arXiv:2507.02998v2 Announce Type: replace Abstract: Rare diseases affect an estimated 300-400 million people worldwide, yet individual conditions remain underdiagnosed and poorly characterized due to their low prevalence and limited clinician familiarity. Computational phenotyping offers a scalable approach to improving rare…
arXiv:2510.15669v1 Announce Type: cross Abstract: Variational autoencoders (VAEs) are a leading approach to address the problem of learning disentangled representations. Typically a single VAE is used and disentangled representations are sought in its continuous latent space. Here we explore a…
arXiv:2411.08590v4 Announce Type: replace Abstract: Associative memory models, such as Hopfield networks and their modern variants, have garnered renewed interest due to advancements in memory capacity and connections with self-attention in transformers. In this work, we introduce a unified framework-Hopfield-Fenchel-Young…
arXiv:2510.15056v1 Announce Type: new Abstract: Reinforcement learning usually assumes a given or sometimes even fixed environment in which an agent seeks an optimal policy to maximize its long-term discounted reward. In contrast, we consider agents that are not limited to…
arXiv:2510.15061v1 Announce Type: new Abstract: Widespread LLM adoption has introduced characteristic repetitive phraseology, termed “slop,” which degrades output quality and makes AI-generated text immediately recognizable. We present Antislop, a comprehensive framework providing tools to both detect and eliminate these overused…
arXiv:2510.15044v1 Announce Type: new Abstract: Credit scoring is a high-stakes task in financial services, where model decisions directly impact individuals’ access to credit and are subject to strict regulatory scrutiny. While Quantum Machine Learning (QML) offers new computational capabilities, its…
arXiv:2510.15047v1 Announce Type: new Abstract: Large Language Models (LLMs) as agents often struggle in out-of-distribution (OOD) scenarios. Real-world environments are complex and dynamic, governed by task-specific rules and stochasticity, which makes it difficult for LLMs to ground their internal knowledge…
arXiv:2510.15038v1 Announce Type: new Abstract: Flow-based Generative Models (FGMs) effectively transform noise into complex data distributions. Incorporating Optimal Transport (OT) to couple noise and data during FGM training has been shown to improve the straightness of flow trajectories, enabling more…
arXiv:2510.15010v1 Announce Type: new Abstract: Wind turbine reliability is critical to the growing renewable energy sector, where early fault detection significantly reduces downtime and maintenance costs. This paper introduces a novel ensemble-based deep learning framework for unsupervised anomaly detection in…
arXiv:2510.15006v1 Announce Type: new Abstract: In most value-based reinforcement learning (RL) algorithms, the agent estimates only the expected reward for each action and selects the action with the highest reward. In contrast, Distributional Reinforcement Learning (DRL) estimates the entire probability…
