arXiv:2601.16382v1 Announce Type: new Abstract: While the filtered-x normalized least mean square (FxNLMS) algorithm is widely applied due to its simple structure and easy implementation for active noise control system, it faces two critical limitations: the fixed step-size causes a trade-off between convergence rate and steady-state residual error, and its performance deteriorates significantly in impulsive noise environments. To address the step-size constraint issue, we propose the switched mbox{step-size} FxNLMS (SSS-FxNLMS) algorithm. Specifically, we derive the mbox{mean-square} deviation (MSD) […]

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arXiv:2603.02228v1 Announce Type: new Abstract: The proof that Large Language Models (LLMs) augmented with external read-write memory constitute a computationally universal system has established the theoretical foundation for general-purpose agents. However, existing implementations face a critical bottleneck: the finite and costly Context Window, which functions not as infinite memory but as a scarce semantic cache. In this work, we introduce textit{Neural Paging}, a hierarchical architecture that decouples symbolic reasoning from information resource management. We formulate the textit{Context Paging […]

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arXiv:2601.05340v1 Announce Type: new Abstract: In this paper, we explore new connections between the cycles in the graph of low-density parity-check (LDPC) codes and the eigenvalues of the corresponding adjacency matrix. The resulting observations are used to derive fast, simple, recursive formulas for the number of cycles $N_{2k}$ of length $2k$, $k

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Despite the success of machine learning (ML) in surrogate modeling, its use in radiation dose assessment is limited by safety-critical constraints, scarce training-ready data, and challenges in selecting suitable architectures for physics-dominated systems. Within this context, rapid and accurate plume shine dose estimation serves as a practical test case, as it is critical for nuclear facility safety assessment and radiological emergency response, while conventional photon-transport-based calculations remain computationally expensive. In this work, an interpolation-assisted ML framework was developed […]

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arXiv:2603.25852v1 Announce Type: new Abstract: The rising adoption of generative AI/ML technologies increases the need to support teens in developing AI/ML literacies. Child-computer interaction research argues that construction activities can support young people in understanding these systems and their implications. Recent exploratory studies demonstrate the feasibility of engaging teens in the construction of very small generative language models (LMs). However, it is unclear how constructing such models may foster the development of teens’ understanding of these systems from […]

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The life of a prescription at Amazon Pharmacy From pricing estimation and regulatory compliance to inventory management and chatbot assistants, machine learning models help Amazon Pharmacy customers stay healthy and save time and money. Conversational AI Alexandre Alves Anita Vila September 30, 01:32 PM October 02, 11:42 AM Pharmacies play a vital role in ensuring patients health, but the process of dispensing medications is far more complex than it may appear. At Amazon Pharmacy, we are using artificial […]

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In this tutorial, we explore how to use Google’s LangExtract library to transform unstructured text into structured, machine-readable information. We begin by installing the required dependencies and securely configuring our OpenAI API key to leverage powerful language models for extraction tasks. Also, we will build a reusable extraction pipeline that enables us to process a range of document types, including contracts, meeting notes, product announcements, and operational logs. Through carefully designed prompts and example annotations, we demonstrate how […]

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arXiv:2511.09425v3 Announce Type: replace-cross Abstract: Theoretically exploring the advantages of neural networks might be one of the most challenging problems in the AI era. An adaptive feature program has recently been proposed to analyze feature learning, the characteristic property of neural networks, in a more abstract way. Motivated by the celebrated Le Cam equivalence, we advocate the over-parameterized sequence models to further simplify the analysis of the training dynamics of adaptive feature program and present several pieces of […]

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arXiv:2603.24304v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have achieved impressive performance in graph-related tasks. However, they suffer from poor generalization on out-of-distribution (OOD) data, as they tend to learn spurious correlations. Such correlations present a phenomenon that GNNs fail to stably learn the mutual information between prediction representations and ground-truth labels under OOD settings. To address these challenges, we formulate a causal graph starting from the essence of node classification, adopt backdoor adjustment to block non-causal […]

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About a year ago I made a Reinforcement Learning From Scratch lecture series and shared it here. It got a great response so I’m posting it again. It covers everything from bandits and Q Learning to DQN REINFORCE and A2C. All implemented from scratch to show how the algorithms actually work. Repo https://github.com/norhum/reinforcement-learning-from-scratch Feedback is always welcomed! submitted by /u/Ok_Cabinet_1397 [link] [comments]

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