17 citations · 68 across the 19 of their papers we have counts for
8 papers · 1 filter
Benchmarking large language models for materials synthesis: the case of atomic layer deposition
Angel Yanguas-Gil, Matthew T. Dearing, Jeffrey W. Elam +5
In this work we introduce an open-ended question benchmark, ALDbench, to evaluate the performance of large language models (LLMs) in materials synthesis, and in particular in the f…
Design Principles for Lifelong Learning AI Accelerators
Dhireesha Kudithipudi, Anurag Daram, Abdullah M. Zyarah +9
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). The…
Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures
Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash
The ability to learn continuously from an incoming data stream without catastrophic forgetting is critical to designing intelligent systems. Many approaches to continual learning r…
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Megan M. Baker, Alexander New, Mario Aguilar-Simon +44
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 enc…
General policy mapping: online continual reinforcement learning inspired on the insect brain
Angel Yanguas-Gil, Sandeep Madireddy
We have developed a model for online continual or lifelong reinforcement learning (RL) inspired on the insect brain. Our model leverages the offline training of a feature extractio…
Machine learning and atomic layer deposition: predicting saturation times from reactor growth profiles using artificial neural networks
Angel Yanguas-Gil, Jeffrey W. Elam
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 a…
Neuromodulated Neural Architectures with Local Error Signals for Memory-Constrained Online Continual Learning
Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash
The ability to learn continuously from an incoming data stream without catastrophic forgetting is critical for designing intelligent systems. Many existing approaches to continual…
Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning
Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash
The ability to learn and adapt in real time is a central feature of biological systems. Neuromorphic architectures demonstrating such versatility can greatly enhance our ability to…