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Top Papers in Deep neural network
Dual Precision Deep Neural Network
On-line Precision scalability of the deep neural networks(DNNs) is a critical
feature to support accuracy and complexity trade-off during the DNN inference.
In this paper, we propose dual-precision DN
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Deep Neural Network Approximation Theory
This paper develops fundamental limits of deep neural network learning by
characterizing what is possible if no constraints are imposed on the learning
algorithm and on the amount of training data. Co
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Deep Neural Network Training without Multiplications
Is multiplication really necessary for deep neural networks? Here we propose
just adding two IEEE754 floating-point numbers with an integer-add instruction
in place of a floating-point multiplication
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Decompiler for Deep Neural Network Executables
Decompiling x86 Deep Neural Network Executables
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Wound Severity Classification using Deep Neural Network
The classification of wound severity is a critical step in wound diagnosis.
An effective classifier can help wound professionals categorize wound
conditions more quickly and affordably, allowing them
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Gradient-enhanced deep neural network approximations
We propose in this work the gradient-enhanced deep neural networks (DNNs)
approach for function approximations and uncertainty quantification. More
precisely, the proposed approach adopts both the fun
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Deep Learning for Scoliosis Detection and Estimation
Scoliosis Detection using Deep Neural Network
This paper aims to review the fundamental concepts of deep learning, analyze how deeplearning is applied to detect spinal curvature, explore the practical deeplearning-based models that have been employed, and implement the most successful one for automated cobbangle prediction.
Deep neural network expressivity for optimal stopping problems
This article studies deep neural network expression rates for optimal
stopping problems of discrete-time Markov processes on high-dimensional state
spaces. A general framework is established in which
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Brain Cancer Segmentation Using YOLOv5 Deep Neural Network
An expansion of aberrant brain cells is referred to as a brain tumor. The
brain's architecture is extremely intricate, with several regions controlling
various nervous system processes. Any portion of
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Conflicting saliency in adversarial samples
Deep neural network loses attention to adversarial images
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Bayesian Learning Based Speaker Adaptation for Speech Recognition
Bayesian Learning for Deep Neural Network Adaptation
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Deep neural network initialization with decision trees
In this work a novel, automated process for constructing and initializing
deep feed-forward neural networks based on decision trees is presented. The
proposed algorithm maps a collection of decision t
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TRADI: Tracking deep neural network weight distributions
During training, the weights of a Deep Neural Network (DNN) are optimized
from a random initialization towards a nearly optimum value minimizing a loss
function. Only this final state of the weights i
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Deep Neural Network Training with Frank-Wolfe
This paper studies the empirical efficacy and benefits of using
projection-free first-order methods in the form of Conditional Gradients,
a.k.a. Frank-Wolfe methods, for training Neural Networks with
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DeepGeo: Photo Localization with Deep Neural Network
In this paper we address the task of determining the geographical location of
an image, a pertinent problem in learning and computer vision. This research
was inspired from playing GeoGuessr, a game t
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Forensicability of Deep Neural Network Inference Pipelines
We propose methods to infer properties of the execution environment of
machine learning pipelines by tracing characteristic numerical deviations in
observable outputs. Results from a series of proof-o
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Embedding Differentiable Sparsity into Deep Neural Network
In this paper, we propose embedding sparsity into the structure of deep neural networks, where model parameters can be exactly zero during training with the stochastic gradient descent. Thus, it can l
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Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural Network
Edge nodes are crucial for detection against multitudes of cyber attacks on
Internet-of-Things endpoints and is set to become part of a multi-billion
industry. The resource constraints in this novel n
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Class Activation Mapping with Gradual Extrapolation
Enhancing Deep Neural Network Saliency Visualizations with Gradual Extrapolation
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Deep Positron: A Deep Neural Network Using the Posit Number System
The recent surge of interest in Deep Neural Networks (DNNs) has led to
increasingly complex networks that tax computational and memory resources. Many
DNNs presently use 16-bit or 32-bit floating poin
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