Latent Process Models for Functional Network Data

Subjects: “…Artificial Intelligence & Machine Learning…”
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Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models

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On the Ability of Deep Networks to Learn Symmetries from Data: A Neural Kernel Theory

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Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems

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Deep Generative Models: Complexity, Dimensionality, and Approximation

Subjects: “…Artificial Intelligence & Machine Learning…”
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ClimSim-Online: A Large Multi-Scale Dataset and Framework for Hybrid Physics-ML Climate Emulation

Subjects: “…Artificial Intelligence & Machine Learning…”
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Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching

Subjects: “…Artificial Intelligence & Machine Learning…”
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Deep Variational Multivariate Information Bottleneck - A Framework for Variational Losses

Subjects: “…Artificial Intelligence & Machine Learning…”
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Diffeomorphism-based feature learning using Poincaré inequalities on augmented input space

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Finite Expression Method for Solving High-Dimensional Partial Differential Equations

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Randomly Projected Convex Clustering Model: Motivation, Realization, and Cluster Recovery Guarantees

Subjects: “…Artificial Intelligence & Machine Learning…”
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Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary

Subjects: “…Artificial Intelligence & Machine Learning…”
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Optimal and Efficient Algorithms for Decentralized Online Convex Optimization

Subjects: “…Artificial Intelligence & Machine Learning…”
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Characterizing Dynamical Stability of Stochastic Gradient Descent in Overparameterized Learning

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PREMAP: A Unifying PREiMage APproximation Framework for Neural Networks

Subjects: “…Artificial Intelligence & Machine Learning…”
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Score-Aware Policy-Gradient and Performance Guarantees using Local Lyapunov Stability

Subjects: “…Artificial Intelligence & Machine Learning…”
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On the O(sqrt(d)/T^(1/4)) Convergence Rate of RMSProp and Its Momentum Extension Measured by l_1 Norm

Subjects: “…Artificial Intelligence & Machine Learning…”
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Categorical Semantics of Compositional Reinforcement Learning

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Transformers from Diffusion: A Unified Framework for Neural Message Passing

Subjects: “…Artificial Intelligence & Machine Learning…”
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Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning

Subjects: “…Artificial Intelligence & Machine Learning…”
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