23 citations · 68 across the 5 of their papers we have counts for
6 papers
Quantitative Overfitting Management for Human-in-the-loop ML Application Development with ease.ml/meter
Frances Ann Hubis, Wentao Wu, Ce Zhang
Simplifying machine learning (ML) application development, including distributed computation, programming interface, resource management, model selection, etc, has attracted intens…
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads
Tian Li, Jie Zhong, Ji Liu +2
We present ease.ml, a declarative machine learning service platform we built to support more than ten research groups outside the computer science departments at ETH Zurich for the…
MLBench: How Good Are Machine Learning Clouds for Binary Classification Tasks on Structured Data?
Yu Liu, Hantian Zhang, Luyuan Zeng +2
We conduct an empirical study of machine learning functionalities provided by major cloud service providers, which we call machine learning clouds. Machine learning clouds hold the…
CYCLADES: Conflict-free Asynchronous Machine Learning
Xinghao Pan, Maximilian Lam, Stephen Tu +6
We present CYCLADES, a general framework for parallelizing stochastic optimization algorithms in a shared memory setting. CYCLADES is asynchronous during shared model updates, and…
Caffe con Troll: Shallow Ideas to Speed Up Deep Learning
Stefan Hadjis, Firas Abuzaid, Ce Zhang +1
We present Caffe con Troll (CcT), a fully compatible end-to-end version of the popular framework Caffe with rebuilt internals. We built CcT to examine the performance characteristi…
Incremental Knowledge Base Construction Using DeepDive
Jaeho Shin, Sen Wu, Feiran Wang +3
Populating a database with unstructured information is a long-standing problem in industry and research that encompasses problems of extraction, cleaning, and integration. Recent n…