Search: “tutorials”

Frequency-Decomposed INR for NIR-Assisted Low-Light RGB Image Denoising

arXiv:2604.16800v1 Announce Type: new Abstract: Addressing the issues of severe noise and high frequency structural degradation in visible images under low-light conditions, this paper proposes a Near

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Tutorials

From Adaptation to Generalization: Adaptive Visual Prompting for Medical Image Segmentation

DGX agent

arXiv:2604.17455v1 Announce Type: new Abstract: Visual prompting has emerged as a powerful method for adapting pre-trained models to new domains without updating model parameters. However, existing pr

tutorialsarxiv-cs-cv

21 Apr 2026

Generalizable Radio-Frequency Radiance Fields for Spatial Spectrum Synthesis

arXiv:2502.05708v3 Announce Type: replace-cross Abstract: We present GRaF, Generalizable Radio-Frequency (RF) Radiance Fields, a framework that models RF signal propagation to synthesize spatial spect

Graph neural network for colliding particles with an application to sea ice floe modeling

arXiv:2602.16213v2 Announce Type: replace-cross Abstract: This paper introduces a novel approach to sea ice modeling using Graph Neural Networks (GNNs), utilizing the natural graph structure of sea ic

Graph Transformer-Based Pathway Embedding for Cancer Prognosis

arXiv:2604.16685v1 Announce Type: new Abstract: Accurate prediction of cancer progression remains a challenge due to the high heterogeneity of molecular omics data across patients. While biologically

Heterogeneity in Formal Linguistic Competence of Language Models: Is Data the Real Bottleneck?

arXiv:2604.17930v1 Announce Type: new Abstract: Large Language Models (LLMs) exhibit a puzzling disparity in their formal linguistic competence: while they learn some linguistic phenomena with near-pe

How to Approximate Inference with Subtractive Mixture Models

arXiv:2604.16714v1 Announce Type: new Abstract: Classical mixture models (MMs) are widely used tractable proposals for approximate inference settings such as variational inference (VI) and importance

HSG: Hyperbolic Scene Graph

arXiv:2604.17454v1 Announce Type: new Abstract: Scene graph representations enable structured visual understanding by modeling objects and their relationships, and have been widely used for multiview

Incremental learning for audio classification with Hebbian Deep Neural Networks

arXiv:2604.18270v1 Announce Type: cross Abstract: The ability of humans for lifelong learning is an inspiration for deep learning methods and in particular for continual learning. In this work, we app

Learning Invariant Modality Representation for Robust Multimodal Learning from a Causal Inference Perspective

arXiv:2604.18460v1 Announce Type: new Abstract: Multimodal affective computing aims to predict humans' sentiment, emotion, intention, and opinion using language, acoustic, and visual modalities. Howev

Learning Whole-Body Humanoid Locomotion via Motion Generation and Motion Tracking

arXiv:2604.17335v1 Announce Type: new Abstract: Whole-body humanoid locomotion is challenging due to high-dimensional control, morphological instability, and the need for real-time adaptation to vario

tutorialsarxiv-cs-ro

Lorentz Framework for Semantic Segmentation

arXiv:2604.16836v1 Announce Type: new Abstract: Semantic segmentation in hyperbolic space enables compact modeling of hierarchical structure while providing inherent uncertainty quantification. Prior

MAPLE: A Meta-learning Framework for Cross-Prompt Essay Scoring

arXiv:2604.17569v1 Announce Type: new Abstract: Automated Essay Scoring (AES) faces significant challenges in cross-prompt settings, where models must generalize to unseen writing prompts. To address

Memorize When Needed: Decoupled Memory Control for Spatially Consistent Long-Horizon Video Generation

arXiv:2604.18215v1 Announce Type: new Abstract: Spatially consistent long-horizon video generation aims to maintain temporal and spatial consistency along predefined camera trajectories. Existing meth

MetaMem: Evolving Meta-Memory for Knowledge Utilization through Self-Reflective Symbolic Optimization

arXiv:2602.11182v2 Announce Type: replace Abstract: Existing memory systems enable Large Language Models (LLMs) to support long-horizon human-LLM interactions by persisting historical interactions bey

mlr3torch: A Deep Learning Framework in R based on mlr3 and torch

arXiv:2604.18152v1 Announce Type: cross Abstract: Deep learning (DL) has become a cornerstone of modern machine learning (ML) praxis. We introduce the R package mlr3torch, which is an extensible DL fr

MM-Hand: A 21-DOF Multi-modal Modular Dexterous Robotic Hand with Remote Actuation

arXiv:2604.17245v1 Announce Type: new Abstract: High-DOF dexterous hands require compact actuation, rich sensing, and reliable thermal behavior, but conventional designs often occupy valuable in-hand

tutorialsarxiv-cs-ro

Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations

arXiv:2604.18146v1 Announce Type: cross Abstract: Recently, large language models (LLMs) have advanced recommendation systems (RSs), and recent works have begun to explore how to integrate LLMs into i

Neural Garbage Collection: Learning to Forget while Learning to Reason

arXiv:2604.18002v1 Announce Type: new Abstract: Chain-of-thought reasoning has driven striking advances in language model capability, yet every reasoning step grows the KV cache, creating a bottleneck

Noise Injection: Improving Out-of-Distribution Generalization for Limited Size Datasets

arXiv:2511.03855v2 Announce Type: replace Abstract: Deep learned (DL) models for image recognition have been shown to fail to generalize to data from different devices, populations, etc. COVID-19 dete

OC-Distill: Ontology-aware Contrastive Learning with Cross-Modal Distillation for ICU Risk Prediction

arXiv:2604.16878v1 Announce Type: new Abstract: Early prediction of severe clinical deterioration and remaining length of stay can enable timely intervention and better resource allocation in high-acu

Optimally Bridging Semantics and Data: Generative Semantic Communication via Schrodinger Bridge

arXiv:2604.17802v1 Announce Type: cross Abstract: Generative Semantic Communication (GSC) is a promising solution for image transmission over narrow-band and high-noise channels. However, existing GSC

Path-Based Quantum Meta-Learning for Adaptive Optimization of Reconfigurable Intelligent Surfaces

arXiv:2604.17690v1 Announce Type: cross Abstract: Reconfigurable intelligent surfaces (RISs) modify signal reflections to enhance wireless communication capabilities. Classical RIS phase optimization

Plausibility as Commonsense Reasoning: Humans Succeed, Large Language Models Do not

arXiv:2604.04825v2 Announce Type: replace Abstract: Large language models achieve strong performance on many language tasks, yet it remains unclear whether they integrate world knowledge with syntacti

Procedural Knowledge at Scale Improves Reasoning

arXiv:2604.01348v2 Announce Type: replace Abstract: Test-time scaling has emerged as an effective way to improve language models on challenging reasoning tasks. However, most existing methods treat ea

PyEPO: A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming

arXiv:2206.14234v3 Announce Type: replace-cross Abstract: In deterministic optimization, it is typically assumed that all problem parameters are fixed and known. In practice, however, some parameters

Randomly Initialized Networks Can Learn from Peer-to-Peer Consensus

arXiv:2604.18390v1 Announce Type: new Abstract: In self-supervised learning, self-distilled methods have shown impressive performance, learning representations useful for downstream tasks and even dis

Reciprocal Co-Training (RCT): Coupling Gradient-Based and Non-Differentiable Models via Reinforcement Learning

arXiv:2604.16378v1 Announce Type: new Abstract: Large language models (LLMs) and classical machine learning methods offer complementary strengths for predictive modeling, yet their fundamentally diffe

RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models

arXiv:2604.17725v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown strong promise for mining Electronic Health Records (EHRs) by reasoning over longitudinal clinical information t

Rethinking Cross-Dose PET Denoising: Mitigating Averaging Effects via Residual Noise Learning

arXiv:2604.16925v1 Announce Type: new Abstract: Cross-dose denoising for low-dose positron emission tomography (LDPET) has been proposed to address the limited generalization of models trained at a si

River-LLM: Large Language Model Seamless Exit Based on KV Share

arXiv:2604.18396v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated exceptional performance across diverse domains but are increasingly constrained by high inference latency

Saccade Attention Networks: Using Transfer Learning of Attention to Reduce Network Sizes

arXiv:2604.16485v1 Announce Type: new Abstract: One of the limitations of transformer networks is the sequence length due to the quadratic nature of the attention matrix. Classical self attention uses

Self-Reinforcing Controllable Synthesis of Rare Relational Data via Bayesian Calibration

arXiv:2604.16817v1 Announce Type: new Abstract: Imbalanced data is commonly present in real-world applications. While data synthesis can effectively mitigate the data scarcity problem of rare-classes,

SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repair

arXiv:2604.17184v1 Announce Type: cross Abstract: Large Language Models (LLMs) show promise for automated code repair but often struggle with the complex semantic and structural correctness required.

Towards a Foundation-Model Paradigm for Aerodynamic Prediction in Three-dimensional Design

arXiv:2604.18062v1 Announce Type: new Abstract: Accurate machine-learning models for aerodynamic prediction are essential for accelerating shape optimization, yet remain challenging to develop for com

UGD: An Unsupervised Geometric Distance for Evaluating Real-world Noisy Point Cloud Denoising

arXiv:2604.16976v1 Announce Type: new Abstract: Point cloud denoising is a fundamental and crucial challenge in real-world point cloud applications. Existing quantitative evaluation metrics for point

UniSim: A Unified Simulator for Time-Coarsened Dynamics of Biomolecules

arXiv:2506.03157v4 Announce Type: replace-cross Abstract: Molecular Dynamics (MD) simulations are essential for understanding the atomic-level behavior of molecular systems, giving insights into their

Unraveling the Key of Machine Learning-based Android Malware Detection

arXiv:2402.02953v2 Announce Type: replace-cross Abstract: With the rapid advancement of machine learning (ML), ML-based Android malware detection has gained significant popularity due to its ability t

When Can LLMs Learn to Reason with Weak Supervision?

arXiv:2604.18574v1 Announce Type: new Abstract: Large language models have achieved significant reasoning improvements through reinforcement learning with verifiable rewards (RLVR). Yet as model capab

A Case Study on the Impact of Anonymization Along the RAG Pipeline

arXiv:2604.15958v1 Announce Type: cross Abstract: Despite the considerable promise of Retrieval-Augmented Generation (RAG), many real-world use cases may create privacy concerns, where the purported u

A Comparative Study on the Impact of Traditional Learning and Interactive Learning on Students' Academic Performance and Emotional Well-Being

arXiv:2604.15335v1 Announce Type: cross Abstract: The growing adoption of interactive learning tools in higher education offers new opportunities to enhance student performance and well-being. This st

A methodology to rank importance of frequencies and channels in electromyography data with Decision Tree classifiers

arXiv:2604.15353v1 Announce Type: cross Abstract: This study presents a methodology for identifying the most informative frequencies and channels in electromyography (EMG) data to evaluate muscle reco

Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks

arXiv:2604.15390v1 Announce Type: cross Abstract: Code deobfuscation is the task of recovering a readable version of a program while preserving its original behavior. In practice, this often requires

Bridging the phenotype-target gap for molecular generation via multi-objective reinforcement learning

arXiv:2509.21010v2 Announce Type: replace-cross Abstract: The de novo generation of drug-like molecules capable of inducing desirable phenotypic changes is receiving increasing attention. However, pre

Cross-modal learning for plankton recognition

arXiv:2603.16427v2 Announce Type: replace Abstract: This paper considers self-supervised cross-modal coordination as a strategy enabling utilization of multiple modalities and large volumes of unlabel

Diffusion Autoencoder for Unsupervised Artifact Restoration in Handheld Fundus Images

arXiv:2604.15723v1 Announce Type: cross Abstract: The advent of handheld fundus imaging devices has made ophthalmologic diagnosis and disease screening more accessible, efficient, and cost-effective.

Discovering quantum phenomena with Interpretable Machine Learning

arXiv:2604.16015v1 Announce Type: cross Abstract: Interpretable machine learning techniques are becoming essential tools for extracting physical insights from complex quantum data. We build on recent

Estimating Joint Interventional Distributions from Marginal Interventional Data

arXiv:2409.01794v2 Announce Type: replace-cross Abstract: In this paper we show how to exploit interventional data to acquire the joint conditional distribution of all the variables using the Maximum