of AI Workloads
https://github.com/swathi-shree-narashiman/alphazip
공식 코드https://github.com/sweetpeach/hummingbird
공식 코드Does BERT Learn as Humans Perceive? Understanding Linguistic Styles through Lexica
https://github.com/sxjscience/automl_multimodal_benchmark
공식 코드Benchmarking Multimodal AutoML for Tabular Data with Text Fields
https://github.com/szc12153/cldyb
공식 코드CLDyB: Towards Dynamic Benchmarking for Continual Learning with Pre-trained Models
https://github.com/taatiteam/motionencoders_parkinsonism_benchmark
공식 코드https://github.com/tablegpt/tablegpt-agent
공식 코드TableGPT2: A Large Multimodal Model with Tabular Data Integration
https://github.com/taco-group/autotrust
공식 코드AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving
https://github.com/taco-group/video-quality-assessment-a-comprehensive-survey
공식 코드https://github.com/tagoyal/snac
공식 코드https://github.com/taisazero/socratic-debugging-benchmark
공식 코드Can Language Models Employ the Socratic Method? Experiments with Code Debugging
https://github.com/talshaharabany/what-is-where-by-looking
공식 코드What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputs
https://github.com/tamlhp/dfd_benchmark
공식 코드A War Beyond Deepfake: Benchmarking Facial Counterfeits and Countermeasures
https://github.com/tangjialiang97/hidfd
공식 코드https://github.com/tanny411/trutheval
공식 코드TruthEval: A Dataset to Evaluate LLM Truthfulness and Reliability
https://github.com/tanvirnwu/hazespace2m
공식 코드https://github.com/tanvirnwu/trifuse
공식 코드LoLI-Street: Benchmarking Low-Light Image Enhancement and Beyond
https://github.com/taohan10200/weather-5k
공식 코드How far are today's time-series models from real-world weather forecasting applications?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.