Search: “tutorials”

Quantifying Noise of Dynamic Vision Sensor

arXiv:2404.01948v3 Announce Type: replace Abstract: Dynamic visual sensors (DVS) are characterized by a large amount of background activity (BA) noise, which it is mixed with the original (cleaned) se

ReTabSyn: Realistic Tabular Data Synthesis via Reinforcement Learning

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DGX agent

arXiv:2603.10823v2 Announce Type: replace-cross Abstract: Deep generative models can help with data scarcity and privacy by producing synthetic training data, but they struggle in low-data, imbalanced

tutorialsarxiv-cs-lg

9 Jun 2026

RGB-S: Image-Aligned Tactile Saliency for Robust Dexterous Manipulation

arXiv:2606.08765v1 Announce Type: cross Abstract: Effective visuo-tactile integration is critical for robotic dexterous manipulation, especially when visual observations are unreliable or occluded. Ho

Sample-Efficient LLM-Based Detection of Malicious Web Server Logs with Forensically Explainable Reasoning

arXiv:2606.08649v1 Announce Type: cross Abstract: Forensic analysis of web server logs demands both accurate detection and human-readable explanations that can satisfy legal requirements. We present C

Shortcuts in the Tail: Debiasing via Post-Hoc Spectral Compression of Fine-Tuning Updates

arXiv:2606.07596v1 Announce Type: new Abstract: Fine-tuning often introduces spurious correlations alongside task knowledge, causing systematic failures on underrepresented groups. Existing mitigation

SmartMixed: A Two-Phase Training Strategy for Adaptive Activation Function Learning in Neural Networks

arXiv:2510.22450v3 Announce Type: replace-cross Abstract: The choice of activation function plays a critical role in neural networks, yet most architectures still rely on fixed, uniform activation fun

SpectraLDS: Provable Distillation for Linear Dynamical Systems

arXiv:2505.17868v2 Announce Type: replace Abstract: We present the first provable method for identifying symmetric linear dynamical systems (LDS) with accuracy guarantees that are independent of the s

SRT: Super-Resolution for Time Series via Disentangled Rectified Flow

arXiv:2606.07605v1 Announce Type: cross Abstract: Fine-grained time series data with high temporal resolution is critical for accurate analytics across a wide range of applications. However, the acqui

Statistical Decision Theory with Counterfactual Loss

arXiv:2505.08908v3 Announce Type: replace-cross Abstract: Many researchers apply classical statistical decision theory to evaluate treatment choices and learn optimal policies. However, because this f

Sustainability and Artificial Intelligence: Necessary, Challenging, and Promising Intersections

arXiv:2606.09006v1 Announce Type: cross Abstract: Both digital economy and digital technology researchers increasingly recognize the need to better address the role that artificial intelligence (AI) p

The CIFAR Synthetic Evidence Corpus for Detecting AI-Generated Evidence

arXiv:2606.07916v1 Announce Type: new Abstract: The growing ability of generative models to produce realistic documents poses a direct challenge to evidentiary workflows in the justice system and the

The Routing Plateau: Understanding and Breaking the Accuracy Limits of LLM Routers

arXiv:2606.07587v1 Announce Type: new Abstract: LLM routing has become a popular approach to improve the cost-quality trade-off of LLM services by dynamically selecting a model for each query. Recent

Train at Moving Edge: Online-Verified Prompt Selection for Efficient RL Training of Large Reasoning Model

arXiv:2603.25184v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become essential for post-training large language models (LLMs) in reasoning tasks. While scaling rollouts can

Vision-Guided Dual-Arm Humanoid Robotic Disassembly of End-of-Life 18650 Lithium-ion Battery Packs

arXiv:2606.08152v1 Announce Type: new Abstract: The growing volume of retired lithium-ion battery packs from electric vehicles and portable electronics calls for automated disassembly that is safe, fl

tutorialsarxiv-cs-ro

Vision-Language Guided Hyperspectral Object Tracking via Semantics Fusion and Contextual Template Updating

arXiv:2606.09167v1 Announce Type: new Abstract: Hyperspectral object tracking (HOT) leverages the rich spectral information provided by hyperspectral videos (HSVs), offering substantial potential for

Vision Language Model Helps Private Information De-Identification in Vision Data

arXiv:2606.09132v1 Announce Type: new Abstract: Visual Language Models (VLMs) have gained significant popularity due to their remarkable ability. While various methods exist to enhance privacy in text

Weak-Driven Learning: How Weak Agents make Strong Agents Stronger

arXiv:2602.08222v2 Announce Type: replace Abstract: As post-training optimization becomes central to improving large language models, we observe a persistent saturation bottleneck: once models grow hi

XAInomaly: Explainable and Interpretable Deep Contractive Autoencoder for O-RAN Traffic Anomaly Detection

arXiv:2502.09194v1 Announce Type: cross Abstract: Generative Artificial Intelligence (AI) techniques have become integral part in advancing next generation wireless communication systems by enabling s

A Mechanism-Coupled Split Window Network for Medium- to High-Resolution Land Surface Temperature Retrieval

arXiv:2509.04991v2 Announce Type: replace-cross Abstract: Land surface temperature (LST) is a fundamental physical variable in land-atmosphere interactions, surface energy budgets, and climate process

A robust PPG foundation model using multimodal physiological supervision

arXiv:2606.07365v1 Announce Type: cross Abstract: Photoplethysmography (PPG), a non-invasive measure of changes in blood volume, is widely used in both wearable devices and clinical settings. Recent P

ARAPDiffusion: ARAP Regularization for Diffusion-Based Deformable Shape Space Learning

arXiv:2606.06887v1 Announce Type: new Abstract: This paper introduces ARAPDiffusion, a latent diffusion model to learn the underlying continuous shape space of a deformation shape collection. The key

AsyncPatch Diffusion: spatially-flexible image generation

arXiv:2606.07079v1 Announce Type: new Abstract: Standard diffusion models corrupt an entire sample with a single shared noise level, forcing all spatial regions to follow the same denoising trajectory

Beyond Backscatter: InSAR coherence from detected SAR images

arXiv:2606.07374v1 Announce Type: cross Abstract: In this work, we propose a deep learning framework for coherence regression directly from detected SAR images, without the need for accurate coregistr

Beyond Linear and Overcomplete Regimes: A Mean-Field Analysis of Bottleneck Autoencoders

arXiv:2606.07120v1 Announce Type: new Abstract: Autoencoders (AEs) learn low-dimensional representations by mapping data into a latent space while minimizing reconstruction error. Despite their empiri

CAPE: Contrastive Action-conditioned Parallel Encoding for Embodied Planning

arXiv:2606.07304v1 Announce Type: new Abstract: Embodied agents need to predict the future consequences of candidate actions in order to plan effectively before execution. Existing visual dynamics mod

tutorialsarxiv-cs-ro

Constructing VAE Latent Spaces with Prescribed Topology

arXiv:2606.07058v1 Announce Type: cross Abstract: Variational autoencoders (VAEs) learn low-dimensional latent representations of high-dimensional data. When the data lies on a manifold with non-Eucli

Database Normalization via Dual-LLM Self-Refinement

arXiv:2508.17693v2 Announce Type: replace-cross Abstract: Database normalization is crucial to preserving data integrity. However, it is time-consuming and error-prone, as it is typically performed ma

Differences in Detection: Explainability Where it Matters

arXiv:2606.07503v1 Announce Type: new Abstract: We propose Differences in Detection (DnD), an intuitive method to compare two object detection models. Based on the same matching algorithm, it compleme

Dreaming when Necessary: Advancing World Action Models with Adaptive Multi-Modal Reasoning

arXiv:2606.07089v1 Announce Type: new Abstract: World Action Models (WAMs) offer a promising approach to embodied intelligence, yet existing methods rely heavily on video prediction as action priors a

tutorialsarxiv-cs-ro

EgoPressDiff: Multimodal Video Diffusion for Egocentric UV-Domain Hand-Pressure Estimation

arXiv:2606.06872v1 Announce Type: cross Abstract: Estimating hand-surface contact pressure from an egocentric view is crucial for AR/VR devices, robotic imitation, and ergonomic analysis. Existing met

ExMesh: EXplicit Mesh Reconstruction with Topology Adaptation

arXiv:2606.07288v1 Announce Type: new Abstract: Reconstructing surface meshes from multi-view images has remained a core challenge in recent years. Most existing methods, whether implicit or explicit,

Geometry of Semantic Space: Comparative Study of Discrete and Continuous Models

arXiv:2606.07183v1 Announce Type: new Abstract: This work examines the semantic geometry underlying NLP models. We compare supervised vector embeddings, such as CamemBERT, with lexical co-occurrence g

tutorialsarxiv-cs-cl

Hard labels sampled from sparse targets mislead rotation invariant algorithms

arXiv:2603.20967v2 Announce Type: replace-cross Abstract: One of the most common machine learning setups is logistic regression. In many classification models, including neural networks, the final pre

Lighting-Aware Representation Learning under Controllable Lighting Variation

arXiv:2606.06899v1 Announce Type: new Abstract: Variations in illumination remain a major challenge for visual representation learning, as they induce substantial appearance changes both across and wi

Mechanistic Evidence for Faithfulness Decay in Chain-of-Thought Reasoning

arXiv:2602.11201v2 Announce Type: replace Abstract: Chain-of-Thought (CoT) explanations are widely used to interpret how language models solve complex problems, yet it remains unclear whether these st

tutorialsarxiv-cs-cl

No-Harm Physics-Informed Inverse Learning with Residual-Calibrated Uncertainty

arXiv:2606.07153v1 Announce Type: cross Abstract: Physics-informed learning is increasingly used for partial differential equation (PDE)-governed inverse problems, but its reliability remains difficul

On the importance of multiple training seeds for evaluating machine unlearning

arXiv:2510.26714v5 Announce Type: replace-cross Abstract: Machine unlearning aims to remove the influence of certain data points from a trained model without costly retraining. Most practical unlearni

Reactivity-Informed Machine Learning for Performance Prediction and Design Space Exploration of Alkali-Activated Slag

arXiv:2606.06765v1 Announce Type: cross Abstract: Establishing quantitative relationships among mix design, raw material properties, curing conditions, and performance remains a long-standing challeng

SS-TPT: Stability and Suitability-Guided Test-Time Prompt Tuning for Adversarially Robust Vision-Language Models

arXiv:2606.06943v1 Announce Type: cross Abstract: Vision-language models (VLMs) such as CLIP achieve strong zero-shot recognition but remain highly fragile under adversarial perturbations. Recent test

Structure-Preserving Correction Learning for Sparse Bayesian Inference in Brain Source Imaging

arXiv:2606.07196v1 Announce Type: new Abstract: Classical sparse Type-II Bayesian methods for M/EEG brain imaging support joint estimation of source and noise hyperparameters, but rely on fixed iterat

Towards Unified Song Generation and Singing Voice Conversion with Accompaniment Co-Generation

arXiv:2606.07015v1 Announce Type: cross Abstract: While song generation and singing voice conversion (SVC) have evolved significantly, they have long been developed isolated: the former lacks zero-sho

Trio: Learning Time-Series Forecasting with Temporal-Spatial-Sample Attention and Structural Causal Priors

arXiv:2606.07291v1 Announce Type: new Abstract: Multivariate time-series forecasting requires models to reason over temporal dynamics, cross-variable dependencies, and historical input-output correspo

Twelve quick tips for designing AI-driven HPC workflows

arXiv:2606.07491v1 Announce Type: cross Abstract: High-performance computing (HPC) clusters remain the backbone of large-scale scientific computation, traditionally executing deterministic, linear pip

EEGDancer: Dynamic Emotion Latent Space Masked Modeling with Reinforcement Learning for EEG Continuous Emotion Prediction

arXiv:2606.05855v1 Announce Type: cross Abstract: Continuous electroencephalography (EEG) emotion prediction aims to model the temporal evolution of human emotional states from EEG signals. Unlike con

GOTabPFN: From Feature Ordering to Compact Tokenization for Tabular Foundation Models on High-Dimensional Data

arXiv:2606.05441v1 Announce Type: cross Abstract: We investigate how to make small tabular foundation models effective for High-Dimensional, Low-Sample Size (HDLSS) tabular prediction without retraini

In-Training Defenses against Emergent Misalignment in Language Models

arXiv:2508.06249v3 Announce Type: replace-cross Abstract: Fine-tuning lets practitioners repurpose aligned large language models (LLMs) for new domains, yet recent work reveals emergent misalignment (

Level Up: Defining and Exploiting Transitional Problems for Curriculum Learning

arXiv:2603.13761v2 Announce Type: replace-cross Abstract: Curriculum learning--ordering training examples in a sequence to aid machine learning--takes inspiration from human learning, but has not gain

Pretraining Recurrent Networks without Recurrence

arXiv:2606.06479v1 Announce Type: cross Abstract: Training recurrent neural networks (RNNs) requires assigning credit across long sequences of computations. Standard backpropagation through time (BPTT