Knowledge catalogue
arXiv:2604.27613v1 Announce Type: new Abstract: Amorphous (disordered) materials are solids that have shown great potential in various domains, including energy storage, thermal management, and advanc
arXiv:2604.27385v1 Announce Type: new Abstract: Robotic-assisted surgery offers significant clinical advantages but largely eliminates direct haptic feedback, increasing the risk of excessive tool-tis
arXiv:2604.27593v1 Announce Type: cross Abstract: Recent technological advances in astronomy, particularly the growing popularity of smart telescopes for the general public, make it possible to develo
arXiv:2604.27102v1 Announce Type: cross Abstract: Soil contamination by heavy metals poses a persistent environmental and public health concern in rapidly urbanising regions of Ghana, particularly at
arXiv:2604.28022v1 Announce Type: new Abstract: Current DeepFake detection scenarios are mostly binary, yet data manipulation can vary across audio, video, or both, whose variability is not captured i
arXiv:2604.28122v1 Announce Type: new Abstract: Modern visual world modeling systems increasingly rely on high-capacity architectures and large-scale data to produce plausible motion, yet they often f
arXiv:2604.27936v1 Announce Type: new Abstract: Animals hear and vocalize across frequency ranges that differ substantially from humans, often extending into the ultrasonic domain. Yet most computatio
arXiv:2604.27551v1 Announce Type: cross Abstract: Large-scale transformers achieve impressive results on program synthesis benchmarks, yet their true generalization capabilities remain obscured by dat
arXiv:2604.27007v1 Announce Type: new Abstract: We provide a causal analysis of Binary Spiking Neural Networks (BSNNs) to explain their behavior. We formally define a BSNN and represent its spiking ac
arXiv:2604.27277v1 Announce Type: cross Abstract: Brain MRI underpins a wide range of neuroscientific and clinical applications, yet most learning-based methods remain task-specific and require substa
arXiv:2604.26981v1 Announce Type: cross Abstract: Large Language Models (LLMs) have revolutionized the field of natural language processing. However, they exhibit some limitations, including a lack of
Bush destroyed Clinton's economy. Obama fixed Bush's mess. Trump destroyed Obama's economy. Biden fixed Trump’s mess and now Trump has destroyed what Biden fixed. The biggest lie that Republicans have
arXiv:2604.27944v1 Announce Type: new Abstract: Large-scale IoT weather sensing networks require incentive mechanisms to sustain participation, yet determining how much value individual data contribut
arXiv:2604.27968v1 Announce Type: new Abstract: The pervasive growth of digital content, specifically short videos on social media platforms, has significantly altered how topics are discussed and und
arXiv:2506.09110v3 Announce Type: replace Abstract: Electroencephalography (EEG) provides real-time insights into brain activity and supports diverse applications in neuroscience. While EEG foundation
arXiv:2604.27251v1 Announce Type: cross Abstract: Large Language Models (LLMs) are known to acquire reasoning capabilities through shared inference patterns in pre-training data, which are further eli
arXiv:2604.26999v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) approximate solutions of partial differential equations (PDEs) by embedding physical laws into the loss functio
arXiv:2604.28186v1 Announce Type: cross Abstract: Most familiar equilibrium concepts, such as Nash and correlated equilibrium, guarantee only that no single player can improve their utility by deviati
arXiv:2604.27172v1 Announce Type: new Abstract: We propose C-MTAD-GAT, an unsupervised, context-aware graph-attention model for anomaly detection in multivariate time series from mobile networks. C-MT
arXiv:2504.19342v3 Announce Type: replace-cross Abstract: Reinforcement Learning from Human Feedback (RLHF) has become a pivotal paradigm in artificial intelligence to align large models with human pr
arXiv:2604.28159v1 Announce Type: new Abstract: Topological features play an essential role in ensuring geometric plausibility and structural consistency in image analysis tasks such as segmentation a
Cybersecurity was already under strain before AI entered the stack. Now, as AI expands the attack surface and adds new complexity, the limits of legacy approaches are becoming harder to ignore. This s
This is a discussion thread from r/MachineLearning where community members ask and answer beginner-level and straightforward questions about machine learning concepts, techniques, and practical implem
arXiv:2604.27775v1 Announce Type: cross Abstract: Shallow nanoindentation enables mechanical characterization of thin films, individual phases and other volume-constrained materials, but measured hard
arXiv:2604.27883v1 Announce Type: cross Abstract: In modern parametric model training, full-batch gradient descent (and its variants) suffers due to progressively stronger biasing towards the exact re
arXiv:2604.27263v1 Announce Type: new Abstract: Subword tokenization is an essential part of modern large language models (LLMs), yet its specific contributions to training efficiency and model perfor
arXiv:2604.26977v1 Announce Type: cross Abstract: In response to a concern raised by Horty, this paper develops a two-tiered, preference-based semantic framework for modeling defeasible conditional ob
arXiv:2604.28148v1 Announce Type: new Abstract: This work presents ThermoMesh, a passive thin-film thermoelectric mesh sensor designed to detect and characterize spatio-temporally sparse heat sources
arXiv:2604.27967v1 Announce Type: new Abstract: Background: Timely, uncertainty-aware forecasting from irregular electronic health records (EHR) can support critical-care decisions, yet most approache
arXiv:2604.27741v1 Announce Type: new Abstract: We study the problem of understanding where two populations differ within a feature space, which we formalize in the concept of a differential subgroup:
arXiv:2604.27952v1 Announce Type: cross Abstract: Joint image compression and wireless transmission remain relatively underexplored compared to generic image restoration, despite its importance in pra
arXiv:2604.28119v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) are widely used to extract interpretable features from neural network representations, often under the implicit assumption
arXiv:2604.27932v1 Announce Type: new Abstract: The computational cost of training a vision-language model (VLM) can be reduced by sampling the training data. Previous work on efficient VLM pre-traini
arXiv:2601.23065v2 Announce Type: replace-cross Abstract: Recent radiance-field-based reconstruction methods, such as NeRF and 3DGS, achieve high visual fidelity for indoor scenes, but often break dow
arXiv:2604.28034v1 Announce Type: new Abstract: The syntactic structure of a sentence can be represented as a tree where edges indicate syntactic dependencies between words. When that structure is a s
arXiv:2512.14067v2 Announce Type: replace-cross Abstract: Diffusion language models (dLMs) have emerged as a promising paradigm that enables parallel, non-autoregressive generation, but their learning
arXiv:2604.28142v1 Announce Type: cross Abstract: Multivector retrieval models achieve state-of-the-art effectiveness through fine-grained token-level representations, but their deployment incurs subs
arXiv:2603.13566v2 Announce Type: replace-cross Abstract: Imbalanced datasets pose a difficulty in fraud detection, as classifiers are often biased toward the majority class and perform poorly on rare
arXiv:2510.19322v3 Announce Type: replace-cross Abstract: Collective communication (CC) is critical for scaling distributed machine learning (DML). The predictable traffic patterns of DML present a gr
arXiv:2604.26998v1 Announce Type: cross Abstract: Automated depression detection often relies on static aggregation of conversational signals, potentially obscuring clinically meaningful behavioral dy
arXiv:2604.27275v1 Announce Type: cross Abstract: Large language model (LLM) reading assistants are increasingly used in settings that require interpretation rather than simple retrieval. In these con
arXiv:2604.27195v1 Announce Type: new Abstract: Accurate prediction of conversion from Mild Cognitive Impairment (MCI) to Alzheimers Diseases (AD) is essential for early intervention, however, develop
arXiv:2604.27695v1 Announce Type: cross Abstract: Long-term conversational memory requires retrieving evidence scattered across multiple sessions, yet single-pass retrieval fails on temporal and multi
arXiv:2604.28149v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) have recently emerged as general-purpose forecasting models and show considerable potential for applications in en
arXiv:2604.27346v1 Announce Type: cross Abstract: This study examines the factors influencing pre-service teachers' behavioral intention to use AI-enabled educational tools during their practicum, usi
arXiv:2604.28036v1 Announce Type: new Abstract: Exponential families encompass the distributions central to modern machine learning -- softmax, Gaussians, and Boltzmann distributions -- and underlie t
arXiv:2604.28016v1 Announce Type: new Abstract: 3D Gaussian Splatting has emerged as a powerful scene representation for real-time novel-view synthesis. However, its standard adaptive density control
arXiv:2604.26976v1 Announce Type: cross Abstract: We study the problem of fitting a description logic (DL) ontology to a given set of positive and negative examples that take the form of an ABox and a
arXiv:2604.27510v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, yet its performance deteriorates und
arXiv:2510.20933v2 Announce Type: replace-cross Abstract: Medical image segmentation is essential for clinical applications such as disease diagnosis, treatment planning, and disease development monit
arXiv:2505.22701v3 Announce Type: replace Abstract: A major challenge in rare animal image classification is the scarcity of data, as many species usually have only a small number of labeled samples.
arXiv:2604.27453v1 Announce Type: new Abstract: Large language models have achieved remarkable progress in text generation but still struggle with generative writing tasks. In terms of evaluation, exi
arXiv:2602.04572v2 Announce Type: replace Abstract: While Generative AI (GenAI) systems draw users away from (Q&A) forums, they also depend on the very data those forums produce to improve their perfo
arXiv:2604.27653v1 Announce Type: new Abstract: Conventional push-broom hyperspectral imaging suffers from slow acquisition speeds, precluding real-time object detection; in contrast, snapshot spectra
arXiv:2502.14698v2 Announce Type: replace-cross Abstract: Decision makers may suffer from uncertainty induced by limited data. This may be mitigated by accounting for epistemic uncertainty, which is h
arXiv:2503.01448v5 Announce Type: replace Abstract: Realistic human geometry generation is an important yet challenging task, requiring both the preservation of fine clothing details and the accurate
arXiv:2604.27636v1 Announce Type: new Abstract: Predicting stable and metastable structures is central to molecular and materials discovery, but remains limited by the cost of searching high-dimension
arXiv:2604.27914v1 Announce Type: new Abstract: When language models lack relevant knowledge for a given query, they frequently generate plausible responses that can be hallucinations, rather than adm
arXiv:2411.18796v2 Announce Type: replace Abstract: Early diagnosis and discovery of therapeutic drug targets are crucial objectives for effective management of Alzheimer's Disease (AD). Current appro
arXiv:2602.11084v2 Announce Type: replace-cross Abstract: Feature selection remains a major challenge in medical prediction, where existing approaches such as LASSO often lack robustness and interpret