Search: “tutorials”

Adversarial Rademacher Complexity of Deep Neural Networks

arXiv:2211.14966v2 Announce Type: replace Abstract: Deep neural networks (DNNs) are highly vulnerable to adversarial attacks. Ideally, a robust model should perform well on both perturbed training dat

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An Adaptive Differentially Private Federated Learning Framework

DGX agent

arXiv:2602.06838v3 Announce Type: replace Abstract: Federated learning enables collaborative model training across distributed clients while preserving data privacy. However, in practical deployments,

tutorialsarxiv-cs-ai

9 Jul 2026

An Edge-aware Prompt-enhanced SAM for Ultrasound Image Segmentation

arXiv:2607.07240v1 Announce Type: new Abstract: Ultrasound image segmentation is essential for delineating anatomical structures and lesions, providing the foundation for accurate diagnosis. While the

Bayesian Optimization of Genetic Algorithm Hyperparameters in a Multi-Fidelity Framework for Efficient Lattice Material Design

arXiv:2607.07289v1 Announce Type: cross Abstract: This study presents a multi-fidelity framework for the systematic optimization of genetic algorithm (GA) hyperparameters. The framework integrates thr

Can We Really Learn One Representation to Optimize All Rewards?

arXiv:2602.11399v2 Announce Type: replace-cross Abstract: As unsupervised pretraining becomes increasingly ubiquitous in reinforcement learning, a more thorough theoretical understanding of these meth

Creating Power Distribution Network Layouts Using Generative Adversarial Networks and Image-Based Representations

arXiv:2607.06622v1 Announce Type: cross Abstract: Utilities increasingly rely on planning and operational tools to cope with the increased penetrations of distributed energy resources, yet the lack of

Ensemble Deep Learning Approaches for AI-Altered Video Detection

arXiv:2607.06872v1 Announce Type: new Abstract: The increasing accessibility of artificial intelligence has led to a rapid rise in AI-generated videos, making it more difficult to distinguish between

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review

arXiv:2508.12265v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable progress in reasoning across diverse domains. However, effective reasoning in real-world t

From system models to class models: An in-context learning paradigm

arXiv:2308.13380v3 Announce Type: replace-cross Abstract: Is it possible to understand the intricacies of a dynamical system not solely from its input/output pattern, but also by observing the behavio

Generative Diffusion Models of Stochastic Graph Signals

arXiv:2607.06833v1 Announce Type: new Abstract: Sampling stochastic signals supported on a graph underlies many graph machine learning tasks, including recommender systems, forecasting in financial ma

HiMoE-VLA: Hierarchical Mixture-of-Experts for Generalist Vision-Language-Action Policies

arXiv:2512.05693v2 Announce Type: replace-cross Abstract: Generalist vision--language--action (VLA) policies are typically trained on heterogeneous mixtures of robot demonstrations spanning diverse em

MILES: Modular Instruction Memory with Learnable Selection for Self-Improving LLM Reasoning

arXiv:2607.06974v1 Announce Type: new Abstract: Large language models (LLMs) increasingly improve their reasoning at test time via additional computation, yet most existing works treat each problem in

On the Principles of Deep Feedforward ReLU Networks

arXiv:2607.07035v1 Announce Type: cross Abstract: The architecture of deep feedforward neural networks is ubiquitous in deep learning, either as a whole system or as a subnetwork of other architecture

Optimal Conformal Prediction under Epistemic Uncertainty

arXiv:2505.19033v2 Announce Type: replace-cross Abstract: Conformal prediction (CP) is a widely used frequentist framework to quantify uncertainty by constructing prediction sets with user-specified m

Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection

arXiv:2607.07192v1 Announce Type: new Abstract: In this paper, we study Single-Domain Generalized Object Detection (Single-DGOD), which aims to transfer a detector trained on a single source domain to

Recovering Candidate Circadian Regulators of Arrhythmic Pituitary Hormone Genes Using Reliability-Weighted Magnetic Laplacian with rwMagLap

arXiv:2607.06579v1 Announce Type: cross Abstract: We study how to recover candidate circadian-clock regulators of pituitary hormone genes that are important for women's health but do not show a clear

Research on Domain Information Mining and Theme Evolution of Scientific Papers

arXiv:2204.08476v2 Announce Type: replace-cross Abstract: In recent years, with the increase of social investment in scientific research, the number of research results in various fields has increased

Smooth Operator: A Real-Time Sampling-Based Algorithm for Kinematic Hand Retargeting

arXiv:2607.07491v1 Announce Type: new Abstract: Advances in learning-based robotic manipulation, such as Vision-Language-Action (VLA) models and Video Action Models (VAMs), heavily rely on high-qualit

tutorialsarxiv-cs-ro

SPECTRA: Context-Conditioned Spectral Movement Primitives for Robot Skill Generalization

arXiv:2607.06978v1 Announce Type: new Abstract: Robot imitation learning for manipulation should preserve demonstrated task geometry while producing dynamically admissible robot motions. Existing pipe

tutorialsarxiv-cs-ro

The Signs Were Always There: Training-Free Concept Detection and Steering in Raw Transformer Dimensions

arXiv:2606.12629v3 Announce Type: replace-cross Abstract: The standard basis of transformer hidden states is a training-free, architecture-general feature basis for detecting concepts and, in language

Towards Understanding Steering Strength

arXiv:2602.02712v2 Announce Type: replace-cross Abstract: A popular approach to post-training control of large language models (LLMs) is the steering of intermediate latent representations. Namely, id

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems

arXiv:2607.06252v1 Announce Type: cross Abstract: Many problems in science and engineering are difficult to model accurately, either due to unknown physical mechanisms, poorly quantified measurement u

A Definition and Roadmap for World Models

arXiv:2607.06401v1 Announce Type: new Abstract: World models -- internal simulators that learn the structure and dynamics of an environment -- have become one of the most actively debated concepts in

Base Models Know How to Reason, Thinking Models Learn When

arXiv:2510.07364v4 Announce Type: replace Abstract: What do thinking language models learn during training that their base models lack? We first present an unsupervised method that discovers a model's

Blind Quality Enhancement of Compressed Video via Fine-Grained Degradation-Guided Sequential Inference

arXiv:2511.16137v2 Announce Type: replace Abstract: Existing studies on quality enhancement for compressed video (QECV) predominantly rely on known quantization parameters (QPs), training separate enh

Breaking Spurious Correlations via Generative Randomization and Cross-Variant Self-Supervised Learning

arXiv:2607.05850v1 Announce Type: new Abstract: Deep neural networks trained with Empirical Risk Minimization (ERM) often fail under distribution shifts because they exploit spurious correlations betw

Canopy: A Heterograph Foundation Model for Metabolic Engineering

arXiv:2607.06224v1 Announce Type: new Abstract: Designing microbial strains that produce high-value chemicals at commercially viable titers remains a central challenge in metabolic engineering. Existi

Contextual Procurement Auctions with Bandit Learning

arXiv:2607.05813v1 Announce Type: cross Abstract: We study repeated contextual procurement auctions in which the platform must learn context-dependent product values from bandit feedback. We give an e

FGAA-FPN: Foreground-Guided Angle-Aware Feature Pyramid Network for Oriented Object Detection

arXiv:2602.10710v2 Announce Type: replace Abstract: With the increasing availability of high-resolution remote sensing and aerial imagery, oriented object detection has become a key capability for geo

FlexAct: Why Learn when you can Pick?

arXiv:2601.06441v2 Announce Type: replace Abstract: Learning activation functions has emerged as a promising direction in deep learning, allowing networks to adapt activation mechanisms to task-specif

Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusion

arXiv:2607.06546v1 Announce Type: cross Abstract: Denoising graphs is a fundamental problem in graph learning and the core operation of graph diffusion models. Attention-based architectures like graph

How Can Mamba Learn In Context with Outliers and Generalize Provably?

arXiv:2510.00399v2 Announce Type: replace Abstract: The Mamba model has gained significant attention for its computational advantages over Transformer-based models, while achieving comparable performa

How Personas Can Influence Agents to Play Split or Steal

arXiv:2607.05398v1 Announce Type: new Abstract: Personas are often employed to guide large language model agents, yet their effectiveness in shaping strategic behavior in social dilemma settings remai

Pro-Pose: Unpaired Full-Body Portrait Synthesis via Canonical UV Maps

arXiv:2512.17143v3 Announce Type: replace Abstract: Photographs of people taken by professional photographers typically present the person in beautiful lighting, with an interesting pose, and flatteri

Rethinking Indic AI from a Lens of Cultural Heritage Preservation

arXiv:2607.06544v1 Announce Type: new Abstract: As Artificial Intelligence (AI) makes inroads into different parts of the Indian subcontinent, there is significant interest in studying how AI impacts

Robust Face Super-Resolution and Recognition Through Multi-Feature Aggregation in Diffusion Models

arXiv:2607.05702v1 Announce Type: new Abstract: Images acquired in surveillance environments often suffer from conditions such as low resolution, variations in pose, irregular illumination, and occlus

RoME: Robust Mixture of Low-Rank Experts against Multiple Adversarial Perturbations

arXiv:2607.06109v1 Announce Type: cross Abstract: Multi-perturbation adversarial training (MAT) aims to achieve robustness against multiple ell_p perturbations but suffers from robustness trade-offs b

Synthetic Consumer Insight Generation with Large Language Models

arXiv:2607.05761v1 Announce Type: new Abstract: Modern data-driven marketing relies on large amounts of consumer data, yet collecting such data can be costly, time-consuming, and difficult to scale. T

Temporal Variational Implicit Neural Representations

arXiv:2506.01544v2 Announce Type: replace Abstract: We introduce Temporal Variational Implicit Neural Representations (TV-INRs), a probabilistic framework for modeling irregular multivariate time seri

Tuned Reverse Distillation: Enhancing Multimodal Industrial Anomaly Detection with Crossmodal Tuners

arXiv:2412.08949v4 Announce Type: replace Abstract: Knowledge distillation (KD) has been widely studied in unsupervised image Anomaly Detection (AD), but its application to unsupervised multimodal AD

Two Sides of the Same Coin: Learning the Backdoor to Remove the Backdoor

arXiv:2607.05748v1 Announce Type: new Abstract: The community has recently developed various training-time defenses to counter neural backdoors introduced through data poisoning. In light of the obser

WING: A Window-Prior-Based Generative Network with Gated Inception for Cross-Modality CT Synthesis

arXiv:2607.06234v1 Announce Type: new Abstract: Generating CT volumes from MRI and CBCT can improve treatment planning in adaptive radiotherapy while avoiding additional radiation exposure. However, d

WristMimic: Full-Body Humanoid Control with Wrist-Guided Manipulation

arXiv:2607.06438v1 Announce Type: cross Abstract: Retargeting human object interaction demonstrations to physics based simulation requires reproducing not only body motion but also the object motion a

A harmonised dataset for Earth system foundation models

arXiv:2607.03298v1 Announce Type: cross Abstract: Foundation models for Earth systems have so far been trained primarily on physical climate and weather data, with limited representation of the human

Aircraft Detection in Satellite Imagery using Deep Learning Object Detectors

arXiv:2607.02699v1 Announce Type: new Abstract: The object detection in satellite imagery has garnered considerable attention due to its extensive real-world applications and the inherent challenges i

Binary Iterative Method for Non-targeted Adversarial Attack

arXiv:2607.04145v1 Announce Type: cross Abstract: Adversarial attacks guide and provide additional training and test data for both adversarial training and adversarial robustness validation, and expos

C^3ASD: Multi-Level Consistency-Driven Representation Learning

arXiv:2607.03018v1 Announce Type: new Abstract: Active Speaker Detection determines whether a visible person in a video is speaking at each moment. While recent audio-visual fusion methods perform wel

Canonical quantization of neurons

arXiv:2607.05000v1 Announce Type: cross Abstract: Canonical quantization provides a systematic procedure for constructing quantum models from classical Hamiltonians. Here, we apply this principle to a