Grids versus graphs: Partitioning space for improved taxi demand-supply forecasts
N Davis, G Raina… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Accurate taxi demand-supply forecasting is a challenging application of ITS (Intelligent
Transportation Systems), due to the complex spatial and temporal patterns involved. We …
Transportation Systems), due to the complex spatial and temporal patterns involved. We …
Taxi demand forecasting: A HEDGE-based tessellation strategy for improved accuracy
N Davis, G Raina… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
A key problem in location-based modeling and forecasting lies in identifying suitable spatial
and temporal resolutions. In particular, judicious spatial partitioning can play a significant …
and temporal resolutions. In particular, judicious spatial partitioning can play a significant …
The robust way to stack and bag: the local Lipschitz way
Recent research has established that the local Lipschitz constant of a neural network directly
influences its adversarial robustness. We exploit this relationship to construct an ensemble …
influences its adversarial robustness. We exploit this relationship to construct an ensemble …
Leveraging online learning for CSS in frugal IoT network
We present a novel method for centralized collaborative spectrum sensing for IoT network
leveraging cognitive radio network. Based on an online learning framework, we propose an …
leveraging cognitive radio network. Based on an online learning framework, we propose an …
[PDF][PDF] What is the optimal depth for deep-unfolding architectures at deployment?
N Nayak, T Tholeti, M Srinivasan… - arXiv preprint arXiv …, 2020 - academia.edu
Recently, many iterative algorithms proposed for various applications such as compressed
sensing, MIMO Detection, etc. have been unfolded and presented as deep networks; these …
sensing, MIMO Detection, etc. have been unfolded and presented as deep networks; these …