Knowledge catalogue
arXiv:2605.08282v1 Announce Type: cross Abstract: Purpose: We aim to enhance the image quality of point-of-care ultrasound (POCUS) devices using deep learning and a novel paired dataset of POCUS and h
arXiv:2605.08150v1 Announce Type: new Abstract: We present a PyTorch package that compiles neural networks and their weights from Turing machine descriptions, producing models that exactly simulate th
arXiv:2605.08297v1 Announce Type: cross Abstract: The scaling behavior, in which test performance often improves as model size and data increase, is a central empirical phenomenon in modern deep learn
arXiv:2605.10650v1 Announce Type: new Abstract: Proper weight initialization prior to training has historically been one of the key factors that helped kick off the deep learning revolution. Initializ
arXiv:2605.10592v1 Announce Type: new Abstract: Aging combined sewer systems in many historical cities are increasingly stressed by extreme rainfall events, which can trigger combined sewer overflows
arXiv:2605.08191v1 Announce Type: cross Abstract: Reliable out-of-distribution (OOD) detection is a critical requirement for the safe deployment of machine learning systems. Despite recent progress, s
arXiv:2605.10643v1 Announce Type: new Abstract: Modern language models scale depth by stacking layers, each holding its own state - a per-layer KV cache in transformers, a per-layer matrix in Mamba, G
arXiv:2605.08504v1 Announce Type: new Abstract: We investigate the origins of massive activations in large language models (LLMs) and identify a specific layer named the extbf{Massive Emergence Layer
arXiv:2605.10668v1 Announce Type: new Abstract: We propose a closed-form spectral framework for relative log-density estimation in linearly parameterized probabilistic models, including unnormalized a
arXiv:2605.09094v1 Announce Type: new Abstract: In recent years, bilevel optimization (BLO) has attracted significant attention for its broad applications in machine learning. However, most existing w
arXiv:2605.08530v1 Announce Type: new Abstract: Millimeter-wave (mmWave) radar has emerged as a promising sensing modality for human perception due to its robustness under challenging environmental co
arXiv:2605.08488v1 Announce Type: cross Abstract: We develop a unified Lyapunov-integral quadratic constraint (IQC) framework for establishing uniform stability of first-order accelerated optimization
arXiv:2605.08757v1 Announce Type: new Abstract: We present a visuo-tactile data-collection system that generates temporally structured, contact-rich demonstrations for imitation learning. Conventional
arXiv:2602.06457v2 Announce Type: replace Abstract: Online bilevel optimization (OBO) has emerged as a powerful framework for many machine learning problems. Prior works have developed several algorit
arXiv:2605.10075v1 Announce Type: new Abstract: Large language models (LLMs) require reliable evaluation from pre-training to test-time scaling, making evaluation a recurring rather than one-off cost.
arXiv:2605.09623v1 Announce Type: cross Abstract: In recent years, the use of artificial intelligence on resource-constrained IoT devices has grown significantly. However, existing approaches to DNN p
arXiv:2510.04988v3 Announce Type: replace Abstract: The vast majority of modern deep learning models are trained with momentum-based first-order optimizers. The momentum term governs the optimizer's m
arXiv:2605.10239v1 Announce Type: new Abstract: This work explores a simple yet powerful lightweight adapter design for feed-forward 3D Gaussian Splatting (3DGS). Existing methods typically apply comp
arXiv:2605.08130v1 Announce Type: new Abstract: We present a framework for the simultaneous symbolic recovery of a function and its antiderivative from data. The framework rests on three ideas. First,
arXiv:2605.08161v1 Announce Type: new Abstract: Tumor segmentation in whole-body PET/CT imaging is crucial for precise disease evaluation and treatment planning. However, it remains challenging due to
arXiv:2605.10566v1 Announce Type: cross Abstract: Probabilistic linear solvers (PLSs) return probability distributions that quantify uncertainty due to limited computation in the solution of linear sy
arXiv:2605.10723v1 Announce Type: cross Abstract: Generating long-horizon music videos (MVs) is frequently constrained by prohibitive computational costs and difficulty maintaining cross-shot consiste
arXiv:2602.18866v2 Announce Type: replace Abstract: Despite the widespread use of boosting in structured prediction, a general theoretical understanding of aggregation beyond scalar prediction remains
arXiv:2605.10590v1 Announce Type: cross Abstract: Causal sensitivity analysis aims to provide bounds for causal effect estimates in the presence of unobserved confounding. However, existing methods fo
arXiv:2605.08329v1 Announce Type: new Abstract: Refining visual representations by eliminating their internal feature-level redundancy is crucial for simultaneously optimizing the performance and comp
arXiv:2605.09231v1 Announce Type: new Abstract: Deep generative models provide flexible frameworks for modeling complex, structured data such as images, videos, 3D objects, and texts. However, when ap
arXiv:2605.08242v1 Announce Type: cross Abstract: Clinical dietary assessment can generate detailed but high-dimensional nutrient and food-group information that is difficult to translate quickly into
arXiv:2605.10328v1 Announce Type: new Abstract: A central challenge in large-scale decision-making under incomplete information is estimating reliable probabilities. Recent approaches leverage Large L
arXiv:2605.10001v1 Announce Type: new Abstract: The increasing prevalence of large-scale hypergraphs poses significant computational challenges for hypergraph neural network (HNN) training. To address
arXiv:2605.08764v1 Announce Type: cross Abstract: Deep vision models degrade sharply in low-data regimes, particularly in medical imaging where labeled samples are scarce. We show this arises not mere
arXiv:2605.09575v1 Announce Type: cross Abstract: Background: Prenatal germinal matrix-intraventricular hemorrhage (GMH-IVH) is a leading cause of infant mortality and neurodevelopmental impairment. M
arXiv:2605.09622v1 Announce Type: cross Abstract: Voxel-wise dose prediction is a critical yet challenging task in practical radiotherapy (RT) planning, as bespoke models trained from scratch often st
arXiv:2605.10153v1 Announce Type: cross Abstract: Explainable AI (XAI) has achieved remarkable success in image classification, yet the audio domain lacks equally mature solutions. Current methods app
arXiv:2510.12811v2 Announce Type: replace-cross Abstract: Malware proliferation is increasing at a tremendous rate, with hundreds of thousands of new samples identified daily. Manual investigation of
arXiv:2605.09190v1 Announce Type: new Abstract: We present AQMP, a novel image codec combining Adaptive Quadtree Refinement with Matching Pursuit. Unlike conventional Matching Pursuit methods that ope
arXiv:2603.10126v2 Announce Type: replace-cross Abstract: We propose a standalone autoregressive (AR) Action Expert that generates actions as a continuous causal sequence while conditioning on refresh
arXiv:2502.09891v4 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has proven effective in integrating external knowledge into large language models (LLMs) for solving ques
arXiv:2605.08612v1 Announce Type: new Abstract: Addressing the escalating security vulnerabilities in Vision-Language-Action (VLA) models, this study investigates backdoor attacks targeting the visual
arXiv:2605.09644v1 Announce Type: new Abstract: Visual Geometry Grounded Transformer (VGGT) advances 3D reconstruction via scalable Transformer architecture, but the quadratic complexity of global att
arXiv:2605.10871v1 Announce Type: cross Abstract: This work proposes Attractor-Vascular Coupling Theory (AVCT), a mathematical framework showing that cardiac attractor geometry encodes blood pressure
arXiv:2605.09780v1 Announce Type: new Abstract: Attribution techniques explain the outcome of an AI model by assigning a numerical score to its inputs. So far, these techniques have mainly focused on
arXiv:2509.08031v3 Announce Type: replace-cross Abstract: Large Audio Language Models (LALMs) are rapidly advancing, but evaluating them remains challenging due to inefficient and non-standardized too
arXiv:2605.08172v1 Announce Type: new Abstract: Anatomical mesh segmentation requires models that operate directly on irregular surface geometry while remaining robust to arbitrary patient pose and me
arXiv:2409.19379v2 Announce Type: replace-cross Abstract: TxGraffiti is a data-driven, heuristic-based computer program developed to automate the process of generating conjectures across various mathe
arXiv:2605.10464v1 Announce Type: new Abstract: Zebrafish embryos are a valuable model for drug discovery due to their optical transparency and genetic similarity to humans. However, current evaluatio
arXiv:2605.10449v1 Announce Type: new Abstract: Quantifying fish community structure is essential for understanding biodiversity and ecosystem responses in a changing environment, yet existing survey
arXiv:2605.09050v1 Announce Type: cross Abstract: Monitoring moisture level of land in a large-scale plantation is tedious. The main objective of this project is to use a robotic kit in collaboration
arXiv:2605.10455v1 Announce Type: new Abstract: Short-term ocean forecast skill depends strongly on the three-dimensional ocean structure of the upper ocean, which governs stratification, subsurface h
arXiv:2605.08110v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) has become the standard for fine-tuning large pre-trained models at reduced computational cost. However, its low-rank point
arXiv:2605.08214v1 Announce Type: cross Abstract: Automatic Speech Recognition (ASR) and speaker diarization in Bangla remain challenging due to long form recordings, diverse acoustic conditions, and
arXiv:2601.03042v4 Announce Type: replace Abstract: Reliable confidence is essential for trusting the outputs of LLMs, yet widely deployed post-trained LLMs (PoLLMs) typically compromise this trust wi
arXiv:2510.09877v3 Announce Type: replace-cross Abstract: Over the past couple of decades, many active learning acquisition functions have been proposed, leaving practitioners with an unclear choice o
arXiv:2605.09775v1 Announce Type: new Abstract: Bayesian optimization (BO) is an efficient framework for optimizing expensive black-box functions. However, it is typically formulated as learning an en
arXiv:2308.13222v3 Announce Type: replace-cross Abstract: We introduce an evidence-driven Bayesian formulation of physics-informed neural networks that enables automatic optimization of loss weights b
arXiv:2605.09662v1 Announce Type: new Abstract: Most Gaussian Splatting techniques that provide a 3D semantic representation of the scene do not optimize the underlying 3D geometry, making object-leve
arXiv:2605.08136v1 Announce Type: cross Abstract: Visual perception plays a central role in competitive robotics, where environmental variations can directly affect real-time detection performance. Th
arXiv:2407.12173v2 Announce Type: replace-cross Abstract: Generative diffusion models have emerged as a powerful tool for high-quality image synthesis, yet their iterative nature demands significant c
arXiv:2605.09285v1 Announce Type: new Abstract: Null-space-based methods have garnered considerable attention in model editing by constraining updates to the null space of the pre-existing knowledge r
arXiv:2601.15686v2 Announce Type: replace Abstract: Model editing updates a pre-trained LLM with new facts or rules without retraining while preserving unrelated behavior. In real deployment, edits ar
arXiv:2605.09955v1 Announce Type: new Abstract: Disagreement in annotation is a common phenomenon in the development of NLP datasets and serves as a valuable source of insight. While majority voting r