Knowledge catalogue
🎓 Register now for ML & Advanced Stats Summer School, an intensive set of courses on theoretical foundations & applications of ML methods & modern stats analysis techniques. With @ELLISUnitMadrid. 📍 M
Michele Catasta (@pirroh) at Google Cloud Next talking about what's new with Gemini, agentic applications, and what's next for builders. Fresh off Replit being named Google Cloud Partner of the Year.
Kai Nicol-Schwarz / CNBC: Vast Data, which makes software infrastructure for managing large amounts of data with a focus on AI applications, raised a 1B Series F at a 30B valuation — Vast Data announc
arXiv:2604.15775v1 Announce Type: new Abstract: Learning with large-scale datasets and information-critical applications, such as in High Energy Physics (HEP), demands highly complex, large-scale mode
arXiv:2604.15238v1 Announce Type: cross Abstract: This paper investigates continuous-time and discrete-time firing-rate and Hopfield recurrent neural networks (RNNs), with applications in nonlinear co
I just updated our license. For personal use, you’re free to run the software on your own servers for coding, building applications, agents, tools, or integrations, as well as for research, experiment
I'm excited about voice as a UI layer for existing visual applications — where speech and screen update together. This goes well beyond voice-only use cases like call center automation. The barrier ha
arXiv:2504.16355v2 Announce Type: replace-cross Abstract: Perceptual hashing is used to detect whether an input image is similar to a reference image with a variety of security applications. Recently,
Meet Zul Mohamed. He pleaded guily to 106 felony voter fraud charges for forging mail-in ballot applications. He’s now running for mayor in Carrollton, Texas and wants to be in charge of passing laws.
arXiv:2603.13970v2 Announce Type: replace Abstract: In High Energy Physics, as in many other fields of science, the application of machine learning techniques has been crucial in advancing our underst
arXiv:2306.02781v4 Announce Type: replace-cross Abstract: Generative artificial intelligence, and large language models in particular, have emerged as one of the most transformative paradigms in moder
arXiv:2610.05174v1 Announce Type: cross Abstract: A kernel k(x,y) is LSHable if there exists a locality sensitive hashing scheme H such that k(x,y)=Pr_{hsim H}[h(x)=h(y)] for all x,y. This notion play
arXiv:2609.36044v1 Announce Type: cross Abstract: We develop two hybrid techniques that approach the performance of neural simulation-based inference (NSBI) analyses while substantially reducing the c
arXiv:2609.36911v1 Announce Type: new Abstract: In this thesis I develop methods for statistical inference when the distributions arising from complex biological systems are multi-modal, geometrically
arXiv:2408.14518v3 Announce Type: replace-cross Abstract: Simultaneous localization and mapping (SLAM) allows a mobile robot or autonomous vehicle to build a map of an unknown environment while estima
arXiv:2503.19225v4 Announce Type: replace Abstract: We introduce CoinFT, a capacitive 6-axis force/torque (F/T) sensor that is compact, light, low-cost, and robust with an average root-mean-squared er
arXiv:2609.35429v1 Announce Type: cross Abstract: Estimating conditional statistics and learning representations of a population of conditional distributions are central problems in many data-driven a
arXiv:2609.31712v1 Announce Type: new Abstract: Multiple instance learning (MIL) is widely used in computational pathology because it enables weakly supervised analysis of whole-slide images (WSIs) wi
arXiv:2601.07823v2 Announce Type: replace-cross Abstract: Video world models have emerged as promising candidates for high-fidelity world models, offering the potential to synthesize high-quality vide
arXiv:2609.12412v1 Announce Type: cross Abstract: Foundation models, including large language models, vision-language models, and time-series foundation models, are increasingly deployed on embedded a
arXiv:2609.11168v1 Announce Type: new Abstract: Tensor singular value decomposition (T-SVD), which is built upon the tensor-tensor product (t-product), has emerged as a powerful tool for processing hi
arXiv:2609.09856v1 Announce Type: cross Abstract: The rapid adoption of generative AI tools such as ChatGPT has sparked intense debate about their risks and opportunities for education, as well as the
arXiv:2609.07254v1 Announce Type: cross Abstract: Mathematical programming provides a common language for many decisions embedded in modern machine-learning (ML) and artificial-intelligence (AI) syste
arXiv:2603.25259v2 Announce Type: replace Abstract: Research on mobile manipulation systems that physically interact with humans has expanded rapidly in recent years, opening the way to tasks which co
arXiv:2609.09569v1 Announce Type: new Abstract: News articles often contain omitted temporal expressions, such as day-only or month-only mentions, which must be interpreted with reference to the publi
arXiv:2412.06494v2 Announce Type: replace Abstract: Background. Federated learning (FL) has gained wide popularity as a collaborative learning paradigm enabling collaborative AI in sensitive healthcar
arXiv:2609.01427v1 Announce Type: new Abstract: Dense self-supervised learning (SSL) is a powerful paradigm for learning without annotations the local descriptors required to solve dense medical imagi
arXiv:2608.29478v1 Announce Type: cross Abstract: Scholarly work which aims to describe potential societal impacts (e.g., risks) of proliferating technology (especially related to artificial intellige
arXiv:2608.25598v1 Announce Type: new Abstract: The purpose of this paper is to provide a general, comprehensive, theoretical framework that allows one to deal with fibrations on graphs labelled on a
arXiv:2608.20379v1 Announce Type: new Abstract: Advances in large language models (LLMs) have fueled a wave of research into agency: the ability to reason, plan, and act. This effort has produced agen
arXiv:2608.18110v1 Announce Type: new Abstract: Agentic AI is gaining new insights and advancements in the field of Artificial Intelligence, fostering significant potential to enable rapid transformat
arXiv:2608.14701v1 Announce Type: new Abstract: Soft-biometric attributes such as gender, age, and ethnicity provide valuable ancillary evidence when full identity recognition is not feasible, support
arXiv:2608.07522v1 Announce Type: cross Abstract: We present a structured review of commonly used Explainable machine learning (XML) methodologies, including global and local interpretability tools su
arXiv:2608.04942v1 Announce Type: cross Abstract: CheMLFlow is an open-source platform for building and executing end-to-end, high-throughput, and agentic workflows for scientific and technological ap
An exclusive report by Reuters today has surfaced evidence that suggests Chinese artificial intelligence firms have been leveraging the outputs of American frontier models developed by OpenAI Group PB
arXiv:2303.13102v2 Announce Type: replace Abstract: Existing Optimal Transport (OT) methods mainly derive the optimal transport plan/matching under the criterion of transport cost/distance minimizatio
arXiv:2607.23326v1 Announce Type: new Abstract: The rapid rise of large language models (LLMs) has driven transformative adoption across enterprises. However, deploying these models in real-world sett
arXiv:2109.11057v2 Announce Type: replace-cross Abstract: Weighted low-rank matrix approximation (WLRMA) generalizes classical low-rank approximation and matrix completion by allowing arbitrary elemen
arXiv:2601.23225v2 Announce Type: replace-cross Abstract: Deep reinforcement learning (RL) is increasingly deployed in resource-constrained environments, yet go-to function approximators - multilayer
arXiv:2512.15231v3 Announce Type: replace Abstract: The automated and intelligent processing of massive remote sensing (RS) datasets is critical in Earth observation (EO). Existing automated systems a
Not content with its push into autonomous artificial intelligence workers, WisdomAI Inc. is bringing its comprehensive analytics capabilities to third-party software-as-a-service platforms with today’
arXiv:2602.05961v2 Announce Type: replace Abstract: Sampling from a distribution p(x) propto e^{-E(x)} known up to a normalising constant is an important and challenging problem in statistics. Recent
arXiv:2605.23936v1 Announce Type: new Abstract: This book presents a comprehensive and systematic survey of graph theory under uncertainty, with particular emphasis on the unifying role of the uncerta
arXiv:2605.19646v1 Announce Type: cross Abstract: Advancements in clinical Brain-Computer Interfaces (BCIs) depend on precise and reliable signal interpretation. However, the high-dimensional and nois
arXiv:2408.12385v3 Announce Type: replace-cross Abstract: We study the problem of approximately recovering a probability distribution given noisy measurements of its Chebyshev polynomial moments. This
arXiv:2605.00879v1 Announce Type: new Abstract: Rehabilitation aims to help patients with limited mobility regain their physical abilities through targeted movements, exercises, stimulation, and other
arXiv:2512.23748v2 Announce Type: replace Abstract: For complex simulation problems, inferring parameters often precludes the use of classical likelihood-based techniques due to intractable likelihood
arXiv:2610.12494v1 Announce Type: new Abstract: In this article, we propose algebraic integer number formats (AINF) as a framework for highly configurable and accurate compressed computation. A key pr
arXiv:2610.12865v1 Announce Type: new Abstract: Existing convergence guarantees for non-log-concave sampling with stochastic gradient Langevin Monte Carlo (SG-LMC) typically assume globally Lipschitz
arXiv:2610.11899v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly embedded as components in software systems, marketed under labels such as chatbot, copilot, retrieval-au
arXiv:2610.07072v1 Announce Type: new Abstract: Variational autoencoders (VAEs) are a key part of modern text-to-image models, which generate images within their latent space. VAEs are known to disent
arXiv:2610.03117v1 Announce Type: new Abstract: Deep Neural Networks (DNNs) inherently exhibit a degree of robustness to bit-level faults due to their distributed representation of information. As a m
arXiv:2509.02109v3 Announce Type: replace Abstract: The Expectation-Maximisation (EM) algorithm is a central tool in statistics and machine learning, widely used for latent-variable models such as Gau
arXiv:2609.26508v1 Announce Type: new Abstract: Streaming principal component analysis (PCA) seeks to recover a leading spectral subspace in a single pass over a data stream. We give a new analysis of
arXiv:2411.15042v3 Announce Type: replace Abstract: Addressing the challenge of ensuring safety in ever-changing and unpredictable environments, particularly in the swiftly advancing realm of autonomo
arXiv:2609.21123v1 Announce Type: new Abstract: The dominant method of processing sonar data is using image-based representations, requiring the preprocessing of image data on autonomous systems. We p
arXiv:2609.15897v1 Announce Type: cross Abstract: The last decade has seen the development of powerful methods for learning complex structure from high-dimensional data. These advances have brought to
arXiv:2405.00065v4 Announce Type: replace-cross Abstract: This paper introduces the notion of upper-linearizable/quadratizable functions, a class that extends concavity and DR-submodularity in various
arXiv:2609.14959v1 Announce Type: new Abstract: We study decentralized learning of Nash equilibria (NE) in infinite-horizon discounted Markov games under bandit feedback, focusing on Markov alpha-pote
arXiv:2609.04778v1 Announce Type: new Abstract: Diffusion language models (DLMs) offer a non-autoregressive alternative for mobile edge agentic artificial intelligence (AI) by refining tokens through