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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/owenzx/met-primaug

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Mutual Exclusivity Training and Primitive Augmentation to Induce Compositionality

https://github.com/ozgurkara99/ISNAS-DIP

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ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior

https://github.com/p2333/score

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Robustness and Accuracy Could Be Reconcilable by (Proper) Definition

https://github.com/patricia-a-apellaniz/low_sample_data_generator

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Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce Scenarios

https://github.com/pauliusrauba/SMART_Testing

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Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models

https://github.com/peerdavid/gamma-capsule-network

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Increasing the adversarial robustness and explainability of capsule networks with $γ$-capsules

https://github.com/peiman-m/specconvcnp

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Spectral Convolutional Conditional Neural Processes

https://github.com/phiandark/SiftingFeatures

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Sifting out the features by pruning: Are convolutional networks the winning lottery ticket of fully connected ones?

https://github.com/phlippe/CategoricalNF

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Categorical Normalizing Flows via Continuous Transformations

https://github.com/pigozzif/attentionvsrs

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

https://github.com/pkicki/neural_path_planning

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Speeding up deep neural network-based planning of local car maneuvers via efficient B-spline path construction

https://github.com/pkmtum/physics-enhanced_nn_smalldata

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Physics-enhanced Neural Networks in the Small Data Regime

https://github.com/pkrsn4/gnn-inductive-bias

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Graph Classification with GNNs: Optimisation, Representation and Inductive Bias

https://github.com/pku-ml/architecturemattersgcl

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Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning

https://github.com/pkuai-lingroup/etsmlp

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Incorporating Exponential Smoothing into MLP: A Simple but Effective Sequence Model

https://github.com/pranavphoenix/ViX

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Vision Xformers: Efficient Attention for Image Classification

https://github.com/pranavphoenix/WaveMix

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WaveMix: Resource-efficient Token Mixing for Images

https://github.com/pranavphoenix/WaveMixSR

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WaveMixSR: A Resource-efficient Neural Network for Image Super-resolution

https://github.com/prlab21/maxvit-unet

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MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation

https://github.com/programmerhyy/cm-mlp

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CM-MLP: Cascade Multi-scale MLP with Axial Context Relation Encoder for Edge Segmentation of Medical Image

https://github.com/prometheusXN/D-LADAN

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Distinguish Confusion in Legal Judgment Prediction via Revised Relation Knowledge

https://github.com/pwangcs/hisvit

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Hierarchical Separable Video Transformer for Snapshot Compressive Imaging

https://github.com/qinglaiweicasia/ravensolver

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Multi-Viewpoint and Multi-Evaluation with Felicitous Inductive Bias Boost Machine Abstract Reasoning Ability

https://github.com/qiyaowei/expertise

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Defining Expertise: Applications to Treatment Effect Estimation

https://github.com/raymondyeh07/equivariance_discovery

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Equivariance Discovery by Learned Parameter-Sharing

https://github.com/raymondyeh07/tv_layers_for_cv

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Total Variation Optimization Layers for Computer Vision

https://github.com/realcrane/human-trajectory-prediction-via-neural-social-physics

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Human Trajectory Prediction via Neural Social Physics

https://github.com/reysam93/adaptive_agg_gcn

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Redesigning graph filter-based GNNs to relax the homophily assumption

https://github.com/reysam93/dag_conv_nn

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Convolutional Learning on Directed Acyclic Graphs

https://github.com/rezkaaufar/fast-and-flexible

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The Fast and the Flexible: training neural networks to learn to follow instructions from small data

https://github.com/rfeinman/learning-to-learn

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Learning Inductive Biases with Simple Neural Networks

https://github.com/rick-ali/parameter-free-approximate-equivariance

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Parameter-free approximate equivariance for tasks with finite group symmetry

https://github.com/ringbdstack/pastel

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Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing

https://github.com/roberttlange/es-lottery

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Lottery Tickets in Evolutionary Optimization: On Sparse Backpropagation-Free Trainability

https://github.com/robintyh1/onpolicybaselines

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Discretizing Continuous Action Space for On-Policy Optimization

https://github.com/robot-learning-freiburg/TAPAS

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The Art of Imitation: Learning Long-Horizon Manipulation Tasks from Few Demonstrations

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