We deploy an intrusion detection classifier on the STM32N6570-DK, a Cortex-M55 MCU with the Neural-ART NPU. Using the approximate T = 1 SNN–INT8 ANN equivalence, we compile a lightweight MLP to the NPU and evaluate four datasets: NSL-KDD (5-class), UNSW-NB15 (10-class), CICIDS2017 (15-class), and IoT-23 (5-class). Results are reported as mean ± std over multi-seed runs (5–20 seeds), with paired Wilcoxon signed-rank tests and Holm–Bonferroni correction. Across all datasets, INT8 NPU inference runs in 0.29–0.46 ms (2.7–4.2× faster […]

Ver mais

arXiv:2603.10049v1 Announce Type: new Abstract: Ensemble learning is a well established body of methods for machine learning to enhance predictive performance by combining multiple algorithms/models. Combinatorial Fusion Analysis (CFA) has provided method and practice for combining multiple scoring systems, using rank-score characteristic (RSC) function and cognitive diversity (CD), including ensemble method and model fusion. However, there is no general-purpose Python tool available that incorporate these techniques. In this paper we introduce texttt{InFusionLayer}, a machine learning architecture inspired by […]

Ver mais

Artificial intelligence holds strong potential to support clinical decision making in intensive care units where timely and accurate risk assessment is critical. However, many existing models focus on isolated outcomes or limited data types, while clinicians integrate longitudinal history, real time physiology, and heterogeneous clinical information. To address this gap, we developed MDS ICU, a unified multimodal machine learning framework that fuses routinely collected data including demographics, biometrics, vital signs, laboratory values, ECG waveforms, surgical procedures, and medical […]

Ver mais

In this tutorial, we build a complete end-to-end pipeline using NVIDIA Model Optimizer to train, prune, and fine-tune a deep learning model directly in Google Colab. We start by setting up the environment and preparing the CIFAR-10 dataset, then define a ResNet architecture and train it to establish a strong baseline. From there, we apply FastNAS pruning to systematically reduce the model’s complexity under FLOPs constraints while preserving performance. We also handle real-world compatibility issues, restore the optimized […]

Ver mais

arXiv:2601.09855v1 Announce Type: new Abstract: Sequential test-time scaling is a promising training-free method to improve large reasoning model accuracy, but as currently implemented, significant limitations have been observed. Inducing models to think for longer can increase their accuracy, but as the length of reasoning is further extended, it has also been shown to result in accuracy degradation and model instability. This work presents a novel sequential test-time scaling method, Min-Seek, which improves model accuracy significantly over a wide […]

Ver mais

Table of Links Abstract and 1. Introduction Some recent trends in theoretical ML 2.1 Deep Learning via continuous-time controlled dynamical system 2.2 Probabilistic modeling and inference in DL 2.3 Deep Learning in non-Euclidean spaces 2.4 Physics Informed ML Kuramoto model 3.1 Kuramoto models from the geometric point of view 3.2 Hyperbolic geometry of Kuramoto ensembles 3.3 Kuramoto models with several globally coupled sub-ensembles Kuramoto models on higher-dimensional manifolds 4.1 Non-Abelian Kuramoto models on Lie groups 4.2 Kuramoto models […]

Ver mais

arXiv:2603.06609v1 Announce Type: new Abstract: Modern machine learning models are highly expressive but notoriously difficult to analyze statistically. In particular, while black-box predictors can achieve strong empirical performance, they rarely provide valid hypothesis tests or p-values for assessing whether individual features contain information about a target variable. This article presents a practical approach to feature-level hypothesis testing that combines the Conditional Randomization Test (CRT) with TabPFN, a probabilistic foundation model for tabular data. The resulting procedure yields finite-sample […]

Ver mais

Federated Learning (FL) suffers significant performance degradation in real-world deployments characterized by moderate to extreme statistical heterogeneity (non-IID client data). While global aggregation strategies promote broad generalization, they often fail to capture the diversity of local data distributions, leading to suboptimal personalization. We address this problem with FedPrism, a framework that uses two main strategies. First, it uses a Prism Decomposition method that builds each client’s model from three parts: a global foundation, a shared group part for […]

Ver mais

arXiv:2505.10919v3 Announce Type: replace-cross Abstract: Fluid thermodynamics underpins atmospheric dynamics, climate science, industrial applications, and energy systems. However, direct numerical simulations (DNS) of such systems can be computationally prohibitive. To address this, we present a novel physics-informed spatiotemporal surrogate model for Rayleigh-Benard convection (RBC), a canonical example of convective fluid flow. Our approach combines convolutional neural networks, for spatial dimension reduction, with an innovative recurrent architecture, inspired by large language models, to model long-range temporal dynamics. Inference is […]

Ver mais

I’ve been following the renamed COVID-19 epidemic closely since potential exponentials deserve that kind of attention. The last few days have convinced me it’s a good idea to start making contingency plans for machine learning conferences like ICML. The plausible options happen to be structurally aligned with calls to enable reduced travel to machine learning conferences, but of course the need is much more immediate. I’ll discuss relevant observations about COVID-19 and then the impact on machine learning […]

Ver mais