1 code implementation • 2 May 2024 • Lakshmi Nair, Evana Gizzi, Jivko Sinapov
In this paper, we discuss approaches for integrating Computational Creativity (CC) with research in large language and vision models (LLVMs) to address a key limitation of these models, i. e., creative problem solving.
2 code implementations • 9 Apr 2024 • Lakshmi Nair
Contrastive Language-Image Pre-training (CLIP) has been shown to improve zero-shot generalization capabilities of language and vision models.
no code implementations • 28 Sep 2023 • Lakshmi Nair, David Widemann, Brad Turcott, Nick Moore, Alexandra Wleklinski, Darius Bunandar, Ioannis Papavasileiou, Shihu Wang, Eric Logan
Photonic computing promises faster and more energy-efficient deep neural network (DNN) inference than traditional digital hardware.
no code implementations • 19 Sep 2023 • Cansu Demirkiran, Lakshmi Nair, Darius Bunandar, Ajay Joshi
Our study demonstrates that analog accelerators utilizing the RNS-based approach can achieve ${\geq}99\%$ of FP32 accuracy for state-of-the-art DNN inference using data converters with only $6$-bit precision whereas a conventional analog core requires more than $8$-bit precision to achieve the same accuracy in the same DNNs.
1 code implementation • 7 Jul 2023 • Lakshmi Nair, Mikhail Bernadskiy, Arulselvan Madhavan, Craig Chan, Ayon Basumallik, Darius Bunandar
To supplement this ongoing effort, we propose INT-FP-QSim: an open-source simulator that enables flexible evaluation of LLMs and vision transformers at various numerical precisions and formats.
no code implementations • 5 Jun 2023 • Lakshmi Nair, Darius Bunandar
Existing methods to recover model accuracy on analog-digital hardware in the presence of quantization and analog noise include noise-injection training.
no code implementations • 12 May 2022 • Ayon Basumallik, Darius Bunandar, Nicholas Dronen, Nicholas Harris, Ludmila Levkova, Calvin Mccarter, Lakshmi Nair, David Walter, David Widemann
Analog mixed-signal (AMS) devices promise faster, more energy-efficient deep neural network (DNN) inference than their digital counterparts.
no code implementations • 21 Apr 2022 • Evana Gizzi, Lakshmi Nair, Sonia Chernova, Jivko Sinapov
Creative Problem Solving (CPS) is a sub-area within Artificial Intelligence (AI) that focuses on methods for solving off-nominal, or anomalous problems in autonomous systems.
no code implementations • 10 May 2021 • Angel Daruna, Lakshmi Nair, Weiyu Liu, Sonia Chernova
We validated the approach on a physical platform, which resulted in the successful generalization of initial task plans to 38 of 50 execution environments with errors resulting from autonomous robot operation included.
no code implementations • 11 Nov 2019 • Nithin Shrivatsav, Lakshmi Nair, Sonia Chernova
This paper explores the problem of tool substitution, namely, identifying substitute tools for performing a task from a given set of candidate tools.
no code implementations • 26 Apr 2018 • Lakshmi Nair, Sonia Chernova
This paper explores the problem of task learning and planning, contributing the Action-Category Representation (ACR) to improve computational performance of both Planning and Reinforcement Learning (RL).