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Found 7 papers, 0 papers with code
Accelerating Deep Neuroevolution on Distributed FPGAs for Reinforcement Learning Problems
Recently, alternative approaches such as evolutionary strategies and deep neuroevolution demonstrated competitive results with faster training time on distributed CPU cores.
Simulation of neural function in an artificial Hebbian network
Artificial neural networks have diverged far from their early inspiration in neurology.
Transfer Learning in Visual and Relational Reasoning
Transfer learning has become the de facto standard in computer vision and natural language processing, especially where labeled data is scarce.
Leveraging Medical Visual Question Answering with Supporting Facts
In this working notes paper, we describe IBM Research AI (Almaden) team's participation in the ImageCLEF 2019 VQA-Med competition.
On transfer learning using a MAC model variant
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.
Learning to Remember, Forget and Ignore using Attention Control in Memory
Typical neural networks with external memory do not effectively separate capacity for episodic and working memory as is required for reasoning in humans.
Using Multi-task and Transfer Learning to Solve Working Memory Tasks
We propose a new architecture called Memory-Augmented Encoder-Solver (MAES) that enables transfer learning to solve complex working memory tasks adapted from cognitive psychology.
