Knowledge catalogue
arXiv:2606.22220v1 Announce Type: new Abstract: Memorization in machine learning models enables high performance on rare in-distribution samples by capturing their atypical patterns. However, it also
arXiv:2606.20722v1 Announce Type: cross Abstract: Grayscale images are commonly found in historical photography restoration, medical imaging, and artistic media. However, automatically applying color
arXiv:2606.21605v1 Announce Type: new Abstract: Vision foundation models have substantially advanced computer vision, enabling state-of-the-art performance in zero- and few-shot settings. They have be
arXiv:2606.23637v1 Announce Type: new Abstract: Matrix-aware optimizers such as Muon and Muown have recently shown strong empirical performance for pre-training Transformers. In particular, Muown sepa
arXiv:2606.22460v1 Announce Type: cross Abstract: Music streaming services such as Deezer often recommend personalized playlists to users. Playlist captioning, which involves describing these playlist
arXiv:2606.21088v1 Announce Type: new Abstract: Achieving robust and generalizable manipulation across diverse environments remains a fundamental challenge in embodied robotics. Recent world action mo
arXiv:2606.22924v1 Announce Type: new Abstract: Text-to-image generation has seen rapid advancements, especially with the development of generative models. However, challenges remain in achieving high
arXiv:2509.18671v2 Announce Type: replace Abstract: Determining where to execute the manipulation policy is a fundamental challenge in mobile manipulation. Most approaches have formulated this as a ge
arXiv:2606.21372v1 Announce Type: cross Abstract: Vision-language-action (VLA) models rely on discrete action tokenizers to bridge continuous robot control and autoregressive sequence modeling, yet ex
arXiv:2606.21297v1 Announce Type: new Abstract: Augmenting model-free reinforcement learning (RL) with representations learned through observation dynamics prediction (observation-predictive RL) can i
arXiv:2606.21463v1 Announce Type: new Abstract: Accurate segmentation of subcortical regions is critical for neurosurgical planning and functional research. Most automated methods rely on template spa
arXiv:2606.22537v1 Announce Type: new Abstract: Out-of-Distribution (OOD) detection is essential for ensuring the robustness and reliability of object detection systems deployed in safety-critical app
arXiv:2606.21024v1 Announce Type: cross Abstract: AI-assisted research systems generate many failed attempts, but those failures rarely become a durable, shared knowledge asset. We propose a negative
arXiv:2606.20689v1 Announce Type: new Abstract: Neonatal jaundice (hyperbilirubinemia) is one of the most common conditions affecting newborns worldwide, with India alone recording roughly 15 million
arXiv:2606.20823v1 Announce Type: new Abstract: Facial landmark localisation is a prerequisite for developing automated, non-contact neonatal pain assessment methods. Clinicians use pain scales to jud
arXiv:2403.06828v4 Announce Type: replace Abstract: Navigating a nonholonomic robot in a cluttered, unknown environment requires accurate perception and precise motion control for real-time collision
arXiv:2606.21061v1 Announce Type: new Abstract: Stochastic segmentation seeks to represent multiple plausible masks for a single image, which is essential in safety- and quality-critical applications
arXiv:2606.22991v1 Announce Type: new Abstract: Sample reweighting is a major approach to addressing distribution shifts, such as label noise and class imbalance. Meta-Weight-Net (MW-Net) is a promisi
arXiv:2507.07532v4 Announce Type: replace Abstract: While Prover-Verifier Games (PVGs) offer a promising path toward verifiability in nonlinear classification models, they have not yet been applied to
arXiv:2606.22200v1 Announce Type: new Abstract: We study regression-based data fusion under uncertainty, where multiple noisy and biased measurement sources are available but ground-truth labels are a
arXiv:2606.23601v1 Announce Type: cross Abstract: Neural networks are a commonly used prediction tool in computer science and statistics. However, the barrier to entry of this interesting field remain
arXiv:2606.22946v1 Announce Type: new Abstract: Neural operators learn mappings between function spaces, but are typically developed with dense input-output training fields and fully observed inputs a
arXiv:2606.23155v1 Announce Type: cross Abstract: Mean field games efficiently approximate a very large population of strategic agents. While these games can aid the understanding of complex systems,
arXiv:2606.20673v1 Announce Type: cross Abstract: A central challenge in EEG authentication is that models are typically tied to the acquisition settings in which they are trained. In particular, vari
arXiv:2606.22068v1 Announce Type: new Abstract: The performance of supervised machine learning models is directly related to the quality of the training dataset. In particular, the presence of signifi
arXiv:2601.17207v2 Announce Type: replace Abstract: We introduce NewPINNs, a physics-informing learning framework that couples neural networks with conventional numerical solvers for solving different
arXiv:2602.09012v2 Announce Type: replace Abstract: The rapid evolution of GUI-enabled agents has rendered traditional CAPTCHAs obsolete. While previous benchmarks like OpenCaptchaWorld established a
arXiv:2606.23200v1 Announce Type: cross Abstract: Neighboring-slice self-supervised denoising is attractive for volumetric medical imaging, yet inter-slice misalignment breaks anatomical correspondenc
arXiv:2603.00180v2 Announce Type: replace Abstract: Generative modeling of neural network parameters is often tied to architectures because standard parameter representations rely on known weight-matr
arXiv:2606.22331v1 Announce Type: cross Abstract: Quantum machine learning is often motivated by the exponentially large state space of quantum systems, but this promise leaves a basic generalization
arXiv:2606.21290v1 Announce Type: new Abstract: We propose NoduLoCC2026, a challenge on lung nodule detection and localization in chest X-ray images. We have provided a dataset for both tasks and rece
arXiv:2606.22769v1 Announce Type: new Abstract: Standard NLP pipelines for occupational clustering discard the 10-15% of job postings that density-based methods assign to noise. We argue this is an er
arXiv:2603.22713v2 Announce Type: replace Abstract: Adversarial imitation learning (AIL) achieves high-quality imitation by mitigating compounding errors inherent to behavioral cloning (BC), yet its a
arXiv:2606.23295v1 Announce Type: new Abstract: In this paper we prove some concentration inequalities for two types of error probabilities in the Empirical Risk Principle (ERP) in statistical learnin
arXiv:2511.11466v2 Announce Type: replace-cross Abstract: Recently, several instances of non-Euclidean SGD, including SignSGD, Lion, and Muon, have attracted significant interest from the optimization
arXiv:2606.21270v1 Announce Type: cross Abstract: Imaging objects hidden outside the direct line of sight expands the effective field of view and is critical for applications such as autonomous drivin
arXiv:2110.03950v2 Announce Type: replace-cross Abstract: We study the problem of finding approximate first-order stationary points in optimization problems of the form min_{x in X} max_{y in Y} f(x,y
arXiv:2606.22030v1 Announce Type: cross Abstract: We present Nous, a novel agent memory architecture grounded in the principle that knowledge is prediction, not storage. Rather than persisting facts a
arXiv:2606.22336v1 Announce Type: cross Abstract: Conformal selection aims to identify test candidates whose unknown responses fall in a target region while controlling the false discovery rate. Exist
arXiv:2511.12024v3 Announce Type: replace Abstract: Lensless imaging reconstructs scenes from highly multiplexed measurements, resulting in a severely ill-posed inverse problem. In this work, we ident
arXiv:2606.22696v1 Announce Type: new Abstract: We propose NullFlow, a principled framework for one-step generative image reconstruction. Our key idea is to confine the generative flow to a measuremen
arXiv:2503.10251v2 Announce Type: replace-cross Abstract: Transformers are the state-of-the-art architecture for large language models, and a key to their scalability is the strategic usage of low-pre
arXiv:2503.03523v3 Announce Type: replace-cross Abstract: O-RAN hosts many intelligent applications known as eXtended Applications (xApps). xApps are applications that leverage advanced algorithms to
arXiv:2606.21113v1 Announce Type: new Abstract: Object-centric image generation is important in settings with few labeled examples, including pedestrian analysis in smart-city scenes, traffic-sign ins
arXiv:2606.21321v1 Announce Type: new Abstract: Real-world decision-making often requires optimizing multiple competing objectives simultaneously. In reinforcement learning (RL), this is typically add
arXiv:2606.23298v1 Announce Type: new Abstract: Underwater 4D reconstruction remains challenging due to the coupling between degraded light transport in participating media and dynamic water variation
arXiv:2606.21135v1 Announce Type: new Abstract: Human motion generation has been widely studied across diverse input modalities, text, music, and video, and recent efforts have unified these into sing
arXiv:2509.22483v2 Announce Type: replace Abstract: Large language models deployed in sensitive applications increasingly require the ability to unlearn specific knowledge, such as user requests, copy
arXiv:2602.00012v3 Announce Type: replace Abstract: We present OGD4All, a transparent, auditable, and reproducible framework based on Large Language Models (LLMs) to enhance citizens' interaction with
arXiv:2606.20920v1 Announce Type: new Abstract: Characterizing non-Gaussian posterior distributions in partially observed high-dimensional nonlinear systems remains a fundamental challenge in data ass
arXiv:2510.18313v5 Announce Type: replace Abstract: Autonomous driving world models are expected to work effectively across three core dimensions: state, action, and reward. However, existing methods
arXiv:2606.22617v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable performance on 2D visual tasks, yet enhancing their spatial intelligence for real-worl
arXiv:2606.21165v1 Announce Type: new Abstract: We present OmniV2X, a generative foundation model for vehicle-to-everything (V2X) cooperative driving. The model directly interprets independent context
arXiv:2606.18658v2 Announce Type: replace Abstract: Learning unsupervised representations of medical imaging cohorts can reveal clinically meaningful prototypes without expert labels, which are often
arXiv:2503.17308v2 Announce Type: replace-cross Abstract: With the growing interest in quantum machine learning, the perceptron, a fundamental building block in traditional machine learning, has emerg
arXiv:2606.21951v1 Announce Type: new Abstract: We study high-dimensional differentially private (DP) covariance estimation in the operator norm, and principal component analysis (PCA), under k-row-co
arXiv:2606.22249v1 Announce Type: new Abstract: In recent years, weight quantization that encodes the learnable parameters of large language models in an n-bit format has garnered significant attentio
arXiv:2606.23668v1 Announce Type: new Abstract: Large Language Models (LLMs) are frequently portrayed as general-purpose solvers capable of solving arbitrary tasks. We argue that this view overlooks a
arXiv:2606.17276v3 Announce Type: replace-cross Abstract: Generative recommendation (GR) has emerged as a promising direction for recommender systems. Recently, large language models (LLMs) have been
arXiv:2606.22600v1 Announce Type: new Abstract: On-Policy Distillation (OPD) improves the learning efficiency of standard reinforcement learning through dense, token-level supervision from teachers. I