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Top Papers in Deep neural networks

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Explaining Deep Neural Networks

Deep neural networks are becoming more and more popular due to their
revolutionary success in diverse areas, such as computer vision, natural
language processing, and speech recognition. However, the

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Accelerating Sparse Deep Neural Networks

As neural network model sizes have dramatically increased, so has the
interest in various techniques to reduce their parameter counts and accelerate
their execution. An active area of research in this

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Any-Precision Deep Neural Networks

We present any-precision deep neural networks (DNNs), which are trained with
a new method that allows the learned DNNs to be flexible in numerical precision
during inference. The same model in runtime

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Evolving Deep Neural Networks

The success of deep learning depends on finding an architecture to fit the
task. As deep learning has scaled up to more challenging tasks, the
architectures have become difficult to design by hand. Th

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Invariant Imbedding in Continuous Neural Networks

Imbedding Deep Neural Networks

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Vulnerability Under Adversarial Machine Learning: Bias or Variance?

Prior studies have unveiled the vulnerability of the deep neural networks in the context of adversarial machine learning, leading to great recent attention into this area. One interesting question tha

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Handcrafted Backdoors in Deep Neural Networks

Deep neural networks (DNNs), while accurate, are expensive to train. Many
practitioners, therefore, outsource the training process to third parties or
use pre-trained DNNs. This practice makes DNNs vu

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Safety Verification of Deep Neural Networks

Deep neural networks have achieved impressive experimental results in image
classification, but can surprisingly be unstable with respect to adversarial
perturbations, that is, minimal changes to the

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Probabilistic Models with Deep Neural Networks

Recent advances in statistical inference have significantly expanded the
toolbox of probabilistic modeling. Historically, probabilistic modeling has
been constrained to (i) very restricted model class

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Foiling Explanations in Deep Neural Networks

Deep neural networks (DNNs) have greatly impacted numerous fields over the
past decade. Yet despite exhibiting superb performance over many problems,
their black-box nature still poses a significant c

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Implicit Saliency in Deep Neural Networks

In this paper, we show that existing recognition and localization deep architectures, that have not been exposed to eye tracking data or any saliency datasets, are capable of predicting the human visu

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Hyperbolic Deep Neural Networks: A Survey

Recently, there has been a raising surge of momentum for deep representation
learning in hyperbolic spaces due to theirhigh capacity of modeling data like
knowledge graphs or synonym hierarchies, poss

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Deep Conspecific Neural Networks based on Partial integro-Differential Equations

Deep Neural Networks and PIDE discretizations

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Property Inference for Deep Neural Networks

We present techniques for automatically inferring formal properties of
feed-forward neural networks. We observe that a significant part (if not all)
of the logic of feed forward networks is captured i

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Theoretical Analysis of the Advantage of Deepening Neural Networks

We propose two new criteria to understand the advantage of deepening neural
networks. It is important to know the expressivity of functions computable by
deep neural networks in order to understand th

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Topology of deep neural networks

We study how the topology of a data set $M = M_a \cup M_b \subseteq
\mathbb{R}^d$, representing two classes $a$ and $b$ in a binary classification
problem, changes as it passes through the layers of a

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Graph-Based Partitioning of Neural Networks

Detecting Modularity in Deep Neural Networks

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Architecture Disentanglement for Deep Neural Networks

Understanding the inner workings of deep neural networks (DNNs) is essential
to provide trustworthy artificial intelligence techniques for practical
applications. Existing studies typically involve li

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On the Approximation and Complexity of Deep Neural Networks to Invariant Functions

Recent years have witnessed a hot wave of deep neural networks in various
domains; however, it is not yet well understood theoretically. A theoretical
characterization of deep neural networks should p

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Are Deep Neural Networks "Robust"?

Separating outliers from inliers is the definition of robustness in computer
vision. This essay delineates how deep neural networks are different than
typical robust estimators. Deep neural networks n

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