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Newest papers, page 96
Machine learning applications in the heliosphere
Space weather is the study of the near-Earth impact of the processes on the Sun.Understanding Large Language Models as Numerical Optimizers
Large language models (LLMs) have emerged as potential tools for numerical optimization.Bi-Phase Training: Learning Efficiently From Scratch
Pre-training foundational models is computationally expensive as models scale.SkillMem: Compact External Skill Memory for Latent Multi-Agent Reasoning
Latent multi-agent systems reduce inference cost by keeping intermediate reasoning and inter-agent communication in continuous representations.\ Reusable skills, however, are commonly supplied as…Finite-Step Geometry of Marginal-Preserving Velocity Fields in Generative Flows
Generative flows can admit different velocity fields that induce the same continuous marginal evolution but behave differently after numerical discretization.You Don't Need Aligned Representations: Knowledge Distillation via Random Prototype Spaces
A widely held assumption in knowledge distillation is that teacher and student representations must occupy a compatible or explicitly aligned feature space.Federated Learning for Semantic Communications over Multiple Access Channels
This paper presents a federated learning (FL) framework for semantic communication (SemCom) over the multiple access channel (MAC), where multiple distributed transmitters simultaneously convey…SAG-Sep: Sparse Augmented Graphs and Onion-Guided Search for Rounded Capacity Cut Separation
Rounded capacity cuts (RCCs) are among the most effective cuts for capacitated vehicle-routing relaxations, but exact separation is computationally prohibitive at scale.Self-Supervised Latent Action-Conditioned World Modeling for Time Series
Time-series data play an important role in various tasks, such as weather forecasting, web traffic analysis, and speech recognition.Meta-Initialization of Implicit Neural Representations to Accelerate Deformable Medical Image Registration
Implicit neural representation (INR) methods for deformable medical image registration require the network parameters to be optimized for each new image pair, making adaptation speed a practical…Application of PDE emulators for a multi-material, multi-physics simulation problem
Machine learning (ML) architectures are being developed for fast emulation of numerical simulators to accelerate both scientific discovery and engineering design.FORGE: Feasible Online Reduced-Order Reference Generation and Whole-Body Execution for Agile Humanoid Locomotion
This paper presents a two-stage learning framework for versatile and agile humanoid locomotion that couples online reduced-order model (ROM) reference generation with whole-body tracking control.Sometimes less is more: Investigating inductive biases of MLPs and Transformers on noisy k-parity features
We empirically investigate how MLPs and Transformers differ when trained on data sets containing one or more features encoded by noisy k-parity.Separability Is Not Alignability: Representations for Spectrum-to-Molecule Retrieval
Identifying a small molecule from its tandem mass spectrum is limited by paired data.Predicting Sparse Observables in Parameterized Unsteady Flows Using Coarse-Mesh Graph Neural Networks
Full-order surrogates for high-dimensional nonlinear flows are often unnecessary when applications require only selected degrees of freedom.Deep learning for non-transvenous ICD screening: tailored neural network architectures, extended experiments, and explainability
This paper studies two non-transvenous implantable cardioverter-defibrillators (ICDs), namely the Subcutaneous ICD (S-ICD) and the Extravascular ICD (EV-ICD).Towards Generalizing Pretrained All-Atom Representations to Unified Binder Design
PairFormer-based all-atom models demonstrate strong capabilities in biomolecular binder design.Preserving Safety During Sequential Fine-Tuning: A Factorial and Geometric Analysis of Replay
Benign fine-tuning can erode safety alignment in large language models, and sequential adaptation substantially compounds this risk.New Multiple Descent Phenomena Induced by Heterogeneous Data
Modern machine learning models are often trained on large datasets assembled from many different sources, whose statistical properties can differ substantially.Are Seismic Foundation Models More Than Glorified Feature Extractors?
Earthquake sequences are stochastic on two separated timescales: the seismic waves of any individual earthquake propagate over seconds to minutes, while the earthquakes themselves arrive as a…