English
Related papers

Related papers: A Grey-box Launch-profile Aware Model for C+L Band…

200 papers

We address a non-unique parameter fitting problem in the context of material science. In particular, we propose to resolve ambiguities in parameter space by augmenting a black-box artificial neural network (ANN) model with two different…

Machine Learning · Computer Science 2020-05-12 Raoul Heese , Michał Walczak , Lukas Morand , Dirk Helm , Michael Bortz

3D ultrasound delivers high-resolution, real-time images of soft tissues, which is essential for pain research. However, manually distinguishing various tissues for quantitative analysis is labor-intensive. To streamline this process, we…

Image and Video Processing · Electrical Eng. & Systems 2025-03-26 Zixue Zeng , Xiaoyan Zhao , Matthew Cartier , Xin Meng , Jiantao Pu

Building on our prior explorations of convolutional neural networks (CNNs) for financial data processing, this paper introduces two significant enhancements to refine our CNN model's predictive performance and robustness for financial…

Computational Finance · Quantitative Finance 2024-08-23 Sina Montazeri , Haseebullah Jumakhan , Sonia Abrasiabian , Amir Mirzaeinia

Traditional analytical reflectance models, while compact and interpretable, lack the capacity to accurately represent physical measurements. Recent neural models, which closely fit input data, are less generalizable and often more expensive…

Graphics · Computer Science 2026-04-28 Xuanzhe Shen , Xiaohe Ma , Kun Zhou , Hongzhi Wu

In massive multi-input multi-output (MIMO) systems, the main bottlenecks of location- and orientation-assisted beam alignment using deep neural networks (DNNs) are large training overhead and significant performance degradation. This paper…

Signal Processing · Electrical Eng. & Systems 2026-01-21 Yuzhu Lei , Qiqi Xiao , Yinghui He , Guanding Yu

Graph Neural Networks (GNNs) excel at learning from structured data, yet fairness in regression tasks remains underexplored. Existing approaches mainly target classification and representation-level debiasing, which cannot fully address the…

Machine Learning · Computer Science 2025-10-24 Soyoung Park , Sungsu Lim

Graph neural networks (GNNs) have been widely applied to numerous fields. A recent work which combines layered structure and residual connection proposes an improved deep architecture to extend CAmouflage-REsistant GNN (CARE-GNN) to deep…

Machine Learning · Computer Science 2022-02-15 Yufan Zeng , Jiashan Tang

Due to the widespread use of complex machine learning models in real-world applications, it is becoming critical to explain model predictions. However, these models are typically black-box deep neural networks, explained post-hoc via…

Machine Learning · Computer Science 2022-10-20 Filip Radenovic , Abhimanyu Dubey , Dhruv Mahajan

This paper presents a new Graph Neural Network (GNN) type using feature-wise linear modulation (FiLM). Many standard GNN variants propagate information along the edges of a graph by computing "messages" based only on the representation of…

Machine Learning · Computer Science 2020-06-29 Marc Brockschmidt

Deep neural networks (DNNs) have made breakthroughs in various fields including image recognition and language processing. DNNs execute hundreds of millions of multiply-and-accumulate (MAC) operations. To efficiently accelerate such…

Systems and Control · Electrical Eng. & Systems 2024-07-08 Amro Eldebiky , Grace Li Zhang , Xunzhao Yin , Cheng Zhuo , Ing-Chao Lin , Ulf Schlichtmann , Bing Li

Recommender Systems (RSs) are used to provide users with personalized item recommendations and help them overcome the problem of information overload. Currently, recommendation methods based on deep learning are gaining ground over…

Information Retrieval · Computer Science 2023-01-19 Nikzad Chizari , Niloufar Shoeibi , María N. Moreno-García

Graph Neural Networks (GNNs) and their message passing framework that leverages both structural and feature information, have become a standard method for solving graph-based machine learning problems. However, these approaches still…

Machine Learning · Computer Science 2024-11-20 Simon Delarue , Thomas Bonald , Tiphaine Viard

Neural Architecture Search methods are effective but often use complex algorithms to come up with the best architecture. We propose an approach with three basic steps that is conceptually much simpler. First we train N random architectures…

Machine Learning · Computer Science 2019-12-03 Wei Wen , Hanxiao Liu , Hai Li , Yiran Chen , Gabriel Bender , Pieter-Jan Kindermans

Cloud and cloud shadow masking is a crucial preprocessing step in hyperspectral satellite imaging, enabling the extraction of high-quality, analysis-ready data. This study evaluates various machine learning approaches, including gradient…

Computer Vision and Pattern Recognition · Computer Science 2025-07-15 Mazen Ali , António Pereira , Fabio Gentile , Aser Cortines , Sam Mugel , Román Orús , Stelios P. Neophytides , Michalis Mavrovouniotis

With the growing demand for interpretable deep learning models, this paper introduces Integrative CAM, an advanced Class Activation Mapping (CAM) technique aimed at providing a holistic view of feature importance across Convolutional Neural…

Computer Vision and Pattern Recognition · Computer Science 2024-12-03 Aniket K. Singh , Debasis Chaudhuri , Manish P. Singh , Samiran Chattopadhyay

Recently regression analysis becomes a popular tool for face recognition. The existing regression methods all use the one-dimensional pixel-based error model, which characterizes the representation error pixel by pixel individually and thus…

Computer Vision and Pattern Recognition · Computer Science 2014-05-07 Jian Yang , Jianjun Qian , Lei Luo , Fanlong Zhang , Yicheng Gao

This paper proposes a method to predict received power in urban area deterministically, which can learn a prediction model from small amount of measurement data by a simulation-aided transfer learning and data augmentation. Recent…

Networking and Internet Architecture · Computer Science 2020-05-05 Masahiro Iwasaki , Takayuki Nishio , Masahiro Morikura , Koji Yamamoto

In this work, we propose a two-stage algorithm based on Bayesian modeling and computation aiming at quantifying analyte concentrations or quantities in complex mixtures with Raman spectroscopy. A hierarchical Bayesian model is built for…

Applications · Statistics 2018-05-22 Ningren Han , Rajeev J. Ram

Next-generation wireless systems such as 6G operate at higher frequency bands, making signal propagation highly sensitive to environmental factors such as buildings and vege- tation. Accurate Radio Environment Map (REM) estimation is…

Signal Processing · Electrical Eng. & Systems 2026-04-08 Ljupcho Milosheski , Fedja Močnik , Mihael Mohorčič , Carolina Fortuna

In this paper, we develop a predictive geometry control framework for jet-based additive manufacturing (AM) based on a physics-guided recurrent neural network (RNN) model. Because of its physically interpretable architecture, the model's…

Systems and Control · Electrical Eng. & Systems 2022-07-28 Uduak Inyang-Udoh , Alvin Chen , Sandipan Mishra