Papers with Code 아카이브

Inductive Bias

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

https://github.com/IBM/LNN

공식 코드

LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking

https://github.com/IntelVCL/MultiObjectiveOptimization

공식 코드

Multi-Task Learning as Multi-Objective Optimization

https://github.com/JRowbottomGit/g-adaptivity

공식 코드

G-Adaptivity: optimised graph-based mesh relocation for finite element methods

https://github.com/JoshuaGhost/expred

공식 코드

Explain and Predict, and then Predict Again

https://github.com/Kaiwen-Hou-KHou/spectralRegularization

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Spectral Regularization: an Inductive Bias for Sequence Modeling

https://github.com/LTS4/neural-anisotropy-directions

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Neural Anisotropy Directions

https://github.com/Liang-Qiu/SVRNN-dialogues

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Structured Attention for Unsupervised Dialogue Structure Induction

https://github.com/LixiaoTHU/PartImageNetPP

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PartImageNet++ Dataset: Scaling up Part-based Models for Robust Recognition

https://github.com/MIC-DKFZ/Longitudinal-Difference-Weighting

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Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

https://github.com/Morefre/Understanding-Generalizability-of-Diffusion-Models-Requires-Rethinking-the-Hidden-Gaussian-Structure

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Understanding Generalizability of Diffusion Models Requires Rethinking the Hidden Gaussian Structure

https://github.com/MurtyShikhar/ExpBERT

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ExpBERT: Representation Engineering with Natural Language Explanations

https://github.com/NSAPH-Projects/topological-equivariant-networks

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E(n) Equivariant Topological Neural Networks

https://github.com/NVlabs/CSG

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Contrastive Syn-to-Real Generalization

https://github.com/NewT123-WM/Task_based_neurons

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No One-Size-Fits-All Neurons: Task-based Neurons for Artificial Neural Networks

https://github.com/Nickil21/weakly-supervised-parsing

공식 코드

Co-training an Unsupervised Constituency Parser with Weak Supervision

https://github.com/Noired/GSEM

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Learning Interpretable Disease Self-Representations for Drug Repositioning

https://github.com/Omid-Nejati/Locality-iN-Locality

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Robust Transformer with Locality Inductive Bias and Feature Normalization

https://github.com/Pehlevan-Group/kernel-generalization

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Spectral Bias and Task-Model Alignment Explain Generalization in Kernel Regression and Infinitely Wide Neural Networks

https://github.com/Physics-aware-AI/DiffCoSim

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Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models

https://github.com/Physics-aware-AI/Symplectic-ODENet

공식 코드

Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

https://github.com/PolymathicAI/xVal

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xVal: A Continuous Numerical Tokenization for Scientific Language Models

https://github.com/ReNginx/ADeLA

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ADeLA: Automatic Dense Labeling with Attention for Viewpoint Adaptation in Semantic Segmentation

https://github.com/RichardEvans/apperception

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Making sense of sensory input

https://github.com/Seokju-Cho/Volumetric-Aggregation-Transformer

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Cost Aggregation Is All You Need for Few-Shot Segmentation

https://github.com/Shawn-Guo-CN/GameBias-EmeCom2020

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Inductive Bias and Language Expressivity in Emergent Communication

https://github.com/Shrajan/AAAI-2022

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3D-OOCS: Learning Prostate Segmentation with Inductive Bias

https://github.com/Shrajan/IB_U_Nets

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IB-U-Nets: Improving medical image segmentation tasks with 3D Inductive Biased kernels

https://github.com/SilenceX12138/ProtoGate

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ProtoGate: Prototype-based Neural Networks with Global-to-local Feature Selection for Tabular Biomedical Data

https://github.com/SitaoLuan/ACM-GNN

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Revisiting Heterophily For Graph Neural Networks

https://github.com/SkafteNicki/unsuper

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Explicit Disentanglement of Appearance and Perspective in Generative Models

https://github.com/SwinTransformer/MIM-Depth-Estimation

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Revealing the Dark Secrets of Masked Image Modeling

https://github.com/SwinTransformer/Video-Swin-Transformer

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Video Swin Transformer

https://github.com/TRI-ML/DDAD

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3D Packing for Self-Supervised Monocular Depth Estimation

https://github.com/TRI-ML/packnet-sfm

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3D Packing for Self-Supervised Monocular Depth Estimation

https://github.com/TencentYoutuResearch/SceneSegmentation-SCRL

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Scene Consistency Representation Learning for Video Scene Segmentation

https://github.com/Tianshu996/METSC

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A microstructure estimation Transformer inspired by sparse representation for diffusion MRI

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