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2020 – today
- 2023
[j5]Aditya Gupta
, Shiva Maharaj, Nicholas Polson, Vadim Sokolov:
On the Value of Chess Squares. Entropy 25(10): 1374 (2023)- [j4]Sergey V. Ershov, Vadim Sokolov, Vladimir A. Galaktionov, Alexey G. Voloboy:
Virtual Light Sensing Technology for Fast Calculation of Daylight Autonomy Metrics. Sensors 23(4): 2255 (2023) - [i15]Aditya Gupta, Shiva Maharaj, Nicholas Polson, Vadim Sokolov:
The Value of Chess Squares. CoRR abs/2307.05330 (2023) - [i14]Nick Polson, Vadim Sokolov:
Deep Learning: A Tutorial. CoRR abs/2310.06251 (2023) - 2022
- [j3]Xianan Huang, Boqi Li, Huei Peng, Joshua A. Auld, Vadim O. Sokolov:
Eco-Mobility-on-Demand Fleet Control With Ride-Sharing. IEEE Trans. Intell. Transp. Syst. 23(4): 3158-3168 (2022) - [i13]Nicholas G. Polson, Vadim Sokolov, Jianeng Xu:
Quantum Bayes AI. CoRR abs/2208.08068 (2022) - 2021
- [i12]Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson, Vadim Sokolov, Jianeng Xu:
Merging Two Cultures: Deep and Statistical Learning. CoRR abs/2110.11561 (2021) - [i11]Farnaz Behnia, Dominik Karbowski, Vadim Sokolov:
Deep Generative Models for Vehicle Speed Trajectories. CoRR abs/2112.08361 (2021)
2010 – 2019
- 2019
- [j2]Josh Warren, Jeff Lipkowitz, Vadim Sokolov:
Clusters of Driving Behavior From Observational Smartphone Data. IEEE Intell. Transp. Syst. Mag. 11(3): 171-180 (2019) - [c5]Haipeng Chen, Sushil Jajodia, Jing Liu, Noseong Park, Vadim Sokolov, V. S. Subrahmanian:
FakeTables: Using GANs to Generate Functional Dependency Preserving Tables with Bounded Real Data. IJCAI 2019: 2074-2080 - [i10]Yuexi Wang, Nicholas G. Polson, Vadim O. Sokolov:
Scalable Data Augmentation for Deep Learning. CoRR abs/1903.09668 (2019) - [i9]Duanshun Li, Jing Liu, Noseong Park, Dongeun Lee, Giridhar Ramachandran, Ali Seyedmazloom, Kookjin Lee, Chen Feng, Vadim Sokolov, Rajesh Ganesan:
Solving Large-Scale 0-1 Knapsack Problems and its Application to Point Cloud Resampling. CoRR abs/1906.05929 (2019) - [i8]Xianan Huang, Boqi Li, Huei Peng, Joshua A. Auld, Vadim O. Sokolov:
Eco-Mobility-on-Demand Fleet Control with Ride-Sharing. CoRR abs/1908.09828 (2019) - 2018
- [j1]Nicholas Polson, Vadim Sokolov:
Bayesian Particle Tracking of Traffic Flows. IEEE Trans. Intell. Transp. Syst. 19(2): 345-356 (2018) - [c4]Laura Schultz, Vadim Sokolov:
Bayesian Optimization for Transportation Simulators. ANT/SEIT 2018: 973-978 - [c3]Vadim Sokolov, Muhammad Imran, David W. Etherington, Dominik Karbowski, Aymeric Rousseau:
Effects of Predictive Real-Time Traffic Signal Information. ITSC 2018: 1834-1839 - [i7]Laura Schultz, Vadim Sokolov:
Deep Reinforcement Learning for Dynamic Urban Transportation Problems. CoRR abs/1806.05310 (2018) - [i6]Nicholas G. Polson, Vadim O. Sokolov:
Deep Learning. CoRR abs/1807.07987 (2018) - [i5]Michael Polson, Vadim Sokolov:
Deep Learning for Energy Markets. CoRR abs/1808.05527 (2018) - [i4]Nicholas Polson, Vadim Sokolov:
Deep Learning: Computational Aspects. CoRR abs/1808.08618 (2018) - 2017
- [i3]Vadim Sokolov, Jeffrey Larson, Todd S. Munson, Joshua Auld, Dominik Karbowski:
Platoon formation maximization through centralized routing and departure time coordination. CoRR abs/1701.01391 (2017) - [i2]Nicholas Polson, Vadim Sokolov:
Deep Learning: A Bayesian Perspective. CoRR abs/1706.00473 (2017) - [i1]Josh Warren, Jeff Lipkowitz, Vadim Sokolov:
Clusters of Driving Behavior from Observational Smartphone Data. CoRR abs/1710.04502 (2017) - 2016
- [c2]Jeffrey Larson, Todd S. Munson, Vadim Sokolov:
Coordinated Platoon Routing in a Metropolitan Network. CSC 2016: 73-82 - 2012
- [c1]Vadim Sokolov, Joshua Auld, Michael Hope:
A Flexible Framework for Developing Integrated Models of Transportation Systems Using an Agent-based Approach. ANT/MobiWIS 2012: 854-859
Coauthor Index
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last updated on 2024-10-07 01:23 CEST by the dblp team
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