Robust XGBoosting for Regression
arXiv:2608.13590v1 Announce Type: new Abstract: XGBoost is a very popular and powerful method for prediction. It iteratively fits simple decision trees to the residuals of the previous step. An effici
Knowledge catalogue
arXiv:2608.13590v1 Announce Type: new Abstract: XGBoost is a very popular and powerful method for prediction. It iteratively fits simple decision trees to the residuals of the previous step. An effici
arXiv:2607.09816v3 Announce Type: replace Abstract: Class imbalance poses a fundamental challenge in risk-sensitive applications such as fraud detection and medical diagnosis, where minority-class sam
arXiv:2608.14029v1 Announce Type: new Abstract: Multimodal dialogue retrieval aims to retrieve dialogues from multimodal dialogue banks that are similar to a target dialogue in terms of both textual s
arXiv:2608.13967v1 Announce Type: new Abstract: Color constancy on pure-color scenes is challenging: when most pixels share a narrow band of hues, every chromaticity-based cue collapses to a single po
arXiv:2608.13947v1 Announce Type: new Abstract: High-quality creative writing data for large language models (LLMs) remains dominated by story-centric data, limiting models' ability to follow the stru
arXiv:2601.11629v2 Announce Type: replace-cross Abstract: We demonstrate that while the current approaches for language model watermarking are effective for open-ended generation, they are inadequate
arXiv:2404.17429v4 Announce Type: replace-cross Abstract: A natural hypothesis for the success of reservoir computing in generic tasks is the ability of the untrained reservoir to map distinct input t
arXiv:2608.14102v1 Announce Type: new Abstract: We consider the problem of sequential prediction of an m-ary sequence, where at each epoch, (i) the environment selects an outcome from an m-ary alphabe
arXiv:2608.13993v1 Announce Type: new Abstract: Urban traffic management relies on sensor networks whose spatial coverage is limited by deployment costs and privacy regulations. Machine learning model
arXiv:2608.14140v1 Announce Type: new Abstract: The problem of route optimization with realistic constraints is becoming extremely relevant in the face of global urban population growth. While we are
arXiv:2608.14046v1 Announce Type: new Abstract: In this work, we propose a source-agnostic framework that dynamically refines a binary mask throughout the reverse diffusion process by computing the di
arXiv:2608.14138v1 Announce Type: cross Abstract: Spatial perception and reasoning from visual observations require recovering geometric structure, establishing correspondences, and understanding spat
arXiv:2608.14509v1 Announce Type: new Abstract: Systems that ask a language model to reach a conclusion from many sources usually concatenate them into one prompt. This conflates two operations with d
arXiv:2608.13591v1 Announce Type: new Abstract: High-confidence errors in large language models are often treated as evidence of fragile internal inference. We study a different possibility: stable mi
arXiv:2608.13717v1 Announce Type: new Abstract: Streaming automatic speech recognition (ASR) underperforms on domain-shifted target audio, where labeled in-domain data is costly to prepare while unlab
arXiv:2608.14435v1 Announce Type: new Abstract: Frozen image embeddings from models such as CLIP are increasingly used to classify paintings by art-historical style, with high reported accuracy. We as
arXiv:2607.28351v2 Announce Type: replace-cross Abstract: Speech deepfake detection has expanded in scope with increasingly heterogeneous spoofing mechanisms, including speech synthesis, voice convers
arXiv:2512.23596v2 Announce Type: replace-cross Abstract: Does more data improve return prediction? In non-stationary financial markets, longer training windows improve prediction of complex models bu
arXiv:2608.13668v1 Announce Type: new Abstract: Linear attention keeps a state of fixed size. At long context, many stored items share this state, and interference between them degrades retrieval. Gat
arXiv:2608.13570v1 Announce Type: cross Abstract: Latent reasoning has emerged as a powerful alternative to text-based Chain-of-Thought (CoT), offering significant gains in computational efficiency by
arXiv:2608.14152v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have reshaped semantic analysis. Opinion Extraction (OE) for Science and Technology Intelligence (STI) r
arXiv:2608.14321v1 Announce Type: new Abstract: The advanced retinal disease diagnosing imaging modality, optical coherence tomography (OCT), encounters a lack of automation because of the high expens
arXiv:2507.04184v3 Announce Type: replace Abstract: Time-to-collision is a commonly employed measure for rear-end collision prediction. However, its conventional formulation, which assumes constant sp
arXiv:2608.13584v1 Announce Type: cross Abstract: UltraArUco - a lightweight multilingual library and framework for low-latency, real-time marker-based tracking in mobile augmented reality. Unlike sta
arXiv:2608.14422v1 Announce Type: cross Abstract: MRI reconstruction from undersampled k-space measurements is an ill-posed inverse problem. Physics-driven deep learning (PD-DL) methods have shown str
arXiv:2608.14502v1 Announce Type: cross Abstract: Free energies govern solid-state phase stability, yet computational materials discovery still relies largely on ground-state energies because free ene
arXiv:2608.13633v1 Announce Type: cross Abstract: Machine learning is now a central tool for solving inverse problems in particle physics and astronomy. Models are trained on simulation and deployed o
arXiv:2608.13882v1 Announce Type: new Abstract: Claims about the benefit of depth depend on the complexity assigned to a representation. We introduce the Variation Brownian Kernel Ladder (VBKL), a pat
arXiv:2603.22580v2 Announce Type: replace Abstract: Age-related mobility decline is frequently accompanied by a redistribution of joint kinetics, where older adults compensate for reduced ankle functi
arXiv:2608.14244v1 Announce Type: new Abstract: In large and heavy structures, vibrations arise during motion, posing significant challenges for precise manipulation. To accomplish the desired motion,
arXiv:2608.13831v1 Announce Type: cross Abstract: Spoken dialogue is a natural form of human--computer interaction, yet most speech language models remain limited to turn-based operation and lack real
arXiv:2608.14366v1 Announce Type: new Abstract: Polar lows are intense maritime cyclones that form rapidly at high latitudes. Deep learning can detect them in Synthetic Aperture Radar (SAR) imagery, b
arXiv:2608.13660v1 Announce Type: new Abstract: Medical image segmentation models are typically trained under the assumption that all data are available simultaneously. However, in clinical practice,
arXiv:2608.14020v1 Announce Type: new Abstract: Adding data known to be correct ought to be safe. Not always. Larsen, Pabbaraju and Shetty model the failure with a monotone adversary, which reads an i
arXiv:2608.13835v1 Announce Type: new Abstract: Dynamic topic models capture evolving word distributions, but traditional coherence metrics may fail when vocabulary changes while semantic meaning pers
arXiv:2601.08151v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress in vision-language understanding, yet how they internally integrate visua
arXiv:2608.13621v1 Announce Type: new Abstract: A hidden Markov model (HMM) combines three roles: inference of a hidden-state belief from observations, propagation through a Markov transition, and emi
Antoine Tardif / Unite.AI: Pathway, which is developing AI models based on what it calls its “Post-Transformer” BDH architecture, raised a 30M seed at a 500M valuation — AI research company Pathway ha
The article describes an end‑to‑end DIY project that builds an AI text detector from scratch, including dataset construction, model training, local deployment, and a reinforcement learning verifier (R
arXiv:2608.12745v1 Announce Type: new Abstract: Medical AI has demonstrated specialist-level diagnostic accuracy, yet these capabilities remain largely inaccessible in resource-constrained rural setti
arXiv:2608.13183v1 Announce Type: new Abstract: Visual foundation models are a cornerstone of image and video understanding but typically require large amounts of data and computation. The current sca
arXiv:2608.12398v1 Announce Type: cross Abstract: We propose IM-LEPP (Integrated Multimodal Latent Energy-based Predictive Processing), a hierarchical, energy-based model of multimodal cognition that
arXiv:2608.13073v1 Announce Type: new Abstract: Significant health risks are associated with the illegal, yet commonly practiced use of industrial-grade Calcium Carbide (CaC2) for ripening climacteric
arXiv:2608.13456v1 Announce Type: new Abstract: World Models (WM) are increasingly seen as a foundation for intelligent agents that can predict, plan, and act beyond their training distribution. In th
arXiv:2505.12599v3 Announce Type: replace-cross Abstract: We propose a class of discrete state sampling algorithms based on Nesterov's accelerated gradient method, which extends the classical Metropol
arXiv:2608.12903v1 Announce Type: new Abstract: The k-Nearest Neighbor~(KNN) algorithm is widely used across various tasks. The selection of the k value is a key issue because it significantly impacts
arXiv:2608.12863v1 Announce Type: new Abstract: As AI systems proliferate in consumer facing applications, questions about liability for AI related harms remain unresolved. This working paper examines
arXiv:2608.12835v1 Announce Type: new Abstract: Unmanned Aerial Vehicle Vision-Language Navigation (UAV-VLN) requires agents to follow language instructions, infer spatial structure from sparse multi-
arXiv:2608.13492v1 Announce Type: new Abstract: This report presents an improved version of AlayaWorld. While the backbone architecture, chunk-wise autoregressive generation scheme, and training data
arXiv:2608.13433v1 Announce Type: cross Abstract: Transformer-based language models are known to sometimes generalize to sequences longer than seen during training, but we lack a precise characterizat
arXiv:2608.12548v1 Announce Type: cross Abstract: Dance imitation integrates motor planning, sensorimotor integration, and social cognition, offering a sensitive framework to characterize motor behavi
arXiv:2608.13430v1 Announce Type: cross Abstract: Instruction-tuned language models achieve strong performance across a range of generation tasks, but have also recently been shown to exhibit verbaliz
arXiv:2608.12374v1 Announce Type: cross Abstract: Language models (LMs) struggle with logical tasks like reasoning on syllogisms. It has been shown that Knowledge Representation (KR) plays a crucial r
arXiv:2608.12840v1 Announce Type: new Abstract: Visual-inertial navigation systems estimate six-degree-of-freedom motion by fusing visual and inertial data. Modern discrete-time methods with IMU prein
arXiv:2508.18829v3 Announce Type: replace Abstract: National Forest Inventory (NFI) serves as the primary source of forest information, however, maintaining these inventories requires labor-intensive
arXiv:2608.12570v1 Announce Type: new Abstract: Fashion retrieval often requires satisfying multiple attributes at once, such as category, color, pattern, and demographic. Monolithic embeddings mix th
arXiv:2603.16946v2 Announce Type: replace-cross Abstract: Currently, an excessive amount of event data is being obtained in four-dimensional inelastic neutron-scattering experiments. A method for auto
arXiv:2608.12936v1 Announce Type: cross Abstract: As quantum computing progresses from proof-of-principle demonstrations toward practical utility, a significant impediment is the need to augment algor
arXiv:2608.13514v1 Announce Type: cross Abstract: We revisit the problem of learning predictors robust to adversarial examples at test-time. We prove that VC classes are adversarially robustly learnab
arXiv:2608.12679v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in discovery domains such as math and science. The usual approach is to present the problem to th