Generative AI has emerged as a transformative force in cybersecurity, offering both opportunities for innovation and challenges in threat detection and mitigation. This systematic literature review and meta-analysis synthesizes existing research to evaluate the efficacy of generative AI in cybersecurity applications, focusing on detection performance, overall impact, and threat detection metrics. We conducted a comprehensive analysis of peer-reviewed studies, employing rigorous statistical methods to quantify effect sizes and their significance. The results reveal a substantial negative effect size […]

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arXiv:2408.00955v2 Announce Type: replace Abstract: Gaussian process (GP) models have received increasing attention in recent years due to their superb prediction accuracy and modeling flexibility. To address the computational burdens of GP models for large-scale datasets, distributed learning for GPs are often adopted. Current aggregation models for distributed GPs is not time-efficient when incorporating correlations between GP experts. In this work, we propose a novel approach for aggregated prediction in distributed GPs. The technique is suitable for both […]

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In this work, we provide a sharp theory of scaling laws for two-layer neural networks trained on a class of hierarchical multi-index targets, in a genuinely representation-limited regime. We derive exact information-theoretic scaling laws for subspace recovery and prediction error, revealing how the hierarchical features of the target are sequentially learned through a cascade of phase transitions. We further show that these optimal rates are achieved by a simple, target-agnostic spectral estimator, which can be interpreted as the […]

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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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:::info Astounding Stories of Super-Science April 2004, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. THE COUNTRY OF THE BLIND – XXII. — A VISION OF JUDGMENT. Astounding Stories of Super-Science April 2004: THE COUNTRY OF THE BLIND – XXII. — A VISION OF JUDGMENT. By H. G. Wells ::: I. — Bru-a-a-a. I listened, not understanding. Wa-ra-ra-ra. “Good Lord!” said I, still only half awake. […]

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Hi everyone, I have a question about anonymizing code for conference submissions. I’m submitting an AI/ML paper to a conference and would like to include the code, but the repository needs to be anonymized. In this situation, is it common to create a separate anonymous GitHub account, upload the code there, and then, if the paper is accepted, move it to your official GitHub account later? I’d really appreciate any guidance. Thanks! submitted by /u/Terrible-Chicken-426 [link] [comments]

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arXiv:2601.10859v1 Announce Type: new Abstract: Inverse design tools such as Topology Optimization (TO) can achieve new levels of improvement for high-performance engineered structures. However, widespread use is hindered by high computational times and a black-box nature that inhibits user interaction. Human-in-the-loop TO approaches are emerging that integrate human intuition into the design generation process. However, these rely on the time-consuming bottleneck of iterative region selection for design modifications. To reduce the number of iterative trials, this contribution presents […]

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arXiv:2602.07203v1 Announce Type: new Abstract: Structural Causal Models (SCM) are a powerful framework for describing complicated dynamics across the natural sciences. A particularly elegant way of interpreting SCMs is do-Shapley, a game-theoretic method of quantifying the average effect of $d$ variables across exponentially many interventions. Like Shapley values, computing do-Shapley values generally requires evaluating exponentially many terms. The foundation of our work is a reformulation of do-Shapley values in terms of the irreducible sets of the underlying SCM. […]

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Reinforcement Learning (RL) has achieved impressive success in post-training Large Language Models (LLMs) and Vision-Language Models (VLMs), with on-policy algorithms such as PPO, GRPO, and REINFORCE++ serving as the dominant paradigm. However, these methods discard all collected trajectories after a single gradient update, resulting in poor sample efficiency, particularly wasteful for agentic tasks where multi-turn environment interactions are expensive. While Experience Replay drives sample efficiency in classic RL by allowing agents to reuse past trajectories and prioritize informative […]

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