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Found 19 papers, 1 papers with code
TinyVQA: Compact Multimodal Deep Neural Network for Visual Question Answering on Resource-Constrained Devices
Traditional machine learning models often require powerful hardware, making them unsuitable for deployment on resource-limited devices.
Squeezed Edge YOLO: Onboard Object Detection on Edge Devices
This model is compressed and optimized to kilobytes of parameters in order to fit onboard such edge devices.
LLM Augmented Hierarchical Agents
However, using LLMs to solve real world problems is hard because they are not grounded in the current task.
ReProHRL: Towards Multi-Goal Navigation in the Real World using Hierarchical Agents
In real-world environments with sparse rewards and multiple goals, learning is still a major challenge and Reinforcement Learning (RL) algorithms fail to learn good policies.
Harnessing the Power of Explanations for Incremental Training: A LIME-Based Approach
In this work, model explanations are fed back to the feed-forward training to help the model generalize better.
Towards an Interpretable Hierarchical Agent Framework using Semantic Goals
Learning to solve long horizon temporally extended tasks with reinforcement learning has been a challenge for several years now.
TinyM$^2$Net: A Flexible System Algorithm Co-designed Multimodal Learning Framework for Tiny Devices
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 IoT.
A Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spotting
We propose a regression-based network exploration technique that considers the scaling of the network filters ($s$) and quantization ($q$) of the network layers, leading to a friendly and energy-efficient configuration for FPGA hardware implementation.
Automatic Goal Generation using Dynamical Distance Learning
In this work, we propose a method for automatic goal generation using a dynamical distance function (DDF) in a self-supervised fashion.
Interactive Hierarchical Guidance using Language
The low-level controller executes the sub-tasks based on the language commands.
Neural Networks for Pulmonary Disease Diagnosis using Auditory and Demographic Information
Pulmonary diseases impact millions of lives globally and annually.
Diverse Knowledge Distillation (DKD): A Solution for Improving The Robustness of Ensemble Models Against Adversarial Attacks
This paper proposes an ensemble learning model that is resistant to adversarial attacks.
Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks
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 by decreasing the overall models' computational complexity.
Learning from Observations Using a Single Video Demonstration and Human Feedback
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 visual representation of the demonstration.
On the use of Deep Autoencoders for Efficient Embedded Reinforcement Learning
This compression model is vital to efficiently learn policies, especially when learning on embedded systems.
Improving Safety in Reinforcement Learning Using Model-Based Architectures and Human Intervention
Recent progress in AI and Reinforcement learning has shown great success in solving complex problems with high dimensional state spaces.
Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection
Ubiquitous bio-sensing for personalized health monitoring is slowly becoming a reality with the increasing availability of small, diverse, robust, high fidelity sensors.
Detecting Epileptic Seizures from EEG Data using Neural Networks
We explore the use of neural networks trained with dropout in predicting epileptic seizures from electroencephalographic data (scalp EEG).
