activity
20122023
most citedSuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks

176 citations · 397 across the 22 of their papers we have counts for

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Showing 2018Show all

8 papers · 1 filter

cs.DB2018

Chiller: Contention-centric Transaction Execution and Data Partitioning for Modern Networks

Erfan Zamanian, Julian Shun, Carsten Binnig +1

Distributed transactions on high-overhead TCP/IP-based networks were conventionally considered to be prohibitively expensive and thus were avoided at all costs. To that end, the pr…

cs.DB2018

VizRec: A framework for secure data exploration via visual representation

Lorenzo De Stefani, Leonhard F. Spiegelberg, Tim Kraska +1

Visual representations of data (visualizations) are tools of great importance and widespread use in data analytics as they provide users visual insight to patterns in the observed…

cs.HC2018

VizML: A Machine Learning Approach to Visualization Recommendation

Kevin Z. Hu, Michiel A. Bakker, Stephen Li +2

Data visualization should be accessible for all analysts with data, not just the few with technical expertise. Visualization recommender systems aim to lower the barrier to explori…

cs.LG2018

Unknown Examples & Machine Learning Model Generalization

Yeounoh Chung, Peter J. Haas, Eli Upfal +1

Over the past decades, researchers and ML practitioners have come up with better and better ways to build, understand and improve the quality of ML models, but mostly under the key…

cs.DB2018

Automated Data Slicing for Model Validation:A Big data - AI Integration Approach

Yeounoh Chung, Tim Kraska, Neoklis Polyzotis +2

As machine learning systems become democratized, it becomes increasingly important to help users easily debug their models. However, current data tools are still primitive when it…

stat.ML2018

Smallify: Learning Network Size while Training

Guillaume Leclerc, Manasi Vartak, Raul Castro Fernandez +2

As neural networks become widely deployed in different applications and on different hardware, it has become increasingly important to optimize inference time and model size along…