Knowledge catalogue
arXiv:2609.32154v1 Announce Type: new Abstract: In this paper, we present a Goal-Aware Uncertainty-Guided Exploration (GAUGE) framework for planner-conditioned active calibration of opaque quadruped v
arXiv:2609.34825v1 Announce Type: new Abstract: Gaussian neural networks (GaNNs) are proposed as a novel regularization mechanism for neural networks. From a Bayesian perspective standard regularizati
arXiv:2407.07670v2 Announce Type: replace-cross Abstract: We investigate the generalization performance of online stochastic gradient descent (SGD) for overparameterized two-layer neural networks unde
arXiv:2504.02272v2 Announce Type: replace Abstract: Domain generalization (DG) aims to improve the generalizability of computer vision models toward distribution shifts. The mainstream DG methods pred
arXiv:2609.34824v1 Announce Type: new Abstract: Under a shared-factor model, the conditional law of the next image patch factors into a posterior over the shared scene factor and a residual kernel giv
arXiv:2609.34579v1 Announce Type: new Abstract: Single-image novel view synthesis remains challenging because the underlying 3D geometry is highly ambiguous. Recent diffusion-based approaches produce
arXiv:2609.32510v1 Announce Type: new Abstract: Cloud removal methods are typically specialized to individual datasets and input configurations, limiting reuse across sensors, spectral bands, and obse
arXiv:2609.35527v1 Announce Type: new Abstract: Embedding structured objects into Euclidean spaces has enabled a wide range of successful machine learning applications. Such objects include words, doc
arXiv:2609.34148v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are far more reliable at recognizing what appears in a video than at reasoning about its spatial and temporal properties,
arXiv:2609.33472v1 Announce Type: new Abstract: Decision-focused surrogates can recover downstream decisions without identifying the quotient report. We characterize the equality set of the convex Sma
arXiv:2609.33363v1 Announce Type: cross Abstract: Generating differentially private synthetic data with meaningful Wasserstein utility guarantees is challenging in high dimensions. For datasets of siz
arXiv:2609.34722v1 Announce Type: new Abstract: Long-horizon camera-controlled video generation requires recovering previously observed content from an ever-growing visual history. Existing approaches
arXiv:2609.33723v1 Announce Type: new Abstract: Diffusion transformers incur substantial inference cost through repeated model evaluations along a sampling trajectory. We introduce GeoShrink, a traini
arXiv:2609.35734v1 Announce Type: new Abstract: Novel view synthesis from sparse images must reconcile faithful reconstruction of observed regions with plausible completion of unseen content, while ma
arXiv:2609.32471v1 Announce Type: new Abstract: Vision-based tactile sensors (VBTS) provide rich contact information for robotic manipulation, but existing designs can be hard to simplify and adapt to
arXiv:2412.08911v4 Announce Type: replace Abstract: Multi-objective learning endeavors to concurrently optimize multiple objectives using a single model, aiming to achieve high and balanced performanc
arXiv:2609.34172v1 Announce Type: new Abstract: Active grounding of a frozen diffusion prior requires jointly determining where new measurements should be taken and how they should be used to refine t
arXiv:2609.34174v1 Announce Type: new Abstract: Test-time training (TTT) allows a model to improve its predictions at inference time by updating weights after every observed token. However, sequential
arXiv:2609.35000v1 Announce Type: new Abstract: Robot-aided operations in space stations are essential for reducing the workload of astronauts and improving the efficiency of on-orbit activities. Mult
arXiv:2609.33561v1 Announce Type: new Abstract: Multimodal graph predictors combine text, images, and relations to classify connected entities. How much of this input is needed to preserve their predi
arXiv:2609.31636v1 Announce Type: cross Abstract: Vision-language models are increasingly used for driving-scene understanding, yet the semantic relations expressed in their outputs are often difficul
arXiv:2609.33400v1 Announce Type: new Abstract: Action boundary detection partitions untrimmed video into intervals without assigning action classes. We present a boundary-detection adapter operating
arXiv:2609.34511v1 Announce Type: new Abstract: Most existing time series generators rely on a two-stage modeling paradigm: the first stage learns discrete latent representations of time series; the s
arXiv:2609.32117v1 Announce Type: new Abstract: Smartphone motion sensors support interactive applications, but their readings may also reveal touchscreen input beyond their intended use. Assuming kno
arXiv:2609.35718v1 Announce Type: new Abstract: Frontier general-purpose systems are rapidly expanding beyond visual understanding into capabilities traditionally handled by dedicated computer-vision
arXiv:2609.34431v1 Announce Type: new Abstract: Conditional Flow Matching (CFM) models for text-to-speech (TTS) suffer from incoherent frequency evolution during inference. While similar spectral imba
arXiv:2609.32551v1 Announce Type: new Abstract: Text-conditioned human-object interaction (HOI) generation requires body motion, object trajectories & rotations, and hand articulation to remain coordi
arXiv:2408.16189v3 Announce Type: replace-cross Abstract: Transfer learning, in regression or classification, concerns problems where one aims to use imperfect data from a source distribution P to imp
arXiv:2609.32789v1 Announce Type: new Abstract: Large-scale point cloud representations of complex geome tries incur prohibitive computational and memory costs, necessitating compressed implicit repre
arXiv:2609.32195v1 Announce Type: cross Abstract: Stochastic gradient descent (SGD) is widely used to train machine learning models, but subsampling the training data introduces noise into its updates
arXiv:2609.34674v1 Announce Type: new Abstract: Learning from demonstration (LfD) has enabled humanoid robots to acquire diverse whole-body skills, but extending this paradigm to human-object interact
arXiv:2609.33093v1 Announce Type: new Abstract: Linear attention replaces the growing key--value (KV) cache of standard attention with a fixed-size recurrent state, substantially reducing memory growt
arXiv:2609.33936v1 Announce Type: new Abstract: Recent research argues that language models exhibit pronounced homogeneity in open-ended generation, framing such behavior as an Artificial Hivemind tha
arXiv:2603.27998v3 Announce Type: replace-cross Abstract: Individualized head-related impulse responses (HRIRs) enable binaural rendering, but dense per-listener measurements are costly. We address HR
arXiv:2609.34430v1 Announce Type: new Abstract: Accurate retrieval of human motions is a crucial first step in text-guided human motion modeling and synthesis, as it selects semantically relevant sequ
arXiv:2609.34396v1 Announce Type: new Abstract: Hyperspectral video provides material cues that can disambiguate targets with similar false-color appearance, yet foundation-model trackers update memor
arXiv:2609.31993v1 Announce Type: cross Abstract: We study how well gravitational-wave phase deviations can be identified using a neural network with classification and regression heads. The classific
arXiv:2609.33372v1 Announce Type: cross Abstract: A special semimartingale admits a unique decomposition X=X_0+M+A into a local martingale M and a predictable finite-variation part A. We consider the
arXiv:2609.33131v1 Announce Type: new Abstract: Black-box Optimization (BO) is a powerful framework for optimizing expensive objective functions or unknown functions with a limited number of evaluatio
arXiv:2609.35726v1 Announce Type: new Abstract: Automated quality assessment of rehabilitation exercises relies heavily on accurate human pose estimation from video data. Although numerous RGB-based p
arXiv:2609.33038v1 Announce Type: cross Abstract: We propose DAST (Diversifying Arithmetic Sampling with TokenTour), a method that increases the diversity of LLM outputs without any change to the marg
arXiv:2609.32882v1 Announce Type: new Abstract: We present VSA2, a frontier trainable sparse attention for video DiTs. VSA2 includes a variety of new architectural features and training procedures tha
arXiv:2609.34798v1 Announce Type: cross Abstract: Recent medical multimodal models have benefited from larger corpora, broader modality coverage, and stronger reasoning-oriented training, yet effectiv
arXiv:2609.35743v1 Announce Type: new Abstract: World-space hand motion estimation from egocentric video requires recovering 3D articulated hand geometry while tracking camera egomotion. Existing appr
arXiv:2609.34731v1 Announce Type: cross Abstract: We develop an information-theoretic framework for generalization in next-token prediction under temporally dependent data. We consider independent tra
arXiv:2609.35516v1 Announce Type: new Abstract: Inspection planning seeks a minimum-length collision-free robot tour that observes a given set of points of interest (POIs). Sampling-based methods redu
arXiv:2609.34976v1 Announce Type: cross Abstract: The integration of artificial intelligence into computer-aided design frameworks has sparked a shift in the design of analog integrated circuits (ICs)
arXiv:2602.04952v2 Announce Type: replace-cross Abstract: We study the sample complexity of shadow tomography in the high-precision regime under realistic measurement constraints. Given an unknown d-d
arXiv:2609.31701v1 Announce Type: new Abstract: A Hybrid Residual Attention Network is proposed for accurately classifying silkworm images into six different classes, including healthy and diseased st
arXiv:2609.31997v1 Announce Type: new Abstract: Decoding imagined speech is an important goal for brain-computer interfaces but remains challenging due to weak neural responses, low signal-to-noise ra
arXiv:2603.14528v2 Announce Type: replace Abstract: In recent years, 3D visual foundation models, pioneered by pointmap-based approaches such as DUSt3R, have attracted a lot of interest, achieving imp
arXiv:2609.32539v1 Announce Type: cross Abstract: Deep learning models can achieve high accuracy for indoor localization, but their black-box nature limits interpretability and the reuse of learned in
arXiv:2609.35073v1 Announce Type: new Abstract: Graph visualization methods and agglomerative clustering have been frequently considered in data analysis and pattern recognition. Because these approac
Biology doesn't operate in silos, and neither should the AI representation of it. Quine is an early-stage research effort to create a multimodal world model of biology. By connecting insights across b
arXiv:2609.34931v1 Announce Type: cross Abstract: Machine learning has made strong progress on music tasks, both as assistive tools and as creative partners. However, most systems train on multitrack
arXiv:2609.33906v1 Announce Type: cross Abstract: Generative models often suffer from mode collapse and limited sample diversity. While prior works attempt to mitigate this by jointly generating a bat
arXiv:2609.34381v1 Announce Type: new Abstract: Video and audio are perceived together, yet most generative models treat them in isolation. We examine methods that model the two modalities jointly, ge
arXiv:2609.34972v1 Announce Type: new Abstract: Long visual token sequences often account for a substantial fraction of the computational overhead in multimodal large language models~(MLLMs). Existing
arXiv:2609.32182v1 Announce Type: new Abstract: Vision-language models are increasingly used to understand long videos and continuous streams. However, dense visual tokens accumulate with video durati
arXiv:2609.31888v1 Announce Type: cross Abstract: X-ray diffraction (XRD) is a experimental technique for determining the phase composition and structure of crystalline materials. However, interpretin