Modern neural translation models based on the Transformer architecture are known for their high performance, particularly when trained on high-resource datasets. A standard next-token prediction training strategy, while widely adopted in practice, may lead to overlooked artifacts such as representation collapse. Previous works have shown that this problem is especially pronounced in the representation of the deeper Transformer layers, where it often fails to efficiently utilize the geometric space. Representation collapse is even more evident in end-to-end training […]

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arXiv:2602.05852v1 Announce Type: cross Abstract: Community detection in networks is a fundamental problem in machine learning and statistical inference, with applications in social networks, biological systems, and communication networks. The stochastic block model (SBM) serves as a canonical framework for studying community structure, and exact recovery, identifying the true communities with high probability, is a central theoretical question. While classical results characterize the phase transition for exact recovery based solely on graph connectivity, many real-world networks contain additional […]

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Hello everyone I am a PhD in ml in bioinformatics and I don’t know which direction to go, i havemultimodal data with very high dimensions I feel everyone is doing foundation models are not as good as a linear regression…somehow it is interesting for to train a foundation model but don’t have resources also as i said it’s still useless. So now I want to do brain storming with you… where to go?what to do? submitted by /u/_A_Lost_Cat_ […]

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Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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arXiv:2601.18981v1 Announce Type: new Abstract: The increasing deployment of Internet-of-Things (IoT)-enabled measurement devices in modern power systems has expanded the cyberattack surface of the grid. As a result, this critical infrastructure is increasingly exposed to cyberattacks, including false data injection attacks (FDIAs) that compromise measurement integrity and threaten reliable system operation. Existing FDIA detection methods primarily exploit spatial correlations and network topology using graph-based learning; however, these approaches often rely on high-dimensional representations and shallow classifiers, limiting their […]

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arXiv:2601.05268v1 Announce Type: new Abstract: We describe a PubMed scale retrieval framework that separates semantic interpretation from metric geometry. A large language model expands a natural language query into concise biomedical phrases; retrieval then operates in a fixed, mean free, approximately isotropic embedding space. Each document and query vector is formed as a weighted mean of token embeddings, projected onto the complement of nuisance axes and compressed by a Johnson Lindenstrauss transform. No parameters are trained. The system […]

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Given several related learning tasks, we propose a nonparametric Bayesian model that captures task relatedness by assuming that the task parameters (i.e., predictors) share a latent subspace. More specifically, the intrinsic dimensionality of the task subspace is not assumed to be known a priori. We use an infinite latent feature model to automatically infer this number (depending on and limited by only the number of tasks). Furthermore, our approach is applicable when the underlying task parameter subspace is […]

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The Box–Cox transformation is widely used to induce approximate normality and linearity in statistical modelling. Within the Power Normal framework, it embeds non-Gaussian variables into a latent Gaussian structure where conditional relationships become linear. However, the inverse transformation does not generally preserve these functional relationships when returning to the original scale. In this paper, we formally analyze the discrepancy between the inverse image of the linear regression function in the transformed domain and the true conditional expectation in […]

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arXiv:2601.07989v1 Announce Type: new Abstract: This paper identifies the Stein-exponent of distributed detection when the sensor communicates to the decision center over a discrete memoryless channel (DMC) subject to one of three stringent communication constraints: 1) The number of channel uses of the DMC grows sublinearly in the number of source observations n; 2) The number of channel uses is n but a block-input cost constraint is imposed almost surely, which grows sublinearly in n; 3) The block-input […]

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