arXiv:2402.06388v4 Announce Type: replace Abstract: Although Multi Armed Bandit (MAB) on one hand and the policy gradient approach on the other hand are among the most used frameworks of Reinforcement Learning, the theoretical properties of the policy gradient algorithm used for MAB have not been given enough attention. We investigate in this work the convergence of such a procedure for the situation when a $L2$ regularization term is present jointly with the ‘softmax’ parametrization. We prove convergence under […]

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Assume a teacher provides examples one by one. An approximate incremental SVM computes a sequence of classifiers that are close to the true SVM solutions computed on the successive incremental training sets. We show that simple algorithms can satisfy an averaged accuracy criterion with a computational cost that scales as well as the best SVM algorithms with the number of examples. Finally, we exhibit some experiments highlighting the benefits of joining fast incremental optimization and curriculum and active […]

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arXiv:2601.05254v1 Announce Type: new Abstract: Retrieval-Augmented Generation enhances language models by retrieving external knowledge to support informed and grounded responses. However, traditional RAG methods rely on fragment-level retrieval, limiting their ability to address query-focused summarization queries. GraphRAG introduces a graph-based paradigm for global knowledge reasoning, yet suffers from inefficiencies in information extraction, costly resource consumption, and poor adaptability to incremental updates. To overcome these limitations, we propose TagRAG, a tag-guided hierarchical knowledge graph RAG framework designed for efficient […]

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We propose a generalization of the Multiple-try Metropolis (MTM) algorithm of Liu et al. (2000), which is based on drawing several proposals at each step and randomly choosing one of them on the basis of weights that may be arbitrary chosen. In particular, for Bayesian estimation we also introduce a method based on weights depending on a quadratic approximation of the posterior distribution. The resulting algorithm cannot be reformulated as an MTM algorithm and leads to a comparable […]

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Nvidia announced today at CES 2026 that it would help Siemens’ electronic design automation (EDA) software run on its GPUs in an attempt to speed up the chip-design process. Nearly all computer chips today are designed using EDA tools, and as chip features get smaller and transistors more numerous, the process has grown more computationally […]

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Dear all, first time writing here. I’m a deep learning PhD student trying to decide between a MacBook Air 15 (M4, 32 GB, 1 TB) and a ThinkPad P14s with Ubuntu and an NVIDIA RTX Pro 1000. For context, I originally used a MacBook for years, then switched to a ThinkPad and have been on Ubuntu for a while now. My current machine is an X1 Carbon 7 gen with no GPU, since all heavy training runs on […]

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