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

https://github.com/facebookresearch/soundspaces-challenge

SoundSpaces: Audio-Visual Navigation in 3D Environments

https://github.com/farama-foundation/vizdoom

ViZDoom Competitions: Playing Doom from Pixels

https://github.com/feipenghe/DYGIE

Extracting a Knowledge Base of Mechanisms from COVID-19 Papers

https://github.com/galsang/BiDAF-pytorch

Bidirectional Attention Flow for Machine Comprehension

https://github.com/ghus75/Question_Answering

Bidirectional Attention Flow for Machine Comprehension

https://github.com/google-deepmind/meltingpot

Melting Pot 2.0

https://github.com/google-deepmind/scalable_agent

Learning to Navigate in Cities Without a Map

https://github.com/google-deepmind/streetlearn

Learning To Follow Directions in Street View

https://github.com/google-research-datasets/RxR

General Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping

https://github.com/hamidthri/navbot_ppo

Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation

https://github.com/hawkrobe/division_of_labor

The division of labor in communication: Speakers help listeners account for asymmetries in visual perspective

https://github.com/hegde95/ViZDoom_with_Sound

ViZDoom Competitions: Playing Doom from Pixels

https://github.com/heiner/scalable_agent

Learning to Navigate in Cities Without a Map

https://github.com/hsd1503/resnet1d

HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units

https://github.com/huang-xx/STGAT

Social Attention: Modeling Attention in Human Crowds

https://github.com/icmlanon58443043/vizdoom

ViZDoom Competitions: Playing Doom from Pixels

https://github.com/icmlanon58443043/vizdoomicmlanon

ViZDoom Competitions: Playing Doom from Pixels

https://github.com/jacobkrantz/VLN-CE

DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames

https://github.com/jinbac/Yolo_Trees

TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios

https://github.com/josephjwilson/cayley_graph_propagation

Expander Graph Propagation

https://github.com/jxwuyi/House3D

Embodied Question Answering

https://github.com/kanji95/carla-nav-tool

Ground then Navigate: Language-guided Navigation in Dynamic Scenes

https://github.com/karayanni/structurease

PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization

https://github.com/karpathy/llama2.c

Training Compute-Optimal Large Language Models

https://github.com/keunwoochoi/dl4mir

A Tutorial on Deep Learning for Music Information Retrieval

https://github.com/kimsoojie/lab_vcl_360panorama

360 Panorama Synthesis from a Sparse Set of Images with Unknown Field of View

https://github.com/kokikwbt/deepsect

Bidirectional Attention Flow for Machine Comprehension

https://github.com/kritiksoman/GIMP-ML

DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better

https://github.com/laurendelong21/mars

Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning

https://github.com/liu-yushan/PoLo

Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning

https://github.com/liuguitao/AVP-SLAM-PLUS

AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking Lot

https://github.com/lmn-extracts/dcn_plus

Bidirectional Attention Flow for Machine Comprehension

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

Towards mental time travel: a hierarchical memory for reinforcement learning agents

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

Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution

https://github.com/m-hasan-n/pooling

Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks

https://github.com/m-hildebrandt/R2D2

Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning

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