Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Nishihara, Robert"'
Autor:
Zhuang, Siyuan, Li, Zhuohan, Zhuo, Danyang, Wang, Stephanie, Liang, Eric, Nishihara, Robert, Moritz, Philipp, Stoica, Ion
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 servi
Externí odkaz:
http://arxiv.org/abs/2002.05814
Monte Carlo Tree Search (MCTS) algorithms perform simulation-based search to improve policies online. During search, the simulation policy is adapted to explore the most promising lines of play. MCTS has been used by state-of-the-art programs for man
Externí odkaz:
http://arxiv.org/abs/1904.03646
Autor:
Liaw, Richard, Liang, Eric, Nishihara, Robert, Moritz, Philipp, Gonzalez, Joseph E., Stoica, Ion
Modern machine learning algorithms are increasingly computationally demanding, requiring specialized hardware and distributed computation to achieve high performance in a reasonable time frame. Many hyperparameter search algorithms have been proposed
Externí odkaz:
http://arxiv.org/abs/1807.05118
Autor:
Liang, Eric, Liaw, Richard, Moritz, Philipp, Nishihara, Robert, Fox, Roy, Goldberg, Ken, Gonzalez, Joseph E., Jordan, Michael I., Stoica, Ion
Reinforcement learning (RL) algorithms involve the deep nesting of highly irregular computation patterns, each of which typically exhibits opportunities for distributed computation. We argue for distributing RL components in a composable way by adapt
Externí odkaz:
http://arxiv.org/abs/1712.09381
Autor:
Moritz, Philipp, Nishihara, Robert, Wang, Stephanie, Tumanov, Alexey, Liaw, Richard, Liang, Eric, Elibol, Melih, Yang, Zongheng, Paul, William, Jordan, Michael I., Stoica, Ion
The next generation of AI applications will continuously interact with the environment and learn from these interactions. These applications impose new and demanding systems requirements, both in terms of performance and flexibility. In this paper, w
Externí odkaz:
http://arxiv.org/abs/1712.05889
Autor:
Nishihara, Robert, Moritz, Philipp, Wang, Stephanie, Tumanov, Alexey, Paul, William, Schleier-Smith, Johann, Liaw, Richard, Niknami, Mehrdad, Jordan, Michael I., Stoica, Ion
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. These applications pose a new set of
Externí odkaz:
http://arxiv.org/abs/1703.03924
This paper establishes the existence of observable footprints that reveal the "causal dispositions" of the object categories appearing in collections of images. We achieve this goal in two steps. First, we take a learning approach to observational ca
Externí odkaz:
http://arxiv.org/abs/1605.08179
Training deep networks is a time-consuming process, with networks for object recognition often requiring multiple days to train. For this reason, leveraging the resources of a cluster to speed up training is an important area of work. However, widely
Externí odkaz:
http://arxiv.org/abs/1511.06051
Algorithms for hyperparameter optimization abound, all of which work well under different and often unverifiable assumptions. Motivated by the general challenge of sequentially choosing which algorithm to use, we study the more specific task of choos
Externí odkaz:
http://arxiv.org/abs/1508.02933
We propose a new stochastic L-BFGS algorithm and prove a linear convergence rate for strongly convex and smooth functions. Our algorithm draws heavily from a recent stochastic variant of L-BFGS proposed in Byrd et al. (2014) as well as a recent appro
Externí odkaz:
http://arxiv.org/abs/1508.02087