Papers with Code 아카이브

Decision Making

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

1

평가표

2

보존된 평가 기록

2

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/YunzhuLi/InfoGAIL

InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations

https://github.com/ZIYU-DEEP/Understanding-Bias-in-Deep-Anomaly-Detection-PyTorch

Causal Confusion in Imitation Learning

https://github.com/ZJUHSY/2020-

ProtoAttend: Attention-Based Prototypical Learning

https://github.com/Zhanghongbin-github/SegNet-Tutorial

Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding

https://github.com/Zoeyyao27/LLM-Semantic-Size-Understanding

WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents

https://github.com/aai-institute/pyDVL

Variance reduced Shapley value estimation for trustworthy data valuation

https://github.com/aangelopoulos/ppi_py

Cross-Prediction-Powered Inference

https://github.com/aaronbae/AnaQA

Multi-hop Reading Comprehension through Question Decomposition and Rescoring

https://github.com/aashish-khub/NearestNeighbors

On adaptivity and minimax optimality of two-sided nearest neighbors

https://github.com/abhshkdz/House3D

Neural Modular Control for Embodied Question Answering

https://github.com/ac-93/soft-actor-critic

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/aced125/sparsemax

ProtoAttend: Attention-Based Prototypical Learning

https://github.com/acm-uiuc/racerbot.rl

End-to-end Driving via Conditional Imitation Learning

https://github.com/activeloopai/Hub

Deep Lake: a Lakehouse for Deep Learning

https://github.com/adamzenith/mapie

Distribution-Free, Risk-Controlling Prediction Sets

https://github.com/agiachris/STAP

Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks

https://github.com/ahmed127011/RNDF

Facial age estimation by deep residual decision making

https://github.com/ai-med/QuickNATv2

QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy

https://github.com/ai-med/quickNAT_pytorch

QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy

https://github.com/ai4co/camp

Equity-Transformer: Solving NP-hard Min-Max Routing Problems as Sequential Generation with Equity Context

https://github.com/aidriver/ChauffeurNet

ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst

https://github.com/aijadugar/commai-django

Assessing the communication gap between AI models and healthcare professionals: explainability, utility and trust in AI-driven clinical decision-making

https://github.com/ain-soph/trojanzoo

Model Agnostic Defence against Backdoor Attacks in Machine Learning

https://github.com/airglow/simitate

Simitate: A Hybrid Imitation Learning Benchmark

https://github.com/airglow/simitate_imitation_learning_benchmark

Simitate: A Hybrid Imitation Learning Benchmark

https://github.com/ajaysub110/rl-pytorch

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/akanimax/Variational_Discriminator_Bottleneck

Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

https://github.com/akashsonowal/ddpo-pytorch

Training Diffusion Models with Reinforcement Learning

https://github.com/alan-turing-institute/tapas

TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data

https://github.com/alescontrela/viper

Mastering Diverse Domains through World Models

https://github.com/alex-petrenko/sample-factory

Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review

https://github.com/alexandonian/relational-set-abstraction

We Have So Much In Common: Modeling Semantic Relational Set Abstractions in Videos

https://github.com/alexandremarcil/DietNetworks

The TCGA Meta-Dataset Clinical Benchmark

https://github.com/alexbarthhd/3DCV_project

A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning

https://github.com/alexgkendall/SegNet-Tutorial

Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding

https://github.com/alexgkendall/caffe-segnet

Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.