arXiv:2602.07378v1 Announce Type: cross Abstract: The dynamics of gradient-based training in neural networks often exhibit nontrivial structures; hence, understanding them remains a central challenge in theoretical machine learning. In particular, a concept of feature unlearning, in which a neural network progressively loses previously learned features over long training, has gained attention. In this study, we consider the infinite-width limit of a two-layer neural network updated with a large-batch stochastic gradient, then derive differential equations with different time scales, […]

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Differentially private federated learning faces a fundamental tension: privacy protection mechanisms that safeguard client data simultaneously create quantifiable privacy costs that discourage participation, undermining the collaborative training process. Existing incentive mechanisms rely on unbiased client selection, forcing servers to compensate even the most privacy-sensitive clients ("privacy stragglers"), leading to systemic inefficiency and suboptimal resource allocation. We introduce JSAM (Joint client Selection and privacy compensAtion Mechanism), a Bayesian-optimal framework that simultaneously optimizes client selection probabilities and privacy compensation to […]

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arXiv:2601.10863v1 Announce Type: new Abstract: Traditional time series forecasting methods optimize for accuracy alone. This objective neglects temporal consistency, in other words, how consistently a model predicts the same future event as the forecast origin changes. We introduce the forecast accuracy and coherence score (forecast AC score for short) for measuring the quality of probabilistic multi-horizon forecasts in a way that accounts for both multi-horizon accuracy and stability. Our score additionally provides for user-specified weights to balance accuracy […]

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Accurate localization of the seizure onset zone (SOZ) from intracranial EEG (iEEG) is essential for epilepsy surgery but is challenged by complex spatiotemporal seizure dynamics. We propose SpaTeoGL, a spatiotemporal graph learning framework for interpretable seizure network analysis. SpaTeoGL jointly learns window-level spatial graphs capturing interactions among iEEG electrodes and a temporal graph linking time windows based on similarity of their spatial structure. The method is formulated within a smooth graph signal processing framework and solved via an […]

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arXiv:2603.23639v1 Announce Type: new Abstract: We contribute two design studies for augmented reality visualizations that support learning musical instruments. First, we designed simple, glanceable encodings for drum kits, which we display through a projector. As second instrument, we chose guitar and designed visualizations to be displayed either on a screen as an augmented mirror or as an optical see-through AR headset. These modalities allow us to also show information around the instrument and in 3D. We evaluated our […]

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Let’s learn about Design Thinking via these 82 free blog posts. They are ordered by HackerNoon reader engagement data. Visit the Learn Repo or LearnRepo.com to find the most read blog posts about any technology. Design thinking is a human-centered approach to innovation that involves empathizing with users, defining problems, ideating solutions, prototyping, and testing. It fosters creative problem-solving and helps develop solutions that are desirable, feasible, and viable for users. 1. Top 10 Principles of Good Web […]

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I built an open-source Python pipeline that scans multiple codebases, extracts structured information using LLMs, and generates Markdown documentation with Mermaid diagrams — all with incremental processing so only changed files get re-analyzed. No wasted LLM calls, no stale docs. ⭐ ==The entire project is open source — grab it, fork it, ship it: full source code on GitHub. Star CocoIndex if it is helpful!== The Problem Every Engineering Team Knows Too Well Documentation rots. It’s one of the […]

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