How to Achieve Prototypical Birth and Death for OOD Detection?
arXiv:2603.15650v1 Announce Type: new
Abstract: Out-of-Distribution (OOD) detection is crucial for the secure deployment of machine learning models, and prototype-based learning methods are among the mainstream strategies for achieving OOD detection. Existing prototype-based learning methods genera...
XLinear: Frequency-Enhanced MLP with CrossFilter for Robust Long-Range Forecasting
arXiv:2603.15645v1 Announce Type: new
Abstract: Time series forecasters are widely used across various domains. Among them, MLP (multi-layer perceptron)-based forecasters have been proven to be more robust to noise compared to Transformer-based forecasters. However, MLP struggles to capture complex...
Human Attribution of Causality to AI Across Agency, Misuse, and Misalignment
arXiv:2603.13236v1 Announce Type: new
Abstract: AI-related incidents are becoming increasingly frequent and severe, ranging from safety failures to misuse by malicious actors. In such complex situations, identifying which elements caused an adverse outcome, the problem of cause selection, is a crit...
Introducing Feature-Based Trajectory Clustering, a clustering algorithm for longitudinal data
arXiv:2603.13254v1 Announce Type: new
Abstract: We present a new algorithm for clustering longitudinal data. Data of this type can be conceptualized as consisting of individuals and, for each such individual, observations of a time-dependent variable made at various times. Generically, the specific...
Continual Fine-Tuning with Provably Accurate and Parameter-Free Task Retrieval
arXiv:2603.13235v1 Announce Type: new
Abstract: Continual fine-tuning aims to adapt a pre-trained backbone to new tasks sequentially while preserving performance on earlier tasks whose data are no longer available. Existing approaches fall into two categories which include input- and parameter-adap...
RFX-Fuse: Breiman and Cutler's Unified ML Engine + Native Explainable Similarity
arXiv:2603.13234v1 Announce Type: new
Abstract: Breiman and Cutler's original Random Forest was designed as a unified ML engine -- not merely an ensemble predictor. Their implementation included classification, regression, unsupervised learning, proximity-based similarity, outlier
detection, miss...
Context-Enriched Natural Language Descriptions of Vessel Trajectories
arXiv:2603.12287v1 Announce Type: new
Abstract: We address the problem of transforming raw vessel trajectory data collected from AIS into structured and semantically enriched representations interpretable by humans and directly usable by machine reasoning systems. We propose a context-aware traject...
On Using Machine Learning to Early Detect Catastrophic Failures in Marine Diesel Engines
arXiv:2603.12733v1 Announce Type: new
Abstract: Catastrophic failures of marine engines imply severe loss of functionality and destroy or damage the systems irreversibly. Being sudden and often unpredictable events, they pose a severe threat to navigation, crew, and passengers. The abrupt nature ma...
No More DeLuLu: Physics-Inspired Kernel Networks for Geometrically-Grounded Neural Computation
arXiv:2603.12276v1 Announce Type: new
Abstract: We introduce the yat-product, a kernel operator combining quadratic alignment with inverse-square proximity. We prove it is a Mercer kernel, analytic, Lipschitz on bounded domains, and self-regularizing, admitting a unique RKHS embedding. Neural Matte...
Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions
arXiv:2603.12296v1 Announce Type: new
Abstract: Deep learning has achieved transformative performance across diverse domains, largely driven by the large-scale, high-quality training data. In contrast, the development of brain-computer interfaces (BCIs) is fundamentally constrained by the limited, ...
Fingerprinting Concepts in Data Streams with Supervised and Unsupervised Meta-Information
arXiv:2603.11094v1 Announce Type: new
Abstract: Streaming sources of data are becoming more common as the ability to collect data in real-time grows. A major concern in dealing with data streams is concept drift, a change in the distribution of data over time, for example, due to changes in environ...
Comparison of Outlier Detection Algorithms on String Data
arXiv:2603.11049v1 Announce Type: new
Abstract: Outlier detection is a well-researched and crucial problem in machine learning. However, there is little research on string data outlier detection, as most literature focuses on outlier detection of numerical data. A robust string data outlier detecti...
Structure-Aware Epistemic Uncertainty Quantification for Neural Operator PDE Surrogates
arXiv:2603.11052v1 Announce Type: new
Abstract: Neural operators (NOs) provide fast, resolution-invariant surrogates for mapping input fields to PDE solution fields, but their predictions can exhibit significant epistemic uncertainty due to finite data, imperfect optimization, and distribution shif...
Gated Adaptation for Continual Learning in Human Activity Recognition
arXiv:2603.10046v1 Announce Type: new
Abstract: Wearable sensors in Internet of Things (IoT) ecosystems increasingly support applications such as remote health monitoring, elderly care, and smart home automation, all of which rely on robust human activity recognition (HAR). Continual learning syste...
Multi-level meta-reinforcement learning with skill-based curriculum
arXiv:2603.08773v1 Announce Type: new
Abstract: We consider problems in sequential decision making with natural multi-level structure, where sub-tasks are assembled together to accomplish complex goals. Systematically inferring and leveraging hierarchical structure has remained a longstanding chall...
arXiv:2603.06602v1 Announce Type: new
Abstract: As datasets continue to grow in size and complexity, finding succinct yet accurate data summaries poses a key challenge. Centroid-based clustering, a widely adopted approach to address this challenge, finds informative summaries of datasets in terms o...
Best-of-Tails: Bridging Optimism and Pessimism in Inference-Time Alignment
arXiv:2603.06797v1 Announce Type: new
Abstract: Inference-time alignment effectively steers large language models (LLMs) by generating multiple candidates from a reference model and selecting among them with an imperfect reward model. However, current strategies face a fundamental dilemma: ``optimi...
Capability Thresholds and Manufacturing Topology: How Embodied Intelligence Triggers Phase Transitions in Economic Geography
arXiv:2603.04457v1 Announce Type: new
Abstract: The fundamental topology of manufacturing has not undergone a paradigm-level transformation since Henry Ford's moving assembly line in 1913. Every major innovation of the past century, from the Toyota Production System to Industry 4.0, has optimized w...
FedEMA-Distill: Exponential Moving Average Guided Knowledge Distillation for Robust Federated Learning
arXiv:2603.04422v1 Announce Type: new
Abstract: Federated learning (FL) often degrades when clients hold heterogeneous non-Independent and Identically Distributed (non-IID) data and when some clients behave adversarially, leading to client drift, slow convergence, and high communication overhead. T...
Estimating Visual Attribute Effects in Advertising from Observational Data: A Deepfake-Informed Double Machine Learning Approach
arXiv:2603.02359v1 Announce Type: new
Abstract: Digital advertising increasingly relies on visual content, yet marketers lack rigorous methods for understanding how specific visual attributes causally affect consumer engagement. This paper addresses a fundamental methodological challenge: estimatin...
arXiv:2603.02365v1 Announce Type: new
Abstract: The paper investigates whether and how AI systems can realize states of uncertainty. By adopting a functionalist and behavioral perspective, it examines how symbolic, connectionist and hybrid architectures make room for uncertainty. The paper distingu...
StaTS: Spectral Trajectory Schedule Learning for Adaptive Time Series Forecasting with Frequency Guided Denoiser
arXiv:2603.00037v1 Announce Type: new
Abstract: Diffusion models have been used for probabilistic time series forecasting and show strong potential. However, fixed noise schedules often produce intermediate states that are hard to invert and a terminal state that deviates from the near noise assump...