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Inductive Bias

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

https://github.com/282857341/nnformer

공식 코드

nnFormer: Interleaved Transformer for Volumetric Segmentation

https://github.com/99731/M2GRL

공식 코드

M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems

https://github.com/AdenosHermes/NeurIPS_2020_Snake

공식 코드

Neural Networks Fail to Learn Periodic Functions and How to Fix It

https://github.com/AishwaryaSivaraman/COMET

공식 코드

Counterexample-Guided Learning of Monotonic Neural Networks

https://github.com/AlexandreChenu/dcil

공식 코드

Divide & Conquer Imitation Learning

https://github.com/AliciaCurth/CATENets

공식 코드

On Inductive Biases for Heterogeneous Treatment Effect Estimation

https://github.com/AlifAshrafee/ViT-pytorch-for-Cooking-State-Recognition

공식 코드

Rethinking Cooking State Recognition with Vision Transformers

https://github.com/Alii-Ganjj/InterpretationsSteeredPruning

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Interpretations Steered Network Pruning via Amortized Inferred Saliency Maps

https://github.com/AndreasMadsen/stable-nalu

공식 코드

Neural Arithmetic Units

https://github.com/Annbless/ViTAE

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ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias

https://github.com/ArnoutDevos/RegressionNet

공식 코드

Regression Networks for Meta-Learning Few-Shot Classification

https://github.com/Baran-phys/DynamicFormer

공식 코드

Can Transformers Do Enumerative Geometry?

https://github.com/BorealisAI/BMI

공식 코드

Better Long-Range Dependency By Bootstrapping A Mutual Information Regularizer

https://github.com/BorealisAI/ranksim-imbalanced-regression

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RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression

https://github.com/CUAI/Non-Homophily-Benchmarks

공식 코드

New Benchmarks for Learning on Non-Homophilous Graphs

https://github.com/Cerenaut/bilateral-brain

공식 코드

Deep learning in a bilateral brain with hemispheric specialization

https://github.com/CompVis/taming-transformers

공식 코드

Taming Transformers for High-Resolution Image Synthesis

https://github.com/CupidJay/Tobias

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A Random CNN Sees Objects: One Inductive Bias of CNN and Its Applications

https://github.com/CupidJay/Training-Vision-Transformers-with-only-2040-images

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Training Vision Transformers with Only 2040 Images

https://github.com/Dandy5721/GNN-PDE-COV

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Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals

https://github.com/DepenM/Exp-WN

공식 코드

Inductive Bias of Gradient Descent for Weight Normalized Smooth Homogeneous Neural Nets

https://github.com/Dynamics-of-Neural-Systems-Lab/MARBLE

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Interpretable statistical representations of neural population dynamics and geometry

https://github.com/ErdunGAO/MissDAG

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MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models

https://github.com/ExpectationMax/Translational-Equivariant-Performers

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Translational Equivariance in Kernelizable Attention

https://github.com/FengheTan9/Medical-Image-Segmentation-Benchmarks

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CMUNeXt: An Efficient Medical Image Segmentation Network based on Large Kernel and Skip Fusion

https://github.com/FieteLab/Exact-Inductive-Bias

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Towards Exact Computation of Inductive Bias

https://github.com/Gitsamshi/WeakVRD-Captioning

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Improving Image Captioning with Better Use of Captions

https://github.com/GraphPKU/GeoNGNN

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On the Completeness of Invariant Geometric Deep Learning Models

https://github.com/HUJI-Deep/inductive-pooling

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Inductive Bias of Deep Convolutional Networks through Pooling Geometry

https://github.com/HanseonJoo/Habaek

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Habaek: High-performance water segmentation through dataset expansion and inductive bias optimization

https://github.com/Haoqing-Wang/CDFSL-ATA

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Cross-Domain Few-Shot Classification via Adversarial Task Augmentation

https://github.com/Harshs27/GLAD

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GLAD: Learning Sparse Graph Recovery

https://github.com/HazeDT/SGWN

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Filter-informed Spectral Graph Wavelet Networks for Multiscale Feature Extraction and Intelligent Fault Diagnosis

https://github.com/Hosein47/IEA-GAN

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Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning

https://github.com/Hritikbansal/MultivariateTimeSeries_Generalization

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Systematic Generalization in Neural Networks-based Multivariate Time Series Forecasting Models

https://github.com/Hritikbansal/RNNs_SVA_OOD

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Can RNNs trained on harder subject-verb agreement instances still perform well on easier ones?

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