Search Results for author:
Found 8 papers, 5 papers with code
HyperSched: Dynamic Resource Reallocation for Model Development on a Deadline
Prior research in resource scheduling for machine learning training workloads has largely focused on minimizing job completion times.
IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks
To address this, we propose a new distributed reinforcement learning algorithm, IMPACT.
Large batch size training of neural networks with adversarial training and second-order information
Our method exceeds the performance of existing solutions in terms of both accuracy and the number of SGD iterations (up to 1\% and $5\times$, respectively).
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.
Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning
We explore how Deep Neural Networks can represent meta-policies that switch among a set of previously learned policies, specifically in settings where the dynamics of a new scenario are composed of a mixture of previously learned dynamics and where the state observation is possibly corrupted by sensing noise.
Real-Time Machine Learning: The Missing Pieces
Machine learning applications are increasingly deployed not only to serve predictions using static models, but also as tightly-integrated components of feedback loops involving dynamic, real-time decision making.
