Search Results for author:
Found 7 papers, 3 papers with code
Active shooter detection and robust tracking utilizing supplemental synthetic data
The increasing concern surrounding gun violence in the United States has led to a focus on developing systems to improve public safety.
MDPGT: Momentum-based Decentralized Policy Gradient Tracking
We propose a novel policy gradient method for multi-agent reinforcement learning, which leverages two different variance-reduction techniques and does not require large batches over iterations.
Multi-agent Reinforcement Learning
Policy Gradient Methods
+3
Granger Causality Based Hierarchical Time Series Clustering for State Estimation
Clustering is an unsupervised learning technique that is useful when working with a large volume of unlabeled data.
Cross-Gradient Aggregation for Decentralized Learning from Non-IID data
Inspired by ideas from continual learning, we propose Cross-Gradient Aggregation (CGA), a novel decentralized learning algorithm where (i) each agent aggregates cross-gradient information, i. e., derivatives of its model with respect to its neighbors' datasets, and (ii) updates its model using a projected gradient based on quadratic programming (QP).
Decentralized Deep Learning using Momentum-Accelerated Consensus
In this context, we propose and analyze a novel decentralized deep learning algorithm where the agents interact over a fixed communication topology (without a central server).
Spatiotemporal Attention for Multivariate Time Series Prediction and Interpretation
Accurate interpretation of such prediction outcomes from a machine learning model that explicitly captures temporal correlations can significantly benefit the domain experts.
On Higher-order Moments in Adam
In this paper, we investigate the popular deep learning optimization routine, Adam, from the perspective of statistical moments.
