https://github.com/google-research-datasets/Textual-Entailment-New-Protocols
공식 코드New Protocols and Negative Results for Textual Entailment Data Collection
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/google-research-datasets/bim
공식 코드Benchmarking Attribution Methods with Relative Feature Importance
https://github.com/google-research-datasets/egotempo
공식 코드Omnia de EgoTempo: Benchmarking Temporal Understanding of Multi-Modal LLMs in Egocentric Videos
https://github.com/google-research-datasets/screen_qa
공식 코드ScreenQA: Large-Scale Question-Answer Pairs over Mobile App Screenshots
https://github.com/google-research/android_world
공식 코드AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents
https://github.com/google-research/deep_ope
공식 코드https://github.com/google-research/federated
공식 코드Motley: Benchmarking Heterogeneity and Personalization in Federated Learning
https://github.com/google-research/federated/tree/master/personalization_benchmark
공식 코드Motley: Benchmarking Heterogeneity and Personalization in Federated Learning
https://github.com/google-research/google-research
공식 코드UGSL: A Unified Framework for Benchmarking Graph Structure Learning
https://github.com/google-research/google-research/tree/master/compositional_rl
공식 코드Adversarial Environment Generation for Learning to Navigate the Web
https://github.com/google-research/google-research/tree/master/quantum_sample_learning
공식 코드https://github.com/google-research/google-research/tree/master/scann
공식 코드Accelerating Large-Scale Inference with Anisotropic Vector Quantization
https://github.com/google-research/graphworld
공식 코드https://github.com/google-research/long-range-arena
공식 코드https://github.com/google-research/mnist-c
공식 코드https://github.com/google-research/nasbench
공식 코드NAS-Bench-101: Towards Reproducible Neural Architecture Search
https://github.com/google-research/population-dynamics
공식 코드General Geospatial Inference with a Population Dynamics Foundation Model
https://github.com/google-research/robopianist
공식 코드RoboPianist: Dexterous Piano Playing with Deep Reinforcement Learning
https://github.com/google/deluca
공식 코드Deluca -- A Differentiable Control Library: Environments, Methods, and Benchmarking
https://github.com/google/haloquest
공식 코드HaloQuest: A Visual Hallucination Dataset for Advancing Multimodal Reasoning
https://github.com/gorilla-lab-scut/bico-net
공식 코드BiCo-Net: Regress Globally, Match Locally for Robust 6D Pose Estimation
https://github.com/gorilla-lab-scut/dcl-net
공식 코드DCL-Net: Deep Correspondence Learning Network for 6D Pose Estimation
https://github.com/gorilla-lab-scut/qs3
공식 코드Quasi-Balanced Self-Training on Noise-Aware Synthesis of Object Point Clouds for Closing Domain Gap
https://github.com/gorilla-lab-scut/ttac
공식 코드Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering
https://github.com/gpbench/tinyversegp
공식 코드TinyverseGP: Towards a Modular Cross-domain Benchmarking Framework for Genetic Programming
https://github.com/gpt-fathom/gpt-fathom
공식 코드https://github.com/graph-com/gdl_ds
공식 코드GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shifts
https://github.com/graph-com/gess
공식 코드GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shifts
https://github.com/graph-com/layerdag
공식 코드LayerDAG: A Layerwise Autoregressive Diffusion Model for Directed Acyclic Graph Generation
https://github.com/graphdeeplearning/benchmarking-gnns
공식 코드https://github.com/gregor-ge/foci-benchmark
공식 코드https://github.com/gsig/actions-for-actions
공식 코드What Actions are Needed for Understanding Human Actions in Videos?
https://github.com/gsisaoki/omnidirectional_blender_3d_dataset
공식 코드OB3D: A New Dataset for Benchmarking Omnidirectional 3D Reconstruction Using Blender
https://github.com/gsk-ai/leak-proof-cmap
공식 코드https://github.com/gt-salt/mic
공식 코드The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systems
https://github.com/gt-star-lab/jaxrobotarium
공식 코드JaxRobotarium: Training and Deploying Multi-Robot Policies in 10 Minutes
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.