When feedback is absorbed faster than task structure can be evaluated, the learner will favor feedback over truth. A two-timescale model shows this feedback-truth gap is inevitable whenever the two rates differ and vanishes only when they match. We test this prediction across neural networks trained with noisy labels (30 datasets, 2,700 runs), human probabilistic reversal learning (N = 292), and human reward/punishment learning with concurrent EEG (N = 25). In each system, truth is defined operationally: held-out […]

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arXiv:2603.12461v1 Announce Type: new Abstract: 3D DRAM has emerged as a promising approach for continued density scaling, but its viability is limited by routing and hybrid bonding constraints to periphery, which may degrade sensing margin, latency, and array efficiency. With device characteristics and array parasitics extracted from TCAD, SPICE simulations are performed with peri logic in a CMOS-Bonded-Array (CBA). The analysis shows that the bitline strap architecture with amorphous oxide semiconductor (AOS) selectors is essential to manage routing […]

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Dynamic graph anomaly detection (DGAD) is critical for many real-world applications but remains challenging due to the scarcity of labeled anomalies. Existing methods are either unsupervised or semi-supervised: unsupervised methods avoid the need for labeled anomalies but often produce ambiguous boundary, whereas semi-supervised methods can overfit to the limited labeled anomalies and generalize poorly to unseen anomalies. To address this gap, we consider a largely underexplored problem in DGAD: learning a discriminative boundary from normal/unlabeled data, while leveraging […]

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arXiv:2601.11897v1 Announce Type: cross Abstract: Fairness-aware machine learning has recently attracted various communities to mitigate discrimination against certain societal groups in data-driven tasks. For fair supervised learning, particularly in pre-processing, there have been two main categories: data fairness and task-tailored fairness. The former directly finds an intermediate distribution among the groups, independent of the type of the downstream model, so a learned downstream classification/regression model returns similar predictive scores to individuals inputting the same covariates irrespective of their […]

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The most popular blogs of Hacker News in 2025 Michael Lynch maintains HN Popularity Contest, a site that tracks personal blogs on Hacker News and scores them based on how well they perform on that platform. The engine behind the project is the domain-meta.csv CSV on GiHub, a hand-curated list of known personal blogs with author and bio and tag metadata, which Michael uses to separate out personal blog posts from other types of content. I came top […]

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Advancing autonomous green technologies in solar photovoltaic (PV) systems is key to improving sustainability and efficiency in renewable energy production. This study presents a reinforcement learning (RL)-based framework to autonomously optimize the cleaning schedules of PV panels in arid regions, where soiling from dust and other airborne particles significantly reduces energy output. By employing advanced RL algorithms, Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC), the framework dynamically adjusts cleaning intervals based on uncertain environmental conditions. The proposed […]

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arXiv:2602.04596v1 Announce Type: new Abstract: TabPFN is a transformer that achieves state-of-the-art performance on supervised tabular tasks by amortizing Bayesian prediction into a single forward pass. However, there is currently no method for uncertainty decomposition in TabPFN. Because it behaves, in an idealised limit, as a Bayesian in-context learner, we cast the decomposition challenge as a Bayesian predictive inference (BPI) problem. The main computational tool in BPI, predictive Monte Carlo, is challenging to apply here as it requires […]

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Zero-shot learning (ZSL) aims to recognize the unseen classes in the open-world guided by the side-information (e.g., attributes). Its key task is how to infer the latent semantic knowledge between visual and attribute features on seen classes, and thus conducting a desirable semantic knowledge transfer from seen classes to unseen ones. Prior works simply utilize unidirectional attention within a weakly-supervised manner to learn the spurious and limited latent semantic representations, which fail to effectively discover the intrinsic semantic […]

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