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Deep Learning

A Topology-aware Graph Coarsening Framework for Continual Graph Learning
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Machine Learning Deep Learning 🏢 Stevens Institute of Technology
TACO, a novel topology-aware graph coarsening framework, tackles catastrophic forgetting in continual graph learning by efficiently preserving topological information during experience replay, signifi…
A Single-Step, Sharpness-Aware Minimization is All You Need to Achieve Efficient and Accurate Sparse Training
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AI Generated Machine Learning Deep Learning 🏢 Clemson University
Single-step Sharpness-Aware Minimization (S2-SAM) achieves efficient and accurate sparse training by approximating sharpness perturbation via prior gradient information, incurring zero extra cost and …
A scalable generative model for dynamical system reconstruction from neuroimaging data
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AI Generated Machine Learning Deep Learning 🏢 Department of Theoretical Neuroscience, Central Institute of Mental Health (CIMH), Medical Faculty Mannheim, Heidelberg University
New scalable algorithm reconstructs brain dynamics from short neuroimaging data, overcoming limitations of existing methods and enabling more accurate, efficient analysis of large-scale brain activity…
A Recipe for Charge Density Prediction
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Machine Learning Deep Learning 🏢 Massachusetts Institute of Technology
A novel machine learning recipe drastically accelerates charge density prediction in density functional theory, achieving state-of-the-art accuracy while being significantly faster than existing metho…
A PID Controller Approach for Adaptive Probability-dependent Gradient Decay in Model Calibration
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Machine Learning Deep Learning 🏢 Jiangnan University
Deep learning models often suffer from overconfidence; this paper introduces a PID controller to adaptively adjust a probability-dependent gradient decay rate, ensuring consistent optimization of both…
A New Neural Kernel Regime: The Inductive Bias of Multi-Task Learning
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AI Generated Machine Learning Deep Learning 🏢 University of Wisconsin-Madison
Multi-task learning with shallow ReLU networks yields almost always unique solutions equivalent to kernel methods, unlike single-task settings.
A Layer-Wise Natural Gradient Optimizer for Training Deep Neural Networks
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Machine Learning Deep Learning 🏢 Ant Group
LNGD: A Layer-Wise Natural Gradient optimizer drastically cuts deep neural network training time without sacrificing accuracy.
A Functional Extension of Semi-Structured Networks
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Machine Learning Deep Learning 🏢 Munich Center for Machine Learning (MCML)
This paper introduces semi-structured functional networks (SSFNNs), a novel approach that combines interpretable functional regression models with deep neural networks, achieving both high accuracy an…
A Foundation Model for Zero-shot Logical Query Reasoning
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Machine Learning Deep Learning 🏢 Intel AI Lab
ULTRAQUERY: a groundbreaking foundation model for zero-shot logical query reasoning on any knowledge graph, surpassing existing methods’ limitations.
A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening
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AI Generated Machine Learning Deep Learning 🏢 Technion - Israel Institute of Technology
Flexible Subgraph GNNs, achieving scalability via graph products and coarsening, consistently outperform baselines and adapt to varying subgraph numbers.
A Canonicalization Perspective on Invariant and Equivariant Learning
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AI Generated Machine Learning Deep Learning 🏢 Peking University
Canonicalization simplifies invariant and equivariant learning by connecting frames to canonical forms, leading to novel, superior frame designs for eigenvector symmetries.
A Bayesian Approach to Data Point Selection
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AI Generated Machine Learning Deep Learning 🏢 Microsoft Research
BADS: a novel Bayesian approach to data point selection efficiently optimizes deep learning models by jointly inferring instance weights and model parameters using stochastic gradient Langevin dynamic…
4-bit Shampoo for Memory-Efficient Network Training
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AI Generated Machine Learning Deep Learning 🏢 Beijing Normal University
4-bit Shampoo achieves comparable performance to its 32-bit counterpart while drastically reducing memory usage, enabling efficient training of significantly larger neural networks.
$psilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise
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Machine Learning Deep Learning 🏢 Faculty of Computing, Harbin Institute of Technology
e-Softmax: A simple plug-and-play module enhances deep learning model robustness against noisy labels by approximating one-hot vectors, achieving noise-tolerant learning with controllable excess risk.