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Inductive Bias

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Showing 76–100 of 1529 papers

TitleStatusHype
The Spectral Bias of Shallow Neural Network Learning is Shaped by the Choice of Non-linearity—0
MIRAM: Masked Image Reconstruction Across Multiple Scales for Breast Lesion Risk Prediction—0
Deep Cut-informed Graph Embedding and Clustering—0
Make Haste Slowly: A Theory of Emergent Structured Mixed Selectivity in Feature Learning ReLU Networks—0
IDInit: A Universal and Stable Initialization Method for Neural Network Training—0
EnigmaToM: Improve LLMs' Theory-of-Mind Reasoning Capabilities with Neural Knowledge Base of Entity StatesCode0
Leap: Inductive Link Prediction via Learnable TopologyAugmentationCode0
Wyckoff Transformer: Generation of Symmetric CrystalsCode1
Riemann Tensor Neural Networks: Learning Conservative Systems with Physics-Constrained Networks—0
Dynamical Decoupling of Generalization and Overfitting in Large Two-Layer Networks—0
Teasing Apart Architecture and Initial Weights as Sources of Inductive Bias in Neural Networks—0
Reversal Blessing: Thinking Backward May Outpace Thinking Forward in Multi-choice Questions—0
MetaSym: A Symplectic Meta-learning Framework for Physical Intelligence—0
Network Tomography with Path-Centric Graph Neural Network—0
Are Sparse Autoencoders Useful? A Case Study in Sparse ProbingCode1
A Gap Between the Gaussian RKHS and Neural Networks: An Infinite-Center Asymptotic Analysis—0
Improving Value-based Process Verifier via Structural Prior Injection—0
Connecting the geometry and dynamics of many-body complex systems with message passing neural operators—0
Shaping Inductive Bias in Diffusion Models through Frequency-Based Noise Control—0
Compress image to patches for Vision TransformerCode0
Exploring the Potential of Encoder-free Architectures in 3D LMMsCode0
Powerformer: A Transformer with Weighted Causal Attention for Time-series ForecastingCode0
Data Augmentation and Regularization for Learning Group EquivarianceCode0
Representational Alignment with Chemical Induced Fit for Molecular Relational Learning—0
Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning—0
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