id stringlengths 10 10 | title stringlengths 26 192 | abstract stringlengths 172 1.92k | authors stringlengths 7 591 | published_date stringlengths 20 20 | link stringlengths 33 33 | markdown stringlengths 269 344k |
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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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