activity
20192024
most citedA Domain-Agnostic Approach for Characterization of Lifelong Learning Systems

17 citations · 49 across the 11 of their papers we have counts for

collaborators

11 papers

cs.MA2024

Data-Driven Distributed Common Operational Picture from Heterogeneous Platforms using Multi-Agent Reinforcement Learning

Indranil Sur, Aswin Raghavan, Abrar Rahman +3

The integration of unmanned platforms equipped with advanced sensors promises to enhance situational awareness and mitigate the "fog of war" in military operations. However, managi…

cs.CV2023

Human Body Model based ID using Shape and Pose Parameters

Aravind Sundaresan, Brian Burns, Indranil Sur +3

We present a Human Body model based IDentification system (HMID) system that is jointly trained for shape, pose and biometric identification. HMID is based on the Human Mesh Recove…

cs.CV2023

TIJO: Trigger Inversion with Joint Optimization for Defending Multimodal Backdoored Models

Indranil Sur, Karan Sikka, Matthew Walmer +5

We present a Multimodal Backdoor Defense technique TIJO (Trigger Inversion using Joint Optimization). Recent work arXiv:2112.07668 has demonstrated successful backdoor attacks on m…

cs.LG2023★ 17 cited

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…

cs.LG2022★ 2 cited

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…

cs.LG2022★ 6 cited

Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2

Zachary Daniels, Aswin Raghavan, Jesse Hostetler +4

One approach to meet the challenges of deep lifelong reinforcement learning (LRL) is careful management of the agent's learning experiences, to learn (without forgetting) and build…