An overview on restricted Boltzmann machines
Abstract The Restricted Boltzmann Machine (RBM) has aroused wide interest in machine
learning fields during the past decade. This review aims to report the recent developments in …
learning fields during the past decade. This review aims to report the recent developments in …
A review of deep learning security and privacy defensive techniques
In recent past years, Deep Learning presented an excellent performance in different areas
like image recognition, pattern matching, and even in cybersecurity. The Deep Learning has …
like image recognition, pattern matching, and even in cybersecurity. The Deep Learning has …
PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network
Partial differential equations (PDEs) are commonly derived based on empirical
observations. However, recent advances of technology enable us to collect and store …
observations. However, recent advances of technology enable us to collect and store …
On the optimization landscape of neural collapse under mse loss: Global optimality with unconstrained features
When training deep neural networks for classification tasks, an intriguing empirical
phenomenon has been widely observed in the last-layer classifiers and features, where (i) …
phenomenon has been widely observed in the last-layer classifiers and features, where (i) …
A geometric analysis of neural collapse with unconstrained features
We provide the first global optimization landscape analysis of Neural Collapse--an intriguing
empirical phenomenon that arises in the last-layer classifiers and features of neural …
empirical phenomenon that arises in the last-layer classifiers and features of neural …
The modern mathematics of deep learning
We describe the new field of the mathematical analysis of deep learning. This field emerged
around a list of research questions that were not answered within the classical framework of …
around a list of research questions that were not answered within the classical framework of …
[HTML][HTML] Universality of deep convolutional neural networks
DX Zhou - Applied and computational harmonic analysis, 2020 - Elsevier
Deep learning has been widely applied and brought breakthroughs in speech recognition,
computer vision, and many other domains. Deep neural network architectures and …
computer vision, and many other domains. Deep neural network architectures and …
Are all losses created equal: A neural collapse perspective
While cross entropy (CE) is the most commonly used loss function to train deep neural
networks for classification tasks, many alternative losses have been developed to obtain …
networks for classification tasks, many alternative losses have been developed to obtain …
ReLU deep neural networks and linear finite elements
In this paper, we investigate the relationship between deep neural networks (DNN) with
rectified linear unit (ReLU) function as the activation function and continuous piecewise …
rectified linear unit (ReLU) function as the activation function and continuous piecewise …
Deep hedging
H Buehler, L Gonon, J Teichmann, B Wood - Quantitative Finance, 2019 - Taylor & Francis
We present a framework for hedging a portfolio of derivatives in the presence of market
frictions such as transaction costs, liquidity constraints or risk limits using modern deep …
frictions such as transaction costs, liquidity constraints or risk limits using modern deep …