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Inductive Bias

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Showing 1201–1250 of 1529 papers

TitleStatusHype
Grid Partitioned Attention: Efficient TransformerApproximation with Inductive Bias for High Resolution Detail GenerationCode0
A Theoretical Analysis of Fine-tuning with Linear Teachers—0
Optimality Inductive Biases and Agnostic Guidelines for Offline Reinforcement LearningCode0
Digging Errors in NMT: Evaluating and Understanding Model Errors from Partial Hypothesis Space—0
Rethinking Token-Mixing MLP for MLP-based Vision Backbone—0
Data efficiency in graph networks through equivariance—0
On Incorporating Inductive Biases into VAEsCode0
Co-advise: Cross Inductive Bias Distillation—0
Unsupervised Deep Learning by Injecting Low-Rank and Sparse Priors—0
Adaptive Cross-Modal Prototypes for Cross-Domain Visual-Language Retrieval—0
The Principles of Deep Learning Theory—0
Model-Based Counterfactual Synthesizer for Interpretation—0
A Self-Supervised Framework for Function Learning and Extrapolation—0
Phase Transitions, Distance Functions, and Implicit Neural RepresentationsCode0
Dynamical Mechanism of Sampling-based Stochastic Inference under Probabilistic Population CodesCode0
Differentiable Robust LQR Layers—0
Simulating Continuum Mechanics with Multi-Scale Graph Neural Networks—0
The Inductive Bias of Quantum KernelsCode0
Unsupervised Representation Disentanglement of Text: An Evaluation on Synthetic DatasetsCode0
A novel Deep Neural Network architecture for non-linear system identification—0
Forced Variational Integrator Networks for Prediction and Control of Mechanical Systems—0
Safeguarding against spurious AI-based predictions: The case of automated verbal memory assessment—0
Symmetry-driven graph neural networks—0
Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images—0
How Gradient Descent Separates Data with Neural Collapse: A Layer-Peeled Perspective—0
Is Heterophily A Real Nightmare For Graph Neural Networks Performing Node Classification?—0
Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach—0
A Cognitive Regularizer for Language Modeling—0
Estimating Disentangled Belief about Hidden State and Hidden Task for Meta-RL—0
ChaRRNets: Channel Robust Representation Networks for RF Fingerprinting—0
Towards Error Measures which Influence a Learners Inductive Bias to the Ground Truth—0
On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning—0
A neural anisotropic view of underspecification in deep learning—0
Sifting out the features by pruning: Are convolutional networks the winning lottery ticket of fully connected ones?Code0
Code Structure Guided Transformer for Source Code Summarization—0
Agent-Centric Representations for Multi-Agent Reinforcement Learning—0
Pareto Self-Supervised Training for Few-Shot Learning—0
Joint Attention for Multi-Agent Coordination and Social Learning—0
Autoequivariant Network Search via Group DecompositionCode0
Towards Enabling Meta-Learning from Target ModelsCode0
Scaling up graph homomorphism for classification via sampling—0
Keep Learning: Self-supervised Meta-learning for Learning from Inference—0
Beyond permutation equivariance in graph networks—0
Spatially Dependent U-Nets: Highly Accurate Architectures for Medical Imaging Segmentation—0
Learning Task Decomposition with Ordered Memory Policy Network—0
ConViT: Improving Vision Transformers with Soft Convolutional Inductive BiasesCode0
Relational Weight Priors in Neural Networks for Abstract Pattern Learning and Language Modelling—0
Self-Supervision by Prediction for Object Discovery in Videos—0
On complementing end-to-end human behavior predictors with planning—0
Neural networks can understand compositional functions that humans do not, in the context of emergent communicationCode0
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