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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/erg0dic/transmon_public

공식 코드

Sample-efficient Model-based Reinforcement Learning for Quantum Control

https://github.com/ericmedvet/jgea

공식 코드

Evolving Modular Soft Robots without Explicit Inter-Module Communication using Local Self-Attention

https://github.com/ermongroup/BiasAndGeneralization

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Bias and Generalization in Deep Generative Models: An Empirical Study

https://github.com/ethanbar11/ssm_2d

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2-D SSM: A General Spatial Layer for Visual Transformers

https://github.com/explainableml/isco

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Iterative Superquadric Recomposition of 3D Objects from Multiple Views

https://github.com/eytan-c/cognitivesimplification

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Cognitive Simplification Operations Improve Text Simplification

https://github.com/facebookresearch/convit

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ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases

https://github.com/facebookresearch/luckmatters

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Student Specialization in Deep ReLU Networks With Finite Width and Input Dimension

https://github.com/facebookresearch/mae_st

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Masked Autoencoders As Spatiotemporal Learners

https://github.com/facebookresearch/mega

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Mega: Moving Average Equipped Gated Attention

https://github.com/facebookresearch/quantized_identifiability

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On the Identifiability of Quantized Factors

https://github.com/facebookresearch/segment_this_thing

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Segment This Thing: Foveated Tokenization for Efficient Point-Prompted Segmentation

https://github.com/fanchy/ci2pvit

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Compress image to patches for Vision Transformer

https://github.com/fehmikahraman/corrloss

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Correlation Loss: Enforcing Correlation between Classification and Localization

https://github.com/feng-yufei/Neural-Natural-Logic

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Exploring End-to-End Differentiable Natural Logic Modeling

https://github.com/fenghetan9/cmunext

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

https://github.com/fenghetan9/mobileutr

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MobileUtr: Revisiting the relationship between light-weight CNN and Transformer for efficient medical image segmentation

https://github.com/ferreirafabio/learningdynamics

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Learning Visual Dynamics Models of Rigid Objects using Relational Inductive Biases

https://github.com/fietelab/inductive-bias-complexity

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Model-agnostic Measure of Generalization Difficulty

https://github.com/flennerhag/warpgrad

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Meta-Learning with Warped Gradient Descent

https://github.com/fsherry/equivariant_image_recon

공식 코드

Equivariant neural networks for inverse problems

https://github.com/fuyu-quant/iblm

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Inductive-bias Learning: Generating Code Models with Large Language Model

https://github.com/gallego-posada/eman

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Equivariant Mesh Attention Networks

https://github.com/galmetzer/self-sample

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Self-Sampling for Neural Point Cloud Consolidation

https://github.com/galvinlim/agtcnet

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AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG Classification

https://github.com/gaopengcuhk/Container

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Container: Context Aggregation Network

https://github.com/gbouritsas/Neural3DMM

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Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation

https://github.com/gchrupala/symbolic-bias

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Symbolic inductive bias for visually grounded learning of spoken language

https://github.com/gemelo-ai/vocos

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Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis

https://github.com/ghazalkhalighinejad/approximating-cky

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Approximating CKY with Transformers

https://github.com/ghazi-f/qkvae

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Exploiting Inductive Bias in Transformers for Unsupervised Disentanglement of Syntax and Semantics with VAEs

https://github.com/giancarlok/relationaltasks

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On Neural Architecture Inductive Biases for Relational Tasks

https://github.com/gistvision/dip-denosing

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Rethinking Deep Image Prior for Denoising

https://github.com/google-research/big_vision

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How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers

https://github.com/google-research/google-research

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On Mutual Information Maximization for Representation Learning

https://github.com/google-research/language/tree/master/language/canine

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CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation

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