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

https://github.com/lanyexiaosa/brltm

Bidirectional Representation Learning from Transformers using Multimodal Electronic Health Record Data to Predict Depression

https://github.com/lbasora/bayesrul

A Benchmark on Uncertainty Quantification for Deep Learning Prognostics

https://github.com/leaffeall/DFS-GDD

Analyzing Fairness in Deepfake Detection With Massively Annotated Databases

https://github.com/learn-to-race/l2r

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

https://github.com/learningmatter-mit/matex

Learning to Extrapolate: A Transductive Approach

https://github.com/leffff/diffusion-forcing

Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion

https://github.com/leo27945875/ASAF-1

Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization

https://github.com/leo27945875/Adversarial-Soft-Advantage-Fitting-1

Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization

https://github.com/leoguelman/BLBF

Doubly Robust Policy Evaluation and Learning

https://github.com/leonguertler/textarena

Evaluating Language Model Agency through Negotiations

https://github.com/lifelong-robot-learning/libero

LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning

https://github.com/lightning-uq-box/lightning-uq-box

Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning

https://github.com/lihenglin/bridge_training_code

FlowRetrieval: Flow-Guided Data Retrieval for Few-Shot Imitation Learning

https://github.com/lily-x/combinatorial-rmab

Neur2SP: Neural Two-Stage Stochastic Programming

https://github.com/lionelblonde/sam-pytorch

Sample-Efficient Imitation Learning via Generative Adversarial Nets

https://github.com/liqs-v2/ctim-rover

ExpeL: LLM Agents Are Experiential Learners

https://github.com/liyuan24/nanodeepresearch

ReAct: Synergizing Reasoning and Acting in Language Models

https://github.com/lkopf/cosy

Labeling Neural Representations with Inverse Recognition

https://github.com/llmbev/talk2bev

Talk2BEV: Language-enhanced Bird's-eye View Maps for Autonomous Driving

https://github.com/lollcat/Soft-Actor-Critic

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

https://github.com/lovelyscientist/rna-seq-vae

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

https://github.com/lucadellalib/sac-beta

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

https://github.com/lucianacendon/simile

Smooth Imitation Learning for Online Sequence Prediction

https://github.com/lucidrains/DALLE2-pytorch

Navigating the Synthetic Realm: Harnessing Diffusion-based Models for Laparoscopic Text-to-Image Generation

https://github.com/lucidrains/imagen-pytorch

Navigating the Synthetic Realm: Harnessing Diffusion-based Models for Laparoscopic Text-to-Image Generation

https://github.com/luck1226/multisource_deepGaussianProcess

Deep Gaussian Processes for Multi-fidelity Modeling

https://github.com/lucylow/Covid_Control

Effective Reinforcement Learning through Evolutionary Surrogate-Assisted Prescription

https://github.com/lukashermann/hulc

What Matters in Language Conditioned Robotic Imitation Learning over Unstructured Data

https://github.com/m-serra/action-inference-for-video-prediction-benchmarking

Predicting Deeper into the Future of Semantic Segmentation

https://github.com/machenslab/dPCA

Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parameters

https://github.com/machine-perception-robotics-group/ABN_CelebA

Attention Branch Network: Learning of Attention Mechanism for Visual Explanation

https://github.com/machine-perception-robotics-group/ABN_CelebA_PyTorch

Attention Branch Network: Learning of Attention Mechanism for Visual Explanation

https://github.com/machine-perception-robotics-group/ST-ABN_PyTorch

ST-ABN: Visual Explanation Taking into Account Spatio-temporal Information for Video Recognition

https://github.com/machine-reasoning-ufrgs/graph-neural-networks

Relational inductive biases, deep learning, and graph networks

https://github.com/machine-reasoning-ufrgs/typed-graph-network

Relational inductive biases, deep learning, and graph networks

https://github.com/magister-informatica-uach/INFO267

An Introduction to Deep Reinforcement Learning

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