Learning with Holographic Reduced Representations

1 code implementation • NeurIPS 2021 • Ashwinkumar Ganesan, Hang Gao, Sunil Gandhi, Edward Raff, Tim Oates, James Holt, Mark McLean

HRRs today are not effective in a differentiable solution due to numerical instability, a problem we solve by introducing a projection step that forces the vectors to exist in a well behaved point in space.

Multi-Label Classification Retrieval

Efficient Retrieval Optimized Multi-task Learning

no code implementations • 20 Apr 2021 • Hengxin Fun, Sunil Gandhi, Sujith Ravi

Our ROM approach presents a unified and generalizable framework that enables scaling efficiently to multiple tasks, varying levels of supervision, and optimization choices such as different learning schedules without changing the model architecture.

Extractive Question-Answering Multi-Task Learning +2

Learning from Observations Using a Single Video Demonstration and Human Feedback

no code implementations • 29 Sep 2019 • Sunil Gandhi, Tim Oates, Tinoosh Mohsenin, Nicholas Waytowich

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.

Denoising Time Series Data Using Asymmetric Generative Adversarial Networks

no code implementations • Advances in Knowledge Discovery and Data Mining. PAKDD 2018 2018 • Sunil Gandhi, Tim Oates, Tinoosh Mohsenin, David Hairston

This step is especially important if the noise in data originates from diverse sources.

Denoising EEG +4