Search: “applications”

arXiv:2610.15571v1 Announce Type: cross Abstract: In this work, we propose a transfer learning algorithm for covariate shift, integrating sample reweighting with representation learning. A key feature

arXiv:2610.16644v1 Announce Type: new Abstract: Learning a response law for continuous decisions from observational time series is central to many real-world decision systems, yet temporal non-station

arXiv:2610.17018v1 Announce Type: new Abstract: Standard procedure in machine learning is to randomly split train and test data, emulating IID draws from one underlying data distribution. In practice,

arXiv:2511.17366v2 Announce Type: replace-cross Abstract: Building a generalist robot that can perceive, reason, and act across diverse tasks remains an open challenge, especially for dexterous manipu

arXiv:2610.18733v1 Announce Type: new Abstract: Block-wise varying exposures are effective for cap- turing appearance of a real-world high dynamic range (HDR) scene via a single-shot. However, brightn

arXiv:2610.16542v1 Announce Type: cross Abstract: This work presents a hybrid numerical approach for solving linear systems arising from the discretization of the two-dimensional parametric Helmholtz

arXiv:2303.07308v3 Announce Type: replace-cross Abstract: We present NeuSE, a novel Neural SE(3)-Equivariant Embedding for objects, and illustrate how it supports object-based Simultaneous Localizatio

arXiv:2506.15660v2 Announce Type: replace-cross Abstract: We consider the problem of estimating the spectral norm of a matrix using only matrix-vector products. We propose a new Counterbalance estimat

Our full lineup is here! We’re back in Baltimore on October 27 to debate: Will Generative AI Enhance or Stunt College Learning? Arguing “Enhance”: Rumman Chowdhury and @RaviPendse Arguing “Stunt”: @ma

arXiv:2610.17843v1 Announce Type: new Abstract: Proactive dynamic grasping requires robots to predict the future state at which physical contact will occur, rather than reacting to the currently obser

arXiv:2610.16504v1 Announce Type: new Abstract: Developing reliable predictive models for multi-physics systems often forces a compromise between computationally demanding physical simulators and data

arXiv:2610.16355v1 Announce Type: new Abstract: Generalist robot manipulation can benefit from pretrained vision-language models (VLMs) for semantic reasoning and video generation models (VGMs) for wo

arXiv:2610.14770v1 Announce Type: new Abstract: Multimodal contrastive learning typically relies on manually specified scoring functions, such as cosine similarity, to quantify the similarity between

arXiv:2610.16967v1 Announce Type: new Abstract: Physical AI is commonly formulated around an explicitly specified task: given a goal or instruction, a system perceives, reasons, and acts. In many real

arXiv:2610.14942v1 Announce Type: new Abstract: Bilevel data selection is a fundamental task with numerous applications in machine learning and statistics. A common strategy for tackling this problem

arXiv:2610.14698v1 Announce Type: new Abstract: Feed-forward 3D reconstruction models have achieved remarkable progress through large-scale training and strong learned priors. However, they remain fra

arXiv:2601.12936v2 Announce Type: replace Abstract: Slot Attention, an approach that binds different objects in a scene to a set of 'slots', has become a leading method in unsupervised object-centric

arXiv:2610.16390v1 Announce Type: cross Abstract: A central goal of quantum machine learning is to identify applications in which small quantum circuits can improve task performance while remaining ex

arXiv:2610.16391v1 Announce Type: cross Abstract: Multi-step retrosynthesis planning is often considered solved once a route to purchasable starting materials is found, but such a route should also be

arXiv:2610.15498v1 Announce Type: new Abstract: The theoretical elegance of Support Vector Machines (SVMs), grounded in statistical learning theory, establishes them as a reference for robust classifi

arXiv:2610.15494v1 Announce Type: cross Abstract: Radio frequency fingerprinting aims to identify transmitters by exploiting small differences in the emitted signal waveform. Common approaches exploit

arXiv:2610.15628v1 Announce Type: new Abstract: Deep learning is now central to radiology, helping detect changes on X-rays, CTs, and MRIs. However, these systems are highly vulnerable to adversarial

arXiv:2610.15157v1 Announce Type: cross Abstract: Efficient perception is a prerequisite for the small, low-power devices targeted by robotics, mixed reality, autonomous driving, and drone application

arXiv:2601.11266v2 Announce Type: replace Abstract: Robust generalization in robotic manipulation is crucial for robots to adapt flexibly to diverse environments. Existing methods usually improve gene

arXiv:2610.15204v1 Announce Type: cross Abstract: Large language models are increasingly used in computer science education. However, the teaching abilities of even the most powerful LLMs are limited:

arXiv:2610.15458v1 Announce Type: new Abstract: Existing analyses of interval length in split conformal prediction largely focus on clean data. However, how contamination at the training and calibrati

arXiv:2610.15585v1 Announce Type: new Abstract: World Action Models (WAMs), often built upon pretrained video-generation world models, have emerged as a promising framework for general-purpose and ope

arXiv:2610.14338v1 Announce Type: cross Abstract: TREGO, a trust-region Bayesian optimization (BO) method for expensive blackbox problems, has convergence guarantees, but only for exact function value

arXiv:2610.15699v1 Announce Type: new Abstract: Generative modeling for tabular data has advanced to the point where models can synthesize realistic records, recover missing values, and generate sampl

arXiv:2610.17574v1 Announce Type: new Abstract: Tracking object motion within a gripper provides essential feedback for robotic manipulation. However, limited contact coverage and uneven deformation m

arXiv:2610.14513v1 Announce Type: new Abstract: Robotic platforms combine multi-modal sensors to operate across diverse conditions, but most dense correspondence methods are limited to RGB-RGB or a sm

arXiv:2506.11804v2 Announce Type: replace Abstract: In recent years, the development of interconnected devices has expanded in many fields, from infotainment to education and industrial applications.

The EU is investigating OpenAI and Anthropic after their AI agents hacked into other organisations. These early enforcement steps might lead to the first legal battle between Brussels and the AI labs

arXiv:2603.18091v2 Announce Type: replace Abstract: Test-time sampling can improve Vision-Language-Action (VLA) policies, but only if the model can efficiently select a good action from multiple candi

arXiv:2602.01581v2 Announce Type: replace Abstract: Aligning large language models (LLMs) depends on high-quality datasets of human preference labels, which are costly to collect. Although active lear

arXiv:2610.14846v1 Announce Type: new Abstract: Indoor human perception is the foundation for smart homes and healthcare. However, RGB cameras raise privacy concerns in private indoor environments, wh

arXiv:2610.15354v1 Announce Type: new Abstract: Sexual harassment represents a pervasive societal challenge, with a substantial proportion of incidents remaining unreported due to insufficient evidenc

arXiv:2610.16793v1 Announce Type: new Abstract: Interacting with unfamiliar objects requires understanding how their parts connect and move. We introduce ViewStruct, a framework for jointly predicting

arXiv:2405.14018v2 Announce Type: replace-cross Abstract: In this paper, we introduce a simple yet effective tabular data watermarking mechanism with statistical guarantees. We show theoretically that

arXiv:2610.18846v1 Announce Type: new Abstract: Reconstructing complete, high-quality meshes of large-scale scenes from unposed multi-view images requires faithful geometric grounding while plausibly

1M-token, multimodal decision models are here! But there is more to this one. You can classify, extract, and summarize with 1M tokens of text and image input. ScaleDown's Constrained Decoding Models r

A worthwhile (and unnerving) reminder from Anthropic that we should be paying attention to AI ability thresholds, not just steady climbs In this case, the threshold is the capability needed for a self

wait so this feels a bit different too in a good way You already gave the LLM the answer. So why is it writing it out from scratch? Introducing a new AI model architecture: Constrained Decoding Models

arXiv:2610.12500v1 Announce Type: cross Abstract: Alzheimer's disease (AD) remains a major public health challenge, and early identification is crucial for timely intervention and care planning. Recen

arXiv:2505.15738v3 Announce Type: replace-cross Abstract: Large language models are increasingly deployed in user-facing products that handle sensitive personal data while interacting with untrusted e

arXiv:2610.12976v1 Announce Type: new Abstract: Accurate daily precipitation forecasts are important for weather-sensitive decisions. Although recent Weather Foundation Models (WFMs) have achieved sig