Search: “tutorials”

Entity Resolution via Batched Oracle Queries

arXiv:2606.24407v1 Announce Type: cross Abstract: We consider an oracle that processes a limited batch of records at a time and clusters those that refer to the same real-world entity. We study how to

Fast and Slow Variational Continual Learning

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arXiv:2606.24007v1 Announce Type: cross Abstract: Continual learning remains a major challenge for modern deep networks, partly because commonly used optimizers lack inherent mechanisms for continual

tutorialsarxiv-cs-ai

24 Jun 2026

Inclusive Interactive Collisions for Multi-View Consistent Compositional 3D Generation

arXiv:2606.24206v1 Announce Type: cross Abstract: Recent breakthroughs in 3D generation have advanced notably with the development of text-to-image diffusion model. However, existing methods remain tw

Invariant Graph Representations for Continuous-Time Dynamic Graphs Under Distribution Shifts

arXiv:2405.19062v2 Announce Type: replace-cross Abstract: Continuous-Time Dynamic Graphs (CTDGs) enable fine-grained modeling of evolving relational systems. However, most existing CTDG representation

IV-CoT: Implicit Visual Chain-of-Thought for Structure-Aware Text-to-Image Generation

arXiv:2606.24849v1 Announce Type: cross Abstract: Unified multi-modal large language models (MLLMs) have achieved strong text-to-image generation quality, but still struggle with structure-aware promp

MVG-KAN: Multi-View Geo-Wind Guided KAN for PM_{2.5} Forecasting

arXiv:2606.24347v1 Announce Type: new Abstract: Accurate short-term PM_{2.5} forecasting is important for public health protection, air-quality early warning, and urban environmental management. Howev

NoContactNoWorries: Estimating Contact through Vision and Proprioception for In-Hand Dexterous Manipulation

arXiv:2606.24450v1 Announce Type: cross Abstract: Perceiving physical contact is fundamental to dexterous manipulation. While robots often rely on dedicated hardware tactile sensors, humans exhibit a

PointVG-R: Internalizing Geometric Reasoning in MLLMs for Precise Pointing Localization via Visual Chain of Thought

arXiv:2606.24539v1 Announce Type: new Abstract: Pointing-based visual grounding requires models to precisely locate target objects by deciphering complex spatial relationships between the visual scene

Predictive variational inference: Learn the predictively optimal posterior distribution

arXiv:2410.14843v4 Announce Type: replace-cross Abstract: Vanilla variational inference finds an optimal approximation to the Bayesian posterior distribution, but even the exact Bayesian posterior is

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective

arXiv:2512.10244v2 Announce Type: replace Abstract: Semi-supervised few-shot learning (SSFSL) resembles real-world applications such as auto-annotation, as it aims to learn a model from a few labeled

Task Decomposition for Efficient Annotation

arXiv:2606.24734v1 Announce Type: cross Abstract: High-quality annotations of structured representations are expensive to collect over large corpora. Manual annotation of structure is laborious, and m

A Hybrid TGN-SEAL Model for Dynamic Graph Link Prediction

arXiv:2602.14239v2 Announce Type: replace-cross Abstract: Predicting links in sparse, continuously evolving networks is a central challenge in network science. Conventional heuristic methods and deep

A large-scale foundation model enables simulation-to-real adaptation for nuclear magnetic resonance-based molecular structure analysis

arXiv:2606.20756v1 Announce Type: cross Abstract: Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful tool for molecular structure analysis, and spectral artificial intelligence offers great p

Adaptive Recurrent Message Passing for Test Time Computing on Graphs

arXiv:2606.22462v1 Announce Type: new Abstract: Pre-trained foundation models have demonstrated remarkable success in many domains, enabling a unified backbone to generalize across diverse downstream

Adversarial Domain Prompt Tuning and Generation for Single Domain Generalization

arXiv:2606.21736v1 Announce Type: new Abstract: Single domain generalization (SDG) aims to learn a robust model, which could perform well on many unseen domains while there is only one single domain a

Asymmetric physics enables efficient learning in quadrupedal robot swarms

arXiv:2606.23153v1 Announce Type: new Abstract: Animal collectives navigate cluttered environments through local coordination, yet robot swarms still struggle to reproduce this capability in the physi

tutorialsarxiv-cs-ro

BASIL: Bayesian Application for Scientific Iteration and Learning

arXiv:2606.21092v1 Announce Type: new Abstract: We introduce BASIL, a user-friendly desktop application for process optimization. BASIL employs a Bayesian approach, incorporating special acquisition f

Bayesian Model Averaging under Predictor Redundancy via Density-Ratio Posterior Compression

arXiv:2606.21080v1 Announce Type: cross Abstract: Bayesian model averaging in support-indexed regression induces a posterior distribution over active predictor supports. Under predictor redundancy, po

Beyond Templates: Revisiting Zero-Shot Remote Sensing through Meta-Prompting

arXiv:2606.20702v1 Announce Type: new Abstract: Vision-language models (VLMs) have sparked growing interest in zero-shot Earth Observation (EO) downstream tasks, with further gains enabled by remote-s

BoxCtrl: 3D-Aware Visual Prompting for Geometric Image Editing

arXiv:2606.23270v1 Announce Type: new Abstract: As instruction-based editing models and multimodal large language models advance, diverse image editing tasks have become feasible. However, achieving p

Brain-Inspired Stochastic Joint Embedding Representation Learning

arXiv:2505.11129v2 Announce Type: replace Abstract: Representation learning is one of the key research topics in machine learning, and the framework of self-supervised learning (SSL) has revolutionize

CogFormer: Learn All Your Models Once

arXiv:2603.20520v2 Announce Type: replace-cross Abstract: Simulation-based inference (SBI) with neural networks has accelerated and transformed cognitive modeling workflows. SBI enables modelers to fi

Contrastive and Adaptive Multi-modal Masked Autoencoder for Spatial Transcriptomics

arXiv:2606.21156v1 Announce Type: new Abstract: The high cost of spatial transcriptomics (ST) has driven extensive studies into predicting gene expression directly from H&E histology images. However,

CurvSegFlow: Time-Conditioned Flow Matching for Robust Segmentation of Curvilinear Structures in Noisy Biomedical Images

arXiv:2606.21608v1 Announce Type: new Abstract: Accurate segmentation of curvilinear structures remains challenging in biomedical imaging due to their thin geometry, complex topology, and sensitivity

Deep Generative Transformers for Probabilistic Time Series and Spatiotemporal Forecasting

arXiv:2603.11909v2 Announce Type: replace Abstract: Reliable uncertainty quantification is paramount for forecasting multivariate time series and spatiotemporal data. While Transformer architectures e

Deep Unrolled Networks in Representation Space Applied to MRI Reconstruction

arXiv:2606.21602v1 Announce Type: cross Abstract: Deep unrolled networks (DUNs) integrate physical forward models with learned regularization in cascaded network architectures, achieving exceptional p

Diffusion-Driven State Space Models

arXiv:2606.21036v1 Announce Type: cross Abstract: In many domains, practitioners seek models that produce accurate forecasts while faithfully capturing latent system dynamics. Existing approaches typi

Diffusion Integrated Gradients: Controllable Path Generation for Flexible Feature Attribution

arXiv:2606.22314v1 Announce Type: cross Abstract: Path-based attribution methods such as Integrated Gradients (IG) are widely adopted for their strong axiomatic properties and effectiveness in attribu

Do Sparse Autoencoders Learn Meaningful Concept Hierarchies?

arXiv:2606.22994v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) have become an important tool for unsupervised concept discovery in large models. To make the resulting feature spaces more i

Evolutionary Discovery of Developmental Reward Schedules in Deep Reinforcement Learning

arXiv:2606.20858v1 Announce Type: new Abstract: The temporal structure of reward composition in reinforcement learning (RL) is typically hand-designed and held fixed throughout training, leaving the p

FPAS: Frontier-Based Path Planning with Adaptive Sampling for Large-Scale Unknown Environments

arXiv:2606.22838v1 Announce Type: new Abstract: In this work, we propose Frontier-based Path Planning with Adaptive Sampling (FPAS), a novel framework designed for efficient goal-reaching in large-sca

tutorialsarxiv-cs-ro

From Uncertainty to Stability and Fidelity: Guiding Sparse-View 3D Gaussian Splatting with Fisher Information

arXiv:2606.20842v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has emerged as a promising technique for novel view synthesis. However, 3DGS requires dense input views to achieve high-qua

Full-Body Golf Swing Kinematic Reconstruction From a Smartwatch IMU

arXiv:2606.22876v1 Announce Type: new Abstract: Quantitative measurement of the golf swing is critical for evaluating technique and enabling individualized feedback. However, existing methods are impr

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning

arXiv:2510.10759v2 Announce Type: replace Abstract: Existing robot locomotion learning techniques rely heavily on the offline selection of proper reward weighting gains and cannot guarantee constraint

tutorialsarxiv-cs-ro

Gen2Balance: Generative Balancing for Long-Tailed Video Action Recognition

arXiv:2606.22416v1 Announce Type: new Abstract: We address the problem of training on long-tailed data for video action recognition. We propose to augment the training set using a text-to-video genera

Keep The Essentials: Efficient Reference Conditioned Generation via Token Dropping

arXiv:2606.23682v1 Announce Type: new Abstract: Reference-based diffusion models enable highly controllable image generation by leveraging elements from input images to guide prompt-driven synthesis.

Learning through Internalization

arXiv:2606.20937v1 Announce Type: new Abstract: We study internalization processes, by which neural-network-based systems absorb an explicit computational procedure into their own weights, and how the

Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks

arXiv:2606.23208v1 Announce Type: new Abstract: Deep Learning (DL) has advanced various fields by extracting complex patterns from large datasets. However, the computational demands of DL models pose

LEViL: Label-Efficient Video Learning via Zero-Shot Distillation over VLM-Generated Pseudo-Label Spaces

arXiv:2606.21358v1 Announce Type: new Abstract: Supervised video pretraining is a common transfer learning practice for improving downstream action recognition performance. However, it requires large-

Lift4D: Harmonizing Single-View 3D Estimation for 4D Reconstruction In-the-Wild

arXiv:2606.23688v1 Announce Type: new Abstract: Reconstructing dynamic non-rigid objects from monocular video requires integrating visual cues from direct observations with data-driven priors over geo

LightOcc: Lightweight Spatial Embedding for Efficient Vision-based 3D Occupancy Prediction

arXiv:2412.05976v2 Announce Type: replace Abstract: Occupancy prediction has garnered increasing attention in recent years for its comprehensive fine-grained environmental representation and strong ge

LLM-Aided A* Search in Non-Geometric Network Graphs

arXiv:2606.23136v1 Announce Type: cross Abstract: Finding the shortest path in non-geometric network graphs, where edge weights encode arbitrary metrics such as latency or monetary cost rather than sp

Look Light, Think Heavy: What Multimodal Chain-of-Thought Reasoning Can and Cannot Do

arXiv:2606.22565v1 Announce Type: cross Abstract: Chain-of-Thought (CoT) has become a standard method for improving reasoning capabilities in large language models (LLMs) by eliciting step-by-step thi

MAVRL: Learning Reward Functions from Multiple Feedback Types with Amortized Variational Inference

arXiv:2602.15206v2 Announce Type: replace Abstract: Reward learning typically relies on a single feedback type or combines multiple feedback types using manually weighted loss terms. Currently, it rem

MIRCaps: A Large-Scale Mixed-Domain Dataset with Image-Level and Region-Level Captions for Fine-Grained Vision-Language Learning

arXiv:2606.21419v1 Announce Type: new Abstract: Despite recent progress in Vision-Language Models (VLMs), mixed-domain image-caption datasets for both general-purpose and CCTV-based video surveillance

Native space based pipelines outperform template space based pipeline in subcortical segmentation

arXiv:2606.21463v1 Announce Type: new Abstract: Accurate segmentation of subcortical regions is critical for neurosurgical planning and functional research. Most automated methods rely on template spa

Neural Operator Processes for Probabilistic Operator Learning under Partial Observations

arXiv:2606.22946v1 Announce Type: new Abstract: Neural operators learn mappings between function spaces, but are typically developed with dense input-output training fields and fully observed inputs a

NewPINNs: Physics-Informing Neural Networks Using Conventional Solvers for Partial Differential Equations

arXiv:2601.17207v2 Announce Type: replace Abstract: We introduce NewPINNs, a physics-informing learning framework that couples neural networks with conventional numerical solvers for solving different