Papers with Code 아카이브
Systematic Generalization
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MichaelKevinKelly/IODINE
Multi-Object Representation Learning with Iterative Variational Inference
https://github.com/Pieter-Cawood/Reinforcement-Learning
https://github.com/Sarah-wookey/RL_NetHack_2020
https://github.com/WeiKangda/clutrr
CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text
https://github.com/a-imamshah/savi-pytorch
https://github.com/allenai/zest
https://github.com/ankursikarwar/grounded-compositional-generalization
A Benchmark for Systematic Generalization in Grounded Language Understanding
https://github.com/facebookresearch/dcd
https://github.com/facebookresearch/minimax
https://github.com/facebookresearch/productive_concept_learning
CURI: A Benchmark for Productive Concept Learning Under Uncertainty
https://github.com/frankaging/Reason-SCAN
A Benchmark for Systematic Generalization in Grounded Language Understanding
https://github.com/jlrussin/syntactic_attention
Compositional generalization in a deep seq2seq model by separating syntax and semantics
https://github.com/koustuvsinha/clutrr
CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text
https://github.com/koustuvsinha/clutrr-baselines
CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text
https://github.com/koustuvsinha/clutrr-workshop
CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text
https://github.com/lucidrains/esbn-transformer
Modelling the development of counting with memory-augmented neural networks
https://github.com/opendilab/DI-engine
https://github.com/pemami4911/IODINE.pytorch
Multi-Object Representation Learning with Iterative Variational Inference
https://github.com/pwc-1/Paper-9/tree/main/1/switch_transformers
The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers
https://github.com/raeidsaqur/mgn
CLOSURE: Assessing Systematic Generalization of CLEVR Models
https://github.com/singhgautam/steve
Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos
https://github.com/singhgautam/sysbinder
https://github.com/unitn-sml/rsbench-code
Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts
https://github.com/vimalabs/VIMA
https://github.com/zhixuan-lin/IODINE
Multi-Object Representation Learning with Iterative Variational Inference
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.