2195 results for "earn" across all locations
Improvements designed to help keep you even safer
Enjoy every Uber ride with more peace of mind. Find out how our new safety tools keep you connected and protected, wherever you go.
Improvements designed to help keep you even safer
Enjoy every Uber ride with more peace of mind. Find out how our new safety tools keep you connected and protected, wherever you go.
Improvements designed to help keep you even safer
Enjoy every Uber ride with more peace of mind. Find out how our new safety tools keep you connected and protected, wherever you go.
Likelihood-free inference with emulator networks
J.-M. Lueckmann, G. Bassetto, T. Karaletsos, J. H. Macke
Approximate Bayesian Computation (ABC) provides methods for Bayesian inference in simulation-based stochastic models which do not permit tractable likelihoods. We present a new ABC method which uses probabilistic neural emulator networks to learn synthetic likelihoods on simulated data — both local emulators which approximate the likelihood for specific observed data, as well as global ones which are applicable to a range of data. […] [PDF]
2018
Bayesian inference on random simple graphs with power law degree distributions
J. Lee, C. Heaukulani, Z. Ghahramani, L. James, S. Choi
We present a model for random simple graphs with a degree distribution that obeys a power law (i.e., is heavy-tailed). To attain this behavior, the edge probabilities in the graph are constructed from Bertoin-Fujita-Roynette-Yor (BFRY) random variables, which have been recently utilized in Bayesian statistics for the construction of power law models in several applications. […] [PDF]
International Conference on Machine Learning (ICML), 2017
Faster Neural Networks Straight from JPEG
L. Gueguen, A. Sergeev, B. Kadlec, R. Liu, J. Yosinski
The simple, elegant approach of training convolutional neural networks (CNNs) directly from RGB pixels has enjoyed overwhelming empirical success. But can more performance be squeezed out of networks by using different input representations? In this paper we propose and explore a simple idea: train CNNs directly on the blockwise discrete cosine transform (DCT) coefficients computed and available in the middle of the JPEG codec. […] [PDF]
Advances in Neural Information Processing Systems (NeurIPS), 2018
Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets
F. Chou, T.-H. Lin, H. Cui, V. Radosavljevic, T. Nguyen, T. Huang, M. Niedoba, J. Schneider, N. Djuric
Following detection and tracking of traffic actors, prediction of their future motion is the next critical component of a self-driving vehicle (SDV), allowing the SDV to move safely and efficiently in its environment. This is particularly important when it comes to vulnerable road users (VRUs), such as pedestrians and bicyclists. We present a deep learning method for predicting VRU movement where we rasterize high-definition maps and actor’s surroundings into bird’s-eye view image used as input to convolutional networks. […] [PDF]
MLITS workshop @ Neural Information Processing Systems (NeurIPS), 2018
Become an EV Champion
At Uber, we are committed to working toward a greener future for our cities by empowering those driving electric vehicles on our platform.
Thank you for launching Uber Auto!
Whenever you want, Uber Auto will arrive at your doorstep at the tap of a button. Book your auto ride through Uber in Delhi NCR.
The best Auckland beaches to visit this summer
Planning a day at the beach? We have 5 tips for the perfect summer picnic.