17 citations · 49 across the 11 of their papers we have counts for
11 papers
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…
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…
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…
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…
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…
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…
Dual-Key Multimodal Backdoors for Visual Question Answering
Matthew Walmer, Karan Sikka, Indranil Sur +2
The success of deep learning has enabled advances in multimodal tasks that require non-trivial fusion of multiple input domains. Although multimodal models have shown potential in…
Detecting Trojaned DNNs Using Counterfactual Attributions
Karan Sikka, Indranil Sur, Susmit Jha +2
We target the problem of detecting Trojans or backdoors in DNNs. Such models behave normally with typical inputs but produce specific incorrect predictions for inputs poisoned with…
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…
Deep Adaptive Semantic Logic (DASL): Compiling Declarative Knowledge into Deep Neural Networks
Karan Sikka, Andrew Silberfarb, John Byrnes +4
We introduce Deep Adaptive Semantic Logic (DASL), a novel framework for automating the generation of deep neural networks that incorporates user-provided formal knowledge to improv…
Data-Efficient Mutual Information Neural Estimator
Xiao Lin, Indranil Sur, Samuel A. Nastase +3
Measuring Mutual Information (MI) between high-dimensional, continuous, random variables from observed samples has wide theoretical and practical applications. Recent work, MINE (B…