https://github.com/sxjscience/automl_multimodal_benchmark
공식 코드Benchmarking Multimodal AutoML for Tabular Data with Text Fields
Papers with Code 아카이브
Benchmarking
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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/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?
https://github.com/taowangzj/llformer
공식 코드Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method
https://github.com/taozhen1110/cudrt
공식 코드Towards Reliable Detection of LLM-Generated Texts: A Comprehensive Evaluation Framework with CUDRT
https://github.com/tapilot-crossing/tapilot_code
공식 코드Tapilot-Crossing: Benchmarking and Evolving LLMs Towards Interactive Data Analysis Agents
https://github.com/tareknaous/readme
공식 코드ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment
https://github.com/taskography/taskography
공식 코드TASKOGRAPHY: Evaluating robot task planning over large 3D scene graphs
https://github.com/taucompling/bliss
공식 코드Benchmarking Neural Network Generalization for Grammar Induction
https://github.com/teddy-xionggz/medrag
공식 코드https://github.com/teddy-xionggz/mirage
공식 코드https://github.com/tekdogan/iccbdc-21
공식 코드Benchmarking Apache Spark and Hadoop MapReduce on Big Data Classification
https://github.com/tencent-ailab/DrugOOD
공식 코드https://github.com/tencent-ailab/rlogist
공식 코드RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement Learning
https://github.com/tencent-alchemy/Alchemy
공식 코드Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
https://github.com/tensorflow/cleverhans
공식 코드Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
https://github.com/terarachang/memdata
공식 코드Do Localization Methods Actually Localize Memorized Data in LLMs? A Tale of Two Benchmarks
https://github.com/terarachang/mempi
공식 코드Do Localization Methods Actually Localize Memorized Data in LLMs? A Tale of Two Benchmarks
https://github.com/ternaus/robot-surgery-segmentation
공식 코드https://github.com/testing-cs/codes
공식 코드CodeS: Towards Code Model Generalization Under Distribution Shift
https://github.com/testing-cs/codes-distribution-shift-benchmark-datasets
공식 코드CodeS: Towards Code Model Generalization Under Distribution Shift
https://github.com/testzer0/ambiqt
공식 코드Benchmarking and Improving Text-to-SQL Generation under Ambiguity
https://github.com/the-ai-alliance/geo-bench-vlm
공식 코드GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial Tasks
https://github.com/the-finai/fino1
공식 코드Fino1: On the Transferability of Reasoning Enhanced LLMs to Finance
https://github.com/theagentcompany/experiments
공식 코드TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.