arXiv Machine Learning
Performance Evaluation of Ising and QUBO Variable Encodings in Boltzmann Machine Learning
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Exact information accounting for SGD methods
The paper presents an exact, information‑theoretic analysis of stochastic gradient descent (SGD) and its variants, showing that a preconditioned SGD step corresponds to a posterior‑mean update in a Gaussian Bayes model. It decomposes one‑step regret into an intrinsic‑time cost and a change in comparator information, extending this split to an identity for the objective itself. The framework links convex convergence, saddle‑point escape, flatness‑generalization trade‑offs, learning‑rate schedules, adaptive optimizers, and various SGD variants, and it is validated on synthetic and real training runs, revealing how different optimizers achieve the same training loss through distinct step characteristics.
Hugging Face Trending Papers
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Perspectives on Tsallis Statistics for Artificial Intelligence
Tsallis statistics generalizes Boltzmann-Gibbs statistical mechanics through a single real parameter $q$ that controls the weight assigned to rare and frequent events. Originally proposed to describe physical systems with long-range correlations, multifractal geometry, and heavy-tailed fluctuations, the framework has become a recurring ingredient in modern artificial intelligence (AI): it underlies sparse attention mechanisms (\textsc{sparsemax} and $α$-\textsc{entmax}), maximum-entropy reinforcement learning with controllable exploration, robust and heavy-tailed probabilistic models, and a family of generalized loss functions and regularizers.
arXiv AI
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The Devil Is in the Reconstruction Loss Scale: Rethinking Optimization in LLM Quantization
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arXiv Machine Learning
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Learning rate adaptive stochastic gradient descent optimization methods: numerical simulations for deep learning methods for partial differential equations and convergence analyses
arXiv:2406. 14340v2 Announce Type: replace-cross Abstract: The standard stochastic gradient descent (SGD) optimization method, as well as adaptive methods such as the Adam optimizer fail to converge if the learning rates do not converge to zero (particularly, in the situation of constant learning rates).
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Gefen: Optimized Stochastic Optimizer
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arXiv Machine Learning
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Gradient-Flow Optimization as Dynamic Random-Effects Inference: Testing and Early Stopping with Applications to Deep Learning
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