Search: “tutorials”

FCUS-rPPG: A Fast-Converging Unsupervised Framework for Remote Photoplethysmography via Gradient Oscillation Suppression

arXiv:2606.03050v1 Announce Type: new Abstract: Remote photoplethysmography (rPPG) enables non-contact extraction of blood volume pulse (BVP) signals using consumer-grade cameras. Recent unsupervised

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FFR: Forward-Forward Learning for Regression

DGX agent

arXiv:2606.03927v1 Announce Type: cross Abstract: The Forward-Forward (FF) algorithm offers a computationally efficient and biologically plausible alternative to backpropagation (BP) by training neura

tutorialsarxiv-cs-ai

3 Jun 2026

Formalizing the Binding Problem

arXiv:2606.03976v1 Announce Type: cross Abstract: Representations of the world, arguably, contain information about features (e.g. something is blue, something is a circle) but also information about

From Video to Control: A Survey of Learning Manipulation Interfaces from Temporal Visual Data

arXiv:2604.04974v2 Announce Type: replace Abstract: Video is a scalable observation of physical dynamics: it captures how objects move, how contact unfolds, and how scenes evolve under interaction --

tutorialsarxiv-cs-ro

Grounding Functional Similarity by Invariance-Aware Model Stitching

arXiv:2505.20142v2 Announce Type: replace Abstract: In deep learning, functional similarity evaluation quantifies the extent to which independently trained models learn similar input--output relations

GS-ROR^2: Bidirectional-guided 3DGS and SDF for Reflective Object Relighting and Reconstruction

arXiv:2406.18544v4 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has shown a powerful capability for novel view synthesis due to its detailed expressive ability and highly efficient re

Hierarchical RBF-KAN and RBF-SKAN Architectures for Multidimensional Function Approximation and Random Field Learning

arXiv:2606.02936v1 Announce Type: new Abstract: In this manuscript, we propose and analyze hierarchical Kolmogorov--Arnold neural network architectures employing radial basis functions as activation f

HiSE: A Lightweight Hierarchical Semantic Explainer for Heterogeneous Graph Neural Networks

arXiv:2606.03495v1 Announce Type: new Abstract: Heterogeneous graph neural networks (HGNNs) have demonstrated remarkable performance in modeling complex relational data, however their interpretability

IDO: Incongruity-aware Distribution Optimization for Multimodal Fake News Detection

arXiv:2606.03418v1 Announce Type: new Abstract: Multimodal fake news detection aims to identify the authenticity of news. Existing multimodal fake news detection methods mainly focus on cross-modal co

Learn When and Where to Connect: Adaptive Virtual Nodes for Dynamic Message Passing on Graphs

arXiv:2606.03068v1 Announce Type: cross Abstract: While Virtual Nodes (VNs) are often utilized in Message Passing Neural Networks (MPNNs) to facilitate effective message passing, existing VN-based met

Learning Coherent Representations: A Topological Approach to Interpretability

arXiv:2606.02841v1 Announce Type: new Abstract: Deep neural networks learn representations where individual features often lack interpretable meaning; a single neuron may activate for scattered, unrel

Learning without training: The implicit dynamics of in-context learning

arXiv:2507.16003v4 Announce Type: replace Abstract: One of the most striking features of Large Language Models (LLMs) is their ability to learn in-context. Namely at inference time an LLM is able to l

Lethe: Adapter-Augmented Dual-Stream Update for Persistent Knowledge Erasure in Federated Unlearning

arXiv:2601.22601v2 Announce Type: replace Abstract: Federated unlearning (FU) aims to erase designated client-level, class-level, or sample-level knowledge from a global model. Existing studies common

Linear Probes Detect Task Format, Not Reasoning Mode in Language Model Hidden States

arXiv:2606.02907v1 Announce Type: cross Abstract: Linear probing of large language model (LLM) hidden states is widely used to claim that models learn distinct representations for different reasoning

Oscillatory State-Space Models as Inductive Biases for Physics-Informed Neural PDE Solvers

arXiv:2606.02623v1 Announce Type: cross Abstract: Solving time-dependent partial differential equations (PDEs) is an important problem in computational science and engineering. Physics-informed neural

Physics-informed diffusion models in spectral space

arXiv:2602.09708v2 Announce Type: replace-cross Abstract: We propose physics-informed spectral diffusion (PISD), a methodology that combines generative latent diffusion models with physics-informed ma

PRISM: Rethinking Atmospheric Scattering Reconstruction as a Unified Understanding and Restoration Model for Real-world Dehazing

arXiv:2604.07048v2 Announce Type: replace Abstract: Real-world image dehazing (RID) aims to remove haze-induced degradation from real scenes. This task remains challenging due to non-uniform haze dist

PURGE: Projected Unlearning via Retain-Guided Erasure

arXiv:2606.03808v1 Announce Type: cross Abstract: We propose PURGE, a machine unlearning algorithm built on a simple but an under-exploited observation: continual learning (CL) and machine unlearning

Re-Evaluating Continual Learning with Few-Shot Adaptation

arXiv:2606.03843v1 Announce Type: cross Abstract: Continual learning methods aim to maximize the stability and plasticity of machine learning models that are trained on a sequence of tasks. The standa

Reasoning over Grammar: Can Synthetic Linguistic Reasoning Traces Enhance Low-Resource Machine Translation?

arXiv:2606.03782v1 Announce Type: new Abstract: Large language models (LLMs) offer a promising approach to machine translation (MT) for extremely low-resource languages by incorporating linguistic res

SAIL: Sound Abstract Interpreters with LLMs

arXiv:2511.13663v2 Announce Type: replace-cross Abstract: How to construct globally sound abstract interpreters to safely approximate program behaviors remains a bottleneck in abstract interpretation.

SaluNet: Enabling Total Plasticity in Normalization-Free Deep Networks

arXiv:2606.02927v1 Announce Type: new Abstract: Normalization layers such as BatchNorm and LayerNorm have long been considered essential for stable training in deep networks. This work demonstrates th

Text-to-Image Models Need Less from Text Encoders Than You Think

arXiv:2606.03715v1 Announce Type: new Abstract: Text-to-image models rely on text prompts as their primary interface to human intent. Prompts are encoded by a text encoder into embeddings that conditi

Toward Gripper-Integrated Active Electrosense for Pre-Contact Sensing in Underwater Soft Grippers

arXiv:2606.03204v1 Announce Type: new Abstract: Underwater manipulation often occurs under degraded visibility due to turbidity, glare, and gripper occlusion, limiting the reliability of vision-based

tutorialsarxiv-cs-ro

Transformer-Guided Content-Adaptive Graph Learning for Hyperspectral Unmixing

arXiv:2509.03376v2 Announce Type: replace Abstract: Hyperspectral unmixing (HU) targets to decompose each mixed pixel in remote sensing images into a set of endmembers and their corresponding abundanc

Video-Mirai: Autoregressive Video Diffusion Models Need Foresight

arXiv:2606.03971v1 Announce Type: new Abstract: Causal video generators must predict from the past, but they need not learn only from it. In streaming autoregressive video diffusion, each emitted segm

Visual Graph Scaffolds for Structural Reasoning in Large Language Models

arXiv:2606.02673v1 Announce Type: new Abstract: Graphs have been used to enhance large language models (LLMs) for structured reasoning, mostly as external knowledge sources are provided to models at t

What Do Students Learn? A Feature-Level Analysis of Dark Knowledge

arXiv:2606.03052v1 Announce Type: new Abstract: Knowledge Distillation (KD) is a powerful tool for model compression, yet the precise mechanisms by which student models acquire feature representations

Your Autoregressive Model Already Reveals the Causal Graph

arXiv:2602.01135v3 Announce Type: replace Abstract: Autoregressive models trained via next-token prediction implicitly learn the conditional independence structure of their data-generating process. We

AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks

arXiv:2606.01283v1 Announce Type: new Abstract: Modeling spatial dependencies is central to spatiotemporal data analysis using Graph Neural Networks (GNNs). Traditional methods rely on distance-based

AdaptiveK: Complexity-Driven Sparse Autoencoders for Interpretable Language Model Representations

arXiv:2508.17320v3 Announce Type: replace Abstract: Understanding the internal representations of large language models (LLMs) remains a central challenge for interpretability research. Sparse autoenc

Advances in Neural 3D Mesh Texturing: A Survey

arXiv:2606.00137v1 Announce Type: new Abstract: Texturing 3D meshes plays a vital role in determining the visual realism of digital objects and scenes. Although recent generative 3D approaches based o

An explainable hierarchical self attention-based approach for tremor detection in the time domain

arXiv:2606.00461v1 Announce Type: new Abstract: Tremor is a common movement disorder associated with conditions like Parkinson's disease and Essential tremor, traditionally diagnosed through expert cl

An Exploratory Study into using Machine-Learning for Fast Step-by-step Emulation of Numerical Mechanical Thrombectomy Simulations for Ischemic Stroke

arXiv:2606.00892v1 Announce Type: new Abstract: The treatment of ischemic stroke using mechanical thrombectomy involves difficult decisions under intense time constraints. Numerical physics simulation

Ask4VG: Risk-Aware Question Selection for Reducing Prior-Driven Answers in Medical VQA

arXiv:2606.01044v1 Announce Type: new Abstract: Medical visual question answering requires models to ground their responses in image evidence, because visually unsupported answers can mislead downstre

Balancing Learning Rates Across Layers: Exact Two-Step Dynamics and Optimal Scaling in Linear Neural Networks

arXiv:2606.00340v1 Announce Type: new Abstract: We study optimal learning-rate selection in two-layer and three-layer linear neural networks trained to learn linear target functions. In particular, we

Before the Model Learns the Bug:Fuzzing RLVR Verifiers

arXiv:2606.01066v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) replaces human preference labels with executable reward functions such as math answer checkers, JS

Beyond Tool Adoption: A Practical Five-Stage Developmental Continuum for AI Literacy in Higher Education

arXiv:2606.00038v1 Announce Type: cross Abstract: Artificial intelligence (AI) literacy is increasingly recognized as a foundational competency for all university graduates. Yet students' engagement w

Bridging the Gap: Transfer Learning from English PLMs to Malaysian English

arXiv:2407.01374v2 Announce Type: replace Abstract: Malaysian English is a low resource creole language, where it carries the elements of Malay, Chinese, and Tamil languages, in addition to Standard E

BRo-JEPA: Learning Modular Arithmetic in Latent Space

arXiv:2606.01372v1 Announce Type: cross Abstract: Can neural networks learn abstract algebraic rules, or do they merely memorize training patterns? We investigate this using MNIST digits as states and

C-LEAD: Contrastive Learning for Enhanced Adversarial Defense

arXiv:2510.27249v2 Announce Type: replace Abstract: Deep neural networks (DNNs) have achieved remarkable success in computer vision tasks such as image classification, segmentation, and object detecti

Can Vision Language Models Learn Intuitive Physics from Interaction?

arXiv:2602.06033v2 Announce Type: replace Abstract: Pre-trained vision language models do not have good intuitions about the physical world. Recent work has shown that supervised fine-tuning can impro

Chroma Clues: Leveraging Color Statistics to Detect Synthetic Images

arXiv:2606.02224v1 Announce Type: new Abstract: The evolution and dissemination of AI-synthesized images is occurring at an unprecedented rate. Image generators are making rapid progress in their goal

CLIP-like Model as a Foundational Density Ratio Estimator

arXiv:2506.22881v3 Announce Type: replace Abstract: Density ratio estimation is a core concept in statistical machine learning because it provides a unified mechanism for tasks such as importance weig

Cluster Analysis with Resampling for Validation and Exploration (CARVE)

arXiv:2606.00327v1 Announce Type: cross Abstract: Clustering is widely used across the sciences as the foundation for downstream data-driven scientific discoveries. However, clustering results are hig

COLLIE: Guiding Skill Discovery in Semantically Coherent Latent Space

arXiv:2606.00950v1 Announce Type: new Abstract: Unsupervised skill discovery (USD) aims to learn diverse behaviors without reward functions, but often results in task-irrelevant or hazardous behaviors

Cortex and subcortex play distinct roles over learning when cortical memory is limited

arXiv:2606.00667v1 Announce Type: cross Abstract: It has been proposed that the brain integrates flexible, computationally expensive cortical processing with simpler, lower-cost subcortical mechanisms

CRAFTQA: A Code-Driven Adaptive Framework for Complex Structured Data Reasoning

arXiv:2606.02170v1 Announce Type: new Abstract: Real-world scenarios involve massive heterogeneous structured data (e.g., tables, knowledge graphs), making effective reasoning over such diverse data i