Papers with Code 아카이브

Decision Making

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보존된 평가 기록

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코드

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

https://github.com/CeroChen/GraphBashedCoordination

Formation Control for Connected and Automated Vehicles on Multi-lane Roads: Relative Motion Planning and Conflict Resolution

https://github.com/Chan-Li/SAS-model

Learning credit assignment

https://github.com/Chanakya-Ekbote/GANs_and_VAC_for_RNA-Sequences

Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification

https://github.com/Cognitive-AI-Systems/MAPF-GPT

Advancing Learnable Multi-Agent Pathfinding Solvers with Active Fine-Tuning

https://github.com/Criss-Wang/BAI-with-Safety-Constraint

Best Arm Identification with Safety Constraints

https://github.com/CurryTang/Graph-LLM

Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning

https://github.com/DLR-RM/stable-baselines3

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

https://github.com/DalBigData/Vessel-RL-Environment

Using Deep Reinforcement Learning Methods for Autonomous Vessels in 2D Environments

https://github.com/DanielTakeshi/IL_ROS_HSR

Deep Transfer Learning of Pick Points on Fabric for Robot Bed-Making

https://github.com/DanielTakeshi/fast_grasp_detect

Deep Transfer Learning of Pick Points on Fabric for Robot Bed-Making

https://github.com/DanielTakeshi/gym-cloth

Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor

https://github.com/Daniil-Selikhanovych/bnn-vi

On the Expressiveness of Approximate Inference in Bayesian Neural Networks

https://github.com/DarLiner/graph_nets

Relational inductive biases, deep learning, and graph networks

https://github.com/DataManagementLab/ParaphraseBench

An End-to-end Neural Natural Language Interface for Databases

https://github.com/DaveFClarke/ml_bias_checking

AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

https://github.com/DavidJanz/successor_uncertainties_atari

Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning

https://github.com/DavidJanz/successor_uncertainties_tabular

Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning

https://github.com/DeepaliVerma/https-github.com-deepmind-graph_nets

Relational inductive biases, deep learning, and graph networks

https://github.com/Denbergvanthijs/imbDRL

Deep Reinforcement Learning for Imbalanced Classification

https://github.com/DonatellaGenovese/Mixture-of-Expert-Graph-Transformer

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection

https://github.com/Dorozhko-Anton/go-explore

Go-Explore: a New Approach for Hard-Exploration Problems

https://github.com/Driesssens/ibp-pytorch

Learning model-based planning from scratch

https://github.com/ENSTA-U2IS-AI/torch-uncertainty

Beyond calibration: estimating the grouping loss of modern neural networks

https://github.com/EbnulMahmood/PatternLab

Deep Learning for Patient-Specific Kidney Graft Survival Analysis

https://github.com/Epsilon10/MARL

Soft Actor-Critic Algorithms and Applications

https://github.com/ErickRosete/MAML-Imitation-Learning

One-Shot Visual Imitation Learning via Meta-Learning

https://github.com/ErikEkstedt/TurnGPT

Hello, It's GPT-2 -- How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems

https://github.com/EvgeniyaMartynova/MLiP_M5

Smooth Pinball Neural Network for Probabilistic Forecasting of Wind Power

https://github.com/FOCAL-ICLR/FOCAL-ICLR

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

https://github.com/FaceOnLive/Face-Recognition-SDK-Android

PIC-Score: Probabilistic Interpretable Comparison Score for Optimal Matching Confidence in Single- and Multi-Biometric (Face) Recognition

https://github.com/FaceOnLive/Realtime-Background-Changer-SDK-Android

PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation

https://github.com/Faceplugin-ltd/FaceRecognition-Android

PIC-Score: Probabilistic Interpretable Comparison Score for Optimal Matching Confidence in Single- and Multi-Biometric (Face) Recognition

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

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

https://github.com/Finspire13/RL-Surgical-Gesture-Segmentation

Deep Reinforcement Learning for Surgical Gesture Segmentation and Classification

https://github.com/GiaLacTRAN/convolutional_deep_gp_random_features

Calibrating Deep Convolutional Gaussian Processes

https://github.com/Habush/nam_jax

Neural Additive Models: Interpretable Machine Learning with Neural Nets

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