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
Neural Network for Biological AI | The Gene Regulatory Network (GRN) of biological cells governs a number of key
functionalities that enables them to adapt and survive through different
environmental conditions. Close observation of the GRN shows that the structure
and operational principles resembles an Artificial Neural Network (ANN), which
can pave t... | 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 AI
###### Abstract
The Gene Regulatory Network (GRN) of biological cells governs a number of key functionalities that enables them to adapt and survive through different environmental conditions. Close observation of th... |
2309.03770 | Neural lasso: a unifying approach of lasso and neural networks | In recent years, there is a growing interest in combining techniques
attributed to the areas of Statistics and Machine Learning in order to obtain
the benefits of both approaches. In this article, the statistical technique
lasso for variable selection is represented through a neural network. It is
observed that, althou... | 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
###### Abstract
In recent years, there is a growing interest in combining techniques attributed to the areas of Statistics and Machine Learning in order to obtain the benefits of both approaches. In this article, the statistical technique lasso for vari... |
2309.04037 | SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression
with Super-resolution Neural Networks | The fast growth of computational power and scales of modern super-computing
systems have raised great challenges for the management of exascale scientific
data. To maintain the usability of scientific data, error-bound lossy
compression is proposed and developed as an essential technique for the size
reduction of scien... | 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 Networks
###### Abstract
The fast growth of computational power and scales of modern super-computing systems have raised great challenges for the management of exascale scientific data. To maintain the usability of scie... |
2309.15728 | Line Graph Neural Networks for Link Weight Prediction | Link weight prediction is of great practical importance, since real-world
networks are often weighted networks. Previous studies have mainly used shallow
graph features for link weight prediction, which limits the prediction
performance. In this paper, we propose a new link weight prediction algorithm,
namely Line Grap... | Jinbi Liang, Cunlai Pu | 2023-09-27T15:34:44Z | http://arxiv.org/abs/2309.15728v1 | # Line Graph Neural Networks for Link Weight Prediction
###### Abstract.
Link weight prediction is of great practical importance, since real-world networks are often weighted networks. Previous studies have mainly used shallow graph features for link weight prediction, which limits the prediction performance. In this... |
2309.03374 | Physics Informed Neural Networks for Modeling of 3D Flow-Thermal
Problems with Sparse Domain Data | Successfully training Physics Informed Neural Networks (PINNs) for highly
nonlinear PDEs on complex 3D domains remains a challenging task. In this paper,
PINNs are employed to solve the 3D incompressible Navier-Stokes (NS) equations
at moderate to high Reynolds numbers for complex geometries. The presented
method utili... | 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
###### Abstract
Successfully training Physics Informed Neural Networks (PINNs) for highly nonlinear PDEs on complex 3D domains remains a challenging task. In this paper, PINNs are employed to solve the 3D incompressibl... |
2309.16022 | GNNHLS: Evaluating Graph Neural Network Inference via High-Level
Synthesis | With the ever-growing popularity of Graph Neural Networks (GNNs), efficient
GNN inference is gaining tremendous attention. Field-Programming Gate Arrays
(FPGAs) are a promising execution platform due to their fine-grained
parallelism, low-power consumption, reconfigurability, and concurrent
execution. Even better, High... | 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
###### Abstract
With the ever-growing popularity of Graph Neural Networks (GNNs), efficient GNN inference is gaining tremendous attention. Field-Programming Gate Arrays (FPGAs) are a promising execution platform due to their fine-grained par... |
2309.04426 | Advanced Computing and Related Applications Leveraging Brain-inspired
Spiking Neural Networks | In the rapid evolution of next-generation brain-inspired artificial
intelligence and increasingly sophisticated electromagnetic environment, the
most bionic characteristics and anti-interference performance of spiking neural
networks show great potential in terms of computational speed, real-time
information processing... | 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
###### Abstract
In the rapid evolution of next-generation brain-inspired artificial intelligence and increasingly sophisticated electromagnetic environment, the most bionic characteristics and anti-interference performance of spiking neural networks show great potential i... |
2303.18083 | Analysis and Comparison of Two-Level KFAC Methods for Training Deep
Neural Networks | As a second-order method, the Natural Gradient Descent (NGD) has the ability
to accelerate training of neural networks. However, due to the prohibitive
computational and memory costs of computing and inverting the Fisher
Information Matrix (FIM), efficient approximations are necessary to make NGD
scalable to Deep Neura... | Abdoulaye Koroko, Ani Anciaux-Sedrakian, Ibtihel Ben Gharbia, Valérie Garès, Mounir Haddou, Quang Huy Tran | 2023-03-31T14:21:53Z | http://arxiv.org/abs/2303.18083v2 | # Analysis and Comparison of Two-Level KFAC Methods for Training Deep Neural Networks
###### Abstract
As a second-order method, the Natural Gradient Descent (NGD) has the ability to accelerate training of neural networks. However, due to the prohibitive computational and memory costs of computing and inverting the Fi... |
2308.16848 | Accurate Computation of Quantum Excited States with Neural Networks | We present a variational Monte Carlo algorithm for estimating the lowest
excited states of a quantum system which is a natural generalization of the
estimation of ground states. The method has no free parameters and requires no
explicit orthogonalization of the different states, instead transforming the
problem of find... | David Pfau, Simon Axelrod, Halvard Sutterud, Ingrid von Glehn, James S. Spencer | 2023-08-31T16:27:08Z | http://arxiv.org/abs/2308.16848v3 | # Natural Quantum Monte Carlo Computation of Excited States
###### Abstract
We present a variational Monte Carlo algorithm for estimating the lowest excited states of a quantum system which is a natural generalization of the estimation of ground states. The method has no free parameters and requires no explicit ortho... |
2310.00496 | The Sparsity Roofline: Understanding the Hardware Limits of Sparse
Neural Networks | We introduce the Sparsity Roofline, a visual performance model for evaluating
sparsity in neural networks. The Sparsity Roofline jointly models network
accuracy, sparsity, and theoretical inference speedup. Our approach does not
require implementing and benchmarking optimized kernels, and the theoretical
speedup become... | Cameron Shinn, Collin McCarthy, Saurav Muralidharan, Muhammad Osama, John D. Owens | 2023-09-30T21:29:31Z | http://arxiv.org/abs/2310.00496v2 | # The Sparsity Roofline: Understanding the Hardware Limits of Sparse Neural Networks
###### Abstract
We introduce the Sparsity Roofline, a visual performance model for evaluating sparsity in neural networks. The Sparsity Roofline jointly models network accuracy, sparsity, and theoretical inference speedup. Our approa... |
2306.17630 | Navigating Noise: A Study of How Noise Influences Generalisation and
Calibration of Neural Networks | Enhancing the generalisation abilities of neural networks (NNs) through
integrating noise such as MixUp or Dropout during training has emerged as a
powerful and adaptable technique. Despite the proven efficacy of noise in NN
training, there is no consensus regarding which noise sources, types and
placements yield maxim... | Martin Ferianc, Ondrej Bohdal, Timothy Hospedales, Miguel Rodrigues | 2023-06-30T13:04:26Z | http://arxiv.org/abs/2306.17630v2 | # Impact of Noise on Calibration and Generalisation of Neural Networks
###### Abstract
Noise injection and data augmentation strategies have been effective for enhancing the generalisation and robustness of neural networks (NNs). Certain types of noise such as label smoothing and MixUp have also been shown to improve... |
2310.04431 | Can neural networks count digit frequency? | In this research, we aim to compare the performance of different classical
machine learning models and neural networks in identifying the frequency of
occurrence of each digit in a given number. It has various applications in
machine learning and computer vision, e.g. for obtaining the frequency of a
target object in a... | Padmaksh Khandelwal | 2023-09-25T03:45:36Z | http://arxiv.org/abs/2310.04431v1 | ## Can Neural Networks Count Digit Frequency?
### Abstract
In this research, we aim to compare the performance of different classical machine learning models and neural networks in identifying the frequency of occurrence of each digit in a given number. It has various applications in machine learning and computer vis... |
2309.05102 | Is Learning in Biological Neural Networks based on Stochastic Gradient
Descent? An analysis using stochastic processes | In recent years, there has been an intense debate about how learning in
biological neural networks (BNNs) differs from learning in artificial neural
networks. It is often argued that the updating of connections in the brain
relies only on local information, and therefore a stochastic gradient-descent
type optimization ... | Sören Christensen, Jan Kallsen | 2023-09-10T18:12:52Z | http://arxiv.org/abs/2309.05102v3 | # Is Learning in Biological Neural Networks based on Stochastic Gradient Descent?
###### Abstract
In recent years, there has been an intense debate about how learning in biological neural networks (BNNs) differs from learning in artificial neural networks. It is often argued that the updating of connections in the br... |
2309.11188 | Rebellions and Impeachments in a Neural Network Society | Basede on a study of the modern presidencial democracies in South America, we
present a statistical mechanics exploration of the collective, coordinated
action of political actors in the legislative chamber that may result on the
impeachment of the executive. By representing the legislative political actors
with neurla... | Juan Neirotti, Nestor Caticha | 2023-09-20T10:18:17Z | http://arxiv.org/abs/2309.11188v2 | # Rebellions and Impeachments in a Neural Network Society
###### Abstract
Based on a study of the modern presidencial democracies in South America, we present a statistical mechanics exploration of the collective, coordinated action of political actors in the legislative chamber that may result on the impeachment of ... |
2301.13817 | Patch Gradient Descent: Training Neural Networks on Very Large Images | Traditional CNN models are trained and tested on relatively low resolution
images (<300 px), and cannot be directly operated on large-scale images due to
compute and memory constraints. We propose Patch Gradient Descent (PatchGD), an
effective learning strategy that allows to train the existing CNN architectures
on lar... | Deepak K. Gupta, Gowreesh Mago, Arnav Chavan, Dilip K. Prasad | 2023-01-31T18:04:35Z | http://arxiv.org/abs/2301.13817v1 | # Patch Gradient Descent: Training Neural Networks
###### Abstract
Traditional CNN models are trained and tested on relatively low resolution images (\(<300\) px), and cannot be directly operated on large-scale images due to compute and memory constraints. We propose Patch Gradient Descent (PatchGD), an effective lea... |
2310.20552 | Privacy-preserving design of graph neural networks with applications to
vertical federated learning | The paradigm of vertical federated learning (VFL), where institutions
collaboratively train machine learning models via combining each other's local
feature or label information, has achieved great success in applications to
financial risk management (FRM). The surging developments of graph
representation learning (GRL... | Ruofan Wu, Mingyang Zhang, Lingjuan Lyu, Xiaolong Xu, Xiuquan Hao, Xinyi Fu, Tengfei Liu, Tianyi Zhang, Weiqiang Wang | 2023-10-31T15:34:59Z | http://arxiv.org/abs/2310.20552v1 | # Privacy-preserving design of graph neural networks with applications to vertical federated learning
###### Abstract
The paradigm of vertical federated learning (VFL), where institutions collaboratively train machine learning models via combining each other's local feature or label information, has achieved great su... |
2306.17418 | ReLU Neural Networks, Polyhedral Decompositions, and Persistent Homolog | A ReLU neural network leads to a finite polyhedral decomposition of input
space and a corresponding finite dual graph. We show that while this dual graph
is a coarse quantization of input space, it is sufficiently robust that it can
be combined with persistent homology to detect homological signals of manifolds
in the ... | Yajing Liu, Christina M Cole, Chris Peterson, Michael Kirby | 2023-06-30T06:20:21Z | http://arxiv.org/abs/2306.17418v1 | # ReLU Neural Networks, Polyhedral Decompositions, and Persistent Homology
###### Abstract
A ReLU neural network leads to a finite polyhedral decomposition of input space and a corresponding finite dual graph. We show that while this dual graph is a coarse quantization of input space, it is sufficiently robust that i... |
2309.15111 | SGD Finds then Tunes Features in Two-Layer Neural Networks with
near-Optimal Sample Complexity: A Case Study in the XOR problem | In this work, we consider the optimization process of minibatch stochastic
gradient descent (SGD) on a 2-layer neural network with data separated by a
quadratic ground truth function. We prove that with data drawn from the
$d$-dimensional Boolean hypercube labeled by the quadratic ``XOR'' function $y
= -x_ix_j$, it is ... | Margalit Glasgow | 2023-09-26T17:57:44Z | http://arxiv.org/abs/2309.15111v2 | SGD Finds then Tunes Features in Two-Layer Neural Networks with Near-Optimal Sample Complexity: A Case Study in the XOR problem
###### Abstract
In this work, we consider the optimization process of minibatch stochastic gradient descent (SGD) on a 2-layer neural network with data separated by a quadratic ground truth ... |
2309.06019 | DSLOT-NN: Digit-Serial Left-to-Right Neural Network Accelerator | We propose a Digit-Serial Left-tO-righT (DSLOT) arithmetic based processing
technique called DSLOT-NN with aim to accelerate inference of the convolution
operation in the deep neural networks (DNNs). The proposed work has the ability
to assess and terminate the ineffective convolutions which results in massive
power an... | Muhammad Sohail Ibrahim, Muhammad Usman, Malik Zohaib Nisar, Jeong-A Lee | 2023-09-12T07:36:23Z | http://arxiv.org/abs/2309.06019v2 | # DSLOT-NN: Digit-Serial Left-to-Right Neural Network Accelerator
###### Abstract
We propose a Digit-Serial Left-to-right (DSLOT) arithmetic based processing technique called _DSLOT-NN_ with aim to accelerate inference of the convolution operation in the deep neural networks (DNNs). The proposed work has the ability ... |
2309.16223 | GInX-Eval: Towards In-Distribution Evaluation of Graph Neural Network
Explanations | Diverse explainability methods of graph neural networks (GNN) have recently
been developed to highlight the edges and nodes in the graph that contribute
the most to the model predictions. However, it is not clear yet how to evaluate
the correctness of those explanations, whether it is from a human or a model
perspectiv... | Kenza Amara, Mennatallah El-Assady, Rex Ying | 2023-09-28T07:56:10Z | http://arxiv.org/abs/2309.16223v2 | # GInX-Eval: Towards In-Distribution Evaluation of Graph Neural Network Explanations
###### Abstract
Diverse explainability methods of graph neural networks (GNN) have recently been developed to highlight the edges and nodes in the graph that contribute the most to the model predictions. However, it is not clear yet ... |
2309.05263 | Brain-inspired Evolutionary Architectures for Spiking Neural Networks | The complex and unique neural network topology of the human brain formed
through natural evolution enables it to perform multiple cognitive functions
simultaneously. Automated evolutionary mechanisms of biological network
structure inspire us to explore efficient architectural optimization for
Spiking Neural Networks (... | Wenxuan Pan, Feifei Zhao, Zhuoya Zhao, Yi Zeng | 2023-09-11T06:39:11Z | http://arxiv.org/abs/2309.05263v1 | # Brain-inspired Evolutionary Architectures for
###### Abstract
The complex and unique neural network topology of the human brain formed through natural evolution enables it to perform multiple cognitive functions simultaneously. Automated evolutionary mechanisms of biological network structure inspire us to explore ... |
2309.16425 | Feed-forward and recurrent inhibition for compressing and classifying
high dynamic range biosignals in spiking neural network architectures | Neuromorphic processors that implement Spiking Neural Networks (SNNs) using
mixed-signal analog/digital circuits represent a promising technology for
closed-loop real-time processing of biosignals. As in biology, to minimize
power consumption, the silicon neurons' circuits are configured to fire with a
limited dynamic ... | Rachel Sava, Elisa Donati, Giacomo Indiveri | 2023-09-28T13:22:51Z | http://arxiv.org/abs/2309.16425v1 | Feed-forward and recurrent inhibition for compressing and classifying high dynamic range biosignals in spiking neural network architectures
###### Abstract
Neuromorphic processors that implement Spiking Neural Networks (SNNs) using mixed-signal analog/digital circuits represent a promising technology for closed-loop ... |
2309.13575 | Probabilistic Weight Fixing: Large-scale training of neural network
weight uncertainties for quantization | Weight-sharing quantization has emerged as a technique to reduce energy
expenditure during inference in large neural networks by constraining their
weights to a limited set of values. However, existing methods for
weight-sharing quantization often make assumptions about the treatment of
weights based on value alone tha... | Christopher Subia-Waud, Srinandan Dasmahapatra | 2023-09-24T08:04:28Z | http://arxiv.org/abs/2309.13575v3 | Probabilistic Weight Fixing: Large-scale training of neural network weight uncertainties for quantization
###### Abstract
Weight-sharing quantization has emerged as a technique to reduce energy expenditure during inference in large neural networks by constraining their weights to a limited set of values. However, exi... |
2309.09934 | Hierarchical Attention and Graph Neural Networks: Toward Drift-Free Pose
Estimation | The most commonly used method for addressing 3D geometric registration is the
iterative closet-point algorithm, this approach is incremental and prone to
drift over multiple consecutive frames. The Common strategy to address the
drift is the pose graph optimization subsequent to frame-to-frame registration,
incorporati... | Kathia Melbouci, Fawzi Nashashibi | 2023-09-18T16:51:56Z | http://arxiv.org/abs/2309.09934v1 | # Hierarchical Attention and Graph Neural Networks: Toward Drift-Free Pose Estimation
###### Abstract
The most commonly used method for addressing 3D geometric registration is the iterative closet-point algorithm, this approach is incremental and prone to drift over multiple consecutive frames. The Common strategy to... |
2307.16416 | MRA-GNN: Minutiae Relation-Aware Model over Graph Neural Network for
Fingerprint Embedding | Deep learning has achieved remarkable results in fingerprint embedding, which
plays a critical role in modern Automated Fingerprint Identification Systems.
However, previous works including CNN-based and Transformer-based approaches
fail to exploit the nonstructural data, such as topology and correlation in
fingerprint... | Yapeng Su, Tong Zhao, Zicheng Zhang | 2023-07-31T05:54:06Z | http://arxiv.org/abs/2307.16416v1 | # MRA-GNN: Minutiae Relation-Aware Model over Graph Neural Network for Fingerprint Embedding
###### Abstract
Deep learning has achieved remarkable results in fingerprint embedding, which plays a critical role in modern Automated Fingerprint Identification Systems. However, previous works including CNN-based and Trans... |
2309.12445 | Ensemble Neural Networks for Remaining Useful Life (RUL) Prediction | A core part of maintenance planning is a monitoring system that provides a
good prognosis on health and degradation, often expressed as remaining useful
life (RUL). Most of the current data-driven approaches for RUL prediction focus
on single-point prediction. These point prediction approaches do not include
the probab... | Ahbishek Srinivasan, Juan Carlos Andresen, Anders Holst | 2023-09-21T19:38:44Z | http://arxiv.org/abs/2309.12445v1 | # Ensemble Neural Networks for Remaining Useful Life (RUL) Prediction
###### Abstract
A core part of maintenance planning is a monitoring system that provides a good prognosis on health and degradation, often expressed as remaining useful life (RUL). Most of the current data-driven approaches for RUL prediction focus... |
2309.13773 | GHN-QAT: Training Graph Hypernetworks to Predict Quantization-Robust
Parameters of Unseen Limited Precision Neural Networks | Graph Hypernetworks (GHN) can predict the parameters of varying unseen CNN
architectures with surprisingly good accuracy at a fraction of the cost of
iterative optimization. Following these successes, preliminary research has
explored the use of GHNs to predict quantization-robust parameters for 8-bit
and 4-bit quantiz... | Stone Yun, Alexander Wong | 2023-09-24T23:01:00Z | http://arxiv.org/abs/2309.13773v1 | GHN-QAT: Training Graph Hypernetworks to Predict Quantization-Robust Parameters of Unseen Limited Precision Neural Networks
###### Abstract
Graph Hypernetworks (GHN) can predict the parameters of varying unseen CNN architectures with surprisingly good accuracy at a fraction of the cost of iterative optimization. Foll... |
2309.12121 | A Multiscale Autoencoder (MSAE) Framework for End-to-End Neural Network
Speech Enhancement | Neural network approaches to single-channel speech enhancement have received
much recent attention. In particular, mask-based architectures have achieved
significant performance improvements over conventional methods. This paper
proposes a multiscale autoencoder (MSAE) for mask-based end-to-end neural
network speech en... | Bengt J. Borgstrom, Michael S. Brandstein | 2023-09-21T14:41:54Z | http://arxiv.org/abs/2309.12121v1 | # A Multiscale Autoencoder (MSAE) Framework for End-to-End Neural Network Speech Enhancement
###### Abstract
Neural network approaches to single-channel speech enhancement have received much recent attention. In particular, mask-based architectures have achieved significant performance improvements over conventional ... |
2309.06626 | Accelerating Deep Neural Networks via Semi-Structured Activation
Sparsity | The demand for efficient processing of deep neural networks (DNNs) on
embedded devices is a significant challenge limiting their deployment.
Exploiting sparsity in the network's feature maps is one of the ways to reduce
its inference latency. It is known that unstructured sparsity results in lower
accuracy degradation ... | Matteo Grimaldi, Darshan C. Ganji, Ivan Lazarevich, Sudhakar Sah | 2023-09-12T22:28:53Z | http://arxiv.org/abs/2309.06626v2 | # Accelerating Deep Neural Networks via Semi-Structured Activation Sparsity
###### Abstract
The demand for efficient processing of deep neural networks (DNNs) on embedded devices is a significant challenge limiting their deployment. Exploiting sparsity in the network's feature maps is one of the ways to reduce its in... |
2303.17823 | An interpretable neural network-based non-proportional odds model for
ordinal regression | This study proposes an interpretable neural network-based non-proportional
odds model (N$^3$POM) for ordinal regression. N$^3$POM is different from
conventional approaches to ordinal regression with non-proportional models in
several ways: (1) N$^3$POM is defined for both continuous and discrete
responses, whereas stan... | Akifumi Okuno, Kazuharu Harada | 2023-03-31T06:40:27Z | http://arxiv.org/abs/2303.17823v4 | # An interpretable neural network-based
###### Abstract
This study proposes an interpretable neural network-based non-proportional odds model (N\({}^{3}\)POM) for ordinal regression. In the model, the response variable can take continuous values, and the regression coefficients vary depending on the predicting ordina... |
2309.06975 | Predicting Expressibility of Parameterized Quantum Circuits using Graph
Neural Network | Parameterized Quantum Circuits (PQCs) are essential to quantum machine
learning and optimization algorithms. The expressibility of PQCs, which
measures their ability to represent a wide range of quantum states, is a
critical factor influencing their efficacy in solving quantum problems.
However, the existing technique ... | Shamminuj Aktar, Andreas Bärtschi, Abdel-Hameed A. Badawy, Diane Oyen, Stephan Eidenbenz | 2023-09-13T14:08:01Z | http://arxiv.org/abs/2309.06975v1 | # Predicting Expressibility of Parameterized Quantum Circuits using Graph Neural Network
###### Abstract
Parameterized Quantum Circuits (PQCs) are essential to quantum machine learning and optimization algorithms. The expressibility of PQCs, which measures their ability to represent a wide range of quantum states, is... |
2309.08569 | Local Differential Privacy in Graph Neural Networks: a Reconstruction
Approach | Graph Neural Networks have achieved tremendous success in modeling complex
graph data in a variety of applications. However, there are limited studies
investigating privacy protection in GNNs. In this work, we propose a learning
framework that can provide node privacy at the user level, while incurring low
utility loss... | Karuna Bhaila, Wen Huang, Yongkai Wu, Xintao Wu | 2023-09-15T17:35:51Z | http://arxiv.org/abs/2309.08569v2 | # Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach
###### Abstract
Graph Neural Networks have achieved tremendous success in modeling complex graph data in a variety of applications. However, there are limited studies investigating privacy protection in GNNs. In this work, we propose a l... |
2309.11763 | Bloch Equation Enables Physics-informed Neural Network in Parametric
Magnetic Resonance Imaging | Magnetic resonance imaging (MRI) is an important non-invasive imaging method
in clinical diagnosis. Beyond the common image structures, parametric imaging
can provide the intrinsic tissue property thus could be used in quantitative
evaluation. The emerging deep learning approach provides fast and accurate
parameter est... | Qingrui Cai, Liuhong Zhu, Jianjun Zhou, Chen Qian, Di Guo, Xiaobo Qu | 2023-09-21T03:53:33Z | http://arxiv.org/abs/2309.11763v1 | # Bloch Equation Enables Physics-informed Neural Network in Parametric Magnetic Resonance Imaging
###### Abstract
Magnetic resonance imaging (MRI) is an important non-invasive imaging method in clinical diagnosis. Beyond the common image structures, parametric imaging can provide the intrinsic tissue property thus co... |
2306.17597 | Razor SNN: Efficient Spiking Neural Network with Temporal Embeddings | The event streams generated by dynamic vision sensors (DVS) are sparse and
non-uniform in the spatial domain, while still dense and redundant in the
temporal domain. Although spiking neural network (SNN), the event-driven
neuromorphic model, has the potential to extract spatio-temporal features from
the event streams, ... | Yuan Zhang, Jian Cao, Ling Zhang, Jue Chen, Wenyu Sun, Yuan Wang | 2023-06-30T12:17:30Z | http://arxiv.org/abs/2306.17597v1 | # Razor SNN: Efficient Spiking Neural Network with Temporal Embeddings
###### Abstract
The event streams generated by dynamic vision sensors (DVS) are sparse and non-uniform in the spatial domain, while still dense and redundant in the temporal domain. Although spiking neural network (SNN), the event-driven neuromorp... |
2309.04434 | Physics-Informed Neural Networks for an optimal counterdiabatic quantum
computation | We introduce a novel methodology that leverages the strength of
Physics-Informed Neural Networks (PINNs) to address the counterdiabatic (CD)
protocol in the optimization of quantum circuits comprised of systems with
$N_{Q}$ qubits. The primary objective is to utilize physics-inspired deep
learning techniques to accurat... | Antonio Ferrer-Sánchez, Carlos Flores-Garrigos, Carlos Hernani-Morales, José J. Orquín-Marqués, Narendra N. Hegade, Alejandro Gomez Cadavid, Iraitz Montalban, Enrique Solano, Yolanda Vives-Gilabert, José D. Martín-Guerrero | 2023-09-08T16:55:39Z | http://arxiv.org/abs/2309.04434v2 | # Physics-Informed Neural Networks for an Optimal Counterdiabatic quantum computation
###### Abstract
We introduce a novel methodology that leverages the strength of Physics-Informed Neural Networks (PINNs) to address the counterdiabatic (CD) protocol in the optimization of quantum circuits comprised of systems with ... |
2309.03617 | NeuroCodeBench: a plain C neural network benchmark for software
verification | Safety-critical systems with neural network components require strong
guarantees. While existing neural network verification techniques have shown
great progress towards this goal, they cannot prove the absence of software
faults in the network implementation. This paper presents NeuroCodeBench - a
verification benchma... | Edoardo Manino, Rafael Sá Menezes, Fedor Shmarov, Lucas C. Cordeiro | 2023-09-07T10:19:33Z | http://arxiv.org/abs/2309.03617v1 | # NeuroCodeBench: a plain C neural network benchmark for software verification
###### Abstract
Safety-critical systems with neural network components require strong guarantees. While existing neural network verification techniques have shown great progress towards this goal, they cannot prove the absence of software ... |
2309.15075 | On Excess Risk Convergence Rates of Neural Network Classifiers | The recent success of neural networks in pattern recognition and
classification problems suggests that neural networks possess qualities
distinct from other more classical classifiers such as SVMs or boosting
classifiers. This paper studies the performance of plug-in classifiers based on
neural networks in a binary cla... | Hyunouk Ko, Namjoon Suh, Xiaoming Huo | 2023-09-26T17:14:10Z | http://arxiv.org/abs/2309.15075v1 | # On Excess Risk Convergence Rates of Neural Network Classifiers
###### Abstract
The recent success of neural networks in pattern recognition and classification problems suggests that neural networks possess qualities distinct from other more classical classifiers such as SVMs or boosting classifiers. This paper stud... |
2310.03760 | Investigating Deep Neural Network Architecture and Feature Extraction
Designs for Sensor-based Human Activity Recognition | The extensive ubiquitous availability of sensors in smart devices and the
Internet of Things (IoT) has opened up the possibilities for implementing
sensor-based activity recognition. As opposed to traditional sensor time-series
processing and hand-engineered feature extraction, in light of deep learning's
proven effect... | Danial Ahangarani, Mohammad Shirazi, Navid Ashraf | 2023-09-26T14:55:32Z | http://arxiv.org/abs/2310.03760v1 | Investigating Deep Neural Network Architecture and Feature Extraction Designs for Sensor-based Human Activity Recognition
###### Abstract
The extensive ubiquitous availability of sensors in smart devices and the Internet of Things (IoT) has opened up the possibilities for implementing sensor-based activity recognitio... |
2309.13410 | Tropical neural networks and its applications to classifying
phylogenetic trees | Deep neural networks show great success when input vectors are in an
Euclidean space. However, those classical neural networks show a poor
performance when inputs are phylogenetic trees, which can be written as vectors
in the tropical projective torus. Here we propose tropical embedding to
transform a vector in the tro... | Ruriko Yoshida, Georgios Aliatimis, Keiji Miura | 2023-09-23T15:47:35Z | http://arxiv.org/abs/2309.13410v1 | # Tropical neural networks and its applications to classifying phylogenetic trees
###### Abstract
Deep neural networks show great success when input vectors are in an Euclidean space. However, those classical neural networks show a poor performance when inputs are phylogenetic trees, which can be written as vectors i... |
2303.17995 | Neural Network Entropy (NNetEn): Entropy-Based EEG Signal and Chaotic
Time Series Classification, Python Package for NNetEn Calculation | Entropy measures are effective features for time series classification
problems. Traditional entropy measures, such as Shannon entropy, use
probability distribution function. However, for the effective separation of
time series, new entropy estimation methods are required to characterize the
chaotic dynamic of the syst... | Andrei Velichko, Maksim Belyaev, Yuriy Izotov, Murugappan Murugappan, Hanif Heidari | 2023-03-31T12:11:21Z | http://arxiv.org/abs/2303.17995v2 | Neural Network Entropy (NNetEn): Entropy-Based EEG Signal and Chaotic Time Series Classification, Python Package for NNetEn Calculation
###### Abstract
Entropy measures are effective features for time series classification problems. Traditional entropy measures, such as Shannon entropy, use probability distribution f... |
2308.00143 | Formally Explaining Neural Networks within Reactive Systems | Deep neural networks (DNNs) are increasingly being used as controllers in
reactive systems. However, DNNs are highly opaque, which renders it difficult
to explain and justify their actions. To mitigate this issue, there has been a
surge of interest in explainable AI (XAI) techniques, capable of pinpointing
the input fe... | Shahaf Bassan, Guy Amir, Davide Corsi, Idan Refaeli, Guy Katz | 2023-07-31T20:19:50Z | http://arxiv.org/abs/2308.00143v3 | # Formally Explaining Neural Networks
###### Abstract
Deep neural networks (DNNs) are increasingly being used as controllers in reactive systems. However, DNNs are highly opaque, which renders it difficult to explain and justify their actions. To mitigate this issue, there has been a surge of interest in explainable ... |
2302.14623 | Fast as CHITA: Neural Network Pruning with Combinatorial Optimization | The sheer size of modern neural networks makes model serving a serious
computational challenge. A popular class of compression techniques overcomes
this challenge by pruning or sparsifying the weights of pretrained networks.
While useful, these techniques often face serious tradeoffs between
computational requirements ... | Riade Benbaki, Wenyu Chen, Xiang Meng, Hussein Hazimeh, Natalia Ponomareva, Zhe Zhao, Rahul Mazumder | 2023-02-28T15:03:18Z | http://arxiv.org/abs/2302.14623v1 | # Fast as CHITA: Neural Network Pruning with Combinatorial Optimization
###### Abstract
The sheer size of modern neural networks makes model serving a serious computational challenge. A popular class of compression techniques overcomes this challenge by pruning or sparsifying the weights of pretrained networks. While... |
2309.04742 | Affine Invariant Ensemble Transform Methods to Improve Predictive
Uncertainty in Neural Networks | We consider the problem of performing Bayesian inference for logistic
regression using appropriate extensions of the ensemble Kalman filter. Two
interacting particle systems are proposed that sample from an approximate
posterior and prove quantitative convergence rates of these interacting
particle systems to their mea... | Diksha Bhandari, Jakiw Pidstrigach, Sebastian Reich | 2023-09-09T10:01:51Z | http://arxiv.org/abs/2309.04742v2 | # Affine Invariant Ensemble Transform Methods to Improve Predictive Uncertainty in ReLU Networks
# Affine Invariant Ensemble Transform Methods to Improve Predictive Uncertainty in ReLU Networks
Diksha Bhandari, Jakiw Pidstrigach, Sebastian Reich
**Abstract** We consider the problem of performing Bayesian inference f... |
2309.12849 | DeepOPF-U: A Unified Deep Neural Network to Solve AC Optimal Power Flow
in Multiple Networks | The traditional machine learning models to solve optimal power flow (OPF) are
mostly trained for a given power network and lack generalizability to today's
power networks with varying topologies and growing plug-and-play distributed
energy resources (DERs). In this paper, we propose DeepOPF-U, which uses one
unified de... | Heng Liang, Changhong Zhao | 2023-09-22T13:22:15Z | http://arxiv.org/abs/2309.12849v1 | # DeepOPF-U: A Unified Deep Neural Network to Solve AC Optimal Power Flow in Multiple Networks
###### Abstract
The traditional machine learning models to solve optimal power flow (OPF) are mostly trained for a given power network and lack generalizability to today's power networks with varying topologies and growing ... |
2310.00137 | On the Disconnect Between Theory and Practice of Neural Networks: Limits
of the NTK Perspective | The neural tangent kernel (NTK) has garnered significant attention as a
theoretical framework for describing the behavior of large-scale neural
networks. Kernel methods are theoretically well-understood and as a result
enjoy algorithmic benefits, which can be demonstrated to hold in wide synthetic
neural network archit... | Jonathan Wenger, Felix Dangel, Agustinus Kristiadi | 2023-09-29T20:51:24Z | http://arxiv.org/abs/2310.00137v2 | # On the Disconnect Between Theory and Practice of Overparametrized Neural Networks
###### Abstract
The infinite-width limit of neural networks (NNs) has garnered significant attention as a theoretical framework for analyzing the behavior of large-scale, overparametrized networks. By approaching infinite width, NNs e... |
2301.01597 | Problem-Dependent Power of Quantum Neural Networks on Multi-Class
Classification | Quantum neural networks (QNNs) have become an important tool for
understanding the physical world, but their advantages and limitations are not
fully understood. Some QNNs with specific encoding methods can be efficiently
simulated by classical surrogates, while others with quantum memory may perform
better than classi... | Yuxuan Du, Yibo Yang, Dacheng Tao, Min-Hsiu Hsieh | 2022-12-29T10:46:40Z | http://arxiv.org/abs/2301.01597v3 | # Demystify Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification
###### Abstract
Quantum neural networks (QNNs) have become an important tool for understanding the physical world, but their advantages and limitations are not fully understood. Some QNNs with specific encoding methods can be... |
2309.08385 | A Unified View Between Tensor Hypergraph Neural Networks And Signal
Denoising | Hypergraph Neural networks (HyperGNNs) and hypergraph signal denoising
(HyperGSD) are two fundamental topics in higher-order network modeling.
Understanding the connection between these two domains is particularly useful
for designing novel HyperGNNs from a HyperGSD perspective, and vice versa. In
particular, the tenso... | Fuli Wang, Karelia Pena-Pena, Wei Qian, Gonzalo R. Arce | 2023-09-15T13:19:31Z | http://arxiv.org/abs/2309.08385v1 | # A Unified View Between Tensor Hypergraph Neural Networks And Signal Denoising
###### Abstract
Hypergraph Neural networks (HyperGNNs) and hypergraph signal denoising (HyperGSD) are two fundamental topics in higher-order network modeling. Understanding the connection between these two domains is particularly useful f... |
2307.16373 | 2D Convolutional Neural Network for Event Reconstruction in IceCube
DeepCore | IceCube DeepCore is an extension of the IceCube Neutrino Observatory designed
to measure GeV scale atmospheric neutrino interactions for the purpose of
neutrino oscillation studies. Distinguishing muon neutrinos from other flavors
and reconstructing inelasticity are especially difficult tasks at GeV scale
energies in I... | J. H. Peterson, M. Prado Rodriguez, K. Hanson | 2023-07-31T02:37:36Z | http://arxiv.org/abs/2307.16373v1 | # 2D Convolutional Neural Network for Event Reconstruction in IceCube DeepCore
###### Abstract:
IceCube DeepCore is an extension of the IceCube Neutrino Observatory designed to measure GeV scale atmospheric neutrino interactions for the purpose of neutrino oscillation studies. Distinguishing muon neutrinos from other... |
2309.09700 | Securing Fixed Neural Network Steganography | Image steganography is the art of concealing secret information in images in
a way that is imperceptible to unauthorized parties. Recent advances show that
is possible to use a fixed neural network (FNN) for secret embedding and
extraction. Such fixed neural network steganography (FNNS) achieves high
steganographic per... | Zicong Luo, Sheng Li, Guobiao Li, Zhenxing Qian, Xinpeng Zhang | 2023-09-18T12:07:37Z | http://arxiv.org/abs/2309.09700v1 | # Securing Fixed Neural Network Steganography
###### Abstract.
Image steganography is the art of concealing secret information in images in a way that is imperceptible to unauthorized parties. Recent advances show that is possible to use a fixed neural network (FNN) for secret embedding and extraction. Such fixed neu... |
2301.00106 | Physics-informed Neural Networks approach to solve the Blasius function | Deep learning techniques with neural networks have been used effectively in
computational fluid dynamics (CFD) to obtain solutions to nonlinear
differential equations. This paper presents a physics-informed neural network
(PINN) approach to solve the Blasius function. This method eliminates the
process of changing the ... | Greeshma Krishna, Malavika S Nair, Pramod P Nair, Anil Lal S | 2022-12-31T03:14:42Z | http://arxiv.org/abs/2301.00106v2 | # Physics-informed Neural Networks approach to solve the Blasius function
###### Abstract
Deep learning techniques with neural networks have been used effectively in computational fluid dynamics (CFD) to obtain solutions to nonlinear differential equations. This paper presents a physics-informed neural network (PINN)... |
2301.13146 | Enhancing Neural Network Differential Equation Solvers | We motivate the use of neural networks for the construction of numerical
solutions to differential equations. We prove that there exists a feed-forward
neural network that can arbitrarily minimise an objective function that is zero
at the solution of Poisson's equation, allowing us to guarantee that neural
network solu... | Matthew J. H. Wright | 2022-12-28T17:26:46Z | http://arxiv.org/abs/2301.13146v1 | # Enhancing Neural Network Differential Equation Solvers
###### Abstract
We motivate the use of neural networks for the construction of numerical solutions to differential equations. We prove that there exists a feed-forward neural network that can arbitrarily minimise an objective function that is zero at the soluti... |
2308.16910 | Robust Variational Physics-Informed Neural Networks | We introduce a Robust version of the Variational Physics-Informed Neural
Networks method (RVPINNs). As in VPINNs, we define the quadratic loss
functional in terms of a Petrov-Galerkin-type variational formulation of the
PDE problem: the trial space is a (Deep) Neural Network (DNN) manifold, while
the test space is a fi... | Sergio Rojas, Paweł Maczuga, Judit Muñoz-Matute, David Pardo, Maciej Paszynski | 2023-08-31T17:59:44Z | http://arxiv.org/abs/2308.16910v3 | # Robust Variational Physics-Informed Neural Networks
###### Abstract
We introduce a Robust version of the Variational Physics-Informed Neural Networks (RVPINNs) to approximate the Partial Differential Equations (PDEs) solution. We start from a weak Petrov-Galerkin formulation of the problem, select a discrete test s... |
2310.00337 | Quantization of Deep Neural Networks to facilitate self-correction of
weights on Phase Change Memory-based analog hardware | In recent years, hardware-accelerated neural networks have gained significant
attention for edge computing applications. Among various hardware options,
crossbar arrays, offer a promising avenue for efficient storage and
manipulation of neural network weights. However, the transition from trained
floating-point models ... | Arseni Ivanov | 2023-09-30T10:47:25Z | http://arxiv.org/abs/2310.00337v1 | Quantization of Deep Neural Networks to facilitate self-correction of weights on Phase Change Memory-based analog hardware
###### Abstract
In recent years, hardware-accelerated neural networks have gained significant attention for edge computing applications. Among various hardware options, crossbar arrays, offer a p... |
2310.10656 | VeriDIP: Verifying Ownership of Deep Neural Networks through Privacy
Leakage Fingerprints | Deploying Machine Learning as a Service gives rise to model plagiarism,
leading to copyright infringement. Ownership testing techniques are designed to
identify model fingerprints for verifying plagiarism. However, previous works
often rely on overfitting or robustness features as fingerprints, lacking
theoretical guar... | Aoting Hu, Zhigang Lu, Renjie Xie, Minhui Xue | 2023-09-07T01:58:12Z | http://arxiv.org/abs/2310.10656v1 | # VeriDIP: Verifying Ownership of Deep Neural Networks through Privacy Leakage Fingerprints
###### Abstract
Deploying Machine Learning as a Service gives rise to model plagiarism, leading to copyright infringement. Ownership testing techniques are designed to identify model fingerprints for verifying plagiarism. Howe... |
2309.12095 | Bayesian sparsification for deep neural networks with Bayesian model
reduction | Deep learning's immense capabilities are often constrained by the complexity
of its models, leading to an increasing demand for effective sparsification
techniques. Bayesian sparsification for deep learning emerges as a crucial
approach, facilitating the design of models that are both computationally
efficient and comp... | Dimitrije Marković, Karl J. Friston, Stefan J. Kiebel | 2023-09-21T14:10:47Z | http://arxiv.org/abs/2309.12095v2 | # Bayesian sparsification for deep neural networks with Bayesian model reduction
###### Abstract
Deep learning's immense capabilities are often constrained by the complexity of its models, leading to an increasing demand for effective sparsification techniques. Bayesian sparsification for deep learning emerges as a c... |
2304.01015 | Adaptive structure evolution and biologically plausible synaptic
plasticity for recurrent spiking neural networks | The architecture design and multi-scale learning principles of the human
brain that evolved over hundreds of millions of years are crucial to realizing
human-like intelligence. Spiking Neural Network (SNN) based Liquid State
Machine (LSM) serves as a suitable architecture to study brain-inspired
intelligence because of... | Wenxuan Pan, Feifei Zhao, Yi Zeng, Bing Han | 2023-03-31T07:36:39Z | http://arxiv.org/abs/2304.01015v1 | Adaptive structure evolution and biologically plausible synaptic plasticity for recurrent spiking neural networks
###### Abstract
The architecture design and multi-scale learning principles of the human brain that evolved over hundreds of millions of years are crucial to realizing human-like intelligence. Spiking Neu... |
2309.03167 | Split-Boost Neural Networks | The calibration and training of a neural network is a complex and
time-consuming procedure that requires significant computational resources to
achieve satisfactory results. Key obstacles are a large number of
hyperparameters to select and the onset of overfitting in the face of a small
amount of data. In this framewor... | Raffaele Giuseppe Cestari, Gabriele Maroni, Loris Cannelli, Dario Piga, Simone Formentin | 2023-09-06T17:08:57Z | http://arxiv.org/abs/2309.03167v1 | # Split-Boost Neural Networks
###### Abstract
The calibration and training of a neural network is a complex and time-consuming procedure that requires significant computational resources to achieve satisfactory results. Key obstacles are a large number of hyperparameters to select and the onset of overfitting in the ... |
2309.06779 | ZKROWNN: Zero Knowledge Right of Ownership for Neural Networks | Training contemporary AI models requires investment in procuring learning
data and computing resources, making the models intellectual property of the
owners. Popular model watermarking solutions rely on key input triggers for
detection; the keys have to be kept private to prevent discovery, forging, and
removal of the... | Nojan Sheybani, Zahra Ghodsi, Ritvik Kapila, Farinaz Koushanfar | 2023-09-13T08:06:13Z | http://arxiv.org/abs/2309.06779v1 | # ZKROWNN: Zero Knowledge Right of Ownership
###### Abstract
Training contemporary AI models requires investment in procuring learning data and computing resources, making the models intellectual property of the owners. Popular model watermarking solutions rely on key input triggers for detection; the keys have to be... |
2309.14816 | A Comparative Study of Population-Graph Construction Methods and Graph
Neural Networks for Brain Age Regression | The difference between the chronological and biological brain age of a
subject can be an important biomarker for neurodegenerative diseases, thus
brain age estimation can be crucial in clinical settings. One way to
incorporate multimodal information into this estimation is through population
graphs, which combine vario... | Kyriaki-Margarita Bintsi, Tamara T. Mueller, Sophie Starck, Vasileios Baltatzis, Alexander Hammers, Daniel Rueckert | 2023-09-26T10:30:45Z | http://arxiv.org/abs/2309.14816v1 | A Comparative Study of Population-Graph Construction Methods and Graph Neural Networks for Brain Age Regression
###### Abstract
The difference between the chronological and biological brain age of a subject can be an important biomarker for neurodegenerative diseases, thus brain age estimation can be crucial in clini... |
2302.14231 | CHGNet: Pretrained universal neural network potential for
charge-informed atomistic modeling | The simulation of large-scale systems with complex electron interactions
remains one of the greatest challenges for the atomistic modeling of materials.
Although classical force fields often fail to describe the coupling between
electronic states and ionic rearrangements, the more accurate
\textit{ab-initio} molecular ... | Bowen Deng, Peichen Zhong, KyuJung Jun, Janosh Riebesell, Kevin Han, Christopher J. Bartel, Gerbrand Ceder | 2023-02-28T01:30:06Z | http://arxiv.org/abs/2302.14231v2 | # CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling
###### Abstract
The simulation of large-scale systems with complex electron interactions remains one of the greatest challenges for the atomistic modeling of materials. Although classical force-fields often fail to describe... |
2309.06221 | Use neural networks to recognize students' handwritten letters and
incorrect symbols | Correcting students' multiple-choice answers is a repetitive and mechanical
task that can be considered an image multi-classification task. Assuming
possible options are 'abcd' and the correct option is one of the four, some
students may write incorrect symbols or options that do not exist. In this
paper, five classifi... | JiaJun Zhu, Zichuan Yang, Binjie Hong, Jiacheng Song, Jiwei Wang, Tianhao Chen, Shuilan Yang, Zixun Lan, Fei Ma | 2023-09-12T13:41:59Z | http://arxiv.org/abs/2309.06221v1 | # Use neural networks to recognize students' handwritten letters and incorrect symbols
###### Abstract
Correcting students' multiple-choice answers is a repetitive and mechanical task that can be considered an image multi-classification task. Assuming possible options are 'abcd' and the correct option is one of the f... |
2309.05067 | Mutation-based Fault Localization of Deep Neural Networks | Deep neural networks (DNNs) are susceptible to bugs, just like other types of
software systems. A significant uptick in using DNN, and its applications in
wide-ranging areas, including safety-critical systems, warrant extensive
research on software engineering tools for improving the reliability of
DNN-based systems. O... | Ali Ghanbari, Deepak-George Thomas, Muhammad Arbab Arshad, Hridesh Rajan | 2023-09-10T16:18:49Z | http://arxiv.org/abs/2309.05067v1 | # Mutation-based Fault Localization
###### Abstract
Deep neural networks (DNNs) are susceptible to bugs, just like other types of software systems. A significant uptick in using DNN, and its applications in wide-ranging areas, including safety-critical systems, warrant extensive research on software engineering tools... |
2309.17113 | Meta-Path Learning for Multi-relational Graph Neural Networks | Existing multi-relational graph neural networks use one of two strategies for
identifying informative relations: either they reduce this problem to low-level
weight learning, or they rely on handcrafted chains of relational dependencies,
called meta-paths. However, the former approach faces challenges in the
presence o... | Francesco Ferrini, Antonio Longa, Andrea Passerini, Manfred Jaeger | 2023-09-29T10:12:30Z | http://arxiv.org/abs/2309.17113v2 | # Meta-Path Learning for Multi-relational Graph Neural Networks
###### Abstract
Existing multi-relational graph neural networks use one of two strategies for identifying informative relations: either they reduce this problem to low-level weight learning, or they rely on handcrafted chains of relational dependencies, ... |
2309.04755 | Towards Real-time Training of Physics-informed Neural Networks:
Applications in Ultrafast Ultrasound Blood Flow Imaging | Physics-informed Neural Network (PINN) is one of the most preeminent solvers
of Navier-Stokes equations, which are widely used as the governing equation of
blood flow. However, current approaches, relying on full Navier-Stokes
equations, are impractical for ultrafast Doppler ultrasound, the
state-of-the-art technique f... | Haotian Guan, Jinping Dong, Wei-Ning Lee | 2023-09-09T11:03:06Z | http://arxiv.org/abs/2309.04755v1 | Towards Real-time Training of Physics-informed Neural Networks: Applications in Ultrafast Ultrasound Blood Flow Imaging
###### Abstract
Physics-informed Neural Network (PINN) is one of the most preeminent solvers of Navier-Stokes equations, which are widely used as the governing equation of blood flow. However, curre... |
2309.04782 | RRCNN$^{+}$: An Enhanced Residual Recursive Convolutional Neural Network
for Non-stationary Signal Decomposition | Time-frequency analysis is an important and challenging task in many
applications. Fourier and wavelet analysis are two classic methods that have
achieved remarkable success in many fields. They also exhibit limitations when
applied to nonlinear and non-stationary signals. To address this challenge, a
series of nonline... | Feng Zhou, Antonio Cicone, Haomin Zhou | 2023-09-09T13:00:30Z | http://arxiv.org/abs/2309.04782v1 | RRCNN\({}^{+}\): An Enhanced Residual Recursive Convolutional Neural Network for Non-stationary Signal Decomposition
###### Abstract
Time-frequency analysis is an important and challenging task in many applications. Fourier and wavelet analysis are two classic methods that have achieved remarkable success in many fie... |
2309.15328 | Exploring Learned Representations of Neural Networks with Principal
Component Analysis | Understanding feature representation for deep neural networks (DNNs) remains
an open question within the general field of explainable AI. We use principal
component analysis (PCA) to study the performance of a k-nearest neighbors
classifier (k-NN), nearest class-centers classifier (NCC), and support vector
machines on ... | Amit Harlev, Andrew Engel, Panos Stinis, Tony Chiang | 2023-09-27T00:18:25Z | http://arxiv.org/abs/2309.15328v1 | # Exploring Learned Representations of Neural Networks with Principal Component Analysis
###### Abstract
Understanding feature representation for deep neural networks (DNNs) remains an open question within the general field of explainable AI. We use principal component analysis (PCA) to study the performance of a k-n... |
2301.00675 | FlatENN: Train Flat for Enhanced Fault Tolerance of Quantized Deep
Neural Networks | Model compression via quantization and sparsity enhancement has gained an
immense interest to enable the deployment of deep neural networks (DNNs) in
resource-constrained edge environments. Although these techniques have shown
promising results in reducing the energy, latency and memory requirements of
the DNNs, their ... | Akul Malhotra, Sumeet Kumar Gupta | 2022-12-29T06:06:14Z | http://arxiv.org/abs/2301.00675v1 | # FlatENN: Train Flat for Enhanced Fault Tolerance of Quantized Deep Neural Networks
###### Abstract
Model compression via quantization and sparsity enhancement has gained an immense interest to enable the deployment of deep neural networks (DNNs) in resource-constrained edge environments. Although these techniques h... |
2307.16366 | Multi-modal Graph Neural Network for Early Diagnosis of Alzheimer's
Disease from sMRI and PET Scans | In recent years, deep learning models have been applied to neuroimaging data
for early diagnosis of Alzheimer's disease (AD). Structural magnetic resonance
imaging (sMRI) and positron emission tomography (PET) images provide structural
and functional information about the brain, respectively. Combining these
features l... | Yanteng Zhanga, Xiaohai He, Yi Hao Chan, Qizhi Teng, Jagath C. Rajapakse | 2023-07-31T02:04:05Z | http://arxiv.org/abs/2307.16366v1 | # Multi-modal Graph Neural Network for Early Diagnosis of Alzheimer's Disease from sMRI and PET Scans
###### Abstract
In recent years, deep learning models have been applied to neuroimaging data for early diagnosis of Alzheimer's disease (AD). Structural magnetic resonance imaging (sMRI) and positron emission tomogra... |
2309.05846 | Designs and Implementations in Neural Network-based Video Coding | The past decade has witnessed the huge success of deep learning in well-known
artificial intelligence applications such as face recognition, autonomous
driving, and large language model like ChatGPT. Recently, the application of
deep learning has been extended to a much wider range, with neural
network-based video codi... | Yue Li, Junru Li, Chaoyi Lin, Kai Zhang, Li Zhang, Franck Galpin, Thierry Dumas, Hongtao Wang, Muhammed Coban, Jacob Ström, Du Liu, Kenneth Andersson | 2023-09-11T22:12:41Z | http://arxiv.org/abs/2309.05846v2 | # Designs and Implementations in Neural Network-based Video Coding
###### Abstract
The past decade has witnessed the huge success of deep learning in well-known artificial intelligence applications such as face recognition, autonomous driving, and large language model like ChatGPT. Recently, the application of deep l... |
2309.05809 | Divergences in Color Perception between Deep Neural Networks and Humans | Deep neural networks (DNNs) are increasingly proposed as models of human
vision, bolstered by their impressive performance on image classification and
object recognition tasks. Yet, the extent to which DNNs capture fundamental
aspects of human vision such as color perception remains unclear. Here, we
develop novel expe... | Ethan O. Nadler, Elise Darragh-Ford, Bhargav Srinivasa Desikan, Christian Conaway, Mark Chu, Tasker Hull, Douglas Guilbeault | 2023-09-11T20:26:40Z | http://arxiv.org/abs/2309.05809v1 | # Divergences in Color Perception between Deep Neural Networks and Humans
###### Abstract
Deep neural networks (DNNs) are increasingly proposed as models of human vision, bolstered by their impressive performance on image classification and object recognition tasks. Yet, the extent to which DNNs capture fundamental a... |
2309.11101 | A New Interpretable Neural Network-Based Rule Model for Healthcare
Decision Making | In healthcare applications, understanding how machine/deep learning models
make decisions is crucial. In this study, we introduce a neural network
framework, $\textit{Truth Table rules}$ (TT-rules), that combines the global
and exact interpretability properties of rule-based models with the high
performance of deep neu... | Adrien Benamira, Tristan Guerand, Thomas Peyrin | 2023-09-20T07:15:48Z | http://arxiv.org/abs/2309.11101v1 | # A New Interpretable Neural Network-Based Rule Model for Healthcare Decision Making
###### Abstract
In healthcare applications, understanding how machine/deep learning models make decisions is crucial. In this study, we introduce a neural network framework, _Truth Table rules_ (TT-rules), that combines the global an... |
2309.08444 | Neural Network Exemplar Parallelization with Go | This paper presents a case for exemplar parallelism of neural networks using
Go as parallelization framework. Further it is shown that also limited
multi-core hardware systems are feasible for these parallelization tasks, as
notebooks and single board computer systems. The main question was how much
speedup can be gene... | Georg Wiesinger, Erich Schikuta | 2023-09-15T14:46:43Z | http://arxiv.org/abs/2309.08444v1 | # Neural Network Exemplar Parallelization with Go
###### Abstract
This paper presents a case for exemplar parallelism of neural networks using Go as parallelization framework. Further it is shown that also limited multi-core hardware systems are feasible for these parallelization tasks, as notebooks and single board ... |
2309.08849 | Learning a Stable Dynamic System with a Lyapunov Energy Function for
Demonstratives Using Neural Networks | Autonomous Dynamic System (DS)-based algorithms hold a pivotal and
foundational role in the field of Learning from Demonstration (LfD).
Nevertheless, they confront the formidable challenge of striking a delicate
balance between achieving precision in learning and ensuring the overall
stability of the system. In respons... | Yu Zhang, Yongxiang Zou, Haoyu Zhang, Xiuze Xia, Long Cheng | 2023-09-16T03:03:53Z | http://arxiv.org/abs/2309.08849v6 | Learning a Stable Dynamic System with a Lyapunov Energy Function for Demonstratives Using Neural Networks
###### Abstract
Autonomous Dynamic System (DS)-based algorithms hold a pivotal and foundational role in the field of Learning from Demonstration (LfD). Nevertheless, they confront the formidable challenge of stri... |
2302.14690 | On the existence of minimizers in shallow residual ReLU neural network
optimization landscapes | Many mathematical convergence results for gradient descent (GD) based
algorithms employ the assumption that the GD process is (almost surely) bounded
and, also in concrete numerical simulations, divergence of the GD process may
slow down, or even completely rule out, convergence of the error function. In
practical rele... | Steffen Dereich, Arnulf Jentzen, Sebastian Kassing | 2023-02-28T16:01:38Z | http://arxiv.org/abs/2302.14690v1 | # On the existence of minimizers in shallow residual
###### Abstract.
Many mathematical convergence results for gradient descent (GD) based algorithms employ the assumption that the GD process is (almost surely) _bounded_ and, also in concrete numerical simulations, divergence of the GD process may _slow down_, or ev... |
2309.10948 | A Novel Deep Neural Network for Trajectory Prediction in Automated
Vehicles Using Velocity Vector Field | Anticipating the motion of other road users is crucial for automated driving
systems (ADS), as it enables safe and informed downstream decision-making and
motion planning. Unfortunately, contemporary learning-based approaches for
motion prediction exhibit significant performance degradation as the prediction
horizon in... | MReza Alipour Sormoli, Amir Samadi, Sajjad Mozaffari, Konstantinos Koufos, Mehrdad Dianati, Roger Woodman | 2023-09-19T22:14:52Z | http://arxiv.org/abs/2309.10948v1 | A Novel Deep Neural Network for Trajectory Prediction in Automated Vehicles Using Velocity Vector Field
###### Abstract
Anticipating the motion of other road users is crucial for automated driving systems (ADS), as it enables safe and informed downstream decision-making and motion planning. Unfortunately, contemporar... |
2308.00127 | DiviML: A Module-based Heuristic for Mapping Neural Networks onto
Heterogeneous Platforms | Datacenters are increasingly becoming heterogeneous, and are starting to
include specialized hardware for networking, video processing, and especially
deep learning. To leverage the heterogeneous compute capability of modern
datacenters, we develop an approach for compiler-level partitioning of deep
neural networks (DN... | Yassine Ghannane, Mohamed S. Abdelfattah | 2023-07-31T19:46:49Z | http://arxiv.org/abs/2308.00127v2 | # DiviML: A Module-based Heuristic for Mapping Neural Networks onto Heterogeneous Platforms
###### Abstract
Datacenters are increasingly becoming heterogeneous, and are starting to include specialized hardware for networking, video processing, and especially deep learning. To leverage the heterogeneous compute capabi... |
2309.08652 | Quantifying Credit Portfolio sensitivity to asset correlations with
interpretable generative neural networks | In this research, we propose a novel approach for the quantification of
credit portfolio Value-at-Risk (VaR) sensitivity to asset correlations with the
use of synthetic financial correlation matrices generated with deep learning
models. In previous work Generative Adversarial Networks (GANs) were employed
to demonstrat... | Sergio Caprioli, Emanuele Cagliero, Riccardo Crupi | 2023-09-15T15:21:14Z | http://arxiv.org/abs/2309.08652v2 | Quantifying Credit Portfolio sensitivity to asset correlations with interpretable generative neural networks
###### Abstract
In this research, we propose a novel approach for the quantification of credit portfolio Value-at-Risk (VaR) sensitivity to asset correlations with the use of synthetic financial correlation ma... |
2308.00053 | T-Fusion Net: A Novel Deep Neural Network Augmented with Multiple
Localizations based Spatial Attention Mechanisms for Covid-19 Detection | In recent years, deep neural networks are yielding better performance in
image classification tasks. However, the increasing complexity of datasets and
the demand for improved performance necessitate the exploration of innovative
techniques. The present work proposes a new deep neural network (called as,
T-Fusion Net) ... | Susmita Ghosh, Abhiroop Chatterjee | 2023-07-31T18:18:01Z | http://arxiv.org/abs/2308.00053v1 | T-Fusion Net: A Novel Deep Neural Network Augmented with Multiple Localizations based Spatial Attention Mechanisms for Covid-19 Detection
###### Abstract
In recent years, deep neural networks are yielding better performance in image classification tasks. However, the increasing complexity of datasets and the demand f... |
2309.07367 | The kernel-balanced equation for deep neural networks | Deep neural networks have shown many fruitful applications in this decade. A
network can get the generalized function through training with a finite
dataset. The degree of generalization is a realization of the proximity scale
in the data space. Specifically, the scale is not clear if the dataset is
complicated. Here w... | Kenichi Nakazato | 2023-09-14T01:00:05Z | http://arxiv.org/abs/2309.07367v1 | # The kernel-balanced equation for deep neural networks
###### Abstract
Deep neural networks have shown many fruitful applications in this decade. A network can get the generalized function through training with a finite dataset. The degree of generalization is a realization of the proximity scale in the data space. ... |
2305.19717 | Is Rewiring Actually Helpful in Graph Neural Networks? | Graph neural networks compute node representations by performing multiple
message-passing steps that consist in local aggregations of node features.
Having deep models that can leverage longer-range interactions between nodes is
hindered by the issues of over-smoothing and over-squashing. In particular, the
latter is a... | Domenico Tortorella, Alessio Micheli | 2023-05-31T10:12:23Z | http://arxiv.org/abs/2305.19717v1 | # Is Rewiring Actually Helpful in
###### Abstract
Graph neural networks compute node representations by performing multiple message-passing steps that consist in local aggregations of node features. Having deep models that can leverage longer-range interactions between nodes is hindered by the issues of over-smoothin... |
2309.06535 | Automatic quantification of abdominal subcutaneous and visceral adipose
tissue in children, through MRI study, using total intensity maps and
Convolutional Neural Networks | Childhood overweight and obesity is one of the main health problems in the
world since it is related to the early appearance of different diseases, in
addition to being a risk factor for later developing obesity in adulthood with
its health and economic consequences. Visceral abdominal tissue (VAT) is
strongly related ... | José Gerardo Suárez-García, Po-Wah So, Javier Miguel Hernández-López, Silvia S. Hidalgo-Tobón, Pilar Dies-Suárez, Benito de Celis-Alonso | 2023-09-12T19:19:47Z | http://arxiv.org/abs/2309.06535v1 | ###### Abstract
###### Abstract
Childhood overweight and obesity is one of the main health problems in the world since it is related to the early appearance of different diseases, in addition to being a risk factor for later developing obesity in adulthood with its health and economic consequences. Visceral abdominal... |
2309.10418 | Graph Neural Networks for Dynamic Modeling of Roller Bearing | In the presented work, we propose to apply the framework of graph neural
networks (GNNs) to predict the dynamics of a rolling element bearing. This
approach offers generalizability and interpretability, having the potential for
scalable use in real-time operational digital twin systems for monitoring the
health state o... | Vinay Sharma, Jens Ravesloot, Cees Taal, Olga Fink | 2023-09-19T08:30:10Z | http://arxiv.org/abs/2309.10418v1 | # Graph Neural Networks for Dynamic Modeling of Roller Bearing
###### Abstract
In the presented work, we propose to apply the framework of graph neural networks (GNNs) to predict the dynamics of a rolling element bearing. This approach offers generalizability and interpretability, having the potential for scalable us... |
2309.17363 | Relational Constraints On Neural Networks Reproduce Human Biases towards
Abstract Geometric Regularity | Uniquely among primates, humans possess a remarkable capacity to recognize
and manipulate abstract structure in the service of task goals across a broad
range of behaviors. One illustration of this is in the visual perception of
geometric forms. Studies have shown a uniquely human bias toward geometric
regularity, with... | Declan Campbell, Sreejan Kumar, Tyler Giallanza, Jonathan D. Cohen, Thomas L. Griffiths | 2023-09-29T16:12:51Z | http://arxiv.org/abs/2309.17363v1 | Relational Constraints on Neural Networks Reproduce Human Biases Towards Abstract Geometric Regularity
###### Abstract
Uniquely among primates, humans possess a remarkable capacity to recognize and manipulate abstract structure in the service of task goals across a broad range of behaviors. One illustration of this i... |
2305.00535 | Nearly Optimal Steiner Trees using Graph Neural Network Assisted Monte
Carlo Tree Search | Graph neural networks are useful for learning problems, as well as for
combinatorial and graph problems such as the Subgraph Isomorphism Problem and
the Traveling Salesman Problem. We describe an approach for computing Steiner
Trees by combining a graph neural network and Monte Carlo Tree Search. We first
train a graph... | Reyan Ahmed, Mithun Ghosh, Kwang-Sung Jun, Stephen Kobourov | 2023-04-30T17:15:38Z | http://arxiv.org/abs/2305.00535v1 | # Nearly Optimal Steiner Trees using Graph Neural Network Assisted Monte Carlo Tree Search
###### Abstract
Graph neural networks are useful for learning problems, as well as for combinatorial and graph problems such as the Subgraph Isomorphism Problem and the Traveling Salesman Problem. We describe an approach for co... |
2307.16506 | Explainable Equivariant Neural Networks for Particle Physics: PELICAN | PELICAN is a novel permutation equivariant and Lorentz invariant or covariant
aggregator network designed to overcome common limitations found in
architectures applied to particle physics problems. Compared to many approaches
that use non-specialized architectures that neglect underlying physics
principles and require ... | Alexander Bogatskiy, Timothy Hoffman, David W. Miller, Jan T. Offermann, Xiaoyang Liu | 2023-07-31T09:08:40Z | http://arxiv.org/abs/2307.16506v4 | # Explainable Equivariant Neural Networks for Particle Physics: PELICAN
###### Abstract
We present a comprehensive study of the PELICAN machine learning algorithm architecture in the context of both tagging (classification) and reconstructing (regression) Lorentz-boosted top quarks, including the difficult task of sp... |
2304.00146 | On the Relationships between Graph Neural Networks for the Simulation of
Physical Systems and Classical Numerical Methods | Recent developments in Machine Learning approaches for modelling physical
systems have begun to mirror the past development of numerical methods in the
computational sciences. In this survey, we begin by providing an example of
this with the parallels between the development trajectories of graph neural
network acceler... | Artur P. Toshev, Ludger Paehler, Andrea Panizza, Nikolaus A. Adams | 2023-03-31T21:51:00Z | http://arxiv.org/abs/2304.00146v1 | On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods
###### Abstract
Recent developments in Machine Learning approaches for modelling physical systems have begun to mirror the past development of numerical methods in the computational sciences. In t... |
2302.14726 | Spiking Neural Network Nonlinear Demapping on Neuromorphic Hardware for
IM/DD Optical Communication | Neuromorphic computing implementing spiking neural networks (SNN) is a
promising technology for reducing the footprint of optical transceivers, as
required by the fast-paced growth of data center traffic. In this work, an SNN
nonlinear demapper is designed and evaluated on a simulated
intensity-modulation direct-detect... | Elias Arnold, Georg Böcherer, Florian Strasser, Eric Müller, Philipp Spilger, Sebastian Billaudelle, Johannes Weis, Johannes Schemmel, Stefano Calabrò, Maxim Kuschnerov | 2023-02-28T16:33:39Z | http://arxiv.org/abs/2302.14726v1 | # Spiking Neural Network Nonlinear Demapping on Neuromorphic Hardware for IM/DD Optical Communication
###### Abstract
Neuromorphic computing implementing spiking neural networks (SNN) is a promising technology for reducing the footprint of optical transceivers, as required by the fast-paced growth of data center traf... |
2309.15762 | Rapid Network Adaptation: Learning to Adapt Neural Networks Using
Test-Time Feedback | We propose a method for adapting neural networks to distribution shifts at
test-time. In contrast to training-time robustness mechanisms that attempt to
anticipate and counter the shift, we create a closed-loop system and make use
of a test-time feedback signal to adapt a network on the fly. We show that this
loop can ... | Teresa Yeo, Oğuzhan Fatih Kar, Zahra Sodagar, Amir Zamir | 2023-09-27T16:20:39Z | http://arxiv.org/abs/2309.15762v1 | # Rapid Network Adaptation:
###### Abstract
We propose a method for adapting neural networks to distribution shifts at test-time. In contrast to **training-time** robustness mechanisms that attempt to **anticipate** and counter the shift, we create a **closed-loop** system and make use of a **test-time** feedback sig... |
2309.04860 | Approximation Results for Gradient Descent trained Neural Networks | The paper contains approximation guarantees for neural networks that are
trained with gradient flow, with error measured in the continuous
$L_2(\mathbb{S}^{d-1})$-norm on the $d$-dimensional unit sphere and targets
that are Sobolev smooth. The networks are fully connected of constant depth and
increasing width. Althoug... | G. Welper | 2023-09-09T18:47:55Z | http://arxiv.org/abs/2309.04860v1 | # Approximation Results for Gradient Descent trained Neural Networks
###### Abstract
The paper contains approximation guarantees for neural networks that are trained with gradient flow, with error measured in the continuous \(L_{2}(\mathbb{S}^{d-1})\)-norm on the \(d\)-dimensional unit sphere and targets that are Sob... |
2309.09203 | Using Artificial Neural Networks to Determine Ontologies Most Relevant
to Scientific Texts | This paper provides an insight into the possibility of how to find ontologies
most relevant to scientific texts using artificial neural networks. The basic
idea of the presented approach is to select a representative paragraph from a
source text file, embed it to a vector space by a pre-trained fine-tuned
transformer, ... | Lukáš Korel, Alexander S. Behr, Norbert Kockmann, Martin Holeňa | 2023-09-17T08:08:50Z | http://arxiv.org/abs/2309.09203v1 | # Using Artificial Neural Networks to Determine Ontologies Most Relevant to Scientific Texts
###### Abstract
This paper provides an insight into the possibility of how to find ontologies most relevant to scientific texts using artificial neural networks. The basic idea of the presented approach is to select a represe... |
2309.04733 | A Spatiotemporal Deep Neural Network for Fine-Grained Multi-Horizon Wind
Prediction | The prediction of wind in terms of both wind speed and direction, which has a
crucial impact on many real-world applications like aviation and wind power
generation, is extremely challenging due to the high stochasticity and
complicated correlation in the weather data. Existing methods typically focus
on a sub-set of i... | Fanling Huang, Yangdong Deng | 2023-09-09T09:36:28Z | http://arxiv.org/abs/2309.04733v1 | # A Spatiotemporal Deep Neural Network for Fine-Grained Multi-Horizon Wind Prediction
###### Abstract
The prediction of wind in terms of both wind speed and direction, which has a crucial impact on many real-world applications like aviation and wind power generation, is extremely challenging due to the high stochasti... |
2302.14685 | DART: Diversify-Aggregate-Repeat Training Improves Generalization of
Neural Networks | Generalization of neural networks is crucial for deploying them safely in the
real world. Common training strategies to improve generalization involve the
use of data augmentations, ensembling and model averaging. In this work, we
first establish a surprisingly simple but strong benchmark for generalization
which utili... | Samyak Jain, Sravanti Addepalli, Pawan Sahu, Priyam Dey, R. Venkatesh Babu | 2023-02-28T15:54:47Z | http://arxiv.org/abs/2302.14685v2 | # DART: Diversify-Aggregate-Repeat Training
###### Abstract
Generalization of Neural Networks is crucial for deploying them safely in the real world. Common training strategies to improve generalization involve the use of data augmentations, ensembling and model averaging. In this work, we first establish a surprisin... |
2309.10975 | SPFQ: A Stochastic Algorithm and Its Error Analysis for Neural Network
Quantization | Quantization is a widely used compression method that effectively reduces
redundancies in over-parameterized neural networks. However, existing
quantization techniques for deep neural networks often lack a comprehensive
error analysis due to the presence of non-convex loss functions and nonlinear
activations. In this p... | Jinjie Zhang, Rayan Saab | 2023-09-20T00:35:16Z | http://arxiv.org/abs/2309.10975v1 | # SPFO: A Stochastic Algorithm and its Error Analysis
###### Abstract.
Quantization is a widely used compression method that effectively reduces redundancies in over-parameterized neural networks. However, existing quantization techniques for deep neural networks often lack a comprehensive error analysis due to the p... |
2309.14050 | NNgTL: Neural Network Guided Optimal Temporal Logic Task Planning for
Mobile Robots | In this work, we investigate task planning for mobile robots under linear
temporal logic (LTL) specifications. This problem is particularly challenging
when robots navigate in continuous workspaces due to the high computational
complexity involved. Sampling-based methods have emerged as a promising avenue
for addressin... | Ruijia Liu, Shaoyuan Li, Xiang Yin | 2023-09-25T11:24:40Z | http://arxiv.org/abs/2309.14050v2 | # NNgTL: Neural Network Guided Optimal Temporal Logic
###### Abstract
In this work, we investigate task planning for mobile robots under linear temporal logic (LTL) specifications. This problem is particularly challenging when robots navigate in continuous workspaces due to the high computational complexity involved.... |
2303.18157 | MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic
Engineering | Current trends in networking propose the use of Machine Learning (ML) for a
wide variety of network optimization tasks. As such, many efforts have been
made to produce ML-based solutions for Traffic Engineering (TE), which is a
fundamental problem in ISP networks. Nowadays, state-of-the-art TE optimizers
rely on tradit... | Guillermo Bernárdez, José Suárez-Varela, Albert López, Xiang Shi, Shihan Xiao, Xiangle Cheng, Pere Barlet-Ros, Albert Cabellos-Aparicio | 2023-03-31T15:47:49Z | http://arxiv.org/abs/2303.18157v1 | # MAGNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering
###### Abstract
Current trends in networking propose the use of Machine Learning (ML) for a wide variety of network optimization tasks. As such, many efforts have been made to produce ML-based solutions for Traffic Engineering (TE), wh... |
2309.14722 | Physics-informed neural network to augment experimental data: an
application to stratified flows | We develop a physics-informed neural network (PINN) to significantly augment
state-of-the-art experimental data and apply it to stratified flows. The PINN
is a fully-connected deep neural network fed with time-resolved,
three-component velocity fields and density fields measured simultaneously in
three dimensions at $R... | Lu Zhu, Xianyang Jiang, Adrien Lefauve, Rich R. Kerswell, P. F. Linden | 2023-09-26T07:29:42Z | http://arxiv.org/abs/2309.14722v1 | # Physics-informed neural network to augment experimental data: an application to stratified flows
###### Abstract
We develop a physics-informed neural network (PINN) to significantly augment state-of-the-art experimental data and apply it to stratified flows. The PINN is a fully-connected deep neural network fed wit... |
2309.05613 | Learning the Geodesic Embedding with Graph Neural Networks | We present GeGnn, a learning-based method for computing the approximate
geodesic distance between two arbitrary points on discrete polyhedra surfaces
with constant time complexity after fast precomputation. Previous relevant
methods either focus on computing the geodesic distance between a single source
and all destina... | Bo Pang, Zhongtian Zheng, Guoping Wang, Peng-Shuai Wang | 2023-09-11T16:54:34Z | http://arxiv.org/abs/2309.05613v2 | # Learning the Geodesic Embedding with Graph Neural Networks
###### Abstract.
We present GrGNN, a learning-based method for computing the approximate geodesic distance between two arbitrary points on discrete polyhedra surfaces with constant time complexity after fast precomputation. Previous relevant methods either ... |
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