Knowledge catalogue
arXiv:2407.15901v2 Announce Type: replace Abstract: Functional near-infrared spectroscopy (fNIRS) is employed as a non-invasive method to monitor functional brain activation by capturing changes in th
arXiv:2606.21378v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have demonstrated significant potential in supporting engineering design tasks, including computer-aided
arXiv:2606.21107v1 Announce Type: new Abstract: Differential privacy (DP) has become the gold standard for ensuring the privacy protection of machine learning and statistical algorithms in recent deca
arXiv:2602.07049v2 Announce Type: replace Abstract: Online handwriting recognition using inertial measurement units opens up handwriting on paper as input for digital devices. Doing it on edge hardwar
arXiv:2606.22429v1 Announce Type: new Abstract: Graph neural networks (GNNs) tightly couple their input-output parameters to dataset-specific feature spaces and target sets, exhibiting limited transfe
arXiv:2606.22307v1 Announce Type: new Abstract: Data augmentation (DA) has been proven to be an effective means for improving protein representation learning (PRL) by generating additional training sa
arXiv:2606.22381v1 Announce Type: cross Abstract: Road traffic accidents remain a critical global crisis, consistently serving as a primary driver of preventable mortality and severe injury. These inc
arXiv:2411.15919v2 Announce Type: replace Abstract: In engineering and applied mathematics, developing accurate mathematical models to predict and understand real-world phenomena is of utmost importan
arXiv:2606.21674v1 Announce Type: new Abstract: We present ENLIGHT, a fast and training free framework for low-light image enhancement based on direct optimization of a perceptual objective. Unlike de
arXiv:2606.21384v1 Announce Type: new Abstract: Multimodal medical imaging fuses complementary anatomical and functional information, yet modalities frequently disagree in pathologically heterogeneous
arXiv:2606.22948v1 Announce Type: cross Abstract: As multimodal agents move from interface understanding to real software control, successful trajectory discovery in live desktop environments becomes
arXiv:2602.21218v2 Announce Type: replace-cross Abstract: High-quality data is essential for modern machine learning, yet many valuable corpora are sensitive and cannot be freely shared. Synthetic dat
arXiv:2606.20960v1 Announce Type: cross Abstract: Nash equilibrium (NE) arises from selfish utility maximization, yet its social welfare can be arbitrarily far from optimal. Moreover, computing an NE
arXiv:2606.22744v1 Announce Type: new Abstract: Predictive coding networks (PCNs) offer a biologically-plausible, local-learning alternative to back-propagation of errors (backprop). Nevertheless, the
arXiv:2606.22433v1 Announce Type: new Abstract: Many central machine learning tasks, from entropy tuning in reinforcement learning to equilibrating generative adversarial networks, are fundamentally s
arXiv:2606.19164v2 Announce Type: replace Abstract: Model merging aims to enable multi-task learning by integrating the capabilities of multiple models fine-tuned from the same pre-trained checkpoint
arXiv:2606.20611v1 Announce Type: cross Abstract: Recent research in educational data mining, especially knowledge tracing, has focused on predicting learners' future knowledge states to support adapt
Harrison Chase suggests that evaluations (evals) are becoming as critical to AI/LLM development as Product Requirements Documents (PRDs) traditionally were, implying a shift in how teams validate and
arXiv:2606.20177v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in various Remote Sensing (RS) tasks. However, their ability to compre
arXiv:2606.21622v1 Announce Type: cross Abstract: Person-level psychological assessment requires aggregating meaning across many messages from the same individual, a task that document-level training
arXiv:2606.22269v1 Announce Type: cross Abstract: We investigate the translation quality of current large language models (LLMs) for English-to-Hausa and English-to-Fongbe - two typologically distinct
arXiv:2606.22943v1 Announce Type: new Abstract: Self-supervised learning (SSL) is increasingly used in medical imaging to reduce annotation requirements, but representation quality is often judged usi
arXiv:2606.20723v1 Announce Type: new Abstract: Chronic wound assessment remains a clinically challenging task that requires accurate interpretation of wound morphology, tissue composition, vascular c
arXiv:2606.20757v1 Announce Type: new Abstract: Recent multimodal survival prediction models have demonstrated strong predictive performance by leveraging complementary information across modalities.
arXiv:2606.22955v1 Announce Type: new Abstract: Large-scale pretrained foundation models have revolutionized general medical screening, but often falter on rare diseases because such conditions are un
arXiv:2606.20858v1 Announce Type: new Abstract: The temporal structure of reward composition in reinforcement learning (RL) is typically hand-designed and held fixed throughout training, leaving the p
arXiv:2606.22765v1 Announce Type: cross Abstract: Biological systems maintain function in fluctuating environments by transforming past stimulation into internal dynamical states that support future-o
arXiv:2606.22252v1 Announce Type: new Abstract: The integration of multiple thematic data layers into a single composite map, known as the cartographic synthesis problem, is typically addressed throug
arXiv:2606.23038v1 Announce Type: new Abstract: Rubric-based rewards offer interpretable and fine-grained optimization signals for reinforcement learning in open-ended tasks where verifiable answers a
Security intelligence and management solutions company Exabeam Inc. today introduced Agent Behavior Verification, a pre-deployment security discipline for artificial intelligence agents, and released
arXiv:2601.01405v2 Announce Type: replace-cross Abstract: Ball Mapper is a tool in topological data analysis that summarizes a finite metric dataset by covering it with metric balls and encoding their
Exactly. If there were mass deaths due to USAID, when are they? I would like to call the bereaved parents! Nobody died is the truth, but a lot of fraud, funding bioweapons and foreign government inter
arXiv:2606.20658v1 Announce Type: cross Abstract: Planning under uncertainty requires agents to balance goal achievement with information gathering. Active inference addresses this through the Expecte
This article explores the Cross-Origin Storage API and its implementation within Transformers.js, a JavaScript library for machine learning models. It likely discusses how this API enables secure cros
arXiv:2606.23131v1 Announce Type: new Abstract: Background: Laparoscopic camera navigation (LCN) is a critical skill, yet its current assessment typically relies on manual rating systems which are tim
arXiv:2606.22780v1 Announce Type: new Abstract: Mental illnesses among drug users are an increasing international issue, particularly in regions where early detection cannot be easily undertaken. The
arXiv:2503.21321v2 Announce Type: replace-cross Abstract: With the rapid development of machine learning and deep learning techniques, actuaries and the broader insurance industry face a persistent tr
arXiv:2302.14062v2 Announce Type: replace-cross Abstract: We address quality assessment for neural network based ASR by providing explanations that help increase our understanding of the system and ul
arXiv:2408.07822v2 Announce Type: replace-cross Abstract: We explore the application of large language models (LLMs), pre-trained models with massive textual data for detecting and improving attention
arXiv:2509.23946v3 Announce Type: replace Abstract: Many LLMs plan before they act, yet planning and execution are often still entangled in one long generation trace, enforced only through prompts, or
arXiv:2606.23276v1 Announce Type: new Abstract: Knowledge Editing (KE) has emerged as a frontier for updating specific facts in LLMs without costly retraining, but its reliability and underlying mecha
arXiv:2606.21687v1 Announce Type: new Abstract: Piecewise-affine neural networks (e.g., with ReLU or LeakyReLU activations) implement continuous piecewise-affine maps, and the number of affine regions
arXiv:2606.21197v1 Announce Type: new Abstract: Vision Language Models (VLMs) have demonstrated impressive performance in tasks requiring joint understanding of images and text, such as image captioni
arXiv:2606.21100v1 Announce Type: new Abstract: The integration of pretrained encoders with diffusion policies has become a dominant paradigm for visual robotic manipulation. However, it still struggl
arXiv:2606.22768v1 Announce Type: new Abstract: To make large-scale distributed training practical outside high-bandwidth datacenters, we must reduce blocking, high-volume synchronization. While DiLoC
arXiv:2606.22149v1 Announce Type: cross Abstract: Failure analysis is being reshaped by heterogeneous integration, chiplet-based architectures, hybrid bonding, backside technologies, & increasingly bu
arXiv:2602.06776v2 Announce Type: replace-cross Abstract: We study the transit stop placement (TrSP) problem in general metric spaces, where agents travel between source-destination pairs and may eith
arXiv:2606.23515v1 Announce Type: cross Abstract: Frameworks for ensuring fairness in machine learning typically focus on learning fair models from existing data. But this endeavor is often undermined
arXiv:2602.23611v2 Announce Type: replace-cross Abstract: Algorithmic decisions about individuals require predictions that are not only accurate but also fair with respect to sensitive attributes such
arXiv:2503.22934v2 Announce Type: replace Abstract: Image classification models trained on clean data often degrade sharply when exposed to corrupted test or deployment data, such as images with impul
arXiv:2508.12042v2 Announce Type: replace Abstract: Federated learning (FL) allows collaborative training of machine learning models across multiple parties without sharing raw data. However, heteroge
arXiv:2606.23354v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable success in Visual Question Answering (VQA), yet their 'black-box' nature hinders deplo
Falcon 9 successfully launched the Starfall Demo mission from a Florida launch facility, marking another operational flight for SpaceX's reusable rocket. The mission demonstrates continued progress in
arXiv:2606.20785v1 Announce Type: cross Abstract: Collecting computer use data from human demonstrations is expensive and slow, motivating the need for scalable generation strategies. This requires tw
arXiv:2606.15476v2 Announce Type: replace Abstract: Robots operating in homes, warehouses, and other object-rich environments need memory systems that can find specific object instances on demand. Obj
arXiv:2606.21587v1 Announce Type: new Abstract: Deep reinforcement learning is pivotal for closed-loop autonomous driving yet remains constrained by severe bottlenecks in sampling efficiency. Standard
arXiv:2606.18011v2 Announce Type: replace-cross Abstract: Constraint-based causal discovery relies on repeated conditional independence tests, but fast nonparametric tests often sacrifice calibration,
arXiv:2606.21448v1 Announce Type: new Abstract: As large language models scale, memory bandwidth for key-value caches and retrieval-augmented generation systems becomes a critical bottleneck. While 1-
arXiv:2508.18031v2 Announce Type: replace Abstract: Craniofacial reconstruction in forensics is one of the processes to identify victims of crime and natural disasters. Identifying an individual from
arXiv:2606.22510v1 Announce Type: new Abstract: While federated learning enables collaborative modelling on decentralised data, standard methods merely fit historical observations. This purely observa