Search: “tutorials”

AdvDINO: Domain-Adversarial Self-Supervised Representation Learning for Spatial Proteomics

arXiv:2508.04955v2 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) has emerged as a powerful approach for learning visual representations without manual annotations. However, the

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Tutorials

Artificial Intelligence for All? Brazilian Teachers on Ethics, Equity, and the Everyday Challenges of AI in Education

DGX agent

arXiv:2512.23834v2 Announce Type: replace-cross Abstract: This study examines the perceptions of Brazilian K-12 education teachers regarding the use of AI in education, specifically General Purpose AI

tutorialsarxiv-cs-ai

14 Apr 2026

Asymptotic Learning Curves for Diffusion Models with Random Features Score and Manifold Data

arXiv:2603.22962v2 Announce Type: replace Abstract: We study the theoretical behavior of denoising score matching--the learning task associated to diffusion models--when the data distribution is suppo

Can Small Training Runs Reliably Guide Data Curation? Rethinking Proxy-Model Practice

arXiv:2512.24503v2 Announce Type: replace-cross Abstract: Data teams at frontier AI companies routinely train small proxy models to make critical decisions about pretraining data recipes for full-scal

CapyMOA: Efficient Machine Learning for Data Streams and Online Continual Learning in Python

arXiv:2502.07432v2 Announce Type: replace Abstract: CapyMOA is an open-source Python library for efficient machine learning on data streams and online continual learning. It provides a structured fram

Causally Sufficient and Necessary Feature Expansion for Class-Incremental Learning

arXiv:2603.09145v2 Announce Type: replace-cross Abstract: Current expansion-based methods for Class Incremental Learning (CIL) effectively mitigate catastrophic forgetting by freezing old features. Ho

CFMS: A Coarse-to-Fine Multimodal Synthesis Framework for Enhanced Tabular Reasoning

arXiv:2604.10973v1 Announce Type: new Abstract: Reasoning over tabular data is a crucial capability for tasks like question answering and fact verification, as it requires models to comprehend both fr

Context-Guided Decompilation: A Step Towards Re-executability

arXiv:2511.01763v2 Announce Type: replace-cross Abstract: Binary decompilation plays an important role in software security analysis, reverse engineering, and malware understanding when source code is

Continuous Adversarial Flow Models

arXiv:2604.11521v1 Announce Type: cross Abstract: We propose continuous adversarial flow models, a type of continuous-time flow model trained with an adversarial objective. Unlike flow matching, which

Controlling Multimodal Conversational Agents with Coverage-Enhanced Latent Actions

arXiv:2601.07516v2 Announce Type: replace-cross Abstract: Vision-language models are increasingly employed as multimodal conversational agents (MCAs) for diverse conversational tasks. Recently, reinfo

CoRe-ECG: Advancing Self-Supervised Representation Learning for 12-Lead ECG via Contrastive and Reconstructive Synergy

arXiv:2604.11359v1 Announce Type: new Abstract: Accurate interpretation of electrocardiogram (ECG) remains challenging due to the scarcity of labeled data and the high cost of expert annotation. Self-

Dynamic Summary Generation for Interpretable Multimodal Depression Detection

arXiv:2604.11334v1 Announce Type: new Abstract: Depression remains widely underdiagnosed and undertreated because stigma and subjective symptom ratings hinder reliable screening. To address this chall

Emulating Non-Differentiable Metrics via Knowledge-Guided Learning: Introducing the Minkowski Image Loss

arXiv:2604.11422v1 Announce Type: cross Abstract: The ``differentiability gap'' presents a primary bottleneck in Earth system deep learning: since models cannot be trained directly on non-differentiab

Energy-oriented Diffusion Bridge for Image Restoration with Foundational Diffusion Models

arXiv:2604.10983v1 Announce Type: new Abstract: Diffusion bridge models have shown great promise in image restoration by explicitly connecting clean and degraded image distributions. However, they oft

Engineering Resource-constrained Software Systems with DNN Components: a Concept-based Pruning Approach

arXiv:2604.09988v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) are widely used by engineers to solve difficult problems that require predictive modeling from data. However, these models

EviCare: Enhancing Diagnosis Prediction with Deep Model-Guided Evidence for In-Context Reasoning

arXiv:2604.10455v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have enabled promising progress in diagnosis prediction from electronic health records (EHRs). However,

tutorialsarxiv-cs-cl

Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation

arXiv:2510.10925v2 Announce Type: replace-cross Abstract: Training student models on synthetic data generated by strong teacher models is a promising way to distilling the capabilities of teachers. Ho

tutorialsarxiv-cs-cl

GIF: A Conditional Multimodal Generative Framework for IR Drop Imaging in Chip Layouts

arXiv:2604.09999v1 Announce Type: new Abstract: IR drop analysis is essential in physical chip design to ensure the power integrity of on-chip power delivery networks. Traditional Electronic Design Au

Harnessing Photonics for Machine Intelligence

arXiv:2604.10841v1 Announce Type: cross Abstract: The exponential growth of machine-intelligence workloads is colliding with the power, memory, and interconnect limits of the post-Moore era, motivatin

HECTOR: Human-centric Hierarchical Coordination and Supervision of Robotic Fleets under Continual Temporal Tasks

arXiv:2604.10892v1 Announce Type: new Abstract: Robotic fleets can be extremely efficient when working concurrently and collaboratively, e.g., for delivery, surveillance, search and rescue. However, i

tutorialsarxiv-cs-ro

HiddenObjects: Scalable Diffusion-Distilled Spatial Priors for Object Placement

arXiv:2604.10675v1 Announce Type: new Abstract: We propose a method to learn explicit, class-conditioned spatial priors for object placement in natural scenes by distilling the implicit placement know

Hierarchical Textual Knowledge for Enhanced Image Clustering

arXiv:2604.11144v1 Announce Type: cross Abstract: Image clustering aims to group images in an unsupervised fashion. Traditional methods focus on knowledge from visual space, making it difficult to dis

tutorialsarxiv-cs-cl

HO-Flow: Generalizable Hand-Object Interaction Generation with Latent Flow Matching

arXiv:2604.10836v1 Announce Type: new Abstract: Generating realistic 3D hand-object interactions (HOI) is a fundamental challenge in computer vision and robotics, requiring both temporal coherence and

How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks

arXiv:2507.07067v3 Announce Type: replace-cross Abstract: Training effective artificial intelligence models for telecommunications is challenging due to the scarcity of deployment-specific data. Real

How to Design a Compact High-Throughput Video Camera?

arXiv:2604.10619v1 Announce Type: new Abstract: High throughput video acquisition is a challenging problem and has been drawing increasing attention. Existing high throughput imaging systems splice hu

How to Spin an Object: First, Get the Shape Right

arXiv:2412.10273v3 Announce Type: replace Abstract: Image-to-3D models increasingly rely on hierarchical generation to disentangle geometry and texture. However, the design choices underlying these tw

Improving the Reasoning of Multi-Image Grounding in MLLMs via Reinforcement Learning

arXiv:2507.00748v3 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) perform well in single-image visual grounding but struggle with real-world tasks that demand cross-image re

INCRT: An Incremental Transformer That Determines Its Own Architecture

arXiv:2604.10703v1 Announce Type: new Abstract: Transformer architectures are designed by trial and error: the number of attention heads, the depth, and the head size are fixed before training begins,

Interpretable Alzheimer's Diagnosis via Multimodal Fusion of Regional Brain Experts

arXiv:2512.10966v2 Announce Type: replace-cross Abstract: Accurate and early diagnosis of Alzheimer's disease (AD) is critical for effective intervention and requires integrating complementary informa

KL Divergence Between Gaussians: A Step-by-Step Derivation for the Variational Autoencoder Objective

arXiv:2604.11744v1 Announce Type: new Abstract: Kullback-Leibler (KL) divergence is a fundamental concept in information theory that quantifies the discrepancy between two probability distributions. I

Language Reconstruction with Brain Predictive Coding from fMRI Data

arXiv:2405.11597v2 Announce Type: replace-cross Abstract: Many recent studies have shown that the perception of speech can be decoded from brain signals and subsequently reconstructed as continuous la

Learning and Enforcing Context-Sensitive Control for LLMs

arXiv:2604.10667v1 Announce Type: cross Abstract: Controlling the output of Large Language Models (LLMs) through context-sensitive constraints has emerged as a promising approach to overcome the limit

Learning from Contrasts: Synthesizing Reasoning Paths from Diverse Search Trajectories

arXiv:2604.11365v1 Announce Type: new Abstract: Monte Carlo Tree Search (MCTS) has been widely used for automated reasoning data exploration, but current supervision extraction methods remain ineffici

Learning Long-term Motion Embeddings for Efficient Kinematics Generation

arXiv:2604.11737v1 Announce Type: new Abstract: Understanding and predicting motion is a fundamental component of visual intelligence. Although modern video models exhibit strong comprehension of scen

Learning to Forget -- Hierarchical Episodic Memory for Lifelong Robot Deployment

arXiv:2604.11306v1 Announce Type: cross Abstract: Robots must verbalize their past experiences when users ask 'Where did you put my keys?' or 'Why did the task fail?' Yet maintaining life-long episodi

LitPivot: Developing Well-Situated Research Ideas Through Dynamic Contextualization and Critique within the Literature Landscape

arXiv:2604.02600v2 Announce Type: replace-cross Abstract: Developing a novel research idea is hard. It must be distinct enough from prior work to claim a contribution while also building on it. This r

MetroGS: Efficient and Stable Reconstruction of Geometrically Accurate High-Fidelity Large-Scale Scenes

arXiv:2511.19172v4 Announce Type: replace Abstract: Recently, 3D Gaussian Splatting and its derivatives have achieved significant breakthroughs in large-scale scene reconstruction. However, how to eff

MorphoFlow: Sparse-Supervised Generative Shape Modeling with Adaptive Latent Relevance

arXiv:2604.11636v1 Announce Type: new Abstract: Statistical shape modeling (SSM) is central to population level analysis of anatomical variability, yet most existing approaches rely on densely annotat

Multimodal Diffusion Forcing for Forceful Manipulation

arXiv:2511.04812v2 Announce Type: replace-cross Abstract: Given a dataset of expert trajectories, standard imitation learning approaches typically learn a direct mapping from observations (e.g., RGB i

Neural Surface Reconstruction from Sparse Views Using Epipolar Geometry

arXiv:2406.04301v3 Announce Type: replace Abstract: Reconstructing accurate surfaces from sparse multi-view images remains challenging due to severe geometric ambiguity and occlusions. Existing genera

NeuroPath: Practically Adopting Motor Imagery Decoding through EEG Signals

arXiv:2604.09654v1 Announce Type: cross Abstract: Motor Imagery (MI) is an emerging Brain-Computer Interface (BCI) paradigm where a person imagines body movements without physical action. By decoding

Not Your Stereo-Typical Estimator: Combining Vision and Language for Volume Perception

arXiv:2604.09886v1 Announce Type: cross Abstract: Accurate volume estimation of objects from visual data is a long-standing challenge in computer vision with significant applications in robotics, logi

PASTA: Vision Transformer Patch Aggregation for Weakly Supervised Target and Anomaly Segmentation

arXiv:2604.09701v1 Announce Type: new Abstract: Detecting unseen anomalies in unstructured environments presents a critical challenge for industrial and agricultural applications such as material recy

Predicting integers from continuous parameters

arXiv:2602.10751v2 Announce Type: replace Abstract: We study the problem of predicting numeric labels that are constrained to the integers or to a subrange of the integers. For example, the number of

Preventing Latent Rehearsal Decay in Online Continual SSL with SOLAR

arXiv:2604.10586v1 Announce Type: cross Abstract: This paper explores Online Continual Self-Supervised Learning (OCSSL), a scenario in which models learn from continuous streams of unlabeled, non-stat

Probabilistic Prediction of Neural Dynamics via Autoregressive Flow Matching

arXiv:2604.11178v1 Announce Type: cross Abstract: Forecasting neural activity in response to naturalistic stimuli remains a key challenge for understanding brain dynamics and enabling downstream neuro

Quantum-Gated Task-interaction Knowledge Distillation for Pre-trained Model-based Class-Incremental Learning

arXiv:2604.11112v1 Announce Type: cross Abstract: Class-incremental learning (CIL) aims to continuously accumulate knowledge from a stream of tasks and construct a unified classifier over all seen cla

Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning

arXiv:2604.11407v1 Announce Type: cross Abstract: We revisit retrieval-augmented generation (RAG) by embedding retrieval control directly into generation. Instead of treating retrieval as an external