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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/idsia/fpainter

공식 코드

Images as Weight Matrices: Sequential Image Generation Through Synaptic Learning Rules

https://github.com/ifujisawa/addition-experiment

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Logical Tasks for Measuring Extrapolation and Rule Comprehension

https://github.com/intellabs/neurocounterfactuals

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NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data Augmentation

https://github.com/irslu/ltrp

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Learning to Rank Patches for Unbiased Image Redundancy Reduction

https://github.com/ismir-24-sub/unsupervised_compositional_representations

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Unsupervised Composable Representations for Audio

https://github.com/ituvisionlab/factorqvae

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Disentanglement with Factor Quantized Variational Autoencoders

https://github.com/ivan-tang-3d/enel

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Exploring the Potential of Encoder-free Architectures in 3D LMMs

https://github.com/jacobandreas/geca

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Good-Enough Compositional Data Augmentation

https://github.com/jakesnell/neural-circuits

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A Metalearned Neural Circuit for Nonparametric Bayesian Inference

https://github.com/jakesnell/prototypical-networks

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Prototypical Networks for Few-shot Learning

https://github.com/james-simon/eigenlearning

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The Eigenlearning Framework: A Conservation Law Perspective on Kernel Regression and Wide Neural Networks

https://github.com/janghyuncho/PiCIE

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PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering

https://github.com/jcchan23/CoMPT

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Learning Attributed Graph Representations with Communicative Message Passing Transformer

https://github.com/jchengai/forecast-mae

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Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoders

https://github.com/jcwang123/BA-Transformer

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Boundary-aware Transformers for Skin Lesion Segmentation

https://github.com/jeremychou28/mtsci

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MTSCI: A Conditional Diffusion Model for Multivariate Time Series Consistent Imputation

https://github.com/jesperhauch/polynomial_deep_learning

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Learning and Generalizing Polynomials in Simulation Metamodeling

https://github.com/jiamang/ibt

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Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation

https://github.com/jieshibo/petl-vit

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Convolutional Bypasses Are Better Vision Transformer Adapters

https://github.com/jincan333/lor-vp

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LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation

https://github.com/jindongwang/transferlearning

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Learning to Match Distributions for Domain Adaptation

https://github.com/jingweio/es-gnn

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ES-GNN: Generalizing Graph Neural Networks Beyond Homophily with Edge Splitting

https://github.com/jkalogero/scalegmn

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Scale Equivariant Graph Metanetworks

https://github.com/jmkuebler/quantumbias

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The Inductive Bias of Quantum Kernels

https://github.com/joeylee-rio/compgen_l2s2

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Learning to Substitute Spans towards Improving Compositional Generalization

https://github.com/john-vastola/gtv-iclr25

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Generalization through variance: how noise shapes inductive biases in diffusion models

https://github.com/johnjaejunlee95/xb-maml

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XB-MAML: Learning Expandable Basis Parameters for Effective Meta-Learning with Wide Task Coverage

https://github.com/johschm/radialbeams

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Learning Continuous Rotation Canonicalization with Radial Beam Sampling

https://github.com/johswald/learning_where_to_learn

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Learning where to learn: Gradient sparsity in meta and continual learning

https://github.com/jonathancrabbe/fourierdiffusion

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Time Series Diffusion in the Frequency Domain

https://github.com/josefina-catoni/ea-vae-uncertainty-in-latent-representations

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Uncertainty in latent representations of variational autoencoders optimized for visual tasks

https://github.com/joshengels/sae-probes

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Are Sparse Autoencoders Useful? A Case Study in Sparse Probing

https://github.com/jrowbottomgit/graff

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Understanding convolution on graphs via energies

https://github.com/juelin/texturesynthesis-stylegan2-pytorch

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Towards Universal Texture Synthesis by Combining Texton Broadcasting with Noise Injection in StyleGAN-2

https://github.com/juyongjiang/adamct

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AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation

https://github.com/jw9730/tokengt

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Pure Transformers are Powerful Graph Learners

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