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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2309.07789 | SOT-MRAM-Enabled Probabilistic Binary Neural Networks for Noise-Tolerant
and Fast Training | We report the use of spin-orbit torque (SOT) magnetoresistive random-access
memory (MRAM) to implement a probabilistic binary neural network (PBNN) for
resource-saving applications. The in-plane magnetized SOT (i-SOT) MRAM not only
enables field-free magnetization switching with high endurance (> 10^11), but
also hosts... | Puyang Huang, Yu Gu, Chenyi Fu, Jiaqi Lu, Yiyao Zhu, Renhe Chen, Yongqi Hu, Yi Ding, Hongchao Zhang, Shiyang Lu, Shouzhong Peng, Weisheng Zhao, Xufeng Kou | 2023-09-14T15:25:36Z | http://arxiv.org/abs/2309.07789v2 | # SOT-MRAM-Enabled Probabilistic Binary Neural Networks for Noise-Tolerant and Fast Training
###### Abstract
We report the use of spin-orbit torque (SOT) magnetoresistive random-access memory (MRAM) to implement a probabilistic binary neural network (PBNN) for resource-saving applications. The in-plane magnetized SOT... |
2310.20519 | Enhancing Graph Neural Networks with Quantum Computed Encodings | Transformers are increasingly employed for graph data, demonstrating
competitive performance in diverse tasks. To incorporate graph information into
these models, it is essential to enhance node and edge features with positional
encodings. In this work, we propose novel families of positional encodings
tailored for gra... | Slimane Thabet, Romain Fouilland, Mehdi Djellabi, Igor Sokolov, Sachin Kasture, Louis-Paul Henry, Loïc Henriet | 2023-10-31T14:56:52Z | http://arxiv.org/abs/2310.20519v1 | # Enhancing Graph Neural Networks with
###### Abstract
Transformers are increasingly employed for graph data, demonstrating competitive performance in diverse tasks. To incorporate graph information into these models, it is essential to enhance node and edge features with positional encodings. In this work, we propos... |
2310.20294 | Robust nonparametric regression based on deep ReLU neural networks | In this paper, we consider robust nonparametric regression using deep neural
networks with ReLU activation function. While several existing theoretically
justified methods are geared towards robustness against identical heavy-tailed
noise distributions, the rise of adversarial attacks has emphasized the
importance of s... | Juntong Chen | 2023-10-31T09:05:09Z | http://arxiv.org/abs/2310.20294v1 | # Robust nonparametric regression based on deep relu neural networks
###### Abstract.
In this paper, we consider robust nonparametric regression using deep neural networks with ReLU activation function. While several existing theoretically justified methods are geared towards robustness against identical heavy-tailed... |
2303.17925 | Beyond Multilayer Perceptrons: Investigating Complex Topologies in
Neural Networks | In this study, we explore the impact of network topology on the approximation
capabilities of artificial neural networks (ANNs), with a particular focus on
complex topologies. We propose a novel methodology for constructing complex
ANNs based on various topologies, including Barab\'asi-Albert,
Erd\H{o}s-R\'enyi, Watts-... | Tommaso Boccato, Matteo Ferrante, Andrea Duggento, Nicola Toschi | 2023-03-31T09:48:16Z | http://arxiv.org/abs/2303.17925v2 | # Beyond Multilayer Perceptrons: Investigating Complex Topologies in Neural Networks
###### Abstract
In this study, we explore the impact of network topology on the approximation capabilities of artificial neural networks (ANNs), with a particular focus on complex topologies. We propose a novel methodology for constr... |
2307.16666 | Improving the temporal resolution of event-based electron detectors
using neural network cluster analysis | Novel event-based electron detector platforms provide an avenue to extend the
temporal resolution of electron microscopy into the ultrafast domain. Here, we
characterize the timing accuracy of a detector based on a TimePix3 architecture
using femtosecond electron pulse trains as a reference. With a large dataset of
eve... | Alexander Schröder, Leon van Velzen, Maurits Kelder, Sascha Schäfer | 2023-07-31T13:45:57Z | http://arxiv.org/abs/2307.16666v1 | Improving the temporal resolution of event-based electron detectors using neural network cluster analysis
###### Abstract
Novel event-based electron detector platforms provide an avenue to extend the temporal resolution of electron microscopy into the ultrafast domain. Here, we characterize the timing accuracy of a d... |
2303.00524 | Semi-decentralized Inference in Heterogeneous Graph Neural Networks for
Traffic Demand Forecasting: An Edge-Computing Approach | Prediction of taxi service demand and supply is essential for improving
customer's experience and provider's profit. Recently, graph neural networks
(GNNs) have been shown promising for this application. This approach models
city regions as nodes in a transportation graph and their relations as edges.
GNNs utilize loca... | Mahmoud Nazzal, Abdallah Khreishah, Joyoung Lee, Shaahin Angizi, Ala Al-Fuqaha, Mohsen Guizani | 2023-02-28T00:21:18Z | http://arxiv.org/abs/2303.00524v2 | Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach
###### Abstract
Prediction of taxi service demand and supply is essential for improving customer's experience and provider's profit. Recently, graph neural networks (GNNs) have been shown pro... |
2309.16335 | End-to-end Risk Prediction of Atrial Fibrillation from the 12-Lead ECG
by Deep Neural Networks | Background: Atrial fibrillation (AF) is one of the most common cardiac
arrhythmias that affects millions of people each year worldwide and it is
closely linked to increased risk of cardiovascular diseases such as stroke and
heart failure. Machine learning methods have shown promising results in
evaluating the risk of d... | Theogene Habineza, Antônio H. Ribeiro, Daniel Gedon, Joachim A. Behar, Antonio Luiz P. Ribeiro, Thomas B. Schön | 2023-09-28T10:47:40Z | http://arxiv.org/abs/2309.16335v1 | # End-to-end Risk Prediction of Atrial Fibrillation from the 12-Lead ECG by Deep Neural Networks
###### Abstract
**Background:** Atrial fibrillation (AF) is one of the most common cardiac arrhythmias that affects millions of people each year worldwide and it is closely linked to increased risk of cardiovascular disea... |
2304.00150 | E($3$) Equivariant Graph Neural Networks for Particle-Based Fluid
Mechanics | We contribute to the vastly growing field of machine learning for engineering
systems by demonstrating that equivariant graph neural networks have the
potential to learn more accurate dynamic-interaction models than their
non-equivariant counterparts. We benchmark two well-studied fluid flow systems,
namely the 3D deca... | Artur P. Toshev, Gianluca Galletti, Johannes Brandstetter, Stefan Adami, Nikolaus A. Adams | 2023-03-31T21:56:35Z | http://arxiv.org/abs/2304.00150v1 | # E(3) Equivariant Graph Neural Networks for Particle-Based Fluid Mechanics
###### Abstract
We contribute to the vastly growing field of machine learning for engineering systems by demonstrating that equivariant graph neural networks have the potential to learn more accurate dynamic-interaction models than their non-... |
2309.16902 | Investigating Shift Equivalence of Convolutional Neural Networks in
Industrial Defect Segmentation | In industrial defect segmentation tasks, while pixel accuracy and
Intersection over Union (IoU) are commonly employed metrics to assess
segmentation performance, the output consistency (also referred to equivalence)
of the model is often overlooked. Even a small shift in the input image can
yield significant fluctuatio... | Zhen Qu, Xian Tao, Fei Shen, Zhengtao Zhang, Tao Li | 2023-09-29T00:04:47Z | http://arxiv.org/abs/2309.16902v1 | # Investigating Shift Equivalence of Convolutional Neural Networks in Industrial Defect Segmentation
###### Abstract
In industrial defect segmentation tasks, while pixel accuracy and Intersection over Union (IoU) are commonly employed metrics to assess segmentation performance, the output consistency (also referred t... |
2308.16424 | Solar horizontal flow evaluation using neural network and numerical
simulation with snapshot data | We suggest a method that evaluates the horizontal velocity in the solar
photosphere with easily observable values using a combination of neural network
and radiative magnetohydrodynamics simulations. All three-component velocities
of thermal convection on the solar surface have important roles in generating
waves in th... | Hiroyuki Masaki, Hideyuki Hotta, Yukio Katsukawa, Ryohtaroh T. Ishikawa | 2023-08-31T03:28:03Z | http://arxiv.org/abs/2308.16424v2 | [
###### Abstract
We suggest a method that evaluates the horizontal velocity in the solar photosphere with easily observable values using a combination of neural network and radiative magnetohydrodynamics simulations. All three-component velocities of thermal convection on the solar surface have important roles in ge... |
2309.08533 | Automated dermatoscopic pattern discovery by clustering neural network
output for human-computer interaction | Background: As available medical image datasets increase in size, it becomes
infeasible for clinicians to review content manually for knowledge extraction.
The objective of this study was to create an automated clustering resulting in
human-interpretable pattern discovery.
Methods: Images from the public HAM10000 dat... | Lidia Talavera-Martinez, Philipp Tschandl | 2023-09-15T16:50:47Z | http://arxiv.org/abs/2309.08533v1 | Automated dermatoscopic pattern discovery by clustering neural network output for human-computer interaction
###### Abstract
Background: As available medical image datasets increase in size, it becomes infeasible for clinicians to review content manually for knowledge extraction. The objective of this study was to cr... |
2309.04452 | Postprocessing of Ensemble Weather Forecasts Using Permutation-invariant
Neural Networks | Statistical postprocessing is used to translate ensembles of raw numerical
weather forecasts into reliable probabilistic forecast distributions. In this
study, we examine the use of permutation-invariant neural networks for this
task. In contrast to previous approaches, which often operate on ensemble
summary statistic... | Kevin Höhlein, Benedikt Schulz, Rüdiger Westermann, Sebastian Lerch | 2023-09-08T17:20:51Z | http://arxiv.org/abs/2309.04452v2 | # Postprocessing of Ensemble Weather Forecasts
###### Abstract
Statistical postprocessing is used to translate ensembles of raw numerical weather forecasts into reliable probabilistic forecast distributions. In this study, we examine the use of permutation-invariant neural networks for this task. In contrast to previ... |
2309.09638 | Neural Network-Based Rule Models With Truth Tables | Understanding the decision-making process of a machine/deep learning model is
crucial, particularly in security-sensitive applications. In this study, we
introduce a neural network framework that combines the global and exact
interpretability properties of rule-based models with the high performance of
deep neural netw... | Adrien Benamira, Tristan Guérand, Thomas Peyrin, Hans Soegeng | 2023-09-18T10:13:59Z | http://arxiv.org/abs/2309.09638v1 | # Neural Network-Based Rule Models With Truth Tables
###### Abstract
Understanding the decision-making process of a machine/deep learning model is crucial, particularly in security-sensitive applications. In this study, we introduce a neural network framework that combines the global and exact interpretability proper... |
2308.16425 | On the Equivalence between Implicit and Explicit Neural Networks: A
High-dimensional Viewpoint | Implicit neural networks have demonstrated remarkable success in various
tasks. However, there is a lack of theoretical analysis of the connections and
differences between implicit and explicit networks. In this paper, we study
high-dimensional implicit neural networks and provide the high dimensional
equivalents for t... | Zenan Ling, Zhenyu Liao, Robert C. Qiu | 2023-08-31T03:28:43Z | http://arxiv.org/abs/2308.16425v1 | # On the Equivalence between Implicit and Explicit Neural Networks:
###### Abstract
Implicit neural networks have demonstrated remarkable success in various tasks. However, there is a lack of theoretical analysis of the connections and differences between implicit and explicit networks. In this paper, we study high-d... |
2309.14523 | Smooth Exact Gradient Descent Learning in Spiking Neural Networks | Artificial neural networks are highly successfully trained with
backpropagation. For spiking neural networks, however, a similar gradient
descent scheme seems prohibitive due to the sudden, disruptive (dis-)appearance
of spikes. Here, we demonstrate exact gradient descent learning based on
spiking dynamics that change ... | Christian Klos, Raoul-Martin Memmesheimer | 2023-09-25T20:51:00Z | http://arxiv.org/abs/2309.14523v1 | # Smooth Exact Gradient Descent Learning in Spiking Neural Networks
###### Abstract
Artificial neural networks are highly successfully trained with backpropagation. For spiking neural networks, however, a similar gradient descent scheme seems prohibitive due to the sudden, disruptive (dis-)appearance of spikes. Here,... |
2309.10976 | Accurate and Scalable Estimation of Epistemic Uncertainty for Graph
Neural Networks | Safe deployment of graph neural networks (GNNs) under distribution shift
requires models to provide accurate confidence indicators (CI). However, while
it is well-known in computer vision that CI quality diminishes under
distribution shift, this behavior remains understudied for GNNs. Hence, we
begin with a case study ... | Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan | 2023-09-20T00:35:27Z | http://arxiv.org/abs/2309.10976v1 | # Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks
###### Abstract
Safe deployment of graph neural networks (GNNs) under distribution shift requires models to provide accurate confidence indicators (CI). However, while it is well-known in computer vision that CI quality diminishes u... |
2303.00055 | Learning time-scales in two-layers neural networks | Gradient-based learning in multi-layer neural networks displays a number of
striking features. In particular, the decrease rate of empirical risk is
non-monotone even after averaging over large batches. Long plateaus in which
one observes barely any progress alternate with intervals of rapid decrease.
These successive ... | Raphaël Berthier, Andrea Montanari, Kangjie Zhou | 2023-02-28T19:52:26Z | http://arxiv.org/abs/2303.00055v3 | # Learning time-scales in two-layers neural networks
###### Abstract
Gradient-based learning in multi-layer neural networks displays a number of striking features. In particular, the decrease rate of empirical risk is non-monotone even after averaging over large batches. Long plateaus in which one observes barely any... |
2306.17485 | Detection-segmentation convolutional neural network for autonomous
vehicle perception | Object detection and segmentation are two core modules of an autonomous
vehicle perception system. They should have high efficiency and low latency
while reducing computational complexity. Currently, the most commonly used
algorithms are based on deep neural networks, which guarantee high efficiency
but require high-pe... | Maciej Baczmanski, Robert Synoczek, Mateusz Wasala, Tomasz Kryjak | 2023-06-30T08:54:52Z | http://arxiv.org/abs/2306.17485v1 | # Detection-segmentation convolutional neural network for autonomous vehicle perception
###### Abstract
Object detection and segmentation are two core modules of an autonomous vehicle perception system. They should have high efficiency and low latency while reducing computational complexity. Currently, the most commo... |
2309.11856 | Activation Compression of Graph Neural Networks using Block-wise
Quantization with Improved Variance Minimization | Efficient training of large-scale graph neural networks (GNNs) has been
studied with a specific focus on reducing their memory consumption. Work by Liu
et al. (2022) proposed extreme activation compression (EXACT) which
demonstrated drastic reduction in memory consumption by performing quantization
of the intermediate ... | Sebastian Eliassen, Raghavendra Selvan | 2023-09-21T07:59:08Z | http://arxiv.org/abs/2309.11856v2 | Activation Compression of Graph Neural Networks Using Block-Wise Quantization With Improved Variance Minimization
###### Abstract
Efficient training of large-scale graph neural networks (GNNs) has been studied with a specific focus on reducing their memory consumption. Work by Liu et al. (2022) proposed extreme activ... |
2310.05950 | Quantization of Neural Network Equalizers in Optical Fiber Transmission
Experiments | The quantization of neural networks for the mitigation of the nonlinear and
components' distortions in dual-polarization optical fiber transmission is
studied. Two low-complexity neural network equalizers are applied in three
16-QAM 34.4 GBaud transmission experiments with different representative
fibers. A number of p... | Jamal Darweesh, Nelson Costa, Antonio Napoli, Bernhard Spinnler, Yves Jaouen, Mansoor Yousefi | 2023-09-09T12:24:55Z | http://arxiv.org/abs/2310.05950v1 | # Quantization of Neural Network Equalizers in Optical Fiber Transmission Experiments
###### Abstract
The quantization of neural networks for the mitigation of the nonlinear and components' distortions in dual-polarization optical fiber transmission is studied. Two low-complexity neural network equalizers are applied... |
2305.19659 | Improving Expressivity of Graph Neural Networks using Localization | In this paper, we propose localized versions of Weisfeiler-Leman (WL)
algorithms in an effort to both increase the expressivity, as well as decrease
the computational overhead. We focus on the specific problem of subgraph
counting and give localized versions of $k-$WL for any $k$. We analyze the
power of Local $k-$WL a... | Anant Kumar, Shrutimoy Das, Shubhajit Roy, Binita Maity, Anirban Dasgupta | 2023-05-31T08:46:11Z | http://arxiv.org/abs/2305.19659v3 | # Improving Expressivity of Graph Neural Networks using Localization
###### Abstract
In this paper, we propose localized versions of Weisfeiler-Leman (WL) algorithms in an effort to both increase the expressivity, as well as decrease the computational overhead. We focus on the specific problem of subgraph counting an... |
2306.17442 | Designing strong baselines for ternary neural network quantization
through support and mass equalization | Deep neural networks (DNNs) offer the highest performance in a wide range of
applications in computer vision. These results rely on over-parameterized
backbones, which are expensive to run. This computational burden can be
dramatically reduced by quantizing (in either data-free (DFQ), post-training
(PTQ) or quantizatio... | Edouard Yvinec, Arnaud Dapogny, Kevin Bailly | 2023-06-30T07:35:07Z | http://arxiv.org/abs/2306.17442v1 | Designing Strong Baselines for Ternary Neural Network Quantization Through Support and Mass Equalization
###### Abstract
Deep neural networks (DNNs) offer the highest performance in a wide range of applications in computer vision. These results rely on over-parameterized backbones, which are expensive to run. This co... |
2309.10225 | VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual
Place Recognition | Spiking Neural Networks (SNNs) are at the forefront of neuromorphic computing
thanks to their potential energy-efficiency, low latencies, and capacity for
continual learning. While these capabilities are well suited for robotics
tasks, SNNs have seen limited adaptation in this field thus far. This work
introduces a SNN... | Adam D. Hines, Peter G. Stratton, Michael Milford, Tobias Fischer | 2023-09-19T00:38:05Z | http://arxiv.org/abs/2309.10225v2 | # VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition
###### Abstract
Spiking Neural Networks (SNNs) are at the forefront of neuromorphic computing thanks to their potential energy-efficiency, low latencies, and capacity for continual learning. While these capabilities are well sui... |
2309.06645 | Bregman Graph Neural Network | Numerous recent research on graph neural networks (GNNs) has focused on
formulating GNN architectures as an optimization problem with the smoothness
assumption. However, in node classification tasks, the smoothing effect induced
by GNNs tends to assimilate representations and over-homogenize labels of
connected nodes, ... | Jiayu Zhai, Lequan Lin, Dai Shi, Junbin Gao | 2023-09-12T23:54:24Z | http://arxiv.org/abs/2309.06645v1 | # Bregman Graph Neural Network
###### Abstract
Numerous recent research on graph neural networks (GNNs) has focused on formulating GNN architectures as an optimization problem with the smoothness assumption. However, in node classification tasks, the smoothing effect induced by GNNs tends to assimilate representation... |
2309.16318 | DeepPCR: Parallelizing Sequential Operations in Neural Networks | Parallelization techniques have become ubiquitous for accelerating inference
and training of deep neural networks. Despite this, several operations are
still performed in a sequential manner. For instance, the forward and backward
passes are executed layer-by-layer, and the output of diffusion models is
produced by app... | Federico Danieli, Miguel Sarabia, Xavier Suau, Pau Rodríguez, Luca Zappella | 2023-09-28T10:15:30Z | http://arxiv.org/abs/2309.16318v2 | # DeepPCR: Parallelizing Sequential Operations in Neural Networks
###### Abstract
Parallelization techniques have become ubiquitous for accelerating inference and training of deep neural networks. Despite this, several operations are still performed in a sequential manner. For instance, the forward and backward passe... |
2309.03846 | Scalable Forward Reachability Analysis of Multi-Agent Systems with
Neural Network Controllers | Neural networks (NNs) have been shown to learn complex control laws
successfully, often with performance advantages or decreased computational cost
compared to alternative methods. Neural network controllers (NNCs) are,
however, highly sensitive to disturbances and uncertainty, meaning that it can
be challenging to mak... | Oliver Gates, Matthew Newton, Konstantinos Gatsis | 2023-09-07T17:02:09Z | http://arxiv.org/abs/2309.03846v1 | # Scalable Forward Reachability Analysis of Multi-Agent Systems with Neural Network Controllers
###### Abstract
Neural networks (NNs) have been shown to learn complex control laws successfully, often with performance advantages or decreased computational cost compared to alternative methods. Neural network controller... |
2303.17883 | Single-ended Recovery of Optical fiber Transmission Matrices using
Neural Networks | Ultra-thin multimode optical fiber imaging promises next-generation medical
endoscopes reaching high image resolution for deep tissues. However, current
technology suffers from severe optical distortion, as the fiber's calibration
is sensitive to bending and temperature and thus requires in vivo
re-measurement with acc... | Yijie Zheng, George S. D. Gordon | 2023-03-31T08:35:22Z | http://arxiv.org/abs/2303.17883v2 | # Single-ended Recovery of Optical fiber Transmission Matrices using Neural Networks
###### Abstract
Ultra-thin multimode optical fiber imaging technology promises next-generation medical endoscopes that provide high image resolution deep in the body (e.g. blood vessels, brain). However, this technology suffers from ... |
2301.00012 | GANExplainer: GAN-based Graph Neural Networks Explainer | With the rapid deployment of graph neural networks (GNNs) based techniques
into a wide range of applications such as link prediction, node classification,
and graph classification the explainability of GNNs has become an indispensable
component for predictive and trustworthy decision-making. Thus, it is critical
to exp... | Yiqiao Li, Jianlong Zhou, Boyuan Zheng, Fang Chen | 2022-12-30T23:11:24Z | http://arxiv.org/abs/2301.00012v1 | # GANExplainer: GAN-based Graph Neural Networks Explainer
###### Abstract
With the rapid deployment of graph neural networks (GNNs) based techniques into a wide range of applications such as link prediction, node classification, and graph classification the explainability of GNNs has become an indispensable component... |
2309.07163 | Systematic Review of Experimental Paradigms and Deep Neural Networks for
Electroencephalography-Based Cognitive Workload Detection | This article summarizes a systematic review of the electroencephalography
(EEG)-based cognitive workload (CWL) estimation. The focus of the article is
twofold: identify the disparate experimental paradigms used for reliably
eliciting discreet and quantifiable levels of cognitive load and the specific
nature and represe... | Vishnu KN, Cota Navin Gupta | 2023-09-11T14:27:22Z | http://arxiv.org/abs/2309.07163v1 | Systematic Review of Experimental Paradigms and Deep Neural Networks for Electroencephalography - Based Cognitive Workload Detection
###### Abstract
This article summarizes a systematic review of the electroencephalography (EEG) - based cognitive workload (CWL) estimation. The focus of the article is two-fold, identi... |
2301.00007 | Selected aspects of complex, hypercomplex and fuzzy neural networks | This short report reviews the current state of the research and methodology
on theoretical and practical aspects of Artificial Neural Networks (ANN). It
was prepared to gather state-of-the-art knowledge needed to construct complex,
hypercomplex and fuzzy neural networks.
The report reflects the individual interests o... | Agnieszka Niemczynowicz, Radosław A. Kycia, Maciej Jaworski, Artur Siemaszko, Jose M. Calabuig, Lluis M. García-Raffi, Baruch Schneider, Diana Berseghyan, Irina Perfiljeva, Vilem Novak, Piotr Artiemjew | 2022-12-29T12:26:56Z | http://arxiv.org/abs/2301.00007v2 | # Selected aspects of complex, hypercomplex and fuzzy neural networks
###### Abstract
We present a new class of hypercomplex and fuzzy neural networks, which are the most common examples of hypercomplex and fuzzy neural networks. We show that hypercomplex and fuzzy neural networks are capable of complex and complex, ... |
2309.13881 | Skip-Connected Neural Networks with Layout Graphs for Floor Plan
Auto-Generation | With the advent of AI and computer vision techniques, the quest for automated
and efficient floor plan designs has gained momentum. This paper presents a
novel approach using skip-connected neural networks integrated with layout
graphs. The skip-connected layers capture multi-scale floor plan information,
and the encod... | Yuntae Jeon, Dai Quoc Tran, Seunghee Park | 2023-09-25T05:20:57Z | http://arxiv.org/abs/2309.13881v2 | # Skip-Connected Neural Networks with Layout Graphs for
###### Abstract
With the advent of AI and computer vision techniques, the quest for automated and efficient floor plan designs has gained momentum. This paper presents a novel approach using skip-connected neural networks integrated with layout graphs. The skip-... |
2309.07412 | Advancing Regular Language Reasoning in Linear Recurrent Neural Networks | In recent studies, linear recurrent neural networks (LRNNs) have achieved
Transformer-level performance in natural language and long-range modeling,
while offering rapid parallel training and constant inference cost. With the
resurgence of interest in LRNNs, we study whether they can learn the hidden
rules in training ... | Ting-Han Fan, Ta-Chung Chi, Alexander I. Rudnicky | 2023-09-14T03:36:01Z | http://arxiv.org/abs/2309.07412v2 | # Advancing Regular Language Reasoning in
###### Abstract
In recent studies, linear recurrent neural networks (LRNNs) have achieved Transformer-level performance in natural language modeling and long-range modeling while offering rapid parallel training and constant inference costs. With the resurged interest in LRNN... |
2309.03890 | XpookyNet: Advancement in Quantum System Analysis through Convolutional
Neural Networks for Detection of Entanglement | The application of machine learning models in quantum information theory has
surged in recent years, driven by the recognition of entanglement and quantum
states, which are the essence of this field. However, most of these studies
rely on existing prefabricated models, leading to inadequate accuracy. This
work aims to ... | Ali Kookani, Yousef Mafi, Payman Kazemikhah, Hossein Aghababa, Kazim Fouladi, Masoud Barati | 2023-09-07T17:52:43Z | http://arxiv.org/abs/2309.03890v4 | XpookyNet: Advancement in Quantum System Analysis through Convolutional Neural Networks for Detection of Entanglement
###### Abstract
The application of machine learning models in quantum information theory has surged in recent years, driven by the recognition of entanglement and quantum states, which are the essence... |
2309.06081 | Information Flow in Graph Neural Networks: A Clinical Triage Use Case | Graph Neural Networks (GNNs) have gained popularity in healthcare and other
domains due to their ability to process multi-modal and multi-relational
graphs. However, efficient training of GNNs remains challenging, with several
open research questions. In this paper, we investigate how the flow of
embedding information ... | Víctor Valls, Mykhaylo Zayats, Alessandra Pascale | 2023-09-12T09:18:12Z | http://arxiv.org/abs/2309.06081v1 | # Information Flow in Graph Neural Networks:
###### Abstract
Graph Neural Networks (GNNs) have gained popularity in healthcare and other domains due to their ability to process multi-modal and multi-relational graphs. However, efficient training of GNNs remains challenging, with several open research questions. In th... |
2309.05826 | KD-FixMatch: Knowledge Distillation Siamese Neural Networks | Semi-supervised learning (SSL) has become a crucial approach in deep learning
as a way to address the challenge of limited labeled data. The success of deep
neural networks heavily relies on the availability of large-scale high-quality
labeled data. However, the process of data labeling is time-consuming and
unscalable... | Chien-Chih Wang, Shaoyuan Xu, Jinmiao Fu, Yang Liu, Bryan Wang | 2023-09-11T21:11:48Z | http://arxiv.org/abs/2309.05826v1 | # KD-FixMatch: Knowledge Distillation Siamese Neural Networks
###### Abstract
Semi-supervised learning (SSL) has become a crucial approach in deep learning as a way to address the challenge of limited labeled data. The success of deep neural networks heavily relies on the availability of large-scale high-quality labe... |
2301.13694 | Are Defenses for Graph Neural Networks Robust? | A cursory reading of the literature suggests that we have made a lot of
progress in designing effective adversarial defenses for Graph Neural Networks
(GNNs). Yet, the standard methodology has a serious flaw - virtually all of the
defenses are evaluated against non-adaptive attacks leading to overly
optimistic robustne... | Felix Mujkanovic, Simon Geisler, Stephan Günnemann, Aleksandar Bojchevski | 2023-01-31T15:11:48Z | http://arxiv.org/abs/2301.13694v1 | # Are Defenses for Graph Neural Networks Robust?
###### Abstract
A cursory reading of the literature suggests that we have made a lot of progress in designing effective adversarial defenses for Graph Neural Networks (GNNs). Yet, the standard methodology has a serious flaw - virtually all of the defenses are evaluated... |
2309.05818 | Rice Plant Disease Detection and Diagnosis using Deep Convolutional
Neural Networks and Multispectral Imaging | Rice is considered a strategic crop in Egypt as it is regularly consumed in
the Egyptian people's diet. Even though Egypt is the highest rice producer in
Africa with a share of 6 million tons per year, it still imports rice to
satisfy its local needs due to production loss, especially due to rice disease.
Rice blast di... | Yara Ali Alnaggar, Ahmad Sebaq, Karim Amer, ElSayed Naeem, Mohamed Elhelw | 2023-09-11T20:51:21Z | http://arxiv.org/abs/2309.05818v1 | Rice Plant Disease Detection and Diagnosis using Deep Convolutional Neural Networks and Multispectral Imaging
###### Abstract
Rice is considered a strategic crop in Egypt as it is regularly consumed in the Egyptian people's diet. Even though Egypt is the highest rice producer in Africa with a share of 6 million tons ... |
2309.04737 | Learning Spiking Neural Network from Easy to Hard task | Starting with small and simple concepts, and gradually introducing complex
and difficult concepts is the natural process of human learning. Spiking Neural
Networks (SNNs) aim to mimic the way humans process information, but current
SNNs models treat all samples equally, which does not align with the principles
of human... | Lingling Tang, Jiangtao Hu, Hua Yu, Surui Liu, Jielei Chu | 2023-09-09T09:46:32Z | http://arxiv.org/abs/2309.04737v3 | # Learning Spiking Neural Network from Easy to Hard task
###### Abstract
Starting with small and simple concepts, and gradually introducing complex and difficult concepts is the natural process of human learning. Spiking Neural Networks (SNNs) aim to mimic the way humans process information, but current SNNs models t... |
2309.13459 | A Model-Agnostic Graph Neural Network for Integrating Local and Global
Information | Graph Neural Networks (GNNs) have achieved promising performance in a variety
of graph-focused tasks. Despite their success, however, existing GNNs suffer
from two significant limitations: a lack of interpretability in results due to
their black-box nature, and an inability to learn representations of varying
orders. T... | Wenzhuo Zhou, Annie Qu, Keiland W. Cooper, Norbert Fortin, Babak Shahbaba | 2023-09-23T19:07:03Z | http://arxiv.org/abs/2309.13459v3 | # A Model-Agnostic Graph Neural Network for Integrating Local and Global Information
###### Abstract
Graph Neural Networks (GNNs) have achieved promising performance in a variety of graph-focused tasks. Despite their success, existing GNNs suffer from two significant limitations: a lack of interpretability in results... |
2309.14691 | On the Computational Complexity and Formal Hierarchy of Second Order
Recurrent Neural Networks | Artificial neural networks (ANNs) with recurrence and self-attention have
been shown to be Turing-complete (TC). However, existing work has shown that
these ANNs require multiple turns or unbounded computation time, even with
unbounded precision in weights, in order to recognize TC grammars. However,
under constraints ... | Ankur Mali, Alexander Ororbia, Daniel Kifer, Lee Giles | 2023-09-26T06:06:47Z | http://arxiv.org/abs/2309.14691v1 | # On the Computational Complexity and Formal Hierarchy of
###### Abstract
Artificial neural networks (ANNs) with recurrence and self-attention have been shown to be Turing-complete (TC). However, existing work has shown that these ANNs require multiple turns or unbounded computation time, even with unbounded precisio... |
2309.09171 | On the Connection Between Riemann Hypothesis and a Special Class of
Neural Networks | The Riemann hypothesis (RH) is a long-standing open problem in mathematics.
It conjectures that non-trivial zeros of the zeta function all have real part
equal to 1/2. The extent of the consequences of RH is far-reaching and touches
a wide spectrum of topics including the distribution of prime numbers, the
growth of ar... | Soufiane Hayou | 2023-09-17T05:50:12Z | http://arxiv.org/abs/2309.09171v1 | # On the Connection Between Riemann Hypothesis
###### Abstract
The Riemann hypothesis (\(\mathcal{RH}\)) is a long-standing open problem in mathematics. It conjectures that non-trivial zeros of the zeta function all lie on the line \(\text{Re}(z)=1/2\). The extent of the consequences of \(\mathcal{RH}\) is far-reachi... |
2305.19921 | Deep Neural Network Estimation in Panel Data Models | In this paper we study neural networks and their approximating power in panel
data models. We provide asymptotic guarantees on deep feed-forward neural
network estimation of the conditional mean, building on the work of Farrell et
al. (2021), and explore latent patterns in the cross-section. We use the
proposed estimat... | Ilias Chronopoulos, Katerina Chrysikou, George Kapetanios, James Mitchell, Aristeidis Raftapostolos | 2023-05-31T14:58:31Z | http://arxiv.org/abs/2305.19921v1 | # Deep Neural Network Estimation in Panel Data Models+
###### Abstract
In this paper we study neural networks and their approximating power in panel data models. We provide asymptotic guarantees on deep feed-forward neural network estimation of the conditional mean, building on the work of Farrell et al. (2021), and ... |
2308.16422 | Dilated convolutional neural network for detecting extreme-mass-ratio
inspirals | The detection of Extreme Mass Ratio Inspirals (EMRIs) is intricate due to
their complex waveforms, extended duration, and low signal-to-noise ratio
(SNR), making them more challenging to be identified compared to compact binary
coalescences. While matched filtering-based techniques are known for their
computational dem... | Tianyu Zhao, Yue Zhou, Ruijun Shi, Zhoujian Cao, Zhixiang Ren | 2023-08-31T03:16:38Z | http://arxiv.org/abs/2308.16422v3 | # DECODE: DilatEd COnvolutional neural network for Detecting Extreme-mass-ratio inspirals
###### Abstract
The detection of Extreme Mass Ratio Inspirals (EMRIs) is intricate due to their complex waveforms, extended duration, and low signal-to-noise ratio (SNR), making them more challenging to be identified compared to... |
2305.19935 | Neural Network Approach to the Simulation of Entangled States with One
Bit of Communication | Bell's theorem states that Local Hidden Variables (LHVs) cannot fully explain
the statistics of measurements on some entangled quantum states. It is natural
to ask how much supplementary classical communication would be needed to
simulate them. We study two long-standing open questions in this field with
neural network... | Peter Sidajaya, Aloysius Dewen Lim, Baichu Yu, Valerio Scarani | 2023-05-31T15:19:00Z | http://arxiv.org/abs/2305.19935v5 | # Neural Network Approach to the Simulation of Entangled States with One Bit of Communication
###### Abstract
Bell's theorem states that Local Hidden Variables (LHVs) cannot fully explain the statistics of measurements on some entangled quantum states. It is natural to ask how much supplementary classical communicati... |
2309.00168 | Pose-Graph Attentional Graph Neural Network for Lidar Place Recognition | This paper proposes a pose-graph attentional graph neural network, called
P-GAT, which compares (key)nodes between sequential and non-sequential
sub-graphs for place recognition tasks as opposed to a common frame-to-frame
retrieval problem formulation currently implemented in SOTA place recognition
methods. P-GAT uses ... | Milad Ramezani, Liang Wang, Joshua Knights, Zhibin Li, Pauline Pounds, Peyman Moghadam | 2023-08-31T23:17:44Z | http://arxiv.org/abs/2309.00168v3 | # Pose-Graph Attentional Graph Neural Network
###### Abstract
This paper proposes a pose-graph attentional graph neural network, called P-GAT, which compares (key)nodes between sequential and non-sequential sub-graphs for place recognition tasks as opposed to a common frame-to-frame retrieval problem formulation curr... |
2309.11717 | A class-weighted supervised contrastive learning long-tailed bearing
fault diagnosis approach using quadratic neural network | Deep learning has achieved remarkable success in bearing fault diagnosis.
However, its performance oftentimes deteriorates when dealing with highly
imbalanced or long-tailed data, while such cases are prevalent in industrial
settings because fault is a rare event that occurs with an extremely low
probability. Conventio... | Wei-En Yu, Jinwei Sun, Shiping Zhang, Xiaoge Zhang, Jing-Xiao Liao | 2023-09-21T01:36:46Z | http://arxiv.org/abs/2309.11717v1 | A class-weighted supervised contrastive learning long-tailed bearing fault diagnosis approach using quadratic neural network
###### Abstract
Deep learning has achieved remarkable success in bearing fault diagnosis. However, its performance oftentimes deteriorates when dealing with highly imbalanced or long-tailed dat... |
2309.15559 | Towards Faithful Neural Network Intrinsic Interpretation with Shapley
Additive Self-Attribution | Self-interpreting neural networks have garnered significant interest in
research. Existing works in this domain often (1) lack a solid theoretical
foundation ensuring genuine interpretability or (2) compromise model
expressiveness. In response, we formulate a generic Additive Self-Attribution
(ASA) framework. Observing... | Ying Sun, Hengshu Zhu, Hui Xiong | 2023-09-27T10:31:48Z | http://arxiv.org/abs/2309.15559v1 | # Towards Faithful Neural Network Intrinsic Interpretation with Shapley Additive Self-Attribution
###### Abstract
Self-interpreting neural networks have garnered significant interest in research. Existing works in this domain often (1) lack a solid theoretical foundation ensuring genuine interpretability or (2) compr... |
2309.04332 | Graph Neural Networks Use Graphs When They Shouldn't | Predictions over graphs play a crucial role in various domains, including
social networks and medicine. Graph Neural Networks (GNNs) have emerged as the
dominant approach for learning on graph data. Although a graph-structure is
provided as input to the GNN, in some cases the best solution can be obtained
by ignoring i... | Maya Bechler-Speicher, Ido Amos, Ran Gilad-Bachrach, Amir Globerson | 2023-09-08T13:59:18Z | http://arxiv.org/abs/2309.04332v2 | # Graph Neural Networks Use Graphs When They Shouldn't
###### Abstract
Predictions over graphs play a crucial role in various domains, including social networks, molecular biology, medicine, and more. Graph Neural Networks (GNNs) have emerged as the dominant approach for learning on graph data. Instances of graph lab... |
2309.13302 | Gaining the Sparse Rewards by Exploring Lottery Tickets in Spiking
Neural Network | Deploying energy-efficient deep learning algorithms on computational-limited
devices, such as robots, is still a pressing issue for real-world applications.
Spiking Neural Networks (SNNs), a novel brain-inspired algorithm, offer a
promising solution due to their low-latency and low-energy properties over
traditional Ar... | Hao Cheng, Jiahang Cao, Erjia Xiao, Mengshu Sun, Renjing Xu | 2023-09-23T08:24:36Z | http://arxiv.org/abs/2309.13302v4 | # Gaining the Sparse Rewards by Exploring Binary Lottery Tickets in Spiking Neural Networks
###### Abstract
Spiking Neural Network (SNN) as a brain-inspired strategy receives lots of attention because of the high-sparsity and low-power properties derived from its inherent spiking information state. To further improve... |
2309.11515 | Towards Differential Privacy in Sequential Recommendation: A Noisy Graph
Neural Network Approach | With increasing frequency of high-profile privacy breaches in various online
platforms, users are becoming more concerned about their privacy. And
recommender system is the core component of online platforms for providing
personalized service, consequently, its privacy preservation has attracted
great attention. As the... | Wentao Hu, Hui Fang | 2023-09-17T03:12:33Z | http://arxiv.org/abs/2309.11515v2 | # Towards Differential Privacy in Sequential Recommendation: A Noisy Graph Neural Network Approach
###### Abstract
With increasing frequency of high-profile privacy breaches in various online platforms, users are becoming more concerned about their privacy. And recommender system is the core component of online platf... |
2310.12157 | Desynchronization of large-scale neural networks by stabilizing unknown
unstable incoherent equilibrium states | In large-scale neural networks, coherent limit cycle oscillations usually
coexist with unstable incoherent equilibrium states, which are not observed
experimentally. We implement a first-order dynamic controller to stabilize
unknown equilibrium states and suppress coherent oscillations. The
stabilization of incoherent ... | Tatjana Pyragiene, Kestutis Pyragas | 2023-09-15T12:00:17Z | http://arxiv.org/abs/2310.12157v1 | Desynchronization of large-scale neural networks by stabilizing unknown unstable incoherent equilibrium states
###### Abstract
In large-scale neural networks, coherent limit cycle oscillations usually coexist with unstable incoherent equilibrium states, which are not observed experimentally. We implement a first-orde... |
2305.19868 | Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANN | Spiking neural networks (SNNs) have shown advantages in computation and
energy efficiency over traditional artificial neural networks (ANNs) thanks to
their event-driven representations. SNNs also replace weight multiplications in
ANNs with additions, which are more energy-efficient and less computationally
intensive. ... | Yangfan Hu, Qian Zheng, Xudong Jiang, Gang Pan | 2023-05-31T14:04:41Z | http://arxiv.org/abs/2305.19868v1 | # Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANN
###### Abstract
Spiking neural networks (SNNs) have shown advantages in computation and energy efficiency over traditional artificial neural networks (ANNs) thanks to their event-driven representations. SNNs also replace weight multiplications in ANN... |
2301.00169 | Generative Graph Neural Networks for Link Prediction | Inferring missing links or detecting spurious ones based on observed graphs,
known as link prediction, is a long-standing challenge in graph data analysis.
With the recent advances in deep learning, graph neural networks have been used
for link prediction and have achieved state-of-the-art performance.
Nevertheless, ex... | Xingping Xian, Tao Wu, Xiaoke Ma, Shaojie Qiao, Yabin Shao, Chao Wang, Lin Yuan, Yu Wu | 2022-12-31T10:07:19Z | http://arxiv.org/abs/2301.00169v1 | # Generative Graph Neural Networks for Link Prediction
###### Abstract
Inferring missing links or detecting spurious ones based on observed graphs, known as link prediction, is a long-standing challenge in graph data analysis. With the recent advances in deep learning, graph neural networks have been used for link pr... |
2305.20028 | A Study of Bayesian Neural Network Surrogates for Bayesian Optimization | Bayesian optimization is a highly efficient approach to optimizing objective
functions which are expensive to query. These objectives are typically
represented by Gaussian process (GP) surrogate models which are easy to
optimize and support exact inference. While standard GP surrogates have been
well-established in Bay... | Yucen Lily Li, Tim G. J. Rudner, Andrew Gordon Wilson | 2023-05-31T17:00:00Z | http://arxiv.org/abs/2305.20028v2 | # A Study of Bayesian Neural Network Surrogates
###### Abstract
Bayesian optimization is a highly efficient approach to optimizing objective functions which are expensive to query. These objectives are typically represented by Gaussian process (GP) surrogate models which are easy to optimize and support exact inferen... |
2305.19468 | Efficient Implementation of a Multi-Layer Gradient-Free Online-Trainable
Spiking Neural Network on FPGA | This paper presents an efficient hardware implementation of the recently
proposed Optimized Deep Event-driven Spiking Neural Network Architecture
(ODESA). ODESA is the first network to have end-to-end multi-layer online local
supervised training without using gradients and has the combined adaptation of
weights and thr... | Ali Mehrabi, Yeshwanth Bethi, André van Schaik, Andrew Wabnitz, Saeed Afshar | 2023-05-31T00:34:15Z | http://arxiv.org/abs/2305.19468v1 | Efficient Implementation of a Multi-Layer Gradient-Free Online-Trainable Spiking Neural Network on FPGA
###### Abstract
This paper presents an efficient hardware implementation of the recently proposed Optimized Deep Event-driven Spiking Neural Network Architecture (ODESA). ODESA is the first network to have end-to-e... |
2310.20671 | Density Matrix Emulation of Quantum Recurrent Neural Networks for
Multivariate Time Series Prediction | Quantum Recurrent Neural Networks (QRNNs) are robust candidates to model and
predict future values in multivariate time series. However, the effective
implementation of some QRNN models is limited by the need of mid-circuit
measurements. Those increase the requirements for quantum hardware, which in
the current NISQ er... | José Daniel Viqueira, Daniel Faílde, Mariamo M. Juane, Andrés Gómez, David Mera | 2023-10-31T17:32:11Z | http://arxiv.org/abs/2310.20671v1 | Density Matrix Emulation of Quantum Recurrent Neural Networks for Multivariate Time Series Prediction
###### Abstract
Quantum Recurrent Neural Networks (QRNNs) are robust candidates to model and predict future values in multivariate time series. However, the effective implementation of some QRNN models is limited by ... |
2309.15018 | Unidirectional brain-computer interface: Artificial neural network
encoding natural images to fMRI response in the visual cortex | While significant advancements in artificial intelligence (AI) have catalyzed
progress across various domains, its full potential in understanding visual
perception remains underexplored. We propose an artificial neural network
dubbed VISION, an acronym for "Visual Interface System for Imaging Output of
Neural activity... | Ruixing Liang, Xiangyu Zhang, Qiong Li, Lai Wei, Hexin Liu, Avisha Kumar, Kelley M. Kempski Leadingham, Joshua Punnoose, Leibny Paola Garcia, Amir Manbachi | 2023-09-26T15:38:26Z | http://arxiv.org/abs/2309.15018v1 | Unidirectional Brain-Computer Interface: Artificial Neural Network Encoding Natural Images to fMRI Response in the Visual Cortex
###### Abstract
While significant advancements in artificial intelligence (AI) have catalyzed progress across various domains, its full potential in understanding visual perception remains ... |
2309.07193 | A Robust SINDy Approach by Combining Neural Networks and an Integral
Form | The discovery of governing equations from data has been an active field of
research for decades. One widely used methodology for this purpose is sparse
regression for nonlinear dynamics, known as SINDy. Despite several attempts,
noisy and scarce data still pose a severe challenge to the success of the SINDy
approach. I... | Ali Forootani, Pawan Goyal, Peter Benner | 2023-09-13T10:50:04Z | http://arxiv.org/abs/2309.07193v1 | # A Robust SINDy Approach by Combining Neural Networks and an Integral Form
###### Abstract
The discovery of governing equations from data has been an active field of research for decades. One widely used methodology for this purpose is sparse regression for nonlinear dynamics, known as SINDy. Despite several attempt... |
2309.14845 | Graph Neural Network Based Method for Path Planning Problem | Sampling-based path planning is a widely used method in robotics,
particularly in high-dimensional state space. Among the whole process of the
path planning, collision detection is the most time-consuming operation. In
this paper, we propose a learning-based path planning method that aims to
reduce the number of collis... | Xingrong Diao, Wenzheng Chi, Jiankun Wang | 2023-09-26T11:20:57Z | http://arxiv.org/abs/2309.14845v2 | # Graph Neural Network Based Method for Path Planning Problem
###### Abstract
Sampling-based path planning is a widely used method in robotics, particularly in high-dimensional state space. Among the whole process of path planning, collision detection is the most time-consuming operation. In this paper, we propose a ... |
2308.16406 | CktGNN: Circuit Graph Neural Network for Electronic Design Automation | The electronic design automation of analog circuits has been a longstanding
challenge in the integrated circuit field due to the huge design space and
complex design trade-offs among circuit specifications. In the past decades,
intensive research efforts have mostly been paid to automate the transistor
sizing with a gi... | Zehao Dong, Weidong Cao, Muhan Zhang, Dacheng Tao, Yixin Chen, Xuan Zhang | 2023-08-31T02:20:25Z | http://arxiv.org/abs/2308.16406v2 | # CktGNN: Circuit Graph Neural Network for Electronic Design Automation
###### Abstract
The electronic design automation of analog circuits has been a longstanding challenge in the integrated circuit field due to the huge design space and complex design trade-offs among circuit specifications. In the past decades, in... |
2309.17357 | Module-wise Training of Neural Networks via the Minimizing Movement
Scheme | Greedy layer-wise or module-wise training of neural networks is compelling in
constrained and on-device settings where memory is limited, as it circumvents a
number of problems of end-to-end back-propagation. However, it suffers from a
stagnation problem, whereby early layers overfit and deeper layers stop
increasing t... | Skander Karkar, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari | 2023-09-29T16:03:25Z | http://arxiv.org/abs/2309.17357v3 | # Module-wise Training of Neural Networks via the Minimizing Movement Scheme
###### Abstract
Greedy layer-wise or module-wise training of neural networks is compelling in constrained and on-device settings where memory is limited, as it circumvents a number of problems of end-to-end back-propagation. However, it suff... |
2309.16049 | Neural Network Augmented Kalman Filter for Robust Acoustic Howling
Suppression | Acoustic howling suppression (AHS) is a critical challenge in audio
communication systems. In this paper, we propose a novel approach that
leverages the power of neural networks (NN) to enhance the performance of
traditional Kalman filter algorithms for AHS. Specifically, our method involves
the integration of NN modul... | Yixuan Zhang, Hao Zhang, Meng Yu, Dong Yu | 2023-09-27T22:07:00Z | http://arxiv.org/abs/2309.16049v1 | # Neural Network Augmented Kalman Filter for Robust Acoustic Howling Suppression
###### Abstract
Acoustic howling suppression (AHS) is a critical challenge in audio communication systems. In this paper, we propose a novel approach that leverages the power of neural networks (NN) to enhance the performance of traditio... |
2309.04303 | Fast Bayesian gravitational wave parameter estimation using
convolutional neural networks | The determination of the physical parameters of gravitational wave events is
a fundamental pillar in the analysis of the signals observed by the current
ground-based interferometers. Typically, this is done using Bayesian inference
approaches which, albeit very accurate, are very computationally expensive. We
propose a... | M. Andrés-Carcasona, M. Martinez, Ll. M. Mir | 2023-09-08T13:04:34Z | http://arxiv.org/abs/2309.04303v2 | # Fast Bayesian gravitational wave parameter estimation using convolutional neural networks
###### Abstract
The determination of the physical parameters of gravitational wave events is a fundamental pillar in the analysis of the signals observed by the current ground-based interferometers. Typically, this is done usi... |
2309.04317 | Actor critic learning algorithms for mean-field control with moment
neural networks | We develop a new policy gradient and actor-critic algorithm for solving
mean-field control problems within a continuous time reinforcement learning
setting. Our approach leverages a gradient-based representation of the value
function, employing parametrized randomized policies. The learning for both the
actor (policy) ... | Huyên Pham, Xavier Warin | 2023-09-08T13:29:57Z | http://arxiv.org/abs/2309.04317v1 | # Actor critic learning algorithms for mean-field control
###### Abstract
We develop a new policy gradient and actor-critic algorithm for solving mean-field control problems within a continuous time reinforcement learning setting. Our approach leverages a gradient-based representation of the value function, employing... |
2309.12212 | SupeRBNN: Randomized Binary Neural Network Using Adiabatic
Superconductor Josephson Devices | Adiabatic Quantum-Flux-Parametron (AQFP) is a superconducting logic with
extremely high energy efficiency. By employing the distinct polarity of current
to denote logic `0' and `1', AQFP devices serve as excellent carriers for
binary neural network (BNN) computations. Although recent research has made
initial strides t... | Zhengang Li, Geng Yuan, Tomoharu Yamauchi, Zabihi Masoud, Yanyue Xie, Peiyan Dong, Xulong Tang, Nobuyuki Yoshikawa, Devesh Tiwari, Yanzhi Wang, Olivia Chen | 2023-09-21T16:14:42Z | http://arxiv.org/abs/2309.12212v1 | # SupeRBNN: Randomized Binary Neural Network Using Adiabatic Superconductor Josephson Devices
###### Abstract
Adiabatic Quantum-Flux-Parametron (AQFP) is a superconducting logic with extremely high energy efficiency. By employing the distinct polarity of current to denote logic '0' and '1', AQFP devices serve as exce... |
2309.16048 | Advancing Acoustic Howling Suppression through Recursive Training of
Neural Networks | In this paper, we introduce a novel training framework designed to
comprehensively address the acoustic howling issue by examining its fundamental
formation process. This framework integrates a neural network (NN) module into
the closed-loop system during training with signals generated recursively on
the fly to closel... | Hao Zhang, Yixuan Zhang, Meng Yu, Dong Yu | 2023-09-27T22:02:53Z | http://arxiv.org/abs/2309.16048v1 | # Advancing Acoustic Howling Suppression Through Recursive Training of Neural Networks
###### Abstract
In this paper, we introduce a novel training framework designed to comprehensively address the acoustic howling issue by examining its fundamental formation process. This framework integrates a neural network (NN) m... |
2309.15555 | Low Latency of object detection for spikng neural network | Spiking Neural Networks, as a third-generation neural network, are
well-suited for edge AI applications due to their binary spike nature. However,
when it comes to complex tasks like object detection, SNNs often require a
substantial number of time steps to achieve high performance. This limitation
significantly hamper... | Nemin Qiu, Chuang Zhu | 2023-09-27T10:26:19Z | http://arxiv.org/abs/2309.15555v1 | # Low Latency Spiking Neural Network for Object Detection
###### Abstract
Spiking Neural Networks (SNNs), as a third-generation neural network, are well-suited for edge AI applications due to their binary spike nature. However, when it comes to complex tasks like object detection, SNNs often require a substantial num... |
2308.16375 | A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and
Applications | Graph Neural Networks (GNNs) have gained significant attention owing to their
ability to handle graph-structured data and the improvement in practical
applications. However, many of these models prioritize high utility
performance, such as accuracy, with a lack of privacy consideration, which is a
major concern in mode... | Yi Zhang, Yuying Zhao, Zhaoqing Li, Xueqi Cheng, Yu Wang, Olivera Kotevska, Philip S. Yu, Tyler Derr | 2023-08-31T00:31:08Z | http://arxiv.org/abs/2308.16375v3 | # A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications
###### Abstract
Graph Neural Networks (GNNs) have gained significant attention owing to their ability to handle graph-structured data and the improvement in practical applications. However, many of these models prioritize high uti... |
2309.11928 | Video Scene Location Recognition with Neural Networks | This paper provides an insight into the possibility of scene recognition from
a video sequence with a small set of repeated shooting locations (such as in
television series) using artificial neural networks. The basic idea of the
presented approach is to select a set of frames from each scene, transform them
by a pre-t... | Lukáš Korel, Petr Pulc, Jiří Tumpach, Martin Holeňa | 2023-09-21T09:42:39Z | http://arxiv.org/abs/2309.11928v1 | # Video Scene Location Recognition with Neural Networks
###### Abstract
This paper provides an insight into the possibility of scene recognition from a video sequence with a small set of repeated shooting locations (such as in television series) using artificial neural networks. The basic idea of the presented approa... |
2303.00498 | Adaptive Hybrid Spatial-Temporal Graph Neural Network for Cellular
Traffic Prediction | Cellular traffic prediction is an indispensable part for intelligent
telecommunication networks. Nevertheless, due to the frequent user mobility and
complex network scheduling mechanisms, cellular traffic often inherits
complicated spatial-temporal patterns, making the prediction incredibly
challenging. Although recent... | Xing Wang, Kexin Yang, Zhendong Wang, Junlan Feng, Lin Zhu, Juan Zhao, Chao Deng | 2023-02-28T06:46:50Z | http://arxiv.org/abs/2303.00498v1 | # Adaptive Hybrid Spatial-Temporal Graph Neural Network for Cellular Traffic Prediction
###### Abstract
Cellular traffic prediction is an indispensable part for intelligent telecommunication networks. Nevertheless, due to the frequent user mobility and complex network scheduling mechanisms, cellular traffic often inh... |
2309.07390 | Unleashing the Power of Depth and Pose Estimation Neural Networks by
Designing Compatible Endoscopic Images | Deep learning models have witnessed depth and pose estimation framework on
unannotated datasets as a effective pathway to succeed in endoscopic
navigation. Most current techniques are dedicated to developing more advanced
neural networks to improve the accuracy. However, existing methods ignore the
special properties o... | Junyang Wu, Yun Gu | 2023-09-14T02:19:38Z | http://arxiv.org/abs/2309.07390v1 | Unleashing the Power of Depth and Pose Estimation Neural Networks by Designing Compatible Endoscopic Images
###### Abstract
Deep learning models have witnessed depth and pose estimation framework on unannotated datasets as a effective pathway to succeed in endoscopic navigation. Most current techniques are dedicated ... |
2309.09550 | Adaptive Reorganization of Neural Pathways for Continual Learning with
Spiking Neural Networks | The human brain can self-organize rich and diverse sparse neural pathways to
incrementally master hundreds of cognitive tasks. However, most existing
continual learning algorithms for deep artificial and spiking neural networks
are unable to adequately auto-regulate the limited resources in the network,
which leads to ... | Bing Han, Feifei Zhao, Wenxuan Pan, Zhaoya Zhao, Xianqi Li, Qingqun Kong, Yi Zeng | 2023-09-18T07:56:40Z | http://arxiv.org/abs/2309.09550v2 | # Adaptive Reorganization of Neural Pathways for Continual Learning with Spiking Neural Networks
###### Abstract
The human brain can self-organize rich and diverse sparse neural pathways to incrementally master hundreds of cognitive tasks. However, most existing continual learning algorithms for deep artificial and s... |
2309.15179 | ParamANN: A Neural Network to Estimate Cosmological Parameters for
$Λ$CDM Universe Using Hubble Measurements | In this article, we employ a machine learning (ML) approach for the
estimations of four fundamental parameters, namely, the Hubble constant
($H_0$), matter ($\Omega_{0m}$), curvature ($\Omega_{0k}$) and vacuum
($\Omega_{0\Lambda}$) densities of non-flat $\Lambda$CDM model. We use $31$
Hubble parameter values measured b... | Srikanta Pal, Rajib Saha | 2023-09-26T18:25:57Z | http://arxiv.org/abs/2309.15179v3 | ParamANN: A Neural Network to Estimate Cosmological Parameters for \(\Lambda\)CDM Universe using Hubble Measurements
###### Abstract
In this article, we employ a machine learning (ML) approach for the estimations of four fundamental parameters, namely, the Hubble constant (\(H_{0}\)), matter (\(\Omega_{0m}\)), curvat... |
2310.12985 | Enabling energy-Efficient object detection with surrogate gradient
descent in spiking neural networks | Spiking Neural Networks (SNNs) are a biologically plausible neural network
model with significant advantages in both event-driven processing and
spatio-temporal information processing, rendering SNNs an appealing choice for
energyefficient object detection. However, the non-differentiability of the
biological neuronal ... | Jilong Luo, Shanlin Xiao, Yinsheng Chen, Zhiyi Yu | 2023-09-07T15:48:00Z | http://arxiv.org/abs/2310.12985v1 | Enabling Energy-Efficient Object Detection with Surrogate Gradient Descent in Spiking Neural Networks
###### Abstract
Spiking Neural Networks (SNNs) are a biologically plausible neural network model with significant advantages in both event-driven processing and spatio-temporal information processing, rendering SNNs ... |
2309.13534 | Comparison of Random Forest and Neural Network Framework for Prediction
of Fatigue Crack Growth Rate in Nickel Superalloys | The rate of fatigue crack growth in Nickle superalloys is a critical factor
of safety in the aerospace industry. A machine learning approach is chosen to
predict the fatigue crack growth rate as a function of the material
composition, material properties and environmental conditions. Random forests
and neural network f... | Raghunandan Pratoori | 2023-09-24T03:08:52Z | http://arxiv.org/abs/2309.13534v1 | Comparison of Random Forest and Neural Network Framework for Prediction of Fatigue Crack Growth Rate in Nickel Superalloys
###### Abstract
The rate of fatigue crack growth in Nickle superalloys is a critical factor of safety in the aerospace industry. A machine learning approach is chosen to predict the fatigue crack... |
2309.16114 | Comparing Active Learning Performance Driven by Gaussian Processes or
Bayesian Neural Networks for Constrained Trajectory Exploration | Robots with increasing autonomy progress our space exploration capabilities,
particularly for in-situ exploration and sampling to stand in for human
explorers. Currently, humans drive robots to meet scientific objectives, but
depending on the robot's location, the exchange of information and driving
commands between th... | Sapphira Akins, Frances Zhu | 2023-09-28T02:45:14Z | http://arxiv.org/abs/2309.16114v1 | Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory Exploration
###### Abstract
Robots with increasing autonomy progress our space exploration capabilities, particularly for in-situ exploration and sampling to stand in for human explorers. Currentl... |
2309.13736 | Geometry of Linear Neural Networks: Equivariance and Invariance under
Permutation Groups | The set of functions parameterized by a linear fully-connected neural network
is a determinantal variety. We investigate the subvariety of functions that are
equivariant or invariant under the action of a permutation group. Examples of
such group actions are translations or $90^\circ$ rotations on images. We
describe s... | Kathlén Kohn, Anna-Laura Sattelberger, Vahid Shahverdi | 2023-09-24T19:40:15Z | http://arxiv.org/abs/2309.13736v2 | # Geometry of Linear Neural Networks:
###### Abstract
The set of functions parameterized by a linear fully-connected neural network is a determinantal variety. We investigate the subvariety of functions that are equivariant or invariant under the action of a permutation group. Examples of such group actions are trans... |
2301.00181 | Smooth Mathematical Function from Compact Neural Networks | This is paper for the smooth function approximation by neural networks (NN).
Mathematical or physical functions can be replaced by NN models through
regression. In this study, we get NNs that generate highly accurate and highly
smooth function, which only comprised of a few weight parameters, through
discussing a few t... | I. K. Hong | 2022-12-31T11:33:24Z | http://arxiv.org/abs/2301.00181v1 | # Smooth Mathematical Function from Compact Neural Networks
###### Abstract
This is paper for the smooth function approximation by neural networks (NN). Mathematical or physical functions can be replaced by NN models through regression. In this study, we get NNs that generate highly accurate and highly smooth functio... |
2309.13907 | HiGNN-TTS: Hierarchical Prosody Modeling with Graph Neural Networks for
Expressive Long-form TTS | Recent advances in text-to-speech, particularly those based on Graph Neural
Networks (GNNs), have significantly improved the expressiveness of short-form
synthetic speech. However, generating human-parity long-form speech with high
dynamic prosodic variations is still challenging. To address this problem, we
expand the... | Dake Guo, Xinfa Zhu, Liumeng Xue, Tao Li, Yuanjun Lv, Yuepeng Jiang, Lei Xie | 2023-09-25T07:07:02Z | http://arxiv.org/abs/2309.13907v2 | # Hignn-Tts: Hierarchical prosody modeling with graph neural networks for expressive long-form TTS
###### Abstract
Recent advances in text-to-speech, particularly those based on Graph Neural Networks (GNNs), have significantly improved the expressiveness of short-form synthetic speech. However, generating human-parit... |
2309.11651 | Drift Control of High-Dimensional RBM: A Computational Method Based on
Neural Networks | Motivated by applications in queueing theory, we consider a stochastic
control problem whose state space is the $d$-dimensional positive orthant. The
controlled process $Z$ evolves as a reflected Brownian motion whose covariance
matrix is exogenously specified, as are its directions of reflection from the
orthant's bou... | Baris Ata, J. Michael Harrison, Nian Si | 2023-09-20T21:32:58Z | http://arxiv.org/abs/2309.11651v4 | # Drift Control of High-Dimensional RBM: A Computational Method Based on Neural Networks
###### Abstract
Motivated by applications in queueing theory, we consider a stochastic control problem whose state space is the \(d\)-dimensional positive orthant. The controlled process \(Z\) evolves as a reflected Brownian moti... |
2306.17670 | Learning Delays in Spiking Neural Networks using Dilated Convolutions
with Learnable Spacings | Spiking Neural Networks (SNNs) are a promising research direction for
building power-efficient information processing systems, especially for
temporal tasks such as speech recognition. In SNNs, delays refer to the time
needed for one spike to travel from one neuron to another. These delays matter
because they influence... | Ilyass Hammouamri, Ismail Khalfaoui-Hassani, Timothée Masquelier | 2023-06-30T14:01:53Z | http://arxiv.org/abs/2306.17670v3 | # Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings
###### Abstract
Spiking Neural Networks (SNNs) are a promising research direction for building power-efficient information processing systems, especially for temporal tasks such as speech recognition. In SNNs, delays refer... |
2309.10759 | A Blueprint for Precise and Fault-Tolerant Analog Neural Networks | Analog computing has reemerged as a promising avenue for accelerating deep
neural networks (DNNs) due to its potential to overcome the energy efficiency
and scalability challenges posed by traditional digital architectures. However,
achieving high precision and DNN accuracy using such technologies is
challenging, as hi... | Cansu Demirkiran, Lakshmi Nair, Darius Bunandar, Ajay Joshi | 2023-09-19T17:00:34Z | http://arxiv.org/abs/2309.10759v1 | # A blueprint for precise and fault-tolerant analog neural networks
###### Abstract
Analog computing has reemerged as a promising avenue for accelerating deep neural networks (DNNs) due to its potential to overcome the energy efficiency and scalability challenges posed by traditional digital architectures. However, a... |
2309.16314 | A Primer on Bayesian Neural Networks: Review and Debates | Neural networks have achieved remarkable performance across various problem
domains, but their widespread applicability is hindered by inherent limitations
such as overconfidence in predictions, lack of interpretability, and
vulnerability to adversarial attacks. To address these challenges, Bayesian
neural networks (BN... | Julyan Arbel, Konstantinos Pitas, Mariia Vladimirova, Vincent Fortuin | 2023-09-28T10:09:15Z | http://arxiv.org/abs/2309.16314v1 | # A Primer on Bayesian Neural Networks: Review and Debates
###### Abstract
Neural networks have achieved remarkable performance across various problem domains, but their widespread applicability is hindered by inherent limitations such as overconfidence in predictions, lack of interpretability, and vulnerability to a... |
2309.06661 | Sound field decomposition based on two-stage neural networks | A method for sound field decomposition based on neural networks is proposed.
The method comprises two stages: a sound field separation stage and a
single-source localization stage. In the first stage, the sound pressure at
microphones synthesized by multiple sources is separated into one excited by
each sound source. I... | Ryo Matsuda, Makoto Otani | 2023-09-13T01:32:46Z | http://arxiv.org/abs/2309.06661v1 | # Sound field decomposition based on two-stage neural networks
###### Abstract
A method for sound field decomposition based on neural networks is proposed. The method comprises two stages: a sound field separation stage and a single-source localization stage. In the first stage, the sound pressure at microphones synt... |
2307.16727 | Multi Agent Navigation in Unconstrained Environments using a Centralized
Attention based Graphical Neural Network Controller | In this work, we propose a learning based neural model that provides both the
longitudinal and lateral control commands to simultaneously navigate multiple
vehicles. The goal is to ensure that each vehicle reaches a desired target
state without colliding with any other vehicle or obstacle in an unconstrained
environmen... | Yining Ma, Qadeer Khan, Daniel Cremers | 2023-07-31T14:48:45Z | http://arxiv.org/abs/2307.16727v2 | Multi Agent Navigation in Unconstrained Environments using a Centralized Attention based Graphical Neural Network Controller
###### Abstract
In this work, we propose a learning based neural model that provides both the longitudinal and lateral control commands to simultaneously navigate multiple vehicles. The goal is... |
2309.05208 | Quaternion MLP Neural Networks Based on the Maximum Correntropy
Criterion | We propose a gradient ascent algorithm for quaternion multilayer perceptron
(MLP) networks based on the cost function of the maximum correntropy criterion
(MCC). In the algorithm, we use the split quaternion activation function based
on the generalized Hamilton-real quaternion gradient. By introducing a new
quaternion ... | Gang Wang, Xinyu Tian, Zuxuan Zhang | 2023-09-11T02:56:55Z | http://arxiv.org/abs/2309.05208v2 | # Quaternion MLP Neural Networks Based on the Maximum Correntropy Criterion
###### Abstract
We propose a gradient ascent algorithm for quaternion multilayer perceptron (MLP) networks based on the cost function of the maximum correntropy criterion (MCC). In the algorithm, we use the split quaternion activation functio... |
2309.10605 | An Active Noise Control System Based on Soundfield Interpolation Using a
Physics-informed Neural Network | Conventional multiple-point active noise control (ANC) systems require
placing error microphones within the region of interest (ROI), inconveniencing
users. This paper designs a feasible monitoring microphone arrangement placed
outside the ROI, providing a user with more freedom of movement. The soundfield
within the R... | Yile Angela Zhang, Fei Ma, Thushara Abhayapala, Prasanga Samarasinghe, Amy Bastine | 2023-09-19T13:20:47Z | http://arxiv.org/abs/2309.10605v1 | An Active Noise Control System Based on Soundfield Interpolation Using a Physics-Informed Neural Network
###### Abstract
Conventional multiple-point active noise control (ANC) systems require placing error microphones within the region of interest (ROI), inconveniencing users. This paper designs a feasible monitoring... |
2309.09195 | SplitEE: Early Exit in Deep Neural Networks with Split Computing | Deep Neural Networks (DNNs) have drawn attention because of their outstanding
performance on various tasks. However, deploying full-fledged DNNs in
resource-constrained devices (edge, mobile, IoT) is difficult due to their
large size. To overcome the issue, various approaches are considered, like
offloading part of the... | Divya J. Bajpai, Vivek K. Trivedi, Sohan L. Yadav, Manjesh K. Hanawal | 2023-09-17T07:48:22Z | http://arxiv.org/abs/2309.09195v1 | # SplitEE: Early Exit in Deep Neural Networks with Split Computing
###### Abstract.
Deep Neural Networks (DNNs) have drawn attention because of their outstanding performance on various tasks. However, deploying full-fledged DNNs in resource-constrained devices (edge, mobile, IoT) is difficult due to their large size.... |
2309.13132 | Understanding Calibration of Deep Neural Networks for Medical Image
Classification | In the field of medical image analysis, achieving high accuracy is not
enough; ensuring well-calibrated predictions is also crucial. Confidence scores
of a deep neural network play a pivotal role in explainability by providing
insights into the model's certainty, identifying cases that require attention,
and establishi... | Abhishek Singh Sambyal, Usma Niyaz, Narayanan C. Krishnan, Deepti R. Bathula | 2023-09-22T18:36:07Z | http://arxiv.org/abs/2309.13132v2 | # Understanding Calibration of Deep Neural Networks for Medical Image Classification
###### Abstract
**Background and Objective -** In the field of medical image analysis, achieving high accuracy is not enough; ensuring well-calibrated predictions is also crucial. Confidence scores of a deep neural network play a piv... |
2301.13710 | On the Initialisation of Wide Low-Rank Feedforward Neural Networks | The edge-of-chaos dynamics of wide randomly initialized low-rank feedforward
networks are analyzed. Formulae for the optimal weight and bias variances are
extended from the full-rank to low-rank setting and are shown to follow from
multiplicative scaling. The principle second order effect, the variance of the
input-out... | Thiziri Nait Saada, Jared Tanner | 2023-01-31T15:40:50Z | http://arxiv.org/abs/2301.13710v1 | # On the Initialisation of Wide Low-Rank Feedforward Neural Networks
###### Abstract
The edge-of-chaos dynamics of wide randomly initialized low-rank feedforward networks are analyzed. Formulae for the optimal weight and bias variances are extended from the full-rank to low-rank setting and are shown to follow from m... |
2306.00091 | A General Framework for Equivariant Neural Networks on Reductive Lie
Groups | Reductive Lie Groups, such as the orthogonal groups, the Lorentz group, or
the unitary groups, play essential roles across scientific fields as diverse as
high energy physics, quantum mechanics, quantum chromodynamics, molecular
dynamics, computer vision, and imaging. In this paper, we present a general
Equivariant Neu... | Ilyes Batatia, Mario Geiger, Jose Munoz, Tess Smidt, Lior Silberman, Christoph Ortner | 2023-05-31T18:09:37Z | http://arxiv.org/abs/2306.00091v1 | # A General Framework for Equivariant Neural Networks on Reductive Lie Groups
###### Abstract
Reductive Lie Groups, such as the orthogonal groups, the Lorentz group, or the unitary groups, play essential roles across scientific fields as diverse as high energy physics, quantum mechanics, quantum chromodynamics, molec... |
2309.12204 | PrNet: A Neural Network for Correcting Pseudoranges to Improve
Positioning with Android Raw GNSS Measurements | We present a neural network for mitigating biased errors in pseudoranges to
improve localization performance with data collected from mobile phones. A
satellite-wise Multilayer Perceptron (MLP) is designed to regress the
pseudorange bias correction from six satellite, receiver, context-related
features derived from And... | Xu Weng, Keck Voon Ling, Haochen Liu | 2023-09-16T10:43:59Z | http://arxiv.org/abs/2309.12204v2 | PrNet: A Neural Network for Correcting Pseudoranges to Improve Positioning with Android Raw GNSS Measurements
###### Abstract
We present a neural network for mitigating pseudorage bias to improve localization performance with data collected from Android smartphones. We represent pseudorange bias using a pragmatic sat... |
2309.09142 | Performance of Graph Neural Networks for Point Cloud Applications | Graph Neural Networks (GNNs) have gained significant momentum recently due to
their capability to learn on unstructured graph data. Dynamic GNNs (DGNNs) are
the current state-of-the-art for point cloud applications; such applications
(viz. autonomous driving) require real-time processing at the edge with tight
latency ... | Dhruv Parikh, Bingyi Zhang, Rajgopal Kannan, Viktor Prasanna, Carl Busart | 2023-09-17T03:05:13Z | http://arxiv.org/abs/2309.09142v1 | # Performance of Graph Neural Networks for Point Cloud Applications
###### Abstract
Graph Neural Networks (GNNs) have gained significant momentum recently due to their capability to learn on unstructured graph data. Dynamic GNNs (DGNNs) are the current state-of-the-art for point cloud applications; such applications ... |
2309.12211 | Physics-informed State-space Neural Networks for Transport Phenomena | This work introduces Physics-informed State-space neural network Models
(PSMs), a novel solution to achieving real-time optimization, flexibility, and
fault tolerance in autonomous systems, particularly in transport-dominated
systems such as chemical, biomedical, and power plants. Traditional data-driven
methods fall s... | Akshay J. Dave, Richard B. Vilim | 2023-09-21T16:14:36Z | http://arxiv.org/abs/2309.12211v2 | # Physics-informed State-space Neural Networks for Transport Phenomena
###### Abstract
This work introduces Physics-informed State-space neural network Models (PSMs), a novel solution to achieving real-time optimization, flexibility, and fault tolerance in autonomous systems, particularly in transport-dominated syste... |
2309.08275 | User Power Measurement Based IRS Channel Estimation via Single-Layer
Neural Network | One main challenge for implementing intelligent reflecting surface (IRS)
aided communications lies in the difficulty to obtain the channel knowledge for
the base station (BS)-IRS-user cascaded links, which is needed to design
high-performance IRS reflection in practice. Traditional methods for estimating
IRS cascaded c... | He Sun, Weidong Mei, Lipeng Zhu, Rui Zhang | 2023-09-15T09:36:22Z | http://arxiv.org/abs/2309.08275v1 | # User Power Measurement Based IRS Channel Estimation via Single-Layer Neural Network
###### Abstract
One main challenge for implementing intelligent reflecting surface (IRS) aided communications lies in the difficulty to obtain the channel knowledge for the base station (BS)-IRS-user cascaded links, which is needed ... |
2309.13866 | On Calibration of Modern Quantized Efficient Neural Networks | We explore calibration properties at various precisions for three
architectures: ShuffleNetv2, GhostNet-VGG, and MobileOne; and two datasets:
CIFAR-100 and PathMNIST. The quality of calibration is observed to track the
quantization quality; it is well-documented that performance worsens with lower
precision, and we obs... | Joey Kuang, Alexander Wong | 2023-09-25T04:30:18Z | http://arxiv.org/abs/2309.13866v2 | # On Calibration of Modern Quantized Efficient Neural Networks
###### Abstract
We explore calibration properties at various precisions for three architectures: ShuffleNetv2, GhostNet-VGG, and MobileOne; and two datasets: CIFAR-100 and PathMNIST. The quality of calibration is observed to track the quantization quality... |
2303.17939 | LyAl-Net: A high-efficiency Lyman-$α$ forest simulation with a
neural network | The inference of cosmological quantities requires accurate and large
hydrodynamical cosmological simulations. Unfortunately, their computational
time can take millions of CPU hours for a modest coverage in cosmological
scales ($\approx (100 {h^{-1}}\,\text{Mpc})^3)$). The possibility to generate
large quantities of moc... | Chotipan Boonkongkird, Guilhem Lavaux, Sebastien Peirani, Yohan Dubois, Natalia Porqueres, Eleni Tsaprazi | 2023-03-31T10:06:59Z | http://arxiv.org/abs/2303.17939v1 | # LyAI-Net: A high-efficiency Lyman-\(\alpha\) forest simulation with a neural network
###### Abstract
Context:The inference of cosmological quantities requires accurate and large hydrodynamical cosmological simulations. Unfortunately, their computational time can take millions of CPU hours for a modest coverage in c... |
2309.15378 | Adversarial Object Rearrangement in Constrained Environments with
Heterogeneous Graph Neural Networks | Adversarial object rearrangement in the real world (e.g., previously unseen
or oversized items in kitchens and stores) could benefit from understanding
task scenes, which inherently entail heterogeneous components such as current
objects, goal objects, and environmental constraints. The semantic
relationships among the... | Xibai Lou, Houjian Yu, Ross Worobel, Yang Yang, Changhyun Choi | 2023-09-27T03:15:45Z | http://arxiv.org/abs/2309.15378v1 | Adversarial Object Rearrangement in Constrained Environments with Heterogeneous Graph Neural Networks
###### Abstract
Adversarial object rearrangement in the real world (e.g., previously unseen or oversized items in kitchens and stores) could benefit from understanding task scenes, which inherently entail heterogeneo... |
2309.12417 | Advances in developing deep neural networks for finding primary vertices
in proton-proton collisions at the LHC | We are studying the use of deep neural networks (DNNs) to identify and locate
primary vertices (PVs) in proton-proton collisions at the LHC. Earlier work
focused on finding primary vertices in simulated LHCb data using a hybrid
approach that started with kernel density estimators (KDEs) derived
heuristically from the e... | Simon Akar, Mohamed Elashri, Rocky Bala Garg, Elliott Kauffman, Michael Peters, Henry Schreiner, Michael Sokoloff, William Tepe, Lauren Tompkins | 2023-09-21T18:34:00Z | http://arxiv.org/abs/2309.12417v2 | Advances in developing deep neural networks for finding primary vertices in proton-proton collisions at the LHC
###### Abstract
We are studying the use of deep neural networks (DNNs) to identify and locate primary vertices (PVs) in proton-proton collisions at the LHC. Earlier work focused on finding primary vertices ... |
2301.13659 | Spyker: High-performance Library for Spiking Deep Neural Networks | Spiking neural networks (SNNs) have been recently brought to light due to
their promising capabilities. SNNs simulate the brain with higher biological
plausibility compared to previous generations of neural networks. Learning with
fewer samples and consuming less power are among the key features of these
networks. Howe... | Shahriar Rezghi Shirsavar, Mohammad-Reza A. Dehaqani | 2023-01-31T14:25:03Z | http://arxiv.org/abs/2301.13659v1 | # Spyker: High-performance Library for Spiking Deep Neural Networks
###### Abstract
Spiking neural networks (SNNs) have been recently brought to light due to their promising capabilities. SNNs simulate the brain with higher biological plausibility compared to previous generations of neural networks. Learning with few... |
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