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ShiftAddNet: A Hardware-Inspired Deep Network

Multiplication (e.g., convolution) is arguably a cornerstone of modern deep
neural networks (DNNs). However, intensive multiplications cause expensive
resource costs that challenge DNNs' deployment on

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End-to-End Deep Neural Network for Single Image RS rectification

Deep network for rolling shutter rectification

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Deep Network Guided Proof Search

Deep learning techniques lie at the heart of several significant AI advances
in recent years including object recognition and detection, image captioning,
machine translation, speech recognition and s

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Matrix Persuading in Deep Networks

Singular Value Perturbation and Deep Network Optimization

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NeuroView: A Interpretable and Explainable Deep Neural Network Architecture

NeuroView: Explainable Deep Network Decision Making

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Deep Network Interpolation for Continuous Imagery Effect Transition

Deep convolutional neural network has demonstrated its capability of learning
a deterministic mapping for the desired imagery effect. However, the large
variety of user flavors motivates the possibili

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Wider or Deeper: Revisiting the ResNet Model for Visual Recognition

The trend towards increasingly deep neural networks has been driven by a
general observation that increasing depth increases the performance of a
network. Recently, however, evidence has been amassing

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A Scale Invariant Flatness Measure for Deep Network Minima

It has been empirically observed that the flatness of minima obtained from
training deep networks seems to correlate with better generalization. However,
for deep networks with positively homogeneous

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Approximating Continuous Convolutions for Deep Network Compression

We present ApproxConv, a novel method for compressing the layers of a
convolutional neural network. Reframing conventional discrete convolution as
continuous convolution of parametrised functions over

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Motivated Propagation in Deep Networks

Max-Affine Spline Insights Into Deep Network Pruning

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Interpretable Split for Deep Neural Networks

I-SPLIT: Deep Network Interpretability for Split Computing

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Image Compressed Sensing Using Non-local Neural Network

Deep network-based image Compressed Sensing (CS) has attracted much attention
in recent years. However, the existing deep network-based CS schemes either
reconstruct the target image in a block-by-blo

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MEMO: A Deep Network for Flexible Combination of Episodic Memories

Recent research developing neural network architectures with external memory
have often used the benchmark bAbI question and answering dataset which
provides a challenging number of tasks requiring re

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DSRN: an Efficient Deep Network for Image Relighting

Custom and natural lighting conditions can be emulated in images of the scene
during post-editing. Extraordinary capabilities of the deep learning framework
can be utilized for such purpose. Deep imag

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Controllable Confidence-Based Image Denoising

Image Denoising with Control over Deep Network Hallucination

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Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep Network Losses

Despite the fact that the loss functions of deep neural networks are highly
non-convex, gradient-based optimization algorithms converge to approximately
the same performance from many random initial p

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Deep Network Ensemble Learning applied to Image Classification using CNN Trees

Traditional machine learning approaches may fail to perform satisfactorily when dealing with complex data. In this context, the importance of data mining evolves w.r.t. building an efficient knowledge

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RobIn: A Robust Interpretable Deep Network for Schizophrenia Diagnosis

Schizophrenia is a severe mental health condition that requires a long and
complicated diagnostic process. However, early diagnosis is vital to control
symptoms. Deep learning has recently become a po

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One-dimensional Deep Low-rank and Sparse Network for Accelerated MRI

Deep learning has shown astonishing performance in accelerated magnetic
resonance imaging (MRI). Most state-of-the-art deep learning reconstructions
adopt the powerful convolutional neural network and

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