technocracy

arXiv:2603.23805v1 Announce Type: cross Abstract: Neural Collapse is a phenomenon that helps identify sparse and low rank structures in deep classifiers. Recent work has extended the definition of neural collapse to regression problems, albeit only measuring the phenomenon at the last layer. In this paper, we establish that Neural Regression Collapse (NRC) also occurs below the last layer across different types of models. We show that in the collapsed layers of neural regression models, features lie in a […]

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arXiv:2602.09169v1 Announce Type: new Abstract: Fully finetuning foundation language models (LMs) with billions of parameters is often impractical due to high computational costs, memory requirements, and the risk of overfitting. Although methods like low-rank adapters help address these challenges by adding small trainable modules to the frozen LM, they also increase memory usage and do not reduce inference latency. We uncover an intriguing phenomenon: sparsifying specific model rows and columns enables efficient task adaptation without requiring weight tuning. […]

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arXiv:2509.18349v3 Announce Type: replace Abstract: Meta-learning aims to leverage information across related tasks to improve prediction on unlabeled data for new tasks when only a small number of labeled observations are available (“few-shot” learning). Increased task diversity is often believed to enhance meta-learning by providing richer information across tasks. However, recent work by Kumar et al. (2022) shows that increasing task diversity, quantified through the overall geometric spread of task representations, can in fact degrade meta-learning prediction performance […]

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After this story was published Google’s spokesperson reached out and asked us to publish a slightly different version of that statement. The new statement no longer stated that “it’s critical that we maintain humans in the loop.” — Emanuel Maiberg, 404 Media, Google Employees Internally Share Memes About How Its AI Sucks Tags: ai-ethics, journalism, ai, google

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arXiv:2604.02455v1 Announce Type: new Abstract: Renewable power sources have low marginal pro-duction costs, but may result in high balancing costs due to the inherent production uncertainty. Current day-ahead markets elicit only point production profiles and neglect the degree of uncertainty associated with each generating asset, preventing the market operator from accounting for balancing costs in day-ahead dispatch and ancillary service procurement. This increases total system costs and undermines market efficiency, especially in renewable-heavy power systems. To address this, […]

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arXiv:2602.10221v1 Announce Type: new Abstract: In this work, we address two major issues in recent Denoising Diffusion Probabilistic Models (DDPM): {bf 1)} geometric key feature extraction and {bf 2)} network equivariance. Since the DDPM prediction network relies on the U-net architecture, which is theoretically only translation equivariant, we introduce a geometric approach combined with an equivariance property of the more general Euclidean group, which includes rotations, reflections, and permutations. We introduce the notion of group morphological convolutions in […]

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Release: sqlite-utils 4.0 See sqlite-utils 4.0, now with database schema migrations for details. Tags: sqlite-utils

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Machine learning and artificial intelligence is a field that drives major innovations across different industries. It is predicted that in 2023, the AI market will reach $500 billion, and in 2030, $1,597.1 billion in size. This means that machine learning technologies will continue to be in high demand in the near future. However, the machine learning industry evolves very rapidly: new technologies and scientific research define how new products and services are built. At the end of 2022, […]

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arXiv:2601.07845v1 Announce Type: new Abstract: Rapid motorization in emerging economies such as India has created severe enforcement asymmetries, with over 11 million recorded violations in 2023 against a human policing density of roughly one officer per 4000 vehicles. Traditional surveillance and manual ticketing cannot scale to this magnitude, motivating the need for an autonomous, cooperative, and energy efficient edge AI perception infrastructure. This paper presents a real time roadside perception node for multi class traffic violation analytics and […]

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