24 citations · 125 across the 25 of their papers we have counts for
29 papers
The Mode of Null-A: Compositional Computation of a Generalized Inverse
Barak A. Pearlmutter, Jeffrey Mark Siskind
We present a novel algorithm for calculating the preimage of an affine space through a product of matrices of special form: finding the largest i…
Gradients without Backpropagation
Atılım Güneş Baydin, Barak A. Pearlmutter, Don Syme +2
Using backpropagation to compute gradients of objective functions for optimization has remained a mainstay of machine learning. Backpropagation, or reverse-mode differentiation, is…
Continuous Convolutional Neural Networks: Coupled Neural PDE and ODE
Mansura Habiba, Barak A. Pearlmutter
Recent work in deep learning focuses on solving physical systems in the Ordinary Differential Equation or Partial Differential Equation. This current work proposed a variant of Con…
Neural Network based on Automatic Differentiation Transformation of Numeric Iterate-to-Fixedpoint
Mansura Habiba, Barak A. Pearlmutter
This work proposes a Neural Network model that can control its depth using an iterate-to-fixed-point operator. The architecture starts with a standard layered Network but with adde…
HeunNet: Extending ResNet using Heun's Methods
Mehrdad Maleki, Mansura Habiba, Barak A. Pearlmutter
There is an analogy between the ResNet (Residual Network) architecture for deep neural networks and an Euler solver for an ODE. The transformation performed by each layer resembles…
Peering Beyond the Gradient Veil with Distributed Auto Differentiation
Bradley T. Baker, Aashis Khanal, Vince D. Calhoun +2
Although distributed machine learning has opened up many new and exciting research frontiers, fragmentation of models and data across different machines, nodes, and sites still res…