Papers with Code 아카이브

Logical Reasoning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

22

평가표

10

보존된 평가 기록

65

코드

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

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/shivam-131/CLA

Continuous online sequence learning with an unsupervised neural network model

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

Bidirectional Attention Flow for Machine Comprehension

https://github.com/simonbing/crlsanitycheck

CITRIS: Causal Identifiability from Temporal Intervened Sequences

https://github.com/simonvino/graphwavenet_brain_connectivity

Graph WaveNet for Deep Spatial-Temporal Graph Modeling

https://github.com/skytliang/multi-agents-debate

Large Language Models are Zero-Shot Reasoners

https://github.com/sleepinyourhat/vector-entailment

Can recursive neural tensor networks learn logical reasoning?

https://github.com/snap-stanford/KGReasoning

Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs

https://github.com/songmzhang/dskd

Knowledge Fusion of Large Language Models

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

Sparse Depth Sensing for Resource-Constrained Robots

https://github.com/sshleifer/Graph-WaveNet

Graph WaveNet for Deep Spatial-Temporal Graph Modeling

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/superli3/CYRMPR

Neural Software Analysis

https://github.com/superli3/codenavi

Neural Software Analysis

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/sustainlab-group/geography-aware-ssl

Functional Map of the World

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

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

https://github.com/tarashakhurana/detecting-invisible-people

Detecting Invisible People

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

Bidirectional Attention Flow for Machine Comprehension

https://github.com/tencent/hunyuan-large

Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent

https://github.com/tencent/tencent-hunyuan-large

Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent

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/tgebhart/sheaf_kg_transind

Inductive Logical Query Answering in Knowledge Graphs

https://github.com/tginart/sglang

SGLang: Efficient Execution of Structured Language Model Programs

https://github.com/thadumi/logictensornetworks-keras

Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge

https://github.com/tidedra/lmm-r1

LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL

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

https://github.com/ty4b112/pytorch_MINERVA

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.