2226 results for "earn" across all locations
MySQL At Uber
How does Uber achieve 99.99% availability across 2,000+ MySQL® clusters? Learn how we manage our MySQL fleet at scale, from architecture to control plane optimizations.
Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity
T. Miconi, A. Rawal, J. Clune, K. Stanley
A grand challenge in reinforcement learning is intelligent exploration, especially when rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard-exploration domains: Montezuma’s Revenge and Pitfall. On both games, current RL algorithms perform poorly, even those with intrinsic motivation, which is the dominant method to improve performance on hard-exploration domains. To address this shortfall, we introduce a new algorithm called Go-Explore. […] [PDF]
International Conference on Learning Representations (ICLR), 2019
Estimating Q(s,s’) with Deep Deterministic Dynamics Gradients
A. Edwards, Himanshu Sahni, R. Liu, J. Hung, A. Jain, R. Wang, A. Ecoffet, T. Miconi, C. Isbell, J. Yosinski
In this paper, we introduce a novel form of value function, Q(s,s′), that expresses the utility of transitioning from a state s to a neighboring state s′ and then acting optimally thereafter. In order to derive an optimal policy, we develop a forward dynamics model that learns to make next-state predictions that maximize this value. […] [PDF]
International Conference on Machine Learning (ICML), 2020
Uber Shuttle: now boarding at AT&T Plano
AT&T’s shuttle program in Dallas will now run through Uber Rides. In this guide, you’ll learn how to join the program, find information on the schedule and location, board your shuttle, and find support when needed.
Conditional Similarity Networks
A. Veit, S. Belongie, T. Karaletsos
What makes images similar? To measure the similarity between images, they are typically embedded in a feature-vector space, in which their distance preserve the relative dissimilarity. However, when learning such similarity embeddings the simplifying assumption is commonly made that images are only compared to one unique measure of similarity. […] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2017
Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents
E. Conti, V. Madhavan, F. Such, J. Lehman, K. Stanley, J. Clune
Evolution strategies (ES) are a family of black-box optimization algorithms able to train deep neural networks roughly as well as Q-learning and policy gradient methods on challenging deep reinforcement learning (RL) problems, but are much faster (e.g. hours vs. days) because they parallelize better. […] [PDF]
ViGIL @ NeurIPS 2017 (NeurIPS), 2017
Ludwig v0.3 Introduces Hyperparameter Optimization, Transformers and TensorFlow 2 support
In February 2019, Uber released Ludwig, an open source, code-free deep learning (DL) toolbox that gives non-programmers and advanced machine learning (ML) practitioners alike the power to develop models for a variety of DL tasks. With use cases spanning text classification, natural language understanding, image classification, and time series forecasting, among many others, Ludwig gives users the ability to easily train and test DL models, and the power to tweak parameters for exploring architectures, comparing models, and improving performance.
Understanding Neural Networks via Feature Visualization: A survey
A. Nguyen, J. Yosinski, J. Clune
A neuroscience method to understanding the brain is to find and study the preferred stimuli that highly activate an individual cell or groups of cells. Recent advances in machine learning enable a family of methods to synthesize preferred stimuli that cause a neuron in an artificial or biological brain to fire strongly. […] [PDF]
Interpretable AI: Interpreting, Explaining and Visualizing Deep Learning, 2019
Hamiltonian Neural Networks
S. Greydanus, M. Dzamba, J. Yosinski
Even though neural networks enjoy widespread use, they still struggle to learn the basic laws of physics. How might we endow them with better inductive biases? In this paper, we draw inspiration from Hamiltonian mechanics to train models that learn and respect exact conservation laws in an unsupervised manner. […] [PDF]
Conference on Neural Information Processing Systems (NeurIPS), 2019
Automatic Discovery of the Statistical Types of Variables in a Dataset
I. Valera, Z. Ghahramani
A common practice in statistics and machine learning is to assume that the statistical data types (e.g., ordinal, categorical or real-valued) of variables, and usually also the likelihood model, is known. However, as the availability of real-world data increases, this assumption becomes too restrictive. […] [PDF]
International Conference on Machine Learning (ICML), 2017