9 citations · 27 across the 8 of their papers we have counts for
8 papers
Accelerating Deep Neuroevolution on Distributed FPGAs for Reinforcement Learning Problems
Alexis Asseman, Nicolas Antoine, Ahmet S. Ozcan
Reinforcement learning augmented by the representational power of deep neural networks, has shown promising results on high-dimensional problems, such as game playing and robotic c…
Overview of the IBM Neural Computer Architecture
Pritish Narayanan, Charles E. Cox, Alexis Asseman +4
The IBM Neural Computer (INC) is a highly flexible, re-configurable parallel processing system that is intended as a research and development platform for emerging machine intellig…
Simulation of neural function in an artificial Hebbian network
J. Campbell Scott, Thomas F. Hayes, Ahmet S. Ozcan +1
Artificial neural networks have diverged far from their early inspiration in neurology. In spite of their technological and commercial success, they have several shortcomings, most…
Transfer Learning in Visual and Relational Reasoning
T. S. Jayram, Vincent Marois, Tomasz Kornuta +3
Transfer learning has become the de facto standard in computer vision and natural language processing, especially where labeled data is scarce. Accuracy can be significantly improv…
Leveraging Medical Visual Question Answering with Supporting Facts
Tomasz Kornuta, Deepta Rajan, Chaitanya Shivade +2
In this working notes paper, we describe IBM Research AI (Almaden) team's participation in the ImageCLEF 2019 VQA-Med competition. The challenge consists of four question-answering…
On transfer learning using a MAC model variant
Vincent Marois, T. S. Jayram, Vincent Albouy +3
We introduce a variant of the MAC model (Hudson and Manning, ICLR 2018) with a simplified set of equations that achieves comparable accuracy, while training faster. We evaluate bot…