Knowledge catalogue
arXiv:2605.01965v1 Announce Type: new Abstract: A classical vector retrieval problem typically considers a single query embedding vector as input and retrieves the most similar embedding vectors from
arXiv:2310.11420v2 Announce Type: replace Abstract: We propose a novel unsupervised learning approach for non-rigid 3D shape matching. Our approach improves upon recent state-of-the art deep functiona
arXiv:2605.02505v1 Announce Type: new Abstract: Semantic Role Labeling (SRL) provides an explicit representation of predicate-argument structure, capturing linguistically grounded relations such as wh
arXiv:2605.01240v1 Announce Type: new Abstract: Self-supervised pretraining is promising for large-scale neuroimaging, yet the impact of region-aware masking and hybrid sequence modeling remains under
arXiv:2506.19133v3 Announce Type: replace Abstract: Euclidean representations distort data with intrinsic non-Euclidean structure. While Riemannian representation learning offers a solution by embeddi
arXiv:2605.02701v1 Announce Type: cross Abstract: We propose a robust gradient estimator based on per-sample gradient clipping and analyze its properties both theoretically and empirically. We show th
arXiv:2605.01868v1 Announce Type: new Abstract: Conformal prediction (CP) constructs prediction sets with marginal coverage guarantees under the assumption that the calibration and test distributions
arXiv:2605.01752v1 Announce Type: new Abstract: We study linear dueling bandits in volatile environments characterized by the simultaneous presence of post-serving contexts, delayed feedback, and adve
arXiv:2605.00904v1 Announce Type: new Abstract: Learning-based fluence map prediction offers a fast alternative to iterative inverse planning in intensity-modulated radiation therapy (IMRT), but its r
arXiv:2208.00335v4 Announce Type: replace Abstract: Rule extraction is a central problem in interpretable machine learning because it seeks to convert opaque predictive behavior into human-readable sy
arXiv:2605.02707v1 Announce Type: new Abstract: Optical coherence tomography (OCT), a commonly used retinal imaging modality, plays a central role in retinal disease diagnosis by providing high-resolu
arXiv:2507.18713v2 Announce Type: replace Abstract: High-fidelity sensor simulation of light-based sensors such as cameras and LiDARs is critical for safe and accurate autonomy testing. Neural radianc
arXiv:2512.10275v2 Announce Type: replace Abstract: Adversarial distillation in the standard min-max adversarial training framework aims to transfer adversarial robustness from a large, robust teacher
arXiv:2605.02147v1 Announce Type: new Abstract: Sampling-based model predictive control methods like MPPI and CEM are essential for real-time control of nonlinear robotic systems, particularly where d
arXiv:2510.04236v3 Announce Type: replace Abstract: We present Kaleido, a family of generative models designed for photorealistic, unified object- and scene-level neural rendering. Kaleido operates on
arXiv:2605.00944v1 Announce Type: cross Abstract: Sample-level rankings are increasingly used in data-centric NLP for analysis, filtering, debugging, and curation, yet existing pipelines typically sco
arXiv:2605.01135v1 Announce Type: new Abstract: Recent progress in generative models has significantly advanced image editing capabilities, yet precise and intuitive user control remains difficult. Sp
arXiv:2605.00886v1 Announce Type: new Abstract: Infrared small target detection (IRSTD) plays a pivotal role in a broad spectrum of mission-critical applications, including maritime surveillance, mili
arXiv:2605.02611v1 Announce Type: new Abstract: We consider selective classification with abstention in the fixed-pool (or transductive) setting, where the unlabeled pool is given beforehand and only
arXiv:2605.01628v1 Announce Type: cross Abstract: Self-normalized martingale inequalities lie at the heart of confidence ellipsoids for online least squares and, more broadly, many bandit and reinforc
arXiv:2605.02575v1 Announce Type: new Abstract: Rotating-view thick-slice acquisition is highly SNR-efficient for mesoscale diffusion MRI (dMRI) but requires numerous rotating views to satisfy Nyquist
arXiv:2511.08303v2 Announce Type: replace-cross Abstract: This study investigates treatment effect estimation in the semi-supervised setting, also can be interpreted as prediction-powered inference. I
arXiv:2605.01317v1 Announce Type: new Abstract: This paper compares Machine Learning and LSTM-based Deep Learning methods for sentiment analysis of Mobile Legends app reviews. Using a dataset of 10,00
arXiv:2605.01383v1 Announce Type: new Abstract: Differentiable physical networks provide a simple setting in which learning can be studied through the interaction between trainable parameters and phys
arXiv:2603.21783v2 Announce Type: replace Abstract: Text-to-image generation powered by Diffusion Transformers (DiTs) has made remarkable strides, yet remote sensing (RS) synthesis lags behind due to
arXiv:2605.01866v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) are promising for edge sensing due to their event-driven computation and temporal filtering capability. However, standa
arXiv:2509.24552v3 Announce Type: replace Abstract: Recent works show that hybrid architectures combining local sliding window attention layers and global attention layers outperform either of these a
arXiv:2605.02737v1 Announce Type: new Abstract: Synthetic training has recently advanced brain MRI segmentation by enabling contrast-agnostic models trained entirely on generated data. However, most e
arXiv:2605.02094v1 Announce Type: new Abstract: Subtle hand differences make sign language recognition challenging, yet many existing methods rely on encoders pretrained on generic action datasets tha
arXiv:2605.01427v1 Announce Type: new Abstract: Humanoid robots are entering our physical world at scale, yet as oversized toys--good at singing and dancing, but short on force-interaction capabilitie
arXiv:2605.01817v1 Announce Type: new Abstract: Diffusion models (DMs) excel on dense continuous data, but are not designed for sparse continuous data. They do not model exact zeros that represent the
arXiv:2605.00515v1 Announce Type: cross Abstract: Leveraging continuous solar energy harvesting at high efficiency, space data centers are envisioned as a promising platform for executing energy-inten
arXiv:2604.07421v2 Announce Type: replace Abstract: Full-waveform inversion (FWI) is pivotal for reconstructing high-resolution subsurface velocity models but remains computationally intensive and ill
arXiv:2605.01382v1 Announce Type: new Abstract: Analyzing human vasculature and vessel-like, tubular structures, such as airways, is crucial for disease diagnosis and treatment. Current methods often
arXiv:2605.00887v1 Announce Type: new Abstract: We propose SparseContrast, a new framework that merges dynamic sparse attention with contrastive learning for medical imaging, with a focus on chest X-r
arXiv:2605.01853v1 Announce Type: new Abstract: Large reasoning models (LRMs) generate extended solutions, yet it remains unclear whether these traces reflect substantive internal computation or merel
arXiv:2605.02888v1 Announce Type: cross Abstract: Speculative decoding accelerates large language model (LLM) inference by using a small draft model to propose candidate tokens that a larger target mo
arXiv:2605.01136v1 Announce Type: new Abstract: Spectral graph sparsification is a classical tool for reducing graph complexity while preserving Laplacian quadratic forms. In graph neural networks (GN
arXiv:2605.01459v1 Announce Type: new Abstract: Single-Image Super-Resolution (SISR) aims to reconstruct a High-Resolution (HR) image from a Low-Resolution (LR) observation, a fundamentally ill-posed
arXiv:2605.01701v1 Announce Type: new Abstract: Stochastic gradient methods are central to large-scale learning, yet their generalization theory typically relies on independent sampling assumptions. I
arXiv:2605.01978v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become the de facto method for achieving locomotion on humanoid robots in practice, yet stability analysis of the corr
arXiv:2605.01492v1 Announce Type: cross Abstract: We study high-dimensional LASSO under differential privacy via objective perturbation with heterogeneous covariate scales. In practical scenarios, cov
arXiv:2605.01856v1 Announce Type: cross Abstract: Stabilized regression aims to identify a set of predictors whose conditional relationship with a response variable remains invariant across different
arXiv:2605.01452v1 Announce Type: cross Abstract: Existing evaluations of conformal prediction, such as prediction efficiency and test-conditional coverage, are defined in expectation over the calibra
arXiv:2510.13668v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) inference has emerged as a fundamental paradigm, however, variations in output length cause severe workload imbalan
arXiv:2605.02116v1 Announce Type: new Abstract: Contrastive representation learning (CRL) underpins many modern foundation models. Despite recent theoretical progress, existing analyses suffer from se
arXiv:2605.00934v1 Announce Type: new Abstract: We introduce Analytic-CPD, a structured analytic variant of coherent point drift for non-rigid point set registration. The method retains the CPD poster
arXiv:2605.02583v1 Announce Type: new Abstract: Text-to-image diffusion models have revolutionized image synthesis and editing, but precise control over stylistic attributes remains a challenge, often
arXiv:2605.00832v1 Announce Type: new Abstract: Current synthetic data pipelines for computer vision generate images without diagnosing what the downstream model actually needs. This open-loop paradig
arXiv:2605.02624v1 Announce Type: new Abstract: There is growing interest in exploring user simulation as an alternative to gathering and scoring real user-chatbot interactions for AI chatbot evaluati
arXiv:2605.02708v1 Announce Type: cross Abstract: Single-view RGB object pose estimators have reached a level of precision and efficiency that makes them good candidates for vision-based robot control
arXiv:2605.02710v1 Announce Type: new Abstract: Purpose: Six million people use crutches as mobile aids in the US. Rigid designs with no axial mobility limit sensory feedback and lead to secondary inj
arXiv:2510.27527v2 Announce Type: replace Abstract: Large Language Models (LLMs) training is prohibitively expensive, driving interest in low-precision fully-quantized training (FQT). While novel 4-bi
arXiv:2605.01637v1 Announce Type: new Abstract: We introduce the Banach-Butterfly Invariant (BBT), an influence-adaptive Banach geometry on the Walsh-Hadamard butterfly factorization. For a Boolean fu
The brain does not predict every upcoming word during language comprehension with maximum precision, contradicting the assumption that next-word prediction serves as the central computational objectiv
arXiv:2605.02177v1 Announce Type: cross Abstract: Pearl's causal hierarchy shows that observational, interventional, and counterfactual queries are qualitatively distinct. We ask a quantitative versio
arXiv:2603.04299v3 Announce Type: replace Abstract: Large Language Models (LLMs) often exhibit highly agreeable and reinforcing conversational styles, also known as AI-sycophancy. Although this behavi
arXiv:2605.01844v1 Announce Type: new Abstract: Steering is a widely used technique for controlling large language models, yet its effects are often unstable and hard to predict. Existing theoretical
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Week one of the Musk v. Altman trial: what it was like in the
arXiv:2603.03235v2 Announce Type: replace-cross Abstract: Selecting the number of clusters remains a fundamental challenge in unsupervised learning. Existing approaches typically focus on identifying