2103 results for "earn" across all locations
Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning
C. DeChant, T. Wiesner-Hanks, S, Chen, E. Stewart, J. Yosinski, M. Gore, R. Nelson, and H. Lipson
Northern leaf blight (NLB) can cause severe yield loss in maize; however, scouting large areas to accurately diagnose the disease is time consuming and difficult. We demonstrate a system capable of automatically identifying NLB lesions in field-acquired images of maize plants with high reliability. […] [PDF]
Phytopathology, 2017
Uber + AutoGuru: rethinking the car-servicing experience
How innovation enabled AutoGuru to rethink the car-servicing experience for customers and mechanics.
Inside Uber HQ: the story behind Share Trip
Get the inside scoop on our Share Trip feature as we interview Uber product manager Ambar.
Austin’s top 5 outdoor activities for summer
With parks, pools, and pretty natural attractions, Austin boasts many places to enjoy the outdoors this summer 2017.
5 Must-see Attractions for Animal Enthusiasts in Miami
Celebrating International Women’s Day in Washington, D.C. with the Family
LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving
G. P. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez, C. Wellington
In this paper, we present LaserNet, a computationally efficient method for 3D object detection from LiDAR data for autonomous driving. The efficiency results from processing LiDAR data in the native range view of the sensor, where the input data is naturally compact. […]
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Computer Vision and Pattern Recognition (CVPR), 2019
Evolvability ES: Scalable and Direct Optimization of Evolvability
A. Gajewski, J. Clune, K. O. Stanley, J. Lehman
Designing evolutionary algorithms capable of uncovering highly evolvable representations is an open challenge; such evolvability is important because it accelerates evolution and enables fast adaptation to changing circumstances. This paper introduces evolvability ES, an evolutionary algorithm designed to explicitly and efficiently optimize for evolvability, i.e. the ability to further adapt. […] [PDF]
The Genetic and Evolutionary Computation Conference (GECCO), 2019
Joint Interaction and Trajectory Prediction for Autonomous Driving using Graph Neural Networks
D. Lee, Y. Gu, J. Hoang, M. Marchetti-Bowick
Using weakly intent label can potentially predict the interaction and the resulting trajectory better. We use a GNN to model the interaction. [PDF]
Conference on Neural Information Processing Systems (NeurIPS), 2019
Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction
A. Jain, S. Casas, R. Liao, Y. Xiong, S. Feng, S. Segal, R. Urtasun
Our research shows that non-parametric distributions can capture extremely well the (erratic) pedestrian behavior. We propose Discrete Residual Flow, a convolutional neural network for human motion prediction that accurately models the temporal dependencies and captures the uncertainty inherent in long-range motion forecasting. In particular, our method captures multi-modal posteriors over future human motion very realistically. [PDF]
Conference on Neural Information Processing Systems (NeurIPS), 2019