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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arXiv:2602.06539v1 Announce Type: new Abstract: The Sliced Wasserstein Kernel (SWK) for persistence diagrams was introduced in (Carri{`e}re et al. 2017) as a powerful tool to implicitly embed persistence diagrams in a Hilbert space with reasonable distortion. This kernel is built on the intuition that the Figalli-Gigli distance-that is the partial matching distance routinely used to compare persistence diagrams-resembles the Wasserstein distance used in the optimal transport literature, and that the later could be sliced to define a positive […]

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arXiv:2501.02364v2 Announce Type: replace-cross Abstract: Deep neural networks have attained remarkable success across diverse classification tasks. Recent empirical studies have shown that deep networks learn features that are linearly separable across classes. However, these findings often lack rigorous justifications, even under relatively simple settings. In this work, we address this gap by examining the linear separation capabilities of shallow nonlinear networks. Specifically, inspired by the low intrinsic dimensionality of image data, we model inputs as a union of […]

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The B2B space is filled with analytical audiences, system thinkers, operators, and decision-makers who need useful information to act. But most content written for this audience does the opposite. Instead of enabling decisions, it focuses on providing intelligent data, insights, and explanations that may signal expertise but fail to guide action. Effective content, therefore, is not defined by how much it explains, but by how efficiently it compresses time-to-value and directs the reader toward a clear outcome. In […]

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Large language models (LLMs) have made it remarkably easy to synthesize plausible source code from natural language prompts. While this accelerates software development and supports learning, it also raises new risks for academic integrity, authorship attribution, and responsible AI use. This paper investigates the problem of distinguishing human-written from machine-generated code by comparing two complementary approaches: feature-based detectors built from lightweight, interpretable stylometric and structural properties of code, and embedding-based detectors leveraging pretrained code encoders. Using a recent […]

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Preliminaries Committee Boosting The committee has an equal weight for every prediction from all models, and it gives little improvement than a single model. Then boosting was built for this problem. Boosting is a technique of combining multiple ‘base’ classifiers to produce a form of the committee that: performances better than any of the base classifiers and each base classifier has a different weight factor Adaboost Adaboost is short for adaptive boosting.

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Muon has quickly been adopted in LLM training, yet we don’t see it being talked about in other contexts. Searches for Muon on ConvNets turn up basically no results, despite its announcement including a new training speed record for Cifar-10. In my experience faster training usually comes with better final models, so what’s the deal? Does it not actually scale? Have I missed papers? submitted by /u/lukeiy [link] [comments]

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arXiv:2602.20232v1 Announce Type: new Abstract: Density functional theory (DFT) is the most widely used method for calculating molecular properties; however, its accuracy is often insufficient for quantitative predictions. Coupled-cluster (CC) theory is the most successful method for achieving accuracy beyond DFT and for predicting properties that closely align with experiment. It is known as the ”gold standard” of quantum chemistry. Unfortunately, the high computational cost of CC limits its widespread applicability. In this work, we present the Molecular […]

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arXiv:2601.20894v1 Announce Type: new Abstract: Prompt-based continual learning methods effectively mitigate catastrophic forgetting. However, most existing methods assign a fixed set of prompts to each task, completely isolating knowledge across tasks and resulting in suboptimal parameter utilization. To address this, we consider the practical needs of continual learning and propose a prompt-sharing framework. This framework constructs a global prompt pool and introduces a task-aware gated routing mechanism that sparsely activates a subset of prompts to achieve dynamic decoupling […]

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