17 citations · 29 across the 12 of their papers we have counts for
7 papers · 1 filter
Automatic Differentiation: Inverse Accumulation Mode
Barak A. Pearlmutter, Jeffrey Mark Siskind
We show that, under certain circumstances, it is possible to automatically compute Jacobian-inverse-vector and Jacobian-inverse-transpose-vector products about as efficiently as Ja…
Creating a Formally Verified Neural Network for Autonomous Navigation: An Experience Report
Syed Ali Asadullah Bukhari, Thomas Flinkow, Medet Inkarbekov +2
The increased reliance of self-driving vehicles on neural networks opens up the challenge of their verification. In this paper we present an experience report, describing a case st…
Recent Trends in Modelling the Continuous Time Series using Deep Learning: A Survey
Mansura Habiba, Barak A. Pearlmutter, Mehrdad Maleki
Continuous-time series is essential for different modern application areas, e.g. healthcare, automobile, energy, finance, Internet of things (IoT) and other related areas. Differen…
Second-Order Forward-Mode Automatic Differentiation for Optimization
Adam D. Cobb, Atılım Güneş Baydin, Barak A. Pearlmutter +1
This paper introduces a second-order hyperplane search, a novel optimization step that generalizes a second-order line search from a line to a -dimensional hyperplane. This, com…
Comparing differentiable logics for learning with logical constraints
Thomas Flinkow, Barak A. Pearlmutter, Rosemary Monahan
Extensive research on formal verification of machine learning systems indicates that learning from data alone often fails to capture underlying background knowledge, such as specif…
Learning Exemplar Representations in Single-Trial EEG Category Decoding
Jack Kilgallen, Barak Pearlmutter, Jeffery Mark Siskind
Within neuroimgaing studies it is a common practice to perform repetitions of trials in an experiment when working with a noisy class of data acquisition system, such as electroenc…
Low-Rank Learning by Design: the Role of Network Architecture and Activation Linearity in Gradient Rank Collapse
Bradley T. Baker, Barak A. Pearlmutter, Robyn Miller +2
Our understanding of learning dynamics of deep neural networks (DNNs) remains incomplete. Recent research has begun to uncover the mathematical principles underlying these networks…