8 citations · 26 across the 9 of their papers we have counts for
6 papers · 1 filter
Graph Neural Networks Gone Hogwild
Olga Solodova, Nick Richardson, Deniz Oktay +1
Graph neural networks (GNNs) appear to be powerful tools to learn state representations for agents in distributed, decentralized multi-agent systems, but generate catastrophically…
Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity
Deniz Oktay, Mehran Mirramezani, Eder Medina +1
Intelligent biological systems are characterized by their embodiment in a complex environment and the intimate interplay between their nervous systems and the nonlinear mechanical…
Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh
Tian Qin, Alex Beatson, Deniz Oktay +2
Partial differential equations (PDEs) are often computationally challenging to solve, and in many settings many related PDEs must be be solved either at every timestep or for a var…
Randomized Automatic Differentiation
Deniz Oktay, Nick McGreivy, Joshua Aduol +2
The successes of deep learning, variational inference, and many other fields have been aided by specialized implementations of reverse-mode automatic differentiation (AD) to comput…
On Predictive Information in RNNs
Zhe Dong, Deniz Oktay, Ben Poole +1
Certain biological neurons demonstrate a remarkable capability to optimally compress the history of sensory inputs while being maximally informative about the future. In this work,…
Scalable Model Compression by Entropy Penalized Reparameterization
Deniz Oktay, Johannes Ballé, Saurabh Singh +1
We describe a simple and general neural network weight compression approach, in which the network parameters (weights and biases) are represented in a "latent" space, amounting to…