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

https://github.com/pku-yuangroup/open-sora-plan

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

https://github.com/poloclub/llm-attribution

WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings

https://github.com/poloclub/llm-attributor

WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings

https://github.com/poloclub/wordflow

WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings

https://github.com/preddy5/A-Deep-Generative-Model-for-Graph-Layout

A Deep Generative Model for Graph Layout

https://github.com/princeton-nlp/swe-agent

SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering

https://github.com/pwc-1/Paper-5/tree/main/mpt

MPTP: Motion-Planning-aware Task Planning for Navigation in Belief Space

https://github.com/qglht/repal

Getting aligned on representational alignment

https://github.com/rajatgermany/qa-nlp

Bidirectional Attention Flow for Machine Comprehension

https://github.com/ram81/il_rl_baselines

PIRLNav: Pretraining with Imitation and RL Finetuning for ObjectNav

https://github.com/rasmusbergpalm/hebbian-evolution

Meta-Learning through Hebbian Plasticity in Random Networks

https://github.com/ray-project/ray/tree/master/rllib

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

https://github.com/riveSunder/harli_learning

Meta-Learning through Hebbian Plasticity in Random Networks

https://github.com/rob530-w22-team25/AVP-SLAM-PLUS

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

https://github.com/rohanchandra30/TrackNPred

Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks

https://github.com/romi514/TLSGAN

Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks

https://github.com/rulematrix/rule-matrix-py

RuleMatrix: Visualizing and Understanding Classifiers with Rules

https://github.com/rvlopes/gloria

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

https://github.com/sagpant/ViZDoom

ViZDoom Competitions: Playing Doom from Pixels

https://github.com/sentient-engineering/agent-q

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

https://github.com/sepsamavi/safe-interactive-crowdnav

Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning

https://github.com/shmsw25/qa-transfer

Bidirectional Attention Flow for Machine Comprehension

https://github.com/sparse-depth-sensing/sparse-depth-sensing

Sparse Depth Sensing for Resource-Constrained Robots

https://github.com/stevezhang1990/dma_rl

Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning

https://github.com/sunnynexus/search-o1

WebThinker: Empowering Large Reasoning Models with Deep Research Capability

https://github.com/surekhamedapati/NLPA_NEO

Bidirectional Attention Flow for Machine Comprehension

https://github.com/suryaiyer/squad-bidaf

Bidirectional Attention Flow for Machine Comprehension

https://github.com/swe-agent/swe-agent

SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering

https://github.com/techit-limtiyakul/bidirectional-attention-flow

Bidirectional Attention Flow for Machine Comprehension

https://github.com/tencentqqgylab/appagent

AppAgent: Multimodal Agents as Smartphone Users

https://github.com/tessavdheiden/SCR

Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning

https://github.com/tikyau/bidaf-quick-test

Bidirectional Attention Flow for Machine Comprehension

https://github.com/tphanson/xupr-drl

Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments

https://github.com/tsinghua-mars-lab/online-hd-map-construction-cvpr2023

VectorMapNet: End-to-end Vectorized HD Map Learning

https://github.com/tub-rip/dvs_mcemvs

Event-based Stereo Depth Estimation: A Survey

https://github.com/tub-rip/es-ptam

Event-based Stereo Depth Estimation: A Survey

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