Stochastic Optimization in Semi-Discrete Optimal Transport: Convergence Analysis and Minimax Rate

The article discusses the application of machine learning in representing subgrid-scale (SGS) dynamics, particularly in the context of quasi-geostrophic turbulence within planetary interiors. It highlights the effectiveness of online end-to-end learning methods, which involve the dynamical solver in the training process, showing superior performance compared to traditional physics-based models. The study focuses on two-dimensional turbulent flow in axisymmetric bounded domains, exploring various geometries and rotation rates.

This technical note discusses the derivative of the truncated singular value and eigenvalue decomposition, which is crucial for applications in machine learning and computational physics. It emphasizes the need for stable and efficient linear algebra gradient computations, particularly in the context of automatic differentiation techniques. The note builds on previous work, providing a detailed explanation of how to derive the relevant terms while focusing on expressing the derivative concerning the truncated part of the decomposition.

The article discusses the challenges faced in machine learning as applications become more complex and require more than just accuracy. It highlights the prevalent method of aggregating penalties for requirement violations into training objectives, which necessitates careful tuning of hyperparameters. This tuning process can become ineffective with a moderate number of requirements, especially when dealing with equality constraints related to fairness. The work presented derives a generalization theory for equality-constrained statistical learning problems.

A new machine learning-based multimodal framework has been developed for wearable sensor-based archery action recognition and stress estimation. This innovative system utilizes a wrist-worn device equipped with an accelerometer and photoplethysmography (PPG) sensor to collect synchronized motion and physiological data during archery sessions. The framework achieves high accuracy in motion recognition and stress estimation, marking a significant advancement in the analysis of athletes' performance in precision sports.

The paper presents SEPAL, a Scalable Embedding Propagation Algorithm aimed at improving the use of large knowledge graphs in machine learning. Current models face limitations in optimizing for link prediction and require extensive engineering for large graphs due to GPU memory constraints. SEPAL addresses these issues by ensuring global embedding consistency through localized optimization and message passing, evaluated across seven large-scale knowledge graphs for various downstream tasks.

Mobile jammers present a significant threat to 5G networks, especially in military settings. An innovative anti-jamming framework has been proposed, utilizing Multiple Signal Classification (MUSIC) for precise Direction-of-Arrival (DoA) estimation and Minimum Variance Distortionless Response (MVDR) beamforming for adaptive interference suppression. Machine learning enhances DoA prediction for mobile jammers. Simulations indicate an average Signal-to-Noise Ratio (SNR) improvement of 9.58 dB and a DoA estimation accuracy of up to 99.8%, showcasing the framework's effectiveness in dynamic environ…

The article discusses the concept of soft-label training in machine learning, which preserves epistemic uncertainty by treating annotation distributions as ground truth. Traditional methods often collapse diverse human judgments into single labels, leading to misalignment between model certainty and human perception. Empirical results show that soft-label training reduces KL divergence from human annotations by 32% and enhances correlation between model and annotation entropy by 61%, while maintaining accuracy comparable to hard-label training.

The article discusses the significance of hyperparameter tuning in ensuring the convergence of machine learning models, particularly through stochastic gradient descent (SGD). It presents a phase diagram of a multilayer neural network, where each phase reflects unique dynamics of singular values in weight matrices. The study draws parallels with disordered systems, interpreting the loss landscape as a disordered feature space, with the initial variance of weight matrices representing disorder strength and temperature linked to the learning rate and batch size.