arXiv:2602.04548v1 Announce Type: cross Abstract: We develop a general mathematical framework to analyze scaling regimes and derive explicit analytic solutions for gradient flow (GF) in large learning problems. Our key innovation is a formal power series expansion of the loss evolution, with coefficients encoded by diagrams akin to Feynman diagrams. We show that this expansion has a well-defined large-size limit that can be used to reveal different learning phases and, in some cases, to obtain explicit solutions of […]

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arXiv:2601.08073v1 Announce Type: new Abstract: For a (possibly partial) Boolean function $fcolon{0,1}^nto{0,1}$ as well as a query complexity measure $M$ which maps Boolean functions to real numbers, define the composition limit of $M$ on $f$ by $M^*(f)=lim_{ktoinfty} M(f^k)^{1/k}$. We study the composition limits of general measures in query complexity. We show this limit converges under reasonable assumptions about the measure. We then give a surprising result regarding the composition limit of randomized query complexity: we show $R_0^*(f)=max{R^*(f),C^*(f)}$. Among […]

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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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:::info Astounding Stories of Super-Science July, 2008, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. The Call of the Wild – The Law of Club and Fang Astounding Stories of Super-Science July 2008: The Call of the Wild – The Law of Club and Fang By Jack London ::: Buck’s first day on the Dyea beach was like a nightmare. Every hour was filled with shock […]

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This study investigates the feasibility and performance of deploying YOLOv11-based elbow fracture detection models on mobile devices for real-time clinical diagnosis. Motivated 3by clinicians’ poor diagnostic accuracy (54.4%) in interpreting elbow radiographs, we 4developed and evaluated a smartphone application capable of performing inference via 5three pathways: photo library analysis, direct camera capture, and live video streaming. Two YOLOv11 models were trained on approximately 1,100 elbow X-ray images using systematic hyperparameter optimisation and deployed in both FP16 and FP32 […]

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When you type a query into a search engine, something has to decide which documents are actually relevant — and how to rank them. BM25 (Best Matching 25), the algorithm powering search engines like Elasticsearch and Lucene, has been the dominant answer to that question for decades.  It scores documents by looking at three things: how often your query terms appear in a document, how rare those terms are across the entire collection, and whether a document is […]

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arXiv:2603.03319v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used as scalable evaluators of model outputs, but their preference judgments exhibit systematic biases and can diverge from human evaluations. Prior work on LLM-as-a-judge has largely focused on a small, predefined set of hypothesized biases, leaving open the problem of automatically discovering unknown drivers of LLM preferences. We address this gap by studying several embedding-level concept extraction methods for analyzing LLM judge behavior. We compare these methods […]

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