Search Results for author:
Found 12 papers, 9 papers with code
Predicting Pedestrian Crosswalk Behavior Using Convolutional Neural Networks
After extensive testing of our system in real-world environments, we conclude that it is feasible as a back-up system that can compliment existing crosswalk buttons, and thereby improve the overall safety of crossing the street.
RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem
Researchers and practitioners in the field of reinforcement learning (RL) frequently leverage parallel computation, which has led to a plethora of new algorithms and systems in the last few years.
Variable Skipping for Autoregressive Range Density Estimation
In this paper, we explore a technique, variable skipping, for accelerating range density estimation over deep autoregressive models.
NeuroCard: One Cardinality Estimator for All Tables
Query optimizers rely on accurate cardinality estimates to produce good execution plans.
Hoplite: Efficient and Fault-Tolerant Collective Communication for Task-Based Distributed Systems
Task-based distributed frameworks (e. g., Ray, Dask, Hydro) have become increasingly popular for distributed applications that contain asynchronous and dynamic workloads, including asynchronous gradient descent, reinforcement learning, and model serving.
IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks
To address this, we propose a new distributed reinforcement learning algorithm, IMPACT.
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
A key challenge in leveraging data augmentation for neural network training is choosing an effective augmentation policy from a large search space of candidate operations.
Ranked #5 on
Image Classification
on SVHN
Deep Unsupervised Cardinality Estimation
To produce a truly usable estimator, we develop a Monte Carlo integration scheme on top of autoregressive models that can efficiently handle range queries with dozens of dimensions or more.
Neural Packet Classification
First, many of the existing solutions are iteratively building a decision tree by splitting nodes in the tree.
Tune: A Research Platform for Distributed Model Selection and Training
We show that this interface meets the requirements for a broad range of hyperparameter search algorithms, allows straightforward scaling of search to large clusters, and simplifies algorithm implementation.
RLlib: Abstractions for Distributed Reinforcement Learning
Reinforcement learning (RL) algorithms involve the deep nesting of highly irregular computation patterns, each of which typically exhibits opportunities for distributed computation.
Ray: A Distributed Framework for Emerging AI Applications
To meet the performance requirements, Ray employs a distributed scheduler and a distributed and fault-tolerant store to manage the system's control state.
