Knowledge catalogue
arXiv:2610.09217v1 Announce Type: new Abstract: Autism spectrum disorder (ASD) is diagnosed through specialist observation of a child's social behavior, and access to that expertise is the bottleneck
arXiv:2610.09056v1 Announce Type: new Abstract: We develop a geometry-based capacity theory for exact-key retrieval in compressed finite-feature Hebbian associative memory. For random or approximately
arXiv:2602.20531v2 Announce Type: replace Abstract: App ratings are among the most significant indicators of the quality, usability, and overall user satisfaction of mobile applications. However, exis
arXiv:2610.08985v1 Announce Type: cross Abstract: The aim of processing and analyzing experimental data from physical experiments is to obtain physically significant information about the phenomenon u
arXiv:2606.05335v2 Announce Type: replace Abstract: Theoretical studies of machine learning models commonly consider different limiting regimes in which the learning dynamics of gradient descent becom
arXiv:2610.09792v1 Announce Type: new Abstract: Electroencephalography (EEG) is highly susceptible to electromyographic (EMG) artifacts, whose temporal heterogeneity and spatial-spectral overlap with
arXiv:2610.09051v1 Announce Type: new Abstract: The large KV-cache size of modern LLMs creates a barrier to efficient deployment. Recent work has explored replacing attention layers' RoPE positional e
arXiv:2610.09150v1 Announce Type: new Abstract: Objective: Data-driven models that estimate physiological states, particularly biological joint moments, are widely used in exoskeleton control. However
arXiv:2610.08822v1 Announce Type: new Abstract: Ageing bridge infrastructure is a growing global concern, yet conventional Structural Health Monitoring (SHM) systems are costly and difficult to scale,
arXiv:2610.08849v1 Announce Type: cross Abstract: Purpose: Current abdominal ultrasound (US) simulation methods often require CT-based anatomical references for ray-casting, limiting deformation and p
arXiv:2610.09782v1 Announce Type: new Abstract: Causal discovery becomes particularly challenging when the available sample size is small relative to the number of variables. This challenge also arise
arXiv:2610.09588v1 Announce Type: new Abstract: Deploying robots as Complex Adaptive Systems (CAS) in unknown and dynamic environments necessitates a transition from rigid command libraries toward int
arXiv:2610.09252v1 Announce Type: new Abstract: Large Vision-Language Models achieve strong VQA performance, but processing high-resolution, information-rich images requires substantial computation, m
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arXiv:2610.09206v1 Announce Type: new Abstract: Loss-difference conditional mutual information (ld-CMI) uses the smallest of the standard observations in the supersample hierarchy of generalization bo
arXiv:2610.09871v1 Announce Type: cross Abstract: Implicit modeling and Relative Geologic Time are geological modeling techniques that enable more efficient, faster, less biased and more reproducible
arXiv:2610.09433v1 Announce Type: new Abstract: Thermo-mechanical fracture couples transient heat conduction on a cracked domain with a crack that grows as the temperature and the displacement evolve.
Announcing ARC Prize Research Summit 2026 Research Speaker Kevin Murphy @sirbayes is a research scientist at Google DeepMind and author of the “Probabilistic Machine Learning” textbooks, developing AI
arXiv:2602.06317v3 Announce Type: replace-cross Abstract: Attention-mass concentration creates an opportunity for sparse decoding, but retained mass alone does not guarantee a stable greedy decision:
arXiv:2610.10135v1 Announce Type: cross Abstract: Sparse attention mechanisms estimate attention over n tokens using a small subset of keys. Many existing approaches use maximum inner product search (
arXiv:2610.09819v1 Announce Type: cross Abstract: Acoustic foundation models (AFMs) have democratized acoustic applications, enabling powerful models for tasks ranging from speech recognition to speak
arXiv:2505.06592v2 Announce Type: replace Abstract: In this paper, we propose batch augmentation with unimodal fine-tuning for multimodal learning. We start with pre-trained unimodal models. We fine-t
arXiv:2610.09052v1 Announce Type: new Abstract: Continuous cardiac monitoring outside clinical settings requires signals that are both informative and practical to collect during daily life. Electroca
arXiv:2610.09207v1 Announce Type: new Abstract: Belief-space planners with separately designed or off-the-shelf estimators may have access to state-relevant information the estimator does not observe.
arXiv:2610.09340v1 Announce Type: new Abstract: Benign overfitting is extensively studied when learning from a single high-dimensional input, but its behavior under heterogeneous input fusion remains
arXiv:2610.10488v1 Announce Type: cross Abstract: We study the best arm identification problem in a stochastic environment with a novel form of adversarial perturbations, which we coin Shifting Means.
arXiv:2610.08809v1 Announce Type: new Abstract: Depression severity among patients with chronic or acute medical conditions is influenced by a complex interaction of baseline psychological state, demo
arXiv:2610.09031v1 Announce Type: new Abstract: Medical imaging models often operate as black boxes, limiting interpretability and systematic debugging. We introduce an easy-to-use, plug-and-play fram
arXiv:2610.09726v1 Announce Type: new Abstract: Estimating remaining shelf life (RSL) from images could provide affordable decision support for perishable produce, but evaluation protocols can substan
arXiv:2610.08842v1 Announce Type: new Abstract: Identifying suicide risk from social networking services (SNS) posts is important for detecting suicide-related signals in online environments. However,
arXiv:2412.00112v5 Announce Type: replace Abstract: Generating natural and expressive human motions from textual descriptions is challenging due to the complexity of coordinating full-body dynamics an
arXiv:2610.09873v1 Announce Type: new Abstract: Binary Neural Networks (BNNs) enable efficient deep learning deployment on resource constrained devices with weights and activations compressed to one b
arXiv:2610.10194v1 Announce Type: cross Abstract: Switching stochastic differential equations (SSDEs) describe continuous-time dynamics whose parameters switch according to a latent regime process tha
arXiv:2610.10105v1 Announce Type: new Abstract: The Fourier Neural Operator (FNO) learns solution operators of partial differential equations (PDEs) through Fourier-space kernel parameterization, but
arXiv:2610.09276v1 Announce Type: cross Abstract: Modern DBMSs expose hundreds of configuration knobs, resulting in a high-dimensional and heterogeneous search space that makes automated tuning costly
arXiv:2610.08827v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) systems often underperform for children and non-native speakers, while adapting adult ASR models to child speech can
arXiv:2610.09315v1 Announce Type: new Abstract: This paper presents an open-sourced MATLAB simulation and analysis platform dedicated to legged climbing robots. This simulator enables the design of an
arXiv:2610.09651v1 Announce Type: new Abstract: DP-SGD protects training data by adding Gaussian noise to clipped gradients. The amount of noise is usually chosen by running a numerical privacy accoun
arXiv:2610.08833v1 Announce Type: new Abstract: Masked diffusion language models (MDLMs) decode by repeatedly committing tokens to masked positions, but these commitments are usually irreversible. A t
arXiv:2610.09587v1 Announce Type: cross Abstract: Reasoning capabilities are critical for advancing Large Language Models, yet current approaches either require massive computational budgets or strugg
arXiv:2602.09270v2 Announce Type: replace-cross Abstract: We present a large scale data analysis of Moltbook, a Reddit-style social media platform exclusively populated by AI agents. Analyzing over 4
arXiv:2610.10206v1 Announce Type: cross Abstract: Links are disjoint unions of circles smoothly embedded in S^3. We use reinforcement learning and Bayesian optimisation to obtain new upper bounds on s
arXiv:2610.09807v1 Announce Type: new Abstract: Camouflaged object detection requires pixel-accurate masks, but obtaining such annotations is slow and costly, making synthetic training images an attra
arXiv:2610.10168v1 Announce Type: cross Abstract: Split conformal prediction uses prediction errors on held-out (calibration) data to determine how wide the prediction intervals should be. It guarante
arXiv:2610.08807v1 Announce Type: new Abstract: Precision pesticide spraying is essential for optimizing application efficiency and ensuring uniform chemical distribution. Spraying performance is infl
arXiv:2610.10021v1 Announce Type: cross Abstract: Mutual information (MI) provides an objective for suppressing or encouraging statistical dependence in implicit generative models. However, direct MI
arXiv:2610.09791v1 Announce Type: new Abstract: Head avatar modeling requires jointly optimizing multiple objectives with different dominant effects on geometry, appearance, and cross-view consistency
arXiv:2610.10398v1 Announce Type: new Abstract: Neural surrogates for computational fluid dynamics (CFD) have the potential to greatly enhance engineering innovation through accelerating simulation. H
arXiv:2610.09212v1 Announce Type: new Abstract: The best two-bit weight quantizers for large language models, such as QTIP and Proteus, rotate each weight matrix by a random orthogonal transform, whic
arXiv:2610.10347v1 Announce Type: cross Abstract: Dataset pruning reduces a large training set to a representative subset while preserving model performance. Existing geometry-based methods typically
arXiv:2610.10527v1 Announce Type: cross Abstract: Heavy-tailed noise has been widely observed in modern machine learning, motivating the use of methods like gradient clipping and normalization. While
arXiv:2610.09977v1 Announce Type: new Abstract: Animal evidence shows that precise voluntary movements arise from rotational neural population dynamics in motor cortex, but their physical effects rema
arXiv:2610.08838v1 Announce Type: cross Abstract: Longitudinal medical imaging analysis is a cornerstone of modern medical practice and patient care. Deep learning applied to longitudinal imaging offe
arXiv:2610.09860v1 Announce Type: new Abstract: 4D point clouds acquired by permanent laser scanning (PLS) enable accurate high-frequency monitoring of surface change in dynamic topographic environmen
arXiv:2610.09853v1 Announce Type: new Abstract: Pose-free feed-forward 3D Gaussian Splatting (3DGS) reconstructs a scene from sparse, unposed images in a single network pass, removing the need for cam
arXiv:2610.08817v1 Announce Type: new Abstract: Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly steady-state visual evoked potential (SSVEP) systems, are highly vulne
arXiv:2610.10428v1 Announce Type: cross Abstract: Gradient observations promise more accurate Gaussian process (GP) surrogates, but the cost of incorporating them has long stood in the way of realizin
arXiv:2610.09645v1 Announce Type: new Abstract: This study proposes a multi-objective optimization method for tendon-driven arm design, addressing the inherent trade-off between joint torque and arm t
arXiv:2610.10436v1 Announce Type: new Abstract: Adversarial perturbations can alter the predictions of frozen vision-language models (VLMs) while leaving their confidence and image--text similarity pa
arXiv:2610.09321v1 Announce Type: new Abstract: Speech Language Model (SLM) performance often degrades on dialects due to data scarcity. Conventional text-to-speech (TTS) augmentation struggles to cov