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Inductive Bias

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Showing 551–575 of 1529 papers

TitleStatusHype
Effective and Interpretable Information Aggregation with Capacity Networks—0
EditVAE: Unsupervised Part-Aware Controllable 3D Point Cloud Shape Generation—0
Echo Planning for Autonomous Driving: From Current Observations to Future Trajectories and Back—0
Can Large Language Models Adapt to Other Agents In-Context?—0
Bundle Neural Networks for message diffusion on graphs—0
Dynamic Recognition of Speakers for Consent Management by Contrastive Embedding Replay—0
A Gap Between the Gaussian RKHS and Neural Networks: An Infinite-Center Asymptotic Analysis—0
Gaining Extra Supervision via Multi-task learning for Multi-Modal Video Question Answering—0
In-Context Translation: Towards Unifying Image Recognition, Processing, and Generation—0
In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning—0
Building Hybrid B-Spline And Neural Network Operators—0
Dynamical Decoupling of Generalization and Overfitting in Large Two-Layer Networks—0
A Principled Permutation Invariant Approach to Mean-Field Multi-Agent Reinforcement Learning—0
Improved EATFormer: A Vision Transformer for Medical Image Classification—0
DUFormer: Solving Power Line Detection Task in Aerial Images using Semantic Segmentation—0
Spectal Harmonics: Bridging Spectral Embedding and Matrix Completion in Self-Supervised Learning—0
Dual Reinforcement-Based Specification Generation for Image De-Rendering—0
Towards Better Understanding of Disentangled Representations via Mutual Information—0
Improved Disentanglement through Learned Aggregation of Convolutional Feature Maps—0
Improved machine learning algorithm for predicting ground state properties—0
Approximately Equivariant Quantum Neural Network for p4m Group Symmetries in Images—0
Sobolev Space Regularised Pre Density Models—0
DSplats: 3D Generation by Denoising Splats-Based Multiview Diffusion Models—0
DSP-informed bandwidth extension using locally-conditioned excitation and linear time-varying filter subnetworks—0
Brain insights improve RNNs' accuracy and robustness for hierarchical control of continually learned autonomous motor motifs—0
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