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2304.00050
kNN-Res: Residual Neural Network with kNN-Graph coherence for point cloud registration
In this paper, we present a residual neural network-based method for point set registration that preserves the topological structure of the target point set. Similar to coherent point drift (CPD), the registration (alignment) problem is viewed as the movement of data points sampled from a target distribution along a re...
Muhammad S. Battikh, Dillon Hammill, Matthew Cook, Artem Lensky
2023-03-31T18:06:26Z
http://arxiv.org/abs/2304.00050v2
# kNN-Res: Residual Neural Network with kNN-Graph coherence for point cloud registration ###### Abstract In this paper, we present a residual neural network-based method for point set registration that preserves the topological structure of the target point set. Similar to coherent point drift (CPD), the registration...
2310.20579
Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks
We analytically investigate how over-parameterization of models in randomized machine learning algorithms impacts the information leakage about their training data. Specifically, we prove a privacy bound for the KL divergence between model distributions on worst-case neighboring datasets, and explore its dependence on ...
Jiayuan Ye, Zhenyu Zhu, Fanghui Liu, Reza Shokri, Volkan Cevher
2023-10-31T16:13:22Z
http://arxiv.org/abs/2310.20579v1
# Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks ###### Abstract We analytically investigate how over-parameterization of models in randomized machine learning algorithms impacts the information leakage about their training data. Specifically, we prove a privacy bound for the KL...
2306.17396
Koopman operator learning using invertible neural networks
In Koopman operator theory, a finite-dimensional nonlinear system is transformed into an infinite but linear system using a set of observable functions. However, manually selecting observable functions that span the invariant subspace of the Koopman operator based on prior knowledge is inefficient and challenging, part...
Yuhuang Meng, Jianguo Huang, Yue Qiu
2023-06-30T04:26:46Z
http://arxiv.org/abs/2306.17396v2
# Physics-informed invertible neural network for the Koopman operator learning 1 ###### Abstract In Koopman operator theory, a finite-dimensional nonlinear system is transformed into an infinite but linear system using a set of observable functions. However, manually selecting observable functions that span the invar...
2310.04424
"Stability Analysis of Non-Linear Classifiers using Gene Regulatory\n Neural Network for Biological(...TRUNCATED)
"The Gene Regulatory Network (GRN) of biological cells governs a number of key\nfunctionalities that(...TRUNCATED)
Adrian Ratwatte, Samitha Somathilaka, Sasitharan Balasubramaniam, Assaf A. Gilad
2023-09-14T21:37:38Z
http://arxiv.org/abs/2310.04424v1
"# Stability Analysis of Non-Linear Classifiers using Gene Regulatory Neural Network for Biological (...TRUNCATED)
2309.03770
Neural lasso: a unifying approach of lasso and neural networks
"In recent years, there is a growing interest in combining techniques\nattributed to the areas of St(...TRUNCATED)
David Delgado, Ernesto Curbelo, Danae Carreras
2023-09-07T15:17:10Z
http://arxiv.org/abs/2309.03770v1
"# Neural lasso: a unifying approach of lasso and neural networks\n\n###### Abstract\n\nIn recent ye(...TRUNCATED)
2309.04037
"SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression\n with Super-resolution Neur(...TRUNCATED)
"The fast growth of computational power and scales of modern super-computing\nsystems have raised gr(...TRUNCATED)
Jinyang Liu, Sheng Di, Sian Jin, Kai Zhao, Xin Liang, Zizhong Chen, Franck Cappello
2023-09-07T22:15:32Z
http://arxiv.org/abs/2309.04037v3
"SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural (...TRUNCATED)
2309.15728
Line Graph Neural Networks for Link Weight Prediction
"Link weight prediction is of great practical importance, since real-world\nnetworks are often weigh(...TRUNCATED)
Jinbi Liang, Cunlai Pu
2023-09-27T15:34:44Z
http://arxiv.org/abs/2309.15728v1
"# Line Graph Neural Networks for Link Weight Prediction\n\n###### Abstract.\n\nLink weight predicti(...TRUNCATED)
2309.03374
"Physics Informed Neural Networks for Modeling of 3D Flow-Thermal\n Problems with Sparse Domain Dat(...TRUNCATED)
"Successfully training Physics Informed Neural Networks (PINNs) for highly\nnonlinear PDEs on comple(...TRUNCATED)
Saakaar Bhatnagar, Andrew Comerford, Araz Banaeizadeh
2023-09-06T21:52:14Z
http://arxiv.org/abs/2309.03374v3
"# Physics Informed Neural Networks for Modeling of 3D Flow-Thermal Problems with Sparse Domain Data(...TRUNCATED)
2309.16022
GNNHLS: Evaluating Graph Neural Network Inference via High-Level Synthesis
"With the ever-growing popularity of Graph Neural Networks (GNNs), efficient\nGNN inference is gaini(...TRUNCATED)
Chenfeng Zhao, Zehao Dong, Yixin Chen, Xuan Zhang, Roger D. Chamberlain
2023-09-27T20:58:33Z
http://arxiv.org/abs/2309.16022v1
"# GNNHLS: Evaluating Graph Neural Network Inference via High-Level Synthesis\n\n###### Abstract\n\n(...TRUNCATED)
2309.04426
Advanced Computing and Related Applications Leveraging Brain-inspired Spiking Neural Networks
"In the rapid evolution of next-generation brain-inspired artificial\nintelligence and increasingly (...TRUNCATED)
Lyuyang Sima, Joseph Bucukovski, Erwan Carlson, Nicole L. Yien
2023-09-08T16:41:08Z
http://arxiv.org/abs/2309.04426v1
"# Advanced Computing and Related Applications\n\n###### Abstract\n\nIn the rapid evolution of next-(...TRUNCATED)
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