Search Results for author:
Found 11 papers, 4 papers with code
Design Principles for Lifelong Learning AI Accelerators
Lifelong learning - an agent's ability to learn throughout its lifetime - is a hallmark of biological learning systems and a central challenge for artificial intelligence (AI).
Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures
The ability to learn continuously from an incoming data stream without catastrophic forgetting is critical to designing intelligent systems.
AutoML for neuromorphic computing and application-driven co-design: asynchronous, massively parallel optimization of spiking architectures
In this work we have extended AutoML inspired approaches to the exploration and optimization of neuromorphic architectures.
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed.
General policy mapping: online continual reinforcement learning inspired on the insect brain
Our model leverages the offline training of a feature extraction and a common general policy layer to enable the convergence of RL algorithms in online settings.
Machine learning and atomic layer deposition: predicting saturation times from reactor growth profiles using artificial neural networks
In this work we explore the application of deep neural networks to the optimization of atomic layer deposition processes based on thickness values obtained at different points of an ALD reactor.
Fast, Smart Neuromorphic Sensors Based on Heterogeneous Networks and Mixed Encodings
Neuromorphic architectures are ideally suited for the implementation of smart sensors able to react, learn, and respond to a changing environment.
Neuromodulated Neural Architectures with Local Error Signals for Memory-Constrained Online Continual Learning
Using high performing configurations metalearned in the single task learning setting, we achieve superior continual learning performance on Split-MNIST, and Split-CIFAR-10 data as compared with other memory-constrained learning approaches, and match that of the state-of-the-art memory-intensive replay-based approaches.
Coarse scale representation of spiking neural networks: backpropagation through spikes and application to neuromorphic hardware
In this work we explore recurrent representations of leaky integrate and fire neurons operating at a timescale equal to their absolute refractory period.
Multilayer Neuromodulated Architectures for Memory-Constrained Online Continual Learning
We focus on the problem of how to achieve online continual learning under memory-constrained conditions where the input data may not be known \emph{a priori}.
Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning
Our results show that optimal learning rules can be dataset-dependent even within similar tasks.
