Search: “tutorials”

Chaotic Contrastive Learning for Robust Texture Classification

arXiv:2605.05012v1 Announce Type: new Abstract: Texture classification is a pivotal task in computer vision, presenting unique challenges due to high inter-class similarity and the sensitivity of stru

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Constraint-Aware Execution Planning for Hybrid Space-Ground Compute Workloads

DGX agent

arXiv:2605.04052v1 Announce Type: cross Abstract: Low Earth orbit (LEO) satellites increasingly carry compute hardware capable of on-board processing, yet each satellite generates roughly two orders o

tutorialsarxiv-cs-cv

7 May 2026

CPCANet: Deep Unfolding Common Principal Component Analysis for Domain Generalization

arXiv:2605.05136v1 Announce Type: new Abstract: Domain Generalization (DG) aims to learn representations that remain robust under out-of-distribution (OOD) shifts and generalize effectively to unseen

Data Augmentation of Contrastive Learning is Estimating Positive-incentive Noise

arXiv:2408.09929v2 Announce Type: replace-cross Abstract: Inspired by the idea of Positive-incentive Noise (Pi-Noise or pi-Noise) that aims at learning the reliable noise beneficial to tasks, we scien

Diffusion-Inspired Masked Fine-Tuning for Knowledge Injection in Autoregressive LLMs

arXiv:2510.09885v5 Announce Type: replace Abstract: Large language models (LLMs) are often used in environments where facts evolve, yet factual knowledge updates via fine-tuning on unstructured text o

Estimating the expected output of wide random MLPs more efficiently than sampling

arXiv:2605.05179v1 Announce Type: new Abstract: By far the most common way to estimate an expected loss in machine learning is to draw samples, compute the loss on each one, and take the empirical ave

Example-Based Object Detection

arXiv:2605.04501v1 Announce Type: new Abstract: In recent years, object detection has achieved significant progress, especially in the field of open-vocabulary object detection. Unlike traditional met

Forget BIT, It is All about TOKEN: Towards Semantic Information Theory for LLMs

arXiv:2511.01202v3 Announce Type: replace-cross Abstract: Despite the unprecedented empirical triumphs of LLMs across diverse real-world applications, the prevailing research paradigm remains overwhel

France or Spain or Germany or France: A Neural Account of Non-Redundant Redundant Disjunctions

arXiv:2602.23547v2 Announce Type: replace Abstract: Sentences like 'She will go to France or Spain, or perhaps to Germany or France.' appear formally redundant, yet become acceptable in contexts such

From Diffusion to Rectified Flow: Rethinking Text-Based Segmentation

arXiv:2605.04590v1 Announce Type: new Abstract: Text-based image segmentation aims to delineate object boundaries within an image from text prompts, offering higher flexibility and broader application

HDFlow: Hierarchical Diffusion-Flow Planning for Long-horizon Tasks

arXiv:2605.04525v1 Announce Type: new Abstract: Recent advances in generative models have shown promise in generating behavior plans for long-horizon, sparse reward tasks. While these approaches have

tutorialsarxiv-cs-ro

HistoMet: A Pan-Cancer Deep Learning Framework for Prognostic Prediction of Metastatic Progression and Site Tropism from Primary Tumor Histopathology

arXiv:2602.07608v2 Announce Type: replace Abstract: Metastatic Progression remains the leading cause of cancer-related mortality, yet predicting whether a primary tumor will metastasize and where it w

Hypergraph Generation via Structured Stochastic Diffusion

arXiv:2605.05024v1 Announce Type: cross Abstract: Hypergraphs model higher-order interactions, but realistic hypergraph generation remains difficult because incidence, hyperedge-size heterogeneity, an

Iso-Riemannian Optimization on Learned Data Manifolds

arXiv:2510.21033v2 Announce Type: replace-cross Abstract: High-dimensional data with intrinsic low-dimensional structure is ubiquitous in machine learning and data science. While various approaches al

LEGO: LoRA-Enabled Generator-Oriented Framework for Synthetic Image Detection

arXiv:2605.04445v1 Announce Type: new Abstract: The rapid advancement of generative technologies has made synthetic images nearly indistinguishable from real ones, thereby creating an urgent need for

Materialist: Physically Based Editing Using Single-Image Inverse Rendering

arXiv:2501.03717v3 Announce Type: replace Abstract: Achieving physically consistent image editing remains a significant challenge in computer vision. Existing image editing methods typically rely on n

Meta-LegNet: A Transferable and Interpretable Framework for Surface Adsorption Prediction via Self-Defined Adsorption-Environment Learning

arXiv:2605.04102v1 Announce Type: cross Abstract: A central challenge in computational catalysis is the identification of low-energy and chemically plausible adsorption configurations, as these direct

Multi Language Models for On-the-Fly Syntax Highlighting

arXiv:2510.04166v2 Announce Type: replace-cross Abstract: Syntax highlighting is a critical feature in modern software development environments, enhancing code readability and developer productivity.

Order-based Rehearsal Learning

arXiv:2605.04955v1 Announce Type: new Abstract: When a machine learning (ML) model forecasts an undesired event, one often seeks a decision to avoid it, known as the avoiding undesired future (AUF) pr

Reference-based Category Discovery: Unsupervised Object Detection with Category Awareness

arXiv:2605.04606v1 Announce Type: new Abstract: Traditional one-shot detection methods have addressed the closed-set problem in object detection, but the high cost of data annotation remains a critica

Self-Improvement for Fast, High-Quality Plan Generation

arXiv:2605.03625v1 Announce Type: new Abstract: Generative models trained on synthetic plan data are a promising approach to generalized planning. Recent work has focused on finding any valid plan, ra

Stream-T1: Test-Time Scaling for Streaming Video Generation

arXiv:2605.04461v1 Announce Type: new Abstract: While Test-Time Scaling (TTS) offers a promising direction to enhance video generation without the surging costs of training, current test-time video ge

SV-GS: Sparse View 4D Reconstruction with Skeleton-Driven Gaussian Splatting

arXiv:2601.00285v2 Announce Type: replace Abstract: Reconstructing a dynamic target moving over a large area is challenging. Standard approaches for dynamic object reconstruction require dense coverag

Towards Adaptive Humanoid Control via Multi-Behavior Distillation and Reinforced Fine-Tuning

arXiv:2511.06371v3 Announce Type: replace Abstract: Humanoid robots are promising to learn a diverse set of human-like locomotion behaviors, including standing up, walking, running, and jumping. Howev

tutorialsarxiv-cs-ro

Towards Open World Sound Event Detection

arXiv:2605.03934v1 Announce Type: cross Abstract: Sound Event Detection (SED) plays a vital role in audio understanding, with applications in surveillance, smart cities, healthcare, and multimedia ind

Understanding In-Context Learning for Nonlinear Regression with Transformers: Attention as Featurizer

arXiv:2605.05176v1 Announce Type: new Abstract: Pre-trained transformers are able to learn from examples provided as part of the prompt without any weight updates, a remarkable ability known as in-con

Unsat Core Prediction through Polarity-Aware Representation Learning over Clause-Literal Hypergraphs

arXiv:2605.04819v1 Announce Type: new Abstract: Graph neural networks have been widely used in Boolean satisfiability (SAT) tasks to learn structural information from SAT formulas. The goal of these s

Velox: Learning Representations of 4D Geometry and Appearance

arXiv:2605.04527v1 Announce Type: new Abstract: We introduce a framework for learning latent representations of 4D objects which are descriptive, faithfully capturing object geometry and appearance; c

ABox Abduction for Inconsistent Knowledge Bases under Repair Semantics

arXiv:2605.01341v1 Announce Type: cross Abstract: Given a knowledge base (KB) with a non-entailed fact, the ABox abduction problem asks for possible extensions of the KB that would entail this fact. T

Approaching human parity in the quality of automated organoid image segmentation

arXiv:2605.03053v1 Announce Type: new Abstract: Organoids are complex, three dimensional, self-organizing cell cultures which manifest organ-like features and represent a powerful platform for studyin

CBV: Clean-label Backdoor Attacks on Vision Language Models via Diffusion Models

arXiv:2605.02202v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have achieved remarkable success in tasks such as image captioning and visual question answering (VQA). However, as their

Chorus: Multi-Teacher Pretraining for Holistic 3D Gaussian Scene Encoding

arXiv:2512.17817v3 Announce Type: replace Abstract: While 3DGS has emerged as a high-fidelity scene representation, encoding rich, general-purpose features directly from its primitives remains under-e

Counterfactual Reasoning in Automated Planning

arXiv:2605.02603v1 Announce Type: new Abstract: Automated planning traditionally assumes that all aspects of a planning task (initial state, goals, and available actions) are fully specified in advanc

Cripping AI: Reimagining AI Through Lived Disability Experiences

arXiv:2605.02080v1 Announce Type: cross Abstract: Drawing on crip theory, this paper proposes cripping AI as a guiding framework to center lived disability experiences in AI research and development.

Direct Simultaneous Translation Activation for Large Audio-Language Models

arXiv:2509.15692v2 Announce Type: replace-cross Abstract: Simultaneous speech-to-text translation (Simul-S2TT) aims to translate speech into target text in real time, outputting translations while rec

Enhance the after-discharge mortality rate prediction via learning from the medical notes

arXiv:2605.03560v1 Announce Type: new Abstract: With the increase of the Electronic Health Records (EHR) data, more and more researchers are developing machine learning models to learn from the medica

Enhancing Visual Question Answering with Multimodal LLMs via Chain-of-Question Guided Retrieval-Augmented Generation

arXiv:2605.03790v1 Announce Type: new Abstract: With advances in multimodal research and deep learning, Multimodal Large Language Models (MLLMs) have emerged as a powerful paradigm for a wide range of

Flow Sampling: Learning to Sample from Unnormalized Densities via Denoising Conditional Processes

arXiv:2605.03984v1 Announce Type: new Abstract: Sampling from unnormalized densities is analogous to the generative modeling problem, but the target distribution is defined by a known energy function

Graph Neural Network based Hierarchy-Aware Embeddings of Knowledge Graphs: Applications to Yeast Phenotype Prediction

arXiv:2605.03690v1 Announce Type: new Abstract: We present a method for finding hierarchy-aware embeddings of knowledge graphs (KGs) using graph neural networks (GNNs) enriched with a semantic loss de

Learning Discriminative Signed Distance Functions from Multi-scale Level-of-detail Features for 3D Anomaly Detection

arXiv:2605.03437v1 Announce Type: new Abstract: Detecting anomalies from 3D point clouds has received increasing attention in the field of computer vision, with some group-based or point-based methods

Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding

arXiv:2605.03620v1 Announce Type: cross Abstract: Syndrome-based neural decoding (SBND) has emerged as a promising deep learning approach for soft-decision decoding of high-rate, short-length codes. H

LLM-Assisted Repository-Level Generation with Structured Spec-Driven Engineering

arXiv:2605.02455v1 Announce Type: cross Abstract: State-of-the-art Large Language Models (LLMs) excel in code generation at the function level. However, the output quality significantly declines when

Multi-modal Relational Item Representation Learning for Inferring Substitutable and Complementary Items

arXiv:2507.22268v3 Announce Type: replace-cross Abstract: We study the problem of inferring substitutable and complementary items, which underpins applications such as alternative and follow-up purcha

Natural Language Processing: A Comprehensive Practical Guide from Tokenisation to RLHF

arXiv:2605.03799v1 Announce Type: new Abstract: This preprint presents a systematic, research-oriented practicum that guides the reader through the entire modern NLP pipeline: from tokenisation and ve

Neural Decision-Propagation for Answer Set Programming

arXiv:2605.01797v1 Announce Type: new Abstract: Integration of Answer Set Programming (ASP) with neural networks has emerged as a promising tool in Neuro-symbolic AI. While existing approaches extend

Parametrization of subgrid scales in long-term simulations of the shallow-water equations using machine learning and convex limiting

arXiv:2602.00378v2 Announce Type: replace-cross Abstract: We present a method for parametrizing sub-grid processes in the Shallow Water equations. We define coarse variables and local spatial averages

Physically Guided Visual Mass Estimation from a Single RGB Image

arXiv:2601.20303v2 Announce Type: replace Abstract: Estimating object mass from visual input is challenging because mass depends jointly on geometric volume and material-dependent density, neither of

Preemptive Solving of Future Problems: Multitask Preplay in Humans and Machines

arXiv:2507.05561v2 Announce Type: replace Abstract: Humans can pursue a near-infinite variety of tasks, but typically can only pursue a small number at the same time. We hypothesize that humans levera