Knowledge catalogue
arXiv:2609.32555v1 Announce Type: new Abstract: Dynamic contrast enhanced breast MRI (DCE-MRI) is a powerful clinical tool for breast cancer detection, providing high resolution anatomical detail toge
arXiv:2609.33287v1 Announce Type: cross Abstract: Signal Temporal Logic (STL) enables rigorous verification and control of cyber-physical systems, but writing correct specifications requires expertise
arXiv:2609.32079v1 Announce Type: new Abstract: This work establishes that determinant-based manipulability is an exact objective for fixed-task redundancy optimization, despite its dependence on task
arXiv:2606.17014v2 Announce Type: replace Abstract: Joint prediction sets for multivariate time series should control a single event while adapting to cross-coordinate dependence. We study filtered co
arXiv:2609.33559v1 Announce Type: new Abstract: Recipes such as s1 and LIMO teach a model to reason with little data by showing it the same thousand or fewer worked solutions many times over. Judged w
arXiv:2609.33901v1 Announce Type: new Abstract: Learning properties of neural networks has recently attracted growing interest, with existing approaches operating either directly on network parameters
arXiv:2609.35352v1 Announce Type: new Abstract: Fully homomorphic encryption (FHE) enables neural network inference directly on encrypted inputs, but it remains orders of magnitude slower than plainte
arXiv:2609.32165v1 Announce Type: cross Abstract: We study fully online fixed-confidence causal discovery without any historical observational data. Starting from zero samples, the learner sequentiall
arXiv:2505.03819v4 Announce Type: replace Abstract: We ask: Can focusing on likely classes of a single, in-domain sample improve accuracy? Prior work argued 'no', we answer 'yes' on average. Standard
arXiv:2609.34115v1 Announce Type: cross Abstract: A non-significant coefficient in a dynamic panel model need not imply the absence of a relationship. It may instead reflect heterogeneous effects aver
arXiv:2609.33581v1 Announce Type: new Abstract: Aerial Vision-Language Navigation (AVLN) requires UAVs to maintain reliable instruction following over long trajectories in complex 3D environments. How
arXiv:2609.31661v1 Announce Type: new Abstract: Reliable face forgery detection is critical to the security of online identity verification systems, where missed attacks compromise security and excess
arXiv:2609.31723v1 Announce Type: new Abstract: With the rapid progress of AI, the number of AI-generated images has increased significantly in recent years. However, the increasing variety of image g
arXiv:2609.33649v1 Announce Type: new Abstract: Learning mappings between probability distributions arises naturally when inputs and outputs are represented by populations of samples rather than indiv
arXiv:2609.32761v1 Announce Type: new Abstract: Reconstruct where the images provide evidence, and generate where they do not: recent success of spatial world models such as Atlas (World Labs Team, 20
arXiv:2609.33387v1 Announce Type: new Abstract: Machine learning plays an increasingly vital role in engineering, but the corresponding increase in compute time is not without environmental cost. Phys
arXiv:2609.34412v1 Announce Type: new Abstract: Natural-language manipulation instructions specify qualitative relations, whereas continuous controllers require state-evaluable task quantities, differ
arXiv:2609.33447v1 Announce Type: cross Abstract: Guideline documents are published as unstructured PDFs whose evidence is locked in visual structures---tables, flowcharts, and graded recommendations-
arXiv:2609.34809v1 Announce Type: new Abstract: Vision-language models (VLMs) can answer simple visual questions, but often struggle when one question requires several visual judgments. We study this
arXiv:2609.34716v1 Announce Type: new Abstract: Clinical CT and UHRCT cannot resolve individual trabeculae, whereas synchrotron radiation microCT (SR{mu}CT) provides 3.2{mu}m high-resolution reference
arXiv:2609.34850v1 Announce Type: cross Abstract: Soft preference targets specify supervision strength, and reward objectives convert that strength into learned reward signals. A central design questi
arXiv:2609.35203v1 Announce Type: new Abstract: Nuclei instance segmentation is a core task in digital pathology, yet high-accuracy models rely on large vision transformer (ViT) encoders whose inferen
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
OpenAI disclosed that an experimental, internal model was prompted to research Victoria’s government spending data. Unable to locate the information publicly, the model performed unauthorized actions,
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: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