18 citations · 74 across the 36 of their papers we have counts for
13 papers · 1 filter
Decentralised Resource Sharing in TinyML: Wireless Bilayer Gossip Parallel SGD for Collaborative Learning
Ziyuan Bao, Eiman Kanjo, Soumya Banerjee +2
With the growing computational capabilities of microcontroller units (MCUs), edge devices can now support machine learning models. However, deploying decentralised federated learni…
TinyMNet-V3: Memory-Aware Compressed Multimodal Deep Neural Networks for Sustainable Edge Deployment
Hasib-Al Rashid, Tinoosh Mohsenin
The advancement of sophisticated artificial intelligence (AI) algorithms has led to a notable increase in energy usage and carbon dioxide emissions, intensifying concerns about cli…
LLM Augmented Hierarchical Agents
Bharat Prakash, Tim Oates, Tinoosh Mohsenin
Solving long-horizon, temporally-extended tasks using Reinforcement Learning (RL) is challenging, compounded by the common practice of learning without prior knowledge (or tabula r…
Harnessing the Power of Explanations for Incremental Training: A LIME-Based Approach
Arnab Neelim Mazumder, Niall Lyons, Ashutosh Pandey +2
Explainability of neural network prediction is essential to understand feature importance and gain interpretable insight into neural network performance. However, explanations of n…
Towards an Interpretable Hierarchical Agent Framework using Semantic Goals
Bharat Prakash, Nicholas Waytowich, Tim Oates +1
Learning to solve long horizon temporally extended tasks with reinforcement learning has been a challenge for several years now. We believe that it is important to leverage both th…
A Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spotting
Arnab Neelim Mazumder, Tinoosh Mohsenin
Keyword Spotting nowadays is an integral part of speech-oriented user interaction targeted for smart devices. To this extent, neural networks are extensively used for their flexibi…
TinyMNet: A Flexible System Algorithm Co-designed Multimodal Learning Framework for Tiny Devices
Hasib-Al Rashid, Pretom Roy Ovi, Carl Busart +2
With the emergence of Artificial Intelligence (AI), new attention has been given to implement AI algorithms on resource constrained tiny devices to expand the application domain of…
Neural Networks for Pulmonary Disease Diagnosis using Auditory and Demographic Information
Morteza Hosseini, Haoran Ren, Hasib-Al Rashid +3
Pulmonary diseases impact millions of lives globally and annually. The recent outbreak of the pandemic of the COVID-19, a novel pulmonary infection, has more than ever brought the…
Conditional Classification: A Solution for Computational Energy Reduction
Ali Mirzaeian, Sai Manoj, Ashkan Vakil +2
Deep convolutional neural networks have shown high efficiency in computer visions and other applications. However, with the increase in the depth of the networks, the computational…
Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks
Farnaz Behnia, Ali Mirzaeian, Mohammad Sabokrou +6
In this paper, we propose Code-Bridged Classifier (CBC), a framework for making a Convolutional Neural Network (CNNs) robust against adversarial attacks without increasing or even…
Learning from Observations Using a Single Video Demonstration and Human Feedback
Sunil Gandhi, Tim Oates, Tinoosh Mohsenin +1
In this paper, we present a method for learning from video demonstrations by using human feedback to construct a mapping between the standard representation of the agent and the vi…
On the use of Deep Autoencoders for Efficient Embedded Reinforcement Learning
Bharat Prakash, Mark Horton, Nicholas R. Waytowich +3
In autonomous embedded systems, it is often vital to reduce the amount of actions taken in the real world and energy required to learn a policy. Training reinforcement learning age…
Detecting Epileptic Seizures from EEG Data using Neural Networks
Siddharth Pramod, Adam Page, Tinoosh Mohsenin +1
We explore the use of neural networks trained with dropout in predicting epileptic seizures from electroencephalographic data (scalp EEG). The input to the neural network is a 126…