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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2309.15803 | ANNCRIPS: Artificial Neural Networks for Cancer Research In Prediction &
Survival | Prostate cancer is a prevalent malignancy among men aged 50 and older.
Current diagnostic methods primarily rely on blood tests, PSA:Prostate-Specific
Antigen levels, and Digital Rectal Examinations (DRE). However, these methods
suffer from a significant rate of false positive results. This study focuses on
the develop... | Amit Mathapati | 2023-09-26T08:11:35Z | http://arxiv.org/abs/2309.15803v1 | # A.N.N.C.R.I.P.S - Artificial Neural Networks for Cancer Research In Prediction & Survival
###### Abstract
Prostate cancer stands as the most frequently diagnosed cancer among men aged 50 and older. Contemporary diagnostic and screening procedures predominantly rely on blood tests to assess prostate-specific antigen... |
2307.16695 | A theory of data variability in Neural Network Bayesian inference | Bayesian inference and kernel methods are well established in machine
learning. The neural network Gaussian process in particular provides a concept
to investigate neural networks in the limit of infinitely wide hidden layers by
using kernel and inference methods. Here we build upon this limit and provide a
field-theor... | Javed Lindner, David Dahmen, Michael Krämer, Moritz Helias | 2023-07-31T14:11:32Z | http://arxiv.org/abs/2307.16695v2 | # A theory of data variability in Neural Network Bayesian inference
###### Abstract
Bayesian inference and kernel methods are well established in machine learning. The neural network Gaussian process in particular provides a concept to investigate neural networks in the limit of infinitely wide hidden layers by using... |
2309.05345 | Empirical study on the efficiency of Spiking Neural Networks with axonal
delays, and algorithm-hardware benchmarking | The role of axonal synaptic delays in the efficacy and performance of
artificial neural networks has been largely unexplored. In step-based
analog-valued neural network models (ANNs), the concept is almost absent. In
their spiking neuroscience-inspired counterparts, there is hardly a systematic
account of their effects... | Alberto Patiño-Saucedo, Amirreza Yousefzadeh, Guangzhi Tang, Federico Corradi, Bernabé Linares-Barranco, Manolis Sifalakis | 2023-09-11T09:45:11Z | http://arxiv.org/abs/2309.05345v1 | Empirical study on the efficiency of Spiking Neural Networks with axonal delays, and algorithm-hardware benchmarking
###### Abstract
The role of axonal synaptic delays in the efficacy and performance of artificial neural networks has been largely unexplored. In step-based analog-valued neural network models (ANNs), t... |
2309.06710 | Crystal structure prediction using neural network potential and
age-fitness Pareto genetic algorithm | While crystal structure prediction (CSP) remains a longstanding challenge, we
introduce ParetoCSP, a novel algorithm for CSP, which combines a
multi-objective genetic algorithm (MOGA) with a neural network inter-atomic
potential (IAP) model to find energetically optimal crystal structures given
chemical compositions. W... | Sadman Sadeed Omee, Lai Wei, Jianjun Hu | 2023-09-13T04:17:28Z | http://arxiv.org/abs/2309.06710v1 | Crystal structure prediction using neural network potential and age-fitness Pareto genetic algorithm +
###### Abstract
While crystal structure prediction (CSP) remains a longstanding challenge, we introduce ParetoCSP, a novel algorithm for CSP, which combines a multi-objective genetic algorithm (MOGA) with a neural n... |
2306.07937 | Gibbs-Duhem-Informed Neural Networks for Binary Activity Coefficient
Prediction | We propose Gibbs-Duhem-informed neural networks for the prediction of binary
activity coefficients at varying compositions. That is, we include the
Gibbs-Duhem equation explicitly in the loss function for training neural
networks, which is straightforward in standard machine learning (ML) frameworks
enabling automatic ... | Jan G. Rittig, Kobi C. Felton, Alexei A. Lapkin, Alexander Mitsos | 2023-05-31T07:36:45Z | http://arxiv.org/abs/2306.07937v2 | **Gibbs-Duhem-Informed Neural Networks**
## Abstract
We propose Gibbs-Duhem-informed neural networks for the prediction of binary activity coefficients at varying compositions. That is, we include the Gibbs-Duhem equation explicitly in the loss function for training neural networks, which is straightforward in standa... |
2309.10370 | Geometric structure of shallow neural networks and constructive
${\mathcal L}^2$ cost minimization | In this paper, we approach the problem of cost (loss) minimization in
underparametrized shallow neural networks through the explicit construction of
upper bounds, without any use of gradient descent. A key focus is on
elucidating the geometric structure of approximate and precise minimizers. We
consider shallow neural ... | Thomas Chen, Patricia Muñoz Ewald | 2023-09-19T07:12:41Z | http://arxiv.org/abs/2309.10370v2 | Geometric structure of shallow neural networks and constructive \(\mathcal{L}^{2}\) cost minimization
###### Abstract.
In this paper, we provide a geometric interpretation of the structure of shallow neural networks, characterized by one hidden layer, a ramp activation function, an \(\mathcal{L}^{2}\) Schatten class ... |
2309.13679 | Neural Network-PSO-based Velocity Control Algorithm for Landing UAVs on
a Boat | Precise landing of Unmanned Aerial Vehicles (UAVs) onto moving platforms like
Autonomous Surface Vehicles (ASVs) is both important and challenging,
especially in GPS-denied environments, for collaborative navigation of
heterogeneous vehicles. UAVs need to land within a confined space onboard ASV
to get energy replenish... | Li-Fan Wu, Zihan Wang, Mo Rastgaar, Nina Mahmoudian | 2023-09-24T16:05:31Z | http://arxiv.org/abs/2309.13679v2 | # Neural Network-PSO-based Velocity Control Algorithm
###### Abstract
Precise landing of Unmanned Aerial Vehicles (UAVs) onto moving platforms like Autonomous Surface Vehicles (ASVs) is both important and challenging, especially in GPS-denied environments, for collaborative navigation of heterogeneous vehicles. UAVs ... |
2309.16729 | SimPINNs: Simulation-Driven Physics-Informed Neural Networks for
Enhanced Performance in Nonlinear Inverse Problems | This paper introduces a novel approach to solve inverse problems by
leveraging deep learning techniques. The objective is to infer unknown
parameters that govern a physical system based on observed data. We focus on
scenarios where the underlying forward model demonstrates pronounced nonlinear
behaviour, and where the ... | Sidney Besnard, Frédéric Jurie, Jalal M. Fadili | 2023-09-27T06:34:55Z | http://arxiv.org/abs/2309.16729v1 | Simpinn's: Simulation-Driven Physics-Informed Neural Networks for Enhanced Performance in Nonlinear Inverse Problems
###### Abstract
This paper introduces a novel approach to solve inverse problems by leveraging deep learning techniques. The objective is to infer unknown parameters that govern a physical system based... |
2309.11046 | Heterogeneous Entity Matching with Complex Attribute Associations using
BERT and Neural Networks | Across various domains, data from different sources such as Baidu Baike and
Wikipedia often manifest in distinct forms. Current entity matching
methodologies predominantly focus on homogeneous data, characterized by
attributes that share the same structure and concise attribute values. However,
this orientation poses c... | Shitao Wang, Jiamin Lu | 2023-09-20T03:49:57Z | http://arxiv.org/abs/2309.11046v1 | # Heterogeneous Entity Matching with Complex Attribute Associations using BERT and Neural Networks
###### Abstract
Across various domains, data from different sources such as Baidu Baike and Wikipedia often manifest in distinct forms. Current entity matching methodologies predominantly focus on homogeneous data, char... |
2309.17437 | Learning Decentralized Flocking Controllers with Spatio-Temporal Graph
Neural Network | Recently a line of researches has delved the use of graph neural networks
(GNNs) for decentralized control in swarm robotics. However, it has been
observed that relying solely on the states of immediate neighbors is
insufficient to imitate a centralized control policy. To address this
limitation, prior studies proposed... | Siji Chen, Yanshen Sun, Peihan Li, Lifeng Zhou, Chang-Tien Lu | 2023-09-29T17:50:57Z | http://arxiv.org/abs/2309.17437v2 | # Learning Decentralized Flocking Controllers with
###### Abstract
Recently a line of researches has delved the use of graph neural networks (GNNs) for decentralized control in swarm robotics. However, it has been observed that relying solely on the states of immediate neighbors is insufficient to imitate a centraliz... |
2301.13715 | Physics-constrained 3D Convolutional Neural Networks for Electrodynamics | We present a physics-constrained neural network (PCNN) approach to solving
Maxwell's equations for the electromagnetic fields of intense relativistic
charged particle beams. We create a 3D convolutional PCNN to map time-varying
current and charge densities J(r,t) and p(r,t) to vector and scalar potentials
A(r,t) and V(... | Alexander Scheinker, Reeju Pokharel | 2023-01-31T15:51:28Z | http://arxiv.org/abs/2301.13715v1 | ## Physics-constrained 3D Convolutional Neural Networks for Electrodynamics
## Abstract
We present a physics-constrained neural network (PCNN) approach to solving Maxwell's equations for the electromagnetic fields of intense relativistic charged particle beams. We create a 3D convolutional PCNN to map time-varying cu... |
2309.17345 | Physics-Informed Neural Network for the Transient Diffusivity Equation
in Reservoir Engineering | Physics-Informed machine learning models have recently emerged with some
interesting and unique features that can be applied to reservoir engineering.
In particular, physics-informed neural networks (PINN) leverage the fact that
neural networks are a type of universal function approximators that can embed
the knowledge... | Daniel Badawi, Eduardo Gildin | 2023-09-29T15:52:04Z | http://arxiv.org/abs/2309.17345v3 | # Physics-Informed Neural Network for the Transient Diffusivity Equation in Reservoir Engineering
###### Abstract
Physics-Informed machine learning models have recently emerged with some interesting and unique features that can be applied to reservoir engineering. In particular, physics-informed neural networks (PINN... |
2308.16372 | Artificial to Spiking Neural Networks Conversion for Scientific Machine
Learning | We introduce a method to convert Physics-Informed Neural Networks (PINNs),
commonly used in scientific machine learning, to Spiking Neural Networks
(SNNs), which are expected to have higher energy efficiency compared to
traditional Artificial Neural Networks (ANNs). We first extend the calibration
technique of SNNs to ... | Qian Zhang, Chenxi Wu, Adar Kahana, Youngeun Kim, Yuhang Li, George Em Karniadakis, Priyadarshini Panda | 2023-08-31T00:21:27Z | http://arxiv.org/abs/2308.16372v1 | # Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning +
###### Abstract
We introduce a method to convert Physics-Informed Neural Networks (PINNs), commonly used in scientific machine learning, to Spiking Neural Networks (SNNs), which are expected to have higher energy efficiency compared ... |
2303.00105 | Scalability and Sample Efficiency Analysis of Graph Neural Networks for
Power System State Estimation | Data-driven state estimation (SE) is becoming increasingly important in
modern power systems, as it allows for more efficient analysis of system
behaviour using real-time measurement data. This paper thoroughly evaluates a
phasor measurement unit-only state estimator based on graph neural networks
(GNNs) applied over f... | Ognjen Kundacina, Gorana Gojic, Mirsad Cosovic, Dragisa Miskovic, Dejan Vukobratovic | 2023-02-28T22:09:12Z | http://arxiv.org/abs/2303.00105v2 | Scalability and Sample Efficiency Analysis of Graph Neural Networks for Power System State Estimation
###### Abstract
Data-driven state estimation (SE) is becoming increasingly important in modern power systems, as it allows for more efficient analysis of system behaviour using real-time measurement data. This paper ... |
2301.13714 | Recursive Neural Networks with Bottlenecks Diagnose
(Non-)Compositionality | A recent line of work in NLP focuses on the (dis)ability of models to
generalise compositionally for artificial languages. However, when considering
natural language tasks, the data involved is not strictly, or locally,
compositional. Quantifying the compositionality of data is a challenging task,
which has been invest... | Verna Dankers, Ivan Titov | 2023-01-31T15:46:39Z | http://arxiv.org/abs/2301.13714v1 | # Recursive Neural Networks with Bottlenecks Diagnose
###### Abstract
A recent line of work in NLP focuses on the (dis)ability of models to generalise compositionally for artificial languages. However, when considering natural language tasks, the data involved is not strictly, or _locally_, compositional. Quantifying... |
2308.16429 | Solving Poisson Problems in Polygonal Domains with Singularity Enriched
Physics Informed Neural Networks | Physics-Informed Neural Networks (PINNs) are a powerful class of numerical
solvers for partial differential equations, employing deep neural networks with
successful applications across a diverse set of problems. However, their
effectiveness is somewhat diminished when addressing issues involving
singularities, such as... | Tianhao Hu, Bangti Jin, Zhi Zhou | 2023-08-31T03:35:12Z | http://arxiv.org/abs/2308.16429v2 | Solving Poisson Problems in Polygonal Domains with Singularity Enriched Physics Informed Neural Networks+
###### Abstract
Physics informed neural networks (PINNs) represent a very powerful class of numerical solvers for partial differential equations using deep neural networks, and have been successfully applied to m... |
2305.19725 | Direct Learning-Based Deep Spiking Neural Networks: A Review | The spiking neural network (SNN), as a promising brain-inspired computational
model with binary spike information transmission mechanism, rich
spatially-temporal dynamics, and event-driven characteristics, has received
extensive attention. However, its intricately discontinuous spike mechanism
brings difficulty to the ... | Yufei Guo, Xuhui Huang, Zhe Ma | 2023-05-31T10:32:16Z | http://arxiv.org/abs/2305.19725v4 | # Direct Learning-Based Deep Spiking Neural Networks: A Review
###### Abstract
The spiking neural network (SNN), as a promising brain-inspired computational model with binary spike information transmission mechanism, rich spatially-temporal dynamics, and event-driven characteristics, has received extensive attention.... |
2309.17032 | Refined Kolmogorov Complexity of Analog, Evolving and Stochastic
Recurrent Neural Networks | We provide a refined characterization of the super-Turing computational power
of analog, evolving, and stochastic neural networks based on the Kolmogorov
complexity of their real weights, evolving weights, and real probabilities,
respectively. First, we retrieve an infinite hierarchy of classes of analog
networks defin... | Jérémie Cabessa, Yann Strozecki | 2023-09-29T07:38:50Z | http://arxiv.org/abs/2309.17032v1 | # Refined Kolmogorov Complexity of Analog, Evolving and Stochastic Recurrent Neural Networks
###### Abstract
We provide a refined characterization of the super-Turing computational power of analog, evolving, and stochastic neural networks based on the Kolmogorov complexity of their real weights, evolving weights, and... |
2309.15631 | Design and Optimization of Residual Neural Network Accelerators for
Low-Power FPGAs Using High-Level Synthesis | Residual neural networks are widely used in computer vision tasks. They
enable the construction of deeper and more accurate models by mitigating the
vanishing gradient problem. Their main innovation is the residual block which
allows the output of one layer to bypass one or more intermediate layers and be
added to the ... | Filippo Minnella, Teodoro Urso, Mihai T. Lazarescu, Luciano Lavagno | 2023-09-27T13:02:14Z | http://arxiv.org/abs/2309.15631v2 | Design and Optimization of Residual Neural Network Accelerators for Low-Power FPGAs Using High-Level Synthesis
###### Abstract
Residual neural networks (ResNets) are widely used in computer vision tasks. They enable the construction of deeper and more accurate models by mitigating the vanishing gradient problem. Thei... |
2309.08853 | Computational Enhancement for Day-Ahead Energy Scheduling with Sparse
Neural Network-based Battery Degradation Model | Battery energy storage systems (BESS) play a pivotal role in future power
systems as they contribute to achiev-ing the net-zero carbon emission
objectives. The BESS systems, predominantly employing lithium-ion batteries,
have been exten-sively deployed. The degradation of these batteries
significantly affects system ef... | Cunzhi Zhao, Xingpeng Li | 2023-09-16T03:11:05Z | http://arxiv.org/abs/2309.08853v1 | Computational Enhancement for Day-Ahead Energy Scheduling with Sparse Neural Network-based Battery Degradation Model
###### Abstract
Battery energy storage systems (BESS) play a pivotal role in future power systems as they contribute to achieving the net-zero carbon emission objectives. The BESS systems, predominantl... |
2306.17648 | Enhancing training of physics-informed neural networks using
domain-decomposition based preconditioning strategies | We propose to enhance the training of physics-informed neural networks
(PINNs). To this aim, we introduce nonlinear additive and multiplicative
preconditioning strategies for the widely used L-BFGS optimizer. The nonlinear
preconditioners are constructed by utilizing the Schwarz domain-decomposition
framework, where th... | Alena Kopaničáková, Hardik Kothari, George Em Karniadakis, Rolf Krause | 2023-06-30T13:35:09Z | http://arxiv.org/abs/2306.17648v2 | Enhancing training of physics-informed neural networks using domain-decomposition based preconditioning strategies +
###### Abstract
We propose to enhance the training of physics-informed neural networks (PINNs). To this aim, we introduce nonlinear additive and multiplicative preconditioning strategies for the widely... |
2310.20299 | Verification of Neural Networks Local Differential Classification
Privacy | Neural networks are susceptible to privacy attacks. To date, no verifier can
reason about the privacy of individuals participating in the training set. We
propose a new privacy property, called local differential classification
privacy (LDCP), extending local robustness to a differential privacy setting
suitable for bl... | Roie Reshef, Anan Kabaha, Olga Seleznova, Dana Drachsler-Cohen | 2023-10-31T09:11:12Z | http://arxiv.org/abs/2310.20299v1 | # Verification of Neural Networks' Local Differential Classification Privacy
###### Abstract
Neural networks are susceptible to privacy attacks. To date, no verifier can reason about the privacy of individuals participating in the training set. We propose a new privacy property, called _local differential classificat... |
2309.03251 | Temporal Inductive Path Neural Network for Temporal Knowledge Graph
Reasoning | Temporal Knowledge Graph (TKG) is an extension of traditional Knowledge Graph
(KG) that incorporates the dimension of time. Reasoning on TKGs is a crucial
task that aims to predict future facts based on historical occurrences. The key
challenge lies in uncovering structural dependencies within historical
subgraphs and ... | Hao Dong, Pengyang Wang, Meng Xiao, Zhiyuan Ning, Pengfei Wang, Yuanchun Zhou | 2023-09-06T17:37:40Z | http://arxiv.org/abs/2309.03251v3 | # Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoning
###### Abstract.
Temporal Knowledge Graph (TKG) is an extension of traditional Knowledge Graph (KG) that incorporates the dimension of time. Reasoning on TKGs is a crucial task that aims to predict future facts based on historical occurre... |
2309.03279 | Let Quantum Neural Networks Choose Their Own Frequencies | Parameterized quantum circuits as machine learning models are typically well
described by their representation as a partial Fourier series of the input
features, with frequencies uniquely determined by the feature map's generator
Hamiltonians. Ordinarily, these data-encoding generators are chosen in advance,
fixing the... | Ben Jaderberg, Antonio A. Gentile, Youssef Achari Berrada, Elvira Shishenina, Vincent E. Elfving | 2023-09-06T18:00:07Z | http://arxiv.org/abs/2309.03279v2 | # Let Quantum Neural Networks Choose Their Own Frequencies
###### Abstract
Parameterized quantum circuits as machine learning models are typically well described by their representation as a partial Fourier series of the input features, with frequencies uniquely determined by the feature map's generator Hamiltonians.... |
2302.14311 | Towards Memory- and Time-Efficient Backpropagation for Training Spiking
Neural Networks | Spiking Neural Networks (SNNs) are promising energy-efficient models for
neuromorphic computing. For training the non-differentiable SNN models, the
backpropagation through time (BPTT) with surrogate gradients (SG) method has
achieved high performance. However, this method suffers from considerable
memory cost and trai... | Qingyan Meng, Mingqing Xiao, Shen Yan, Yisen Wang, Zhouchen Lin, Zhi-Quan Luo | 2023-02-28T05:01:01Z | http://arxiv.org/abs/2302.14311v3 | # Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks
###### Abstract
Spiking Neural Networks (SNNs) are promising energy-efficient models for neuromorphic computing. For training the non-differentiable SNN models, the backpropagation through time (BPTT) with surrogate gradients (S... |
2306.00134 | A Quantum Optical Recurrent Neural Network for Online Processing of
Quantum Times Series | Over the last decade, researchers have studied the synergy between quantum
computing (QC) and classical machine learning (ML) algorithms. However,
measurements in QC often disturb or destroy quantum states, requiring multiple
repetitions of data processing to estimate observable values. In particular,
this prevents onl... | Robbe De Prins, Guy Van der Sande, Peter Bienstman | 2023-05-31T19:19:25Z | http://arxiv.org/abs/2306.00134v1 | # A Quantum Optical Recurrent Neural Network
###### Abstract
Over the last decade, researchers have studied the synergy between quantum computing (QC) and classical machine learning (ML) algorithms. However, measurements in QC often disturb or destroy quantum states, requiring multiple repetitions of data processing ... |
2301.13845 | Interpreting Robustness Proofs of Deep Neural Networks | In recent years numerous methods have been developed to formally verify the
robustness of deep neural networks (DNNs). Though the proposed techniques are
effective in providing mathematical guarantees about the DNNs behavior, it is
not clear whether the proofs generated by these methods are
human-interpretable. In this... | Debangshu Banerjee, Avaljot Singh, Gagandeep Singh | 2023-01-31T18:41:28Z | http://arxiv.org/abs/2301.13845v1 | # Interpreting Robustness Proofs of Deep Neural Networks
###### Abstract
In recent years numerous methods have been developed to formally verify the robustness of deep neural networks (DNNs). Though the proposed techniques are effective in providing mathematical guarantees about the DNNs behavior, it is not clear whe... |
2309.07948 | Complex-Valued Neural Networks for Data-Driven Signal Processing and
Signal Understanding | Complex-valued neural networks have emerged boasting superior modeling
performance for many tasks across the signal processing, sensing, and
communications arenas. However, developing complex-valued models currently
demands development of basic deep learning operations, such as linear or
convolution layers, as modern d... | Josiah W. Smith | 2023-09-14T16:55:28Z | http://arxiv.org/abs/2309.07948v1 | # Complex-Valued Neural Networks for Data-Driven Signal Processing and Signal Understanding
###### Abstract
Complex-valued neural networks have emerged boasting superior modeling performance for many tasks across the signal processing, sensing, and communications arenas. However, developing complex-valued models curr... |
2301.00636 | New Designed Loss Functions to Solve Ordinary Differential Equations
with Artificial Neural Network | This paper investigates the use of artificial neural networks (ANNs) to solve
differential equations (DEs) and the construction of the loss function which
meets both differential equation and its initial/boundary condition of a
certain DE. In section 2, the loss function is generalized to $n^\text{th}$
order ordinary d... | Xiao Xiong | 2022-12-29T11:26:31Z | http://arxiv.org/abs/2301.00636v1 | # New Designed Loss Functions to Solve Ordinary Differential Equations with Artificial Neural Network
###### Abstract
This paper investigates the use of artificial neural networks (ANNs) to solve differential equations (DEs) and the construction of the loss function which meets both differential equation and its init... |
2309.08474 | VulnSense: Efficient Vulnerability Detection in Ethereum Smart Contracts
by Multimodal Learning with Graph Neural Network and Language Model | This paper presents VulnSense framework, a comprehensive approach to
efficiently detect vulnerabilities in Ethereum smart contracts using a
multimodal learning approach on graph-based and natural language processing
(NLP) models. Our proposed framework combines three types of features from
smart contracts comprising so... | Phan The Duy, Nghi Hoang Khoa, Nguyen Huu Quyen, Le Cong Trinh, Vu Trung Kien, Trinh Minh Hoang, Van-Hau Pham | 2023-09-15T15:26:44Z | http://arxiv.org/abs/2309.08474v1 | VulnSense: Efficient Vulnerability Detection in Ethereum Smart Contracts by Multimodal Learning with Graph Neural Network and Language Model
###### Abstract
This paper presents VulnSense framework, a comprehensive approach to efficiently detect vulnerabilities in Ethereum smart contracts using a multimodal learning a... |
2309.09018 | Real-time optimal control for attitude-constrained solar sailcrafts via
neural networks | This work is devoted to generating optimal guidance commands in real time for
attitude-constrained solar sailcrafts in coplanar circular-to-circular
interplanetary transfers. Firstly, a nonlinear optimal control problem is
established, and necessary conditions for optimality are derived by the
Pontryagin's Minimum Prin... | Kun Wang, Fangmin Lu, Zheng Chen, Jun Li | 2023-09-16T15:12:59Z | http://arxiv.org/abs/2309.09018v2 | # Real-time optimal control for attitude-constrained solar sailcrafts via neural networks
###### Abstract
This work is devoted to generating optimal guidance commands in real time for attitude-constrained solar sailcrafts in coplanar circular-to-circular interplanetary transfers. Firstly, a nonlinear optimal control ... |
2309.10987 | SpikingNeRF: Making Bio-inspired Neural Networks See through the Real
World | Spiking neural networks (SNNs) have been thriving on numerous tasks to
leverage their promising energy efficiency and exploit their potentialities as
biologically plausible intelligence. Meanwhile, the Neural Radiance Fields
(NeRF) render high-quality 3D scenes with massive energy consumption, but few
works delve into ... | Xingting Yao, Qinghao Hu, Tielong Liu, Zitao Mo, Zeyu Zhu, Zhengyang Zhuge, Jian Cheng | 2023-09-20T01:04:57Z | http://arxiv.org/abs/2309.10987v3 | # SpikingNeRF: Making Bio-inspired Neural Networks See through the Real World
###### Abstract
Spiking neural networks (SNNs) have been thriving on numerous tasks to leverage their promising energy efficiency and exploit their potentialities as biologically plausible intelligence. Meanwhile, the Neural Radiance Fields... |
2309.15592 | Pulsar Classification: Comparing Quantum Convolutional Neural Networks
and Quantum Support Vector Machines | Well-known quantum machine learning techniques, namely quantum kernel
assisted support vector machines (QSVMs) and quantum convolutional neural
networks (QCNNs), are applied to the binary classification of pulsars. In this
comparitive study it is illustrated with simulations that both quantum methods
successfully achie... | Donovan Slabbert, Matt Lourens, Francesco Petruccione | 2023-09-27T11:46:57Z | http://arxiv.org/abs/2309.15592v1 | Pulsar Classification: Comparing Quantum Convolutional Neural Networks and Quantum Support Vector Machines
###### Abstract
Well-known quantum machine learning techniques, namely quantum kernel assisted support vector machines (QSVMs) and quantum convolutional neural networks (QCNNs), are applied to the binary classif... |
2309.07815 | Nonlinear model order reduction for problems with microstructure using
mesh informed neural networks | Many applications in computational physics involve approximating problems
with microstructure, characterized by multiple spatial scales in their data.
However, these numerical solutions are often computationally expensive due to
the need to capture fine details at small scales. As a result, simulating such
phenomena be... | Piermario Vitullo, Alessio Colombo, Nicola Rares Franco, Andrea Manzoni, Paolo Zunino | 2023-09-14T16:09:29Z | http://arxiv.org/abs/2309.07815v1 | Nonlinear model order reduction for problems with microstructure using mesh informed neural networks
###### Abstract
Many applications in computational physics involve approximating problems with microstructure, characterized by multiple spatial scales in their data. However, these numerical solutions are often compu... |
2309.16918 | ACGAN-GNNExplainer: Auxiliary Conditional Generative Explainer for Graph
Neural Networks | Graph neural networks (GNNs) have proven their efficacy in a variety of
real-world applications, but their underlying mechanisms remain a mystery. To
address this challenge and enable reliable decision-making, many GNN explainers
have been proposed in recent years. However, these methods often encounter
limitations, in... | Yiqiao Li, Jianlong Zhou, Yifei Dong, Niusha Shafiabady, Fang Chen | 2023-09-29T01:20:28Z | http://arxiv.org/abs/2309.16918v2 | # ACGAN-GNNExplainer: Auxiliary Conditional Generative Explainer for Graph Neural Networks
###### Abstract.
Graph neural networks (GNNs) have proven their efficacy in a variety of real-world applications, but their underlying mechanisms remain a mystery. To address this challenge and enable reliable decision-making, ... |
2309.10164 | Asynchronous Perception-Action-Communication with Graph Neural Networks | Collaboration in large robot swarms to achieve a common global objective is a
challenging problem in large environments due to limited sensing and
communication capabilities. The robots must execute a
Perception-Action-Communication (PAC) loop -- they perceive their local
environment, communicate with other robots, and... | Saurav Agarwal, Alejandro Ribeiro, Vijay Kumar | 2023-09-18T21:20:50Z | http://arxiv.org/abs/2309.10164v1 | # Asynchronous Perception-Action-Communication with Graph Neural Networks
###### Abstract
Collaboration in large robot swarms to achieve a common global objective is a challenging problem in large environments due to limited sensing and communication capabilities. The robots must execute a Perception-Action-Communica... |
2309.10616 | Enhancing quantum state tomography via resource-efficient
attention-based neural networks | Resource-efficient quantum state tomography is one of the key ingredients of
future quantum technologies. In this work, we propose a new tomography protocol
combining standard quantum state reconstruction methods with an attention-based
neural network architecture. We show how the proposed protocol is able to
improve t... | Adriano Macarone Palmieri, Guillem Müller-Rigat, Anubhav Kumar Srivastava, Maciej Lewenstein, Grzegorz Rajchel-Mieldzioć, Marcin Płodzień | 2023-09-19T13:46:21Z | http://arxiv.org/abs/2309.10616v2 | # Enhancing quantum state tomography via resource-efficient attention-based neural networks
###### Abstract
Resource-efficient quantum state tomography is one of the key ingredients of future quantum technologies. In this work, we propose a new tomography protocol combining standard quantum state reconstruction metho... |
2305.19487 | SPGNN-API: A Transferable Graph Neural Network for Attack Paths
Identification and Autonomous Mitigation | Attack paths are the potential chain of malicious activities an attacker
performs to compromise network assets and acquire privileges through exploiting
network vulnerabilities. Attack path analysis helps organizations to identify
new/unknown chains of attack vectors that reach critical assets within the
network, as op... | Houssem Jmal, Firas Ben Hmida, Nardine Basta, Muhammad Ikram, Mohamed Ali Kaafar, Andy Walker | 2023-05-31T01:48:12Z | http://arxiv.org/abs/2305.19487v2 | SPGNN-API: A Transferable Graph Neural Network for Attack Paths Identification and Autonomous Mitigation
###### Abstract
Attack paths are the potential chain of malicious activities an attacker performs to compromise network assets and acquire privileges through exploiting network vulnerabilities. Attack path analysi... |
2306.17844 | The Clock and the Pizza: Two Stories in Mechanistic Explanation of
Neural Networks | Do neural networks, trained on well-understood algorithmic tasks, reliably
rediscover known algorithms for solving those tasks? Several recent studies, on
tasks ranging from group arithmetic to in-context linear regression, have
suggested that the answer is yes. Using modular addition as a prototypical
problem, we show... | Ziqian Zhong, Ziming Liu, Max Tegmark, Jacob Andreas | 2023-06-30T17:59:13Z | http://arxiv.org/abs/2306.17844v2 | # The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
###### Abstract
Do neural networks, trained on well-understood algorithmic tasks, reliably re-discover known algorithms for solving those tasks? Several recent studies, on tasks ranging from group arithmetic to in-context linear regr... |
2309.09025 | Efficient Privacy-Preserving Convolutional Spiking Neural Networks with
FHE | With the rapid development of AI technology, we have witnessed numerous
innovations and conveniences. However, along with these advancements come
privacy threats and risks. Fully Homomorphic Encryption (FHE) emerges as a key
technology for privacy-preserving computation, enabling computations while
maintaining data pri... | Pengbo Li, Huifang Huang, Ting Gao, Jin Guo, Jinqiao Duan | 2023-09-16T15:37:18Z | http://arxiv.org/abs/2309.09025v1 | # Efficient Privacy-Preserving Convolutional Spiking Neural Networks with FHE
###### Abstract
With the rapid development of AI technology, we have witnessed numerous innovations and conveniences. However, along with these advancements come privacy threats and risks. Fully Homomorphic Encryption (FHE) emerges as a key... |
2305.00416 | Quaternion Matrix Completion Using Untrained Quaternion Convolutional
Neural Network for Color Image Inpainting | The use of quaternions as a novel tool for color image representation has
yielded impressive results in color image processing. By considering the color
image as a unified entity rather than separate color space components,
quaternions can effectively exploit the strong correlation among the RGB
channels, leading to en... | Jifei Miao, Kit Ian Kou, Liqiao Yang, Juan Han | 2023-04-30T07:20:22Z | http://arxiv.org/abs/2305.00416v1 | Quaternion Matrix Completion Using Untrained Quaternion Convolutional Neural Network for Color Image Inpainting
###### Abstract
The use of quaternions as a novel tool for color image representation has yielded impressive results in color image processing. By considering the color image as a unified entity rather than... |
2310.20570 | Correlation-pattern-based Continuous-variable Entanglement Detection
through Neural Networks | Entanglement in continuous-variable non-Gaussian states provides
irreplaceable advantages in many quantum information tasks. However, the sheer
amount of information in such states grows exponentially and makes a full
characterization impossible. Here, we develop a neural network that allows us
to use correlation patte... | Xiaoting Gao, Mathieu Isoard, Fengxiao Sun, Carlos E. Lopetegui, Yu Xiang, Valentina Parigi, Qiongyi He, Mattia Walschaers | 2023-10-31T16:00:25Z | http://arxiv.org/abs/2310.20570v1 | # Correlation-pattern-based Continuous-variable Entanglement Detection through Neural Networks
###### Abstract
Entanglement in continuous-variable non-Gaussian states provides irreplaceable advantages in many quantum information tasks. However, the sheer amount of information in such states grows exponentially and ma... |
2309.05863 | The bionic neural network for external simulation of human locomotor
system | Muscle forces and joint kinematics estimated with musculoskeletal (MSK)
modeling techniques offer useful metrics describing movement quality.
Model-based computational MSK models can interpret the dynamic interaction
between the neural drive to muscles, muscle dynamics, body and joint
kinematics, and kinetics. Still, s... | Yue Shi, Shuhao Ma, Yihui Zhao | 2023-09-11T23:02:56Z | http://arxiv.org/abs/2309.05863v1 | # The bionic neural network for external simulation of human locomotor system
###### Abstract
Muscle forces and joint kinematics estimated with musculoskeletal (MSK) modeling techniques offer useful metrics describing movement quality. Model-based computational MSK models can interpret the dynamic interaction between... |
2309.07912 | An Observationally Driven Multifield Approach for Probing the
Circum-Galactic Medium with Convolutional Neural Networks | The circum-galactic medium (CGM) can feasibly be mapped by multiwavelength
surveys covering broad swaths of the sky. With multiple large datasets becoming
available in the near future, we develop a likelihood-free Deep Learning
technique using convolutional neural networks (CNNs) to infer broad-scale
physical propertie... | Naomi Gluck, Benjamin D. Oppenheimer, Daisuke Nagai, Francisco Villaescusa-Navarro, Daniel Anglés-Alcázar | 2023-09-14T17:58:49Z | http://arxiv.org/abs/2309.07912v2 | An Observationally Driven Multifield Approach for Probing the Circum-Galactic Medium with Convolutional Neural Networks
###### Abstract
The circum-galactic medium (CGM) can feasibly be mapped by multiwavelength surveys covering broad swaths of the sky. With multiple large datasets becoming available in the near futur... |
2309.17240 | Data-driven localized waves and parameter discovery in the massive
Thirring model via extended physics-informed neural networks with interface
zones | In this paper, we study data-driven localized wave solutions and parameter
discovery in the massive Thirring (MT) model via the deep learning in the
framework of physics-informed neural networks (PINNs) algorithm. Abundant
data-driven solutions including soliton of bright/dark type, breather and rogue
wave are simulate... | Junchao Chen, Jin Song, Zijian Zhou, Zhenya Yan | 2023-09-29T13:50:32Z | http://arxiv.org/abs/2309.17240v1 | Data-driven localized waves and parameter discovery in the massive Thirring model via extended physics-informed neural networks with interface zones
###### Abstract
In this paper, we study data-driven localized wave solutions and parameter discovery in the massive Thirring (MT) model via the deep learning in the fram... |
2309.10275 | Optimizing Crowd-Aware Multi-Agent Path Finding through Local
Communication with Graph Neural Networks | Multi-Agent Path Finding (MAPF) in crowded environments presents a
challenging problem in motion planning, aiming to find collision-free paths for
all agents in the system. MAPF finds a wide range of applications in various
domains, including aerial swarms, autonomous warehouse robotics, and
self-driving vehicles. Curr... | Phu Pham, Aniket Bera | 2023-09-19T03:02:43Z | http://arxiv.org/abs/2309.10275v3 | # Crowd-Aware Multi-Agent Pathfinding With Boosted Curriculum Reinforcement Learning
###### Abstract
Multi-Agent Path Finding (MAPF) in crowded environments presents a challenging problem in motion planning, aiming to find collision-free paths for all agents in the system. MAPF finds a wide range of applications in v... |
2309.11341 | Article Classification with Graph Neural Networks and Multigraphs | Classifying research output into context-specific label taxonomies is a
challenging and relevant downstream task, given the volume of existing and
newly published articles. We propose a method to enhance the performance of
article classification by enriching simple Graph Neural Network (GNN) pipelines
with multi-graph ... | Khang Ly, Yury Kashnitsky, Savvas Chamezopoulos, Valeria Krzhizhanovskaya | 2023-09-20T14:18:04Z | http://arxiv.org/abs/2309.11341v2 | # Improving Article Classification with Edge-Heterogeneous Graph Neural Networks
###### Abstract
Classifying research output into context-specific label taxonomies is a challenging and relevant downstream task, given the volume of existing and newly published articles. We propose a method to enhance the performance o... |
2309.09240 | High-dimensional manifold of solutions in neural networks: insights from
statistical physics | In these pedagogic notes I review the statistical mechanics approach to
neural networks, focusing on the paradigmatic example of the perceptron
architecture with binary an continuous weights, in the classification setting.
I will review the Gardner's approach based on replica method and the derivation
of the SAT/UNSAT ... | Enrico M. Malatesta | 2023-09-17T11:10:25Z | http://arxiv.org/abs/2309.09240v1 | **High-dimensional manifold of solutions in neural networks:**
## Abstract
**In these pedagogic notes I review the statistical mechanics approach to neural networks, focusing on the paradigmatic example of the perceptron architecture with binary an continuous weights, in the classification setting. I will review the ... |
2310.20148 | Decision-Making for Autonomous Vehicles with Interaction-Aware
Behavioral Prediction and Social-Attention Neural Network | Autonomous vehicles need to accomplish their tasks while interacting with
human drivers in traffic. It is thus crucial to equip autonomous vehicles with
artificial reasoning to better comprehend the intentions of the surrounding
traffic, thereby facilitating the accomplishments of the tasks. In this work,
we propose a ... | Xiao Li, Kaiwen Liu, H. Eric Tseng, Anouck Girard, Ilya Kolmanovsky | 2023-10-31T03:31:09Z | http://arxiv.org/abs/2310.20148v2 | Decision-Making for Autonomous Vehicles with Interaction-Aware Behavioral Prediction and Social-Attention Neural Network
###### Abstract
Autonomous vehicles need to accomplish their tasks while interacting with human drivers in traffic. It is thus crucial to equip autonomous vehicles with artificial reasoning to bett... |
2306.00230 | Predictive Limitations of Physics-Informed Neural Networks in Vortex
Shedding | The recent surge of interest in physics-informed neural network (PINN)
methods has led to a wave of studies that attest to their potential for solving
partial differential equations (PDEs) and predicting the dynamics of physical
systems. However, the predictive limitations of PINNs have not been thoroughly
investigated... | Pi-Yueh Chuang, Lorena A. Barba | 2023-05-31T22:59:52Z | http://arxiv.org/abs/2306.00230v1 | # Predictive Limitations of Physics-Informed Neural Networks in Vortex Shedding
###### Abstract
The recent surge of interest in physics-informed neural network (PINN) methods has led to a wave of studies that attest to their potential for solving partial differential equations (PDEs) and predicting the dynamics of ph... |
2301.13770 | Energy-Conserving Neural Network for Turbulence Closure Modeling | In turbulence modeling, we are concerned with finding closure models that
represent the effect of the subgrid scales on the resolved scales. Recent
approaches gravitate towards machine learning techniques to construct such
models. However, the stability of machine-learned closure models and their
abidance by physical s... | Toby van Gastelen, Wouter Edeling, Benjamin Sanderse | 2023-01-31T17:13:17Z | http://arxiv.org/abs/2301.13770v5 | # Energy-Conserving Neural Network for Turbulence Closure Modeling
###### Abstract
In turbulence modeling, and more particularly in the Large-Eddy Simulation (LES) framework, we are concerned with finding closure models that represent the effect of the unresolved subgrid scales on the resolved scales. Recent approach... |
2309.06577 | Efficient Finite Initialization for Tensorized Neural Networks | We present a novel method for initializing layers of tensorized neural
networks in a way that avoids the explosion of the parameters of the matrix it
emulates. The method is intended for layers with a high number of nodes in
which there is a connection to the input or output of all or most of the nodes,
we cannot or do... | Alejandro Mata Ali, Iñigo Perez Delgado, Marina Ristol Roura, Aitor Moreno Fdez. de Leceta | 2023-09-11T08:05:09Z | http://arxiv.org/abs/2309.06577v3 | # Efficient Finite Initialization for Tensorized Neural Networks
###### Abstract
We present a novel method for initializing layers of tensorized neural networks in a way that avoids the explosion of the parameters of the matrix it emulates. The method is intended for layers with a high number of nodes in which there ... |
2301.00056 | A Bayesian Neural Network Approach to identify Stars and AGNs observed
by XMM Newton | In today's era, a tremendous amount of data is generated by different
observatories and manual classification of data is something which is
practically impossible. Hence, to classify and categorize the objects there are
multiple machine and deep learning techniques used. However, these predictions
are overconfident and... | Sarvesh Gharat, Bhaskar Bose | 2022-12-30T21:29:50Z | http://arxiv.org/abs/2301.00056v1 | # A Bayesian Neural Network Approach to identify Stars and AGNs observed by XMM Newton +
###### Abstract
In today's era, a tremendous amount of data is generated by different observatories and manual classification of data is something which is practically impossible. Hence, to classify and categorize the objects the... |
2309.15244 | Homotopy Relaxation Training Algorithms for Infinite-Width Two-Layer
ReLU Neural Networks | In this paper, we present a novel training approach called the Homotopy
Relaxation Training Algorithm (HRTA), aimed at accelerating the training
process in contrast to traditional methods. Our algorithm incorporates two key
mechanisms: one involves building a homotopy activation function that
seamlessly connects the li... | Yahong Yang, Qipin Chen, Wenrui Hao | 2023-09-26T20:18:09Z | http://arxiv.org/abs/2309.15244v3 | # Homotopy Relaxation Training Algorithms for Infinite-Width Two-Layer ReLU Neural Networks
###### Abstract
In this paper, we present a novel training approach called the Homotopy Relaxation Training Algorithm (HRTA), aimed at accelerating the training process in contrast to traditional methods. Our algorithm incorpo... |
2308.16470 | Domain-adaptive Message Passing Graph Neural Network | Cross-network node classification (CNNC), which aims to classify nodes in a
label-deficient target network by transferring the knowledge from a source
network with abundant labels, draws increasing attention recently. To address
CNNC, we propose a domain-adaptive message passing graph neural network
(DM-GNN), which int... | Xiao Shen, Shirui Pan, Kup-Sze Choi, Xi Zhou | 2023-08-31T05:26:08Z | http://arxiv.org/abs/2308.16470v2 | # Domain-adaptive Message Passing Graph Neural Network
###### Abstract
Cross-network node classification (CNNC), which aims to classify nodes in a label-deficient target network by transferring the knowledge from a source network with abundant labels, draws increasing attention recently. To address CNNC, we propose a... |
2309.12259 | Soft Merging: A Flexible and Robust Soft Model Merging Approach for
Enhanced Neural Network Performance | Stochastic Gradient Descent (SGD), a widely used optimization algorithm in
deep learning, is often limited to converging to local optima due to the
non-convex nature of the problem. Leveraging these local optima to improve
model performance remains a challenging task. Given the inherent complexity of
neural networks, t... | Hao Chen, Yusen Wu, Phuong Nguyen, Chao Liu, Yelena Yesha | 2023-09-21T17:07:31Z | http://arxiv.org/abs/2309.12259v1 | Soft Merging: A Flexible and Robust Soft Model Merging Approach for Enhanced Neural Network Performance
###### Abstract
Stochastic Gradient Descent (SGD), a widely used optimization algorithm in deep learning, is often limited to converging to local optima due to the non-convex nature of the problem. Leveraging these... |
2309.06628 | Epistemic Modeling Uncertainty of Rapid Neural Network Ensembles for
Adaptive Learning | Emulator embedded neural networks, which are a type of physics informed
neural network, leverage multi-fidelity data sources for efficient design
exploration of aerospace engineering systems. Multiple realizations of the
neural network models are trained with different random initializations. The
ensemble of model real... | Atticus Beachy, Harok Bae, Jose Camberos, Ramana Grandhi | 2023-09-12T22:34:34Z | http://arxiv.org/abs/2309.06628v1 | # Epistemic Modeling Uncertainty of Rapid Neural Network Ensembles for Adaptive Learning
###### Abstract
**Emulator embedded neural networks, which are a type of physics informed neural network, leverage multi-fidelity data sources for efficient design exploration of aerospace engineering systems. Multiple realizatio... |
2309.09043 | Forward Invariance in Neural Network Controlled Systems | We present a framework based on interval analysis and monotone systems theory
to certify and search for forward invariant sets in nonlinear systems with
neural network controllers. The framework (i) constructs localized first-order
inclusion functions for the closed-loop system using Jacobian bounds and
existing neural... | Akash Harapanahalli, Saber Jafarpour, Samuel Coogan | 2023-09-16T16:49:19Z | http://arxiv.org/abs/2309.09043v2 | # Forward Invariance in Neural Network Controlled Systems
###### Abstract
We present a framework based on interval analysis and monotone systems theory to certify and search for forward invariant sets in nonlinear systems with neural network controllers. The framework (i) constructs localized first-order inclusion fu... |
2309.12463 | Impact of architecture on robustness and interpretability of
multispectral deep neural networks | Including information from additional spectral bands (e.g., near-infrared)
can improve deep learning model performance for many vision-oriented tasks.
There are many possible ways to incorporate this additional information into a
deep learning model, but the optimal fusion strategy has not yet been
determined and can v... | Charles Godfrey, Elise Bishoff, Myles McKay, Eleanor Byler | 2023-09-21T20:11:01Z | http://arxiv.org/abs/2309.12463v2 | # Impact of architecture on robustness and interpretability of multispectral deep neural networks
###### Abstract
Including information from additional spectral bands (e.g., near-infrared) can improve deep learning model performance for many vision-oriented tasks. There are many possible ways to incorporate this addi... |
2310.20203 | Importance Estimation with Random Gradient for Neural Network Pruning | Global Neuron Importance Estimation is used to prune neural networks for
efficiency reasons. To determine the global importance of each neuron or
convolutional kernel, most of the existing methods either use activation or
gradient information or both, which demands abundant labelled examples. In this
work, we use heuri... | Suman Sapkota, Binod Bhattarai | 2023-10-31T06:00:17Z | http://arxiv.org/abs/2310.20203v1 | # Importance Estimation with Random Gradient for Neural Network Pruning
###### Abstract
Global Neuron Importance Estimation is used to prune neural networks for efficiency reasons. To determine the global importance of each neuron or convolutional kernel, most of the existing methods either use activation or gradient... |
2309.08388 | AONN-2: An adjoint-oriented neural network method for PDE-constrained
shape optimization | Shape optimization has been playing an important role in a large variety of
engineering applications. Existing shape optimization methods are generally
mesh-dependent and therefore encounter challenges due to mesh deformation. To
overcome this limitation, we present a new adjoint-oriented neural network
method, AONN-2,... | Xili Wang, Pengfei Yin, Bo Zhang, Chao Yang | 2023-09-15T13:29:53Z | http://arxiv.org/abs/2309.08388v1 | # AONN-2: An adjoint-oriented neural network method for PDE-constrained shape optimization
###### Abstract
Shape optimization has been playing an important role in a large variety of engineering applications. Existing shape optimization methods are generally mesh-dependent and therefore encounter challenges due to me... |
2309.10510 | Logic Design of Neural Networks for High-Throughput and Low-Power
Applications | Neural networks (NNs) have been successfully deployed in various fields. In
NNs, a large number of multiplyaccumulate (MAC) operations need to be
performed. Most existing digital hardware platforms rely on parallel MAC units
to accelerate these MAC operations. However, under a given area constraint, the
number of MAC u... | Kangwei Xu, Grace Li Zhang, Ulf Schlichtmann, Bing Li | 2023-09-19T10:45:46Z | http://arxiv.org/abs/2309.10510v1 | # Logic Design of Neural Networks for High-Throughput and Low-Power Applications
###### Abstract
**Neural networks (NNs) have been successfully deployed in various fields. In NNs, a large number of multiply-accumulate (MAC) operations need to be performed. Most existing digital hardware platforms rely on parallel MAC... |
2308.16664 | What can we learn from quantum convolutional neural networks? | We can learn from analyzing quantum convolutional neural networks (QCNNs)
that: 1) working with quantum data can be perceived as embedding physical
system parameters through a hidden feature map; 2) their high performance for
quantum phase recognition can be attributed to generation of a very suitable
basis set during ... | Chukwudubem Umeano, Annie E. Paine, Vincent E. Elfving, Oleksandr Kyriienko | 2023-08-31T12:12:56Z | http://arxiv.org/abs/2308.16664v2 | # What can we learn from quantum convolutional neural networks?
###### Abstract
We can learn from analyzing quantum convolutional neural networks (QCNNs) that: 1) working with quantum data can be perceived as embedding physical system parameters through a hidden feature map; 2) their high performance for quantum phas... |
2310.20349 | A Low-cost Strategic Monitoring Approach for Scalable and Interpretable
Error Detection in Deep Neural Networks | We present a highly compact run-time monitoring approach for deep computer
vision networks that extracts selected knowledge from only a few (down to
merely two) hidden layers, yet can efficiently detect silent data corruption
originating from both hardware memory and input faults. Building on the insight
that critical ... | Florian Geissler, Syed Qutub, Michael Paulitsch, Karthik Pattabiraman | 2023-10-31T10:45:55Z | http://arxiv.org/abs/2310.20349v1 | A Low-cost Strategic Monitoring Approach for Scalable and Interpretable Error Detection in Deep Neural Networks
###### Abstract
We present a highly compact run-time monitoring approach for deep computer vision networks that extracts selected knowledge from only a few (down to merely two) hidden layers, yet can effici... |
2309.06212 | Long-term drought prediction using deep neural networks based on
geospatial weather data | The problem of high-quality drought forecasting up to a year in advance is
critical for agriculture planning and insurance. Yet, it is still unsolved with
reasonable accuracy due to data complexity and aridity stochasticity. We tackle
drought data by introducing an end-to-end approach that adopts a
spatio-temporal neur... | Alexander Marusov, Vsevolod Grabar, Yury Maximov, Nazar Sotiriadi, Alexander Bulkin, Alexey Zaytsev | 2023-09-12T13:28:06Z | http://arxiv.org/abs/2309.06212v6 | # Long Term Drought Prediction using Deep Neural Networks based on Geospatial Weather Data
###### Abstract
The accurate prediction of drought probability in specific regions is crucial for informed decision-making in agricultural practices. In particular, for long-term decisions it is important to make predictions fo... |
2309.09108 | Neural Network-based Fault Detection and Identification for Quadrotors
using Dynamic Symmetry | Autonomous robotic systems, such as quadrotors, are susceptible to actuator
faults, and for the safe operation of such systems, timely detection and
isolation of these faults is essential. Neural networks can be used for
verification of actuator performance via online actuator fault detection with
high accuracy. In thi... | Kunal Garg, Chuchu Fan | 2023-09-16T22:59:09Z | http://arxiv.org/abs/2309.09108v1 | # Neural Network-based Fault Detection and Identification for Quadrotors using Dynamic Symmetry
###### Abstract
Autonomous robotic systems, such as quadrotors, are susceptible to actuator faults, and for the safe operation of such systems, timely detection and isolation of these faults is essential. Neural networks c... |
2310.00517 | Assessing the Generalizability of Deep Neural Networks-Based Models for
Black Skin Lesions | Melanoma is the most severe type of skin cancer due to its ability to cause
metastasis. It is more common in black people, often affecting acral regions:
palms, soles, and nails. Deep neural networks have shown tremendous potential
for improving clinical care and skin cancer diagnosis. Nevertheless, prevailing
studies ... | Luana Barros, Levy Chaves, Sandra Avila | 2023-09-30T22:36:51Z | http://arxiv.org/abs/2310.00517v2 | # Assessing the Generalizability of Deep Neural Networks-Based Models for Black Skin Lesions
###### Abstract
Melanoma is the most severe type of skin cancer due to its ability to cause metastasis. It is more common in black people, often affecting acral regions: palms, soles, and nails. Deep neural networks have show... |
2307.16792 | Classification with Deep Neural Networks and Logistic Loss | Deep neural networks (DNNs) trained with the logistic loss (i.e., the cross
entropy loss) have made impressive advancements in various binary
classification tasks. However, generalization analysis for binary
classification with DNNs and logistic loss remains scarce. The unboundedness of
the target function for the logi... | Zihan Zhang, Lei Shi, Ding-Xuan Zhou | 2023-07-31T15:58:46Z | http://arxiv.org/abs/2307.16792v2 | # Classification with Deep Neural Networks and Logistic Loss +
###### Abstract
Deep neural networks (DNNs) trained with the logistic loss (also known as the cross entropy loss) have made impressive advancements in various binary classification tasks. Despite the considerable success in practice, generalization analys... |
2308.16665 | Fault Injection on Embedded Neural Networks: Impact of a Single
Instruction Skip | With the large-scale integration and use of neural network models, especially
in critical embedded systems, their security assessment to guarantee their
reliability is becoming an urgent need. More particularly, models deployed in
embedded platforms, such as 32-bit microcontrollers, are physically accessible
by adversa... | Clement Gaine, Pierre-Alain Moellic, Olivier Potin, Jean-Max Dutertre | 2023-08-31T12:14:37Z | http://arxiv.org/abs/2308.16665v1 | # Fault Injection on Embedded Neural Networks: Impact of a Single Instruction Skip
###### Abstract
With the large-scale integration and use of neural network models, especially in critical embedded systems, their security assessment to guarantee their reliability is becoming an urgent need. More particularly, models ... |
2309.08799 | SHAPNN: Shapley Value Regularized Tabular Neural Network | We present SHAPNN, a novel deep tabular data modeling architecture designed
for supervised learning. Our approach leverages Shapley values, a
well-established technique for explaining black-box models. Our neural network
is trained using standard backward propagation optimization methods, and is
regularized with realti... | Qisen Cheng, Shuhui Qu, Janghwan Lee | 2023-09-15T22:45:05Z | http://arxiv.org/abs/2309.08799v1 | # SHAPNN: Shapley Value Regularized Tabular Neural Network
###### Abstract
We present SHAPNN, a novel deep tabular data modeling architecture designed for supervised learning. Our approach leverages Shapley values, a well-established technique for explaining black-box models. Our neural network is trained using stand... |
2301.13376 | Quantized Neural Networks for Low-Precision Accumulation with Guaranteed
Overflow Avoidance | We introduce a quantization-aware training algorithm that guarantees avoiding
numerical overflow when reducing the precision of accumulators during
inference. We leverage weight normalization as a means of constraining
parameters during training using accumulator bit width bounds that we derive.
We evaluate our algorit... | Ian Colbert, Alessandro Pappalardo, Jakoba Petri-Koenig | 2023-01-31T02:46:57Z | http://arxiv.org/abs/2301.13376v1 | # Quantized Neural Networks for Low-Precision Accumulation with Guaranteed Overflow Avoidance
###### Abstract
Quantizing the weights and activations of neural networks significantly reduces their inference costs, often in exchange for minor reductions in model accuracy. This is in large part due to compute and memory... |
2308.00615 | Cardiac MRI Orientation Recognition and Standardization using Deep
Neural Networks | Orientation recognition and standardization play a crucial role in the
effectiveness of medical image processing tasks. Deep learning-based methods
have proven highly advantageous in orientation recognition and prediction
tasks. In this paper, we address the challenge of imaging orientation in
cardiac MRI and present a... | Ruoxuan Zhen | 2023-07-31T00:01:49Z | http://arxiv.org/abs/2308.00615v1 | # Cardiac MRI Orientation Recognition and Standardization using Deep Neural Networks
###### Abstract
Orientation recognition and standardization play a crucial role in the effectiveness of medical image processing tasks. Deep learning-based methods have proven highly advantageous in orientation recognition and predic... |
2309.05646 | A Novel Supervised Deep Learning Solution to Detect Distributed Denial
of Service (DDoS) attacks on Edge Systems using Convolutional Neural Networks
(CNN) | Cybersecurity attacks are becoming increasingly sophisticated and pose a
growing threat to individuals, and private and public sectors. Distributed
Denial of Service attacks are one of the most harmful of these threats in
today's internet, disrupting the availability of essential services. This
project presents a novel... | Vedanth Ramanathan, Krish Mahadevan, Sejal Dua | 2023-09-11T17:37:35Z | http://arxiv.org/abs/2309.05646v1 | A Novel Supervised Deep Learning Solution to Detect Distributed Denial of Service (DDoS) attacks on Edge Systems using Convolutional Neural Networks (CNN)
###### Abstract
Cybersecurity attacks are becoming increasingly sophisticated and pose a growing threat to individuals, and private and public sectors. Distributed... |
2309.04558 | Towards Interpretable Solar Flare Prediction with Attention-based Deep
Neural Networks | Solar flare prediction is a central problem in space weather forecasting and
recent developments in machine learning and deep learning accelerated the
adoption of complex models for data-driven solar flare forecasting. In this
work, we developed an attention-based deep learning model as an improvement
over the standard... | Chetraj Pandey, Anli Ji, Rafal A. Angryk, Berkay Aydin | 2023-09-08T19:21:10Z | http://arxiv.org/abs/2309.04558v1 | # Towards Interpretable Solar Flare Prediction with Attention-based Deep Neural Networks
###### Abstract
Solar flare prediction is a central problem in space weather forecasting and recent developments in machine learning and deep learning accelerated the adoption of complex models for data-driven solar flare forecas... |
2301.13821 | Complete Neural Networks for Complete Euclidean Graphs | Neural networks for point clouds, which respect their natural invariance to
permutation and rigid motion, have enjoyed recent success in modeling geometric
phenomena, from molecular dynamics to recommender systems. Yet, to date, no
model with polynomial complexity is known to be complete, that is, able to
distinguish b... | Snir Hordan, Tal Amir, Steven J. Gortler, Nadav Dym | 2023-01-31T18:07:26Z | http://arxiv.org/abs/2301.13821v4 | # Complete Neural Networks for Euclidean Graphs
###### Abstract
We propose a \(2\)-WL-like geometric graph isomorphism test and prove it is complete when applied to Euclidean Graphs in \(\mathbb{R}^{3}\). We then use recent results on multiset embeddings to devise an efficient geometric GNN model with equivalent sepa... |
2309.11810 | Extragalactic Test of General Relativity from Strong Gravitational
Lensing by using Artificial Neural Networks | This study aims to test the validity of general relativity (GR) on kiloparsec
scales by employing a newly compiled galaxy-scale strong gravitational lensing
(SGL) sample. We utilize the distance sum rule within the
Friedmann-Lema\^{\i}tre-Robertson-Walker metric to obtain cosmology-independent
constraints on both the p... | Jing-Yu Ran, Jun-Jie Wei | 2023-09-21T06:28:39Z | http://arxiv.org/abs/2309.11810v2 | Extragalactic Test of General Relativity from Strong Gravitational Lensing by using Artificial Neural Networks
###### Abstract
This study aims to test the validity of general relativity (GR) on kiloparsec scales by employing a newly compiled galaxy-scale strong gravitational lensing (SGL) sample. We utilize the dista... |
2309.16158 | FireFly v2: Advancing Hardware Support for High-Performance Spiking
Neural Network with a Spatiotemporal FPGA Accelerator | Spiking Neural Networks (SNNs) are expected to be a promising alternative to
Artificial Neural Networks (ANNs) due to their strong biological
interpretability and high energy efficiency. Specialized SNN hardware offers
clear advantages over general-purpose devices in terms of power and
performance. However, there's sti... | Jindong Li, Guobin Shen, Dongcheng Zhao, Qian Zhang, Yi Zeng | 2023-09-28T04:17:02Z | http://arxiv.org/abs/2309.16158v1 | FireFly v2: Advancing Hardware Support for High-Performance Spiking Neural Network with a Spatiotemporal FPGA Accelerator
###### Abstract
Spiking Neural Networks (SNNs) are expected to be a promising alternative to Artificial Neural Networks (ANNs) due to their strong biological interpretability and high energy effic... |
2309.13752 | Improving Robustness of Deep Convolutional Neural Networks via
Multiresolution Learning | The current learning process of deep learning, regardless of any deep neural
network (DNN) architecture and/or learning algorithm used, is essentially a
single resolution training. We explore multiresolution learning and show that
multiresolution learning can significantly improve robustness of DNN models for
both 1D s... | Hongyan Zhou, Yao Liang | 2023-09-24T21:04:56Z | http://arxiv.org/abs/2309.13752v2 | # Improving Robustness of Deep Convolutional Neural Networks via Multiresolution Learning
###### Abstract
The current learning process of deep learning, regardless of any deep neural network (DNN) architecture and/or learning algorithm used, is essentially a single resolution training. We explore multiresolution lear... |
2309.12128 | Convergence and Recovery Guarantees of Unsupervised Neural Networks for
Inverse Problems | Neural networks have become a prominent approach to solve inverse problems in
recent years. While a plethora of such methods was developed to solve inverse
problems empirically, we are still lacking clear theoretical guarantees for
these methods. On the other hand, many works proved convergence to optimal
solutions of ... | Nathan Buskulic, Jalal Fadili, Yvain Quéau | 2023-09-21T14:48:02Z | http://arxiv.org/abs/2309.12128v3 | # Convergence and Recovery Guarantees of Unsupervised Neural Networks for Inverse Problems
###### Abstract
Neural networks have become a prominent approach to solve inverse problems in recent years. While a plethora of such methods was developed to solve inverse problems empirically, we are still lacking clear theore... |
2309.09483 | An Accurate and Efficient Neural Network for OCTA Vessel Segmentation
and a New Dataset | Optical coherence tomography angiography (OCTA) is a noninvasive imaging
technique that can reveal high-resolution retinal vessels. In this work, we
propose an accurate and efficient neural network for retinal vessel
segmentation in OCTA images. The proposed network achieves accuracy comparable
to other SOTA methods, w... | Haojian Ning, Chengliang Wang, Xinrun Chen, Shiying Li | 2023-09-18T04:47:12Z | http://arxiv.org/abs/2309.09483v1 | # An Accurate and Efficient Neural Network for Octa Vessel Segmentation and a New Dataset
###### Abstract
Optical coherence tomography angiography (OCTA) is a noninvasive imaging technique that can reveal high-resolution retinal vessels. In this work, we propose an accurate and efficient neural network for retinal ve... |
2309.15096 | Fixing the NTK: From Neural Network Linearizations to Exact Convex
Programs | Recently, theoretical analyses of deep neural networks have broadly focused
on two directions: 1) Providing insight into neural network training by SGD in
the limit of infinite hidden-layer width and infinitesimally small learning
rate (also known as gradient flow) via the Neural Tangent Kernel (NTK), and 2)
Globally o... | Rajat Vadiraj Dwaraknath, Tolga Ergen, Mert Pilanci | 2023-09-26T17:42:52Z | http://arxiv.org/abs/2309.15096v1 | # Fixing the NTK: From Neural Network Linearizations to Exact Convex Programs
###### Abstract
Recently, theoretical analyses of deep neural networks have broadly focused on two directions: 1) Providing insight into neural network training by SGD in the limit of infinite hidden-layer width and infinitesimally small le... |
2305.19753 | The Tunnel Effect: Building Data Representations in Deep Neural Networks | Deep neural networks are widely known for their remarkable effectiveness
across various tasks, with the consensus that deeper networks implicitly learn
more complex data representations. This paper shows that sufficiently deep
networks trained for supervised image classification split into two distinct
parts that contr... | Wojciech Masarczyk, Mateusz Ostaszewski, Ehsan Imani, Razvan Pascanu, Piotr Miłoś, Tomasz Trzciński | 2023-05-31T11:38:24Z | http://arxiv.org/abs/2305.19753v2 | # The Tunnel Effect: Building Data Representations
###### Abstract
Deep neural networks are widely known for their remarkable effectiveness across various tasks, with the consensus that deeper networks implicitly learn more complex data representations. This paper shows that sufficiently deep networks trained for sup... |
2309.07684 | deepFDEnet: A Novel Neural Network Architecture for Solving Fractional
Differential Equations | The primary goal of this research is to propose a novel architecture for a
deep neural network that can solve fractional differential equations
accurately. A Gaussian integration rule and a $L_1$ discretization technique
are used in the proposed design. In each equation, a deep neural network is
used to approximate the... | Ali Nosrati Firoozsalari, Hassan Dana Mazraeh, Alireza Afzal Aghaei, Kourosh Parand | 2023-09-14T12:58:40Z | http://arxiv.org/abs/2309.07684v1 | # deepFDEnet: A Novel Neural Network Architecture for Solving Fractional Differential Equations
###### Abstract
The primary goal of this research is to propose a novel architecture for a deep neural network that can solve fractional differential equations accurately. A Gaussian integration rule and a \(L_{1}\) discre... |
2304.10476 | HL-nets: Physics-informed neural networks for hydrodynamic lubrication
with cavitation | Recently, physics-informed neural networks (PINNs) have emerged as a
promising method for solving partial differential equations (PDEs). In this
study, we establish a deep learning computational framework, HL-nets, for
computing the flow field of hydrodynamic lubrication involving cavitation
effects. Two classical cavi... | Yiqian Cheng, Qiang He, Weifeng Huang, Ying Liu, Yanwen Li, Decai Li | 2022-12-18T06:19:10Z | http://arxiv.org/abs/2304.10476v1 | HL-nets: Physics-informed neural networks for hydrodynamic lubrication with cavitation
###### Abstract
Recently, physics-informed neural networks (PINNs) have emerged as a promising method for solving partial differential equations (PDEs). In this study, we establish a deep learning computational framework, HL-nets, ... |
2309.03390 | A novel method for iris recognition using BP neural network and parallel
computing by the aid of GPUs (Graphics Processing Units) | In this paper, we seek a new method in designing an iris recognition system.
In this method, first the Haar wavelet features are extracted from iris images.
The advantage of using these features is the high-speed extraction, as well as
being unique to each iris. Then the back propagation neural network (BPNN) is
used a... | Farahnaz Hosseini, Hossein Ebrahimpour, Samaneh Askari | 2023-09-06T22:50:50Z | http://arxiv.org/abs/2309.03390v1 | A novel method for iris recognition using BP neural network and parallel computing by the aid of GPUs (Graphics Processing Units)
###### Abstract
In this paper, we seek a new method in designing an iris recognition system. In this method, first the Haar wavelet features are extracted from iris images. The advantage o... |
2309.09694 | Noise-Augmented Boruta: The Neural Network Perturbation Infusion with
Boruta Feature Selection | With the surge in data generation, both vertically (i.e., volume of data) and
horizontally (i.e., dimensionality), the burden of the curse of dimensionality
has become increasingly palpable. Feature selection, a key facet of
dimensionality reduction techniques, has advanced considerably to address this
challenge. One s... | Hassan Gharoun, Navid Yazdanjoe, Mohammad Sadegh Khorshidi, Amir H. Gandomi | 2023-09-18T11:59:06Z | http://arxiv.org/abs/2309.09694v1 | # Noise-Augmented Boruta: The Neural Network Perturbation Infusion with Boruta Feature Selection
###### Abstract
With the surge in data generation, both vertically (i.e., volume of data) and horizontally (i.e., dimensionality) the burden of the curse of dimensionality has become increasingly palpable. Feature selecti... |
2310.03033 | Benchmarking Local Robustness of High-Accuracy Binary Neural Networks
for Enhanced Traffic Sign Recognition | Traffic signs play a critical role in road safety and traffic management for
autonomous driving systems. Accurate traffic sign classification is essential
but challenging due to real-world complexities like adversarial examples and
occlusions. To address these issues, binary neural networks offer promise in
constructin... | Andreea Postovan, Mădălina Eraşcu | 2023-09-25T01:17:14Z | http://arxiv.org/abs/2310.03033v1 | Benchmarking Local Robustness of High-Accuracy Binary Neural Networks for Enhanced Traffic Sign Recognition
###### Abstract
Traffic signs play a critical role in road safety and traffic management for autonomous driving systems. Accurate traffic sign classification is essential but challenging due to real-world compl... |
2310.03755 | Physics Informed Neural Network Code for 2D Transient Problems
(PINN-2DT) Compatible with Google Colab | We present an open-source Physics Informed Neural Network environment for
simulations of transient phenomena on two-dimensional rectangular domains, with
the following features: (1) it is compatible with Google Colab which allows
automatic execution on cloud environment; (2) it supports two dimensional
time-dependent P... | Paweł Maczuga, Maciej Sikora, Maciej Skoczeń, Przemysław Rożnawski, Filip Tłuszcz, Marcin Szubert, Marcin Łoś, Witold Dzwinel, Keshav Pingali, Maciej Paszyński | 2023-09-24T07:08:36Z | http://arxiv.org/abs/2310.03755v2 | Physics Informed Neural Network Code for 2D Transient Problems (PINN-2DT) Compatible with Google Colab
###### Abstract
We present an open-source Physics Informed Neural Network environment for simulations of transient phenomena on two-dimensional rectangular domains, with the following features: (1) it is compatible ... |
2309.09469 | Spiking-LEAF: A Learnable Auditory front-end for Spiking Neural Networks | Brain-inspired spiking neural networks (SNNs) have demonstrated great
potential for temporal signal processing. However, their performance in speech
processing remains limited due to the lack of an effective auditory front-end.
To address this limitation, we introduce Spiking-LEAF, a learnable auditory
front-end meticu... | Zeyang Song, Jibin Wu, Malu Zhang, Mike Zheng Shou, Haizhou Li | 2023-09-18T04:03:05Z | http://arxiv.org/abs/2309.09469v2 | # Spiking-Leaf: A Learnable Auditory Front-End for
###### Abstract
Brain-inspired spiking neural networks (SNNs) have demonstrated great potential for temporal signal processing. However, their performance in speech processing remains limited due to the lack of an effective auditory front-end. To address this limitat... |
2310.20601 | Functional connectivity modules in recurrent neural networks: function,
origin and dynamics | Understanding the ubiquitous phenomenon of neural synchronization across
species and organizational levels is crucial for decoding brain function.
Despite its prevalence, the specific functional role, origin, and dynamical
implication of modular structures in correlation-based networks remains
ambiguous. Using recurren... | Jacob Tanner, Sina Mansour L., Ludovico Coletta, Alessandro Gozzi, Richard F. Betzel | 2023-10-31T16:37:01Z | http://arxiv.org/abs/2310.20601v1 | # Functional connectivity modules in recurrent neural networks: function, origin and dynamics
###### Abstract
Understanding the ubiquitous phenomenon of neural synchronization across species and organizational levels is crucial for decoding brain function. Despite its prevalence, the specific functional role, origin,... |
2309.08171 | Unveiling Invariances via Neural Network Pruning | Invariance describes transformations that do not alter data's underlying
semantics. Neural networks that preserve natural invariance capture good
inductive biases and achieve superior performance. Hence, modern networks are
handcrafted to handle well-known invariances (ex. translations). We propose a
framework to learn... | Derek Xu, Yizhou Sun, Wei Wang | 2023-09-15T05:38:33Z | http://arxiv.org/abs/2309.08171v1 | # Unveiling Invariances via Neural Network Pruning
###### Abstract
Invariance describes transformations that do not alter data's underlying semantics. Neural networks that preserve natural invariance capture good inductive biases and achieve superior performance. Hence, modern networks are handcrafted to handle well-... |
2309.09290 | Coarse-Graining with Equivariant Neural Networks: A Path Towards
Accurate and Data-Efficient Models | Machine learning has recently entered into the mainstream of coarse-grained
(CG) molecular modeling and simulation. While a variety of methods for
incorporating deep learning into these models exist, many of them involve
training neural networks to act directly as the CG force field. This has
several benefits, the most... | Timothy D. Loose, Patrick G. Sahrmann, Thomas S. Qu, Gregory A. Voth | 2023-09-17T14:55:08Z | http://arxiv.org/abs/2309.09290v2 | # Coarse-Graining with Equivariant Neural Networks: A Path Towards Accurate and Data-Efficient Models
###### Abstract
Machine learning has recently entered into the mainstream of coarse-grained (CG) molecular modeling and simulation. While a variety of methods for incorporating deep learning into these models exist, ... |
2308.16516 | Curvature-based Pooling within Graph Neural Networks | Over-squashing and over-smoothing are two critical issues, that limit the
capabilities of graph neural networks (GNNs). While over-smoothing eliminates
the differences between nodes making them indistinguishable, over-squashing
refers to the inability of GNNs to propagate information over long distances,
as exponential... | Cedric Sanders, Andreas Roth, Thomas Liebig | 2023-08-31T08:00:08Z | http://arxiv.org/abs/2308.16516v1 | # Curvature-based Pooling within Graph Neural Networks
###### Abstract
Over-squashing and over-smoothing are two critical issues, that limit the capabilities of graph neural networks (GNNs). While over-smoothing eliminates the differences between nodes making them indistinguishable, over-squashing refers to the inabi... |
2309.08429 | IHT-Inspired Neural Network for Single-Snapshot DOA Estimation with
Sparse Linear Arrays | Single-snapshot direction-of-arrival (DOA) estimation using sparse linear
arrays (SLAs) has gained significant attention in the field of automotive MIMO
radars. This is due to the dynamic nature of automotive settings, where
multiple snapshots aren't accessible, and the importance of minimizing hardware
costs. Low-rank... | Yunqiao Hu, Shunqiao Sun | 2023-09-15T14:30:38Z | http://arxiv.org/abs/2309.08429v1 | # Hit-Inspired Neural Network for Single-Snapshot Doa Estimation with Sparse Linear Arrays
###### Abstract
Single-snapshot direction-of-arrival (DOA) estimation using sparse linear arrays (SLAs) has gained significant attention in the field of automotive MIMO radios. This is due to the dynamic nature of automotive se... |
2309.06410 | Solving the Pulsar Equation using Physics-Informed Neural Networks | In this study, Physics-Informed Neural Networks (PINNs) are skilfully applied
to explore a diverse range of pulsar magneto-spheric models, specifically
focusing on axisymmetric cases. The study successfully reproduced various
axisymmetric models found in the literature, including those with non-dipolar
configurations, ... | Petros Stefanou, Jorge F. Urbán, José A. Pons | 2023-09-12T17:23:15Z | http://arxiv.org/abs/2309.06410v2 | # Solving the Pulsar Equation using Physics-Informed Neural Networks
###### Abstract
In this study, Physics-Informed Neural Networks (PINNs) are skilfully applied to explore a diverse range of pulsar magnetospheric models, specifically focusing on axisymmetric cases. The study successfully reproduced various axisymme... |
2309.16826 | An Attentional Recurrent Neural Network for Occlusion-Aware Proactive
Anomaly Detection in Field Robot Navigation | The use of mobile robots in unstructured environments like the agricultural
field is becoming increasingly common. The ability for such field robots to
proactively identify and avoid failures is thus crucial for ensuring efficiency
and avoiding damage. However, the cluttered field environment introduces
various sources... | Andre Schreiber, Tianchen Ji, D. Livingston McPherson, Katherine Driggs-Campbell | 2023-09-28T20:15:53Z | http://arxiv.org/abs/2309.16826v1 | # An Attentional Recurrent Neural Network for Occlusion-Aware
###### Abstract
The use of mobile robots in unstructured environments like the agricultural field is becoming increasingly common. The ability for such field robots to proactively identify and avoid failures is thus crucial for ensuring efficiency and avoi... |
2309.07056 | Deep Quantum Graph Dreaming: Deciphering Neural Network Insights into
Quantum Experiments | Despite their promise to facilitate new scientific discoveries, the
opaqueness of neural networks presents a challenge in interpreting the logic
behind their findings. Here, we use a eXplainable-AI (XAI) technique called
$inception$ or $deep$ $dreaming$, which has been invented in machine learning
for computer vision. ... | Tareq Jaouni, Sören Arlt, Carlos Ruiz-Gonzalez, Ebrahim Karimi, Xuemei Gu, Mario Krenn | 2023-09-13T16:13:54Z | http://arxiv.org/abs/2309.07056v2 | # Deep Quantum Graph Dreaming:
###### Abstract
Despite their promise to facilitate new scientific discoveries, the opaqueness of neural networks presents a challenge in interpreting the logic behind their findings. Here, we use a eXplainable-AI (XAI) technique called _inception_ or _deep dreaming_, which has been inv... |
2301.03412 | Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter
Configuration in Cellular Network | The mobile communication enabled by cellular networks is the one of the main
foundations of our modern society. Optimizing the performance of cellular
networks and providing massive connectivity with improved coverage and user
experience has a considerable social and economic impact on our daily life.
This performance ... | Mehrtash Mehrabi, Walid Masoudimansour, Yingxue Zhang, Jie Chuai, Zhitang Chen, Mark Coates, Jianye Hao, Yanhui Geng | 2022-12-29T18:51:36Z | http://arxiv.org/abs/2301.03412v2 | Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter Configuration in Cellular Network
###### Abstract
The mobile communication enabled by cellular networks is the one of the main foundations of our modern society. Optimizing the performance of cellular networks and providing massive connectivity with ... |
2305.19546 | Prediction of Born effective charges using neural network to study ion
migration under electric fields: applications to crystalline and amorphous
Li$_3$PO$_4$ | Understanding ionic behaviour under external electric fields is crucial to
develop electronic and energy-related devices using ion transport. In this
study, we propose a neural network (NN) model to predict the Born effective
charges of ions along an axis parallel to an applied electric field from atomic
structures. Th... | Koji Shimizu, Ryuji Otsuka, Masahiro Hara, Emi Minamitani, Satoshi Watanabe | 2023-05-31T04:24:01Z | http://arxiv.org/abs/2305.19546v1 | Prediction of Born effective charges using neural network to study ion migration under electric fields: applications to crystalline and amorphous Li\({}_{3}\)Po\({}_{4}\)
###### Abstract
Understanding ionic behaviour under external electric fields is crucial to develop electronic and energy-related devices using ion ... |
2307.00134 | Generalization Limits of Graph Neural Networks in Identity Effects
Learning | Graph Neural Networks (GNNs) have emerged as a powerful tool for data-driven
learning on various graph domains. They are usually based on a message-passing
mechanism and have gained increasing popularity for their intuitive
formulation, which is closely linked to the Weisfeiler-Lehman (WL) test for
graph isomorphism to... | Giuseppe Alessio D'Inverno, Simone Brugiapaglia, Mirco Ravanelli | 2023-06-30T20:56:38Z | http://arxiv.org/abs/2307.00134v3 | # Generalization Limits of Graph Neural Networks
###### Abstract
Graph Neural Networks (GNNs) have emerged as a powerful tool for data-driven learning on various graph domains. They are usually based on a message-passing mechanism and have gained increasing popularity for their intuitive formulation, which is closely... |
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