Keep Up With Latest Trending Papers. Computer Science, AI and Machine Learning and more.Subscribe

Top Papers in Deep learning

Share

Deep Learning for Face Recognition: Pride or Prejudiced?

Do very high accuracies of deep networks suggest pride of effective AI or are
deep networks prejudiced? Do they suffer from in-group biases (own-race-bias
and own-age-bias), and mimic the human behavi

More...

Share

Mini-batch Gradient Descent for Differentially Private Gradient Descent

Practical and Private (Deep) Learning without Sampling or Shuffling

Read More...

Share

Shallow or Deep? An Empirical Study on Detecting Vulnerabilities using Deep Learning

Deep learning (DL) techniques are on the rise in the software engineering
research community. More and more approaches have been developed on top of DL
models, also due to the unprecedented amount of

More...

Share

Cautious Deep Learning

Most classifiers operate by selecting the maximum of an estimate of the
conditional distribution $p(y|x)$ where $x$ stands for the features of the
instance to be classified and $y$ denotes its label.

More...

Share

Data Structures and Algorithms

Machine Learning

Neural and Evolutionary Computing

Optimization and Control

Stats Machine Learning

Backward Feature Correction: How Deep Learning Performs Deep Learning

How does a 110-layer ResNet learn a high-complexity classifier using
relatively few training examples and short training time? We present a theory
towards explaining this in terms of hierarchical lear

More...

Share

Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory

We empirically evaluate common assumptions about neural networks that are
widely held by practitioners and theorists alike. In this work, we: (1) prove
the widespread existence of suboptimal local min

More...

Share

Differentially Private Deep Learning Models for Human Mobility Forecast

Differentially Private Multivariate Time Series Forecasting of Aggregated Human Mobility With Deep Learning: Input or Gradient Perturbation?

Read More...

Share

Object Detection for Graphical User Interface: Old Fashioned or Deep Learning or a Combination?

Detecting Graphical User Interface (GUI) elements in GUI images is a
domain-specific object detection task. It supports many software engineering
tasks, such as GUI animation and testing, GUI search a

More...

Share

Deep Learning Volatility

We present a neural network based calibration method that performs the
calibration task within a few milliseconds for the full implied volatility
surface. The framework is consistently applicable thro

More...

Share

Security Risks in Deep Learning Implementations

Advance in deep learning algorithms overshadows their security risk in
software implementations. This paper discloses a set of vulnerabilities in
popular deep learning frameworks including Caffe, Tens

More...

Share

Deep Learning for Symbolic Mathematics

Neural networks have a reputation for being better at solving statistical or
approximate problems than at performing calculations or working with symbolic
data. In this paper, we show that they can be

More...

Share

Algebraic Geometry

High Energy Physics - Phenomenology

High Energy Physics - Theory

Stats Machine Learning

Deep-Learning the Landscape

We propose a paradigm to deep-learn the ever-expanding databases which have
emerged in mathematical physics and particle phenomenology, as diverse as the
statistics of string vacua or combinatorial an

More...

Share

A novel approach to replicate the portrait mode from DSLR using any smartphone to generate high quality portrait images

Portrait Segmentation Using Deep Learning

Read More...

Share

Structure preserving deep learning

Over the past few years, deep learning has risen to the foreground as a topic
of massive interest, mainly as a result of successes obtained in solving
large-scale image processing tasks. There are mul

More...

Share

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks

More...

Share

Model-Based Deep Learning

Signal processing, communications, and control have traditionally relied on
classical statistical modeling techniques. Such model-based methods utilize
mathematical formulations that represent the und

More...

Share

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

More...

Share

Hebbian Deep Learning Without Feedback

Recent approximations to backpropagation (BP) have mitigated many of BP's
computational inefficiencies and incompatibilities with biology, but important
limitations still remain. Moreover, the approxi

More...

Share

True or False: Does the Deep Learning Model Learn to Detect Rumors?

It is difficult for humans to distinguish the true and false of rumors, but
current deep learning models can surpass humans and achieve excellent accuracy
on many rumor datasets. In this paper, we inv

More...

Share

Deep Learning with Logical Constraints

In recent years, there has been an increasing interest in exploiting
logically specified background knowledge in order to obtain neural models (i)
with a better performance, (ii) able to learn from le

More...

Share

More