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pro vyhledávání: '"LIANG, ERIC"'
Autor:
Luan, Frank Sifei, Wang, Stephanie, Yagati, Samyukta, Kim, Sean, Lien, Kenneth, Ong, Isaac, Hong, Tony, Cho, SangBin, Liang, Eric, Stoica, Ion
We present Exoshuffle-CloudSort, a sorting application running on top of Ray using the Exoshuffle architecture. Exoshuffle-CloudSort runs on Amazon EC2, with input and output data stored on Amazon S3. Using 40 i4i.4xlarge workers, Exoshuffle-CloudSor
Externí odkaz:
http://arxiv.org/abs/2301.03734
Autor:
Liang, Eric, Stamp, Mark
A common yet potentially dangerous task is the act of crossing the street. Pedestrian accidents contribute a significant amount to the high number of annual traffic casualties, which is why it is crucial for pedestrians to use safety measures such as
Externí odkaz:
http://arxiv.org/abs/2208.07250
Autor:
Luan, Frank Sifei, Wang, Stephanie, Yagati, Samyukta, Kim, Sean, Lien, Kenneth, Ong, Isaac, Hong, Tony, Cho, SangBin, Liang, Eric, Stoica, Ion
Shuffle is one of the most expensive communication primitives in distributed data processing and is difficult to scale. Prior work addresses the scalability challenges of shuffle by building monolithic shuffle systems. These systems are costly to dev
Externí odkaz:
http://arxiv.org/abs/2203.05072
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. In this paper, we re-examine the challenges posed by
Externí odkaz:
http://arxiv.org/abs/2011.12719
Deep autoregressive models compute point likelihood estimates of individual data points. However, many applications (i.e., database cardinality estimation) require estimating range densities, a capability that is under-explored by current neural dens
Externí odkaz:
http://arxiv.org/abs/2007.05572
Query optimizers rely on accurate cardinality estimates to produce good execution plans. Despite decades of research, existing cardinality estimators are inaccurate for complex queries, due to making lossy modeling assumptions and not capturing inter
Externí odkaz:
http://arxiv.org/abs/2006.08109
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
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The practical usage of reinforcement learning agents is often bottlenecked by the duration of training time. To accelerate training, practitioners often turn to distributed reinforcement learning architectures to parallelize and accelerate the traini
Externí odkaz:
http://arxiv.org/abs/1912.00167
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. Properly chosen augmentation policies can lead to significant generalization i
Externí odkaz:
http://arxiv.org/abs/1905.05393