4 citations · 10 across the 6 of their papers we have counts for
6 papers · 1 filter
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games
Indranil Sur, Zachary Daniels, Abrar Rahman +16
As Artificial and Robotic Systems are increasingly deployed and relied upon for real-world applications, it is important that they exhibit the ability to continually learn and adap…
Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition, and Selective Transfer
Aswin Raghavan, Jesse Hostetler, Indranil Sur +2
We introduce the eigentask framework for lifelong learning. An eigentask is a pairing of a skill that solves a set of related tasks, paired with a generative model that can sample…
Generative Memory for Lifelong Reinforcement Learning
Aswin Raghavan, Jesse Hostetler, Sek Chai
Our research is focused on understanding and applying biological memory transfers to new AI systems that can fundamentally improve their performance, throughout their fielded lifet…
Generalized Ternary Connect: End-to-End Learning and Compression of Multiplication-Free Deep Neural Networks
Samyak Parajuli, Aswin Raghavan, Sek Chai
The use of deep neural networks in edge computing devices hinges on the balance between accuracy and complexity of computations. Ternary Connect (TC) \cite{lin2015neural} addresses…
GPU Activity Prediction using Representation Learning
Aswin Raghavan, Mohamed Amer, Timothy Shields +2
GPU activity prediction is an important and complex problem. This is due to the high level of contention among thousands of parallel threads. This problem was mostly addressed usin…
Low Precision Neural Networks using Subband Decomposition
Sek Chai, Aswin Raghavan, David Zhang +2
Large-scale deep neural networks (DNN) have been successfully used in a number of tasks from image recognition to natural language processing. They are trained using large training…