John W. Lambert
John W. Lambert
Verified email at gatech.edu - Homepage
Title
Cited by
Cited by
Year
Argoverse: 3d tracking and forecasting with rich maps
MF Chang, J Lambert, P Sangkloy, J Singh, S Bak, A Hartnett, D Wang, ...
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
1012019
Deep learning under privileged information using heteroscedastic dropout
J Lambert, O Sener, S Savarese
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
282018
A Fully-Automated Pipeline for Detection and Segmentation of Liver Lesions and Pathological Lymph Nodes
A Hoogi, JW Lambert, Y Zheng, D Comaniciu, DL Rubin
NIPS 2016 Workshop on Machine Learning for Health, 2016
72016
YOLOFlow: Real-time Object Tracking in Video
K Buhler, J Lambert, M Vilim
1*2016
Quantifying Mammalian Learning: Large-Scale Detection of Dendritic Spines
I Bahtchevanov, S Hildick-Smith, J Lambert
12016
Is the Price Right
J Lambert, J Greenland
Prediction of Monthly Rental Prices in Provo, Utah, 2015
12015
Stacked RNNs for Encoder-Decoder Networks: Accurate Machine Understanding of Images
J Lambert
URL https://cs224d. stanford. edu/reports/Lambert. pdf, 0
1
MSeg: A Composite Dataset for Multi-domain Semantic Segmentation
J Lambert, Z Liu, O Sener, J Hays, V Koltun
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
2020
Markov Chain Monte Carlo Multi-target Tracking
J Lambert, B Jackson
2018
Training Regime Modifications for Deep Q-Network Learning Acceleration
S Hildick-Smith, J Lambert, B Weems
2016
Argoverse: 3D Tracking and Forecasting with Rich Maps Supplementary Material
MF Chang, J Lambert, P Sangkloy, J Singh, S Bak, A Hartnett, ...
network 14, 2, 0
Fully-Convolutional Networks for Semantic Segmentation of Fluorescence Microscopies
J Lambert
Deep Learning under Privileged Information Using Heteroscedastic Dropout Supplementary Material
J Lambert, O Sener, S Savarese
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Articles 1–13