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20222025
most citedAngler: Helping Machine Translation Practitioners Prioritize Model Improvements

20 citations · 44 across the 9 of their papers we have counts for

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6 papers · 1 filter

cs.HC20248 cited

Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference

Fred Hohman, Chaoqun Wang, Jinmook Lee +7

On-device machine learning (ML) moves computation from the cloud to personal devices, protecting user privacy and enabling intelligent user experiences. However, fitting models on…

cs.HC2023

Draco 2: An Extensible Platform to Model Visualization Design

Junran Yang, Péter Ferenc Gyarmati, Zehua Zeng +1

Draco introduced a constraint-based framework to model visualization design in an extensible and testable form. It provides a way to abstract design guidelines from theoretical and…

cs.HC20233 cited

Dead or Alive: Continuous Data Profiling for Interactive Data Science

Will Epperson, Vaishnavi Gorantla, Dominik Moritz +1

Profiling data by plotting distributions and analyzing summary statistics is a critical step throughout data analysis. Currently, this process is manual and tedious since analysts…

cs.HC2023

DashQL -- Complete Analysis Workflows with SQL

André Kohn, Dominik Moritz, Thomas Neumann

We present DashQL, a language that describes complete analysis workflows in self-contained scripts. DashQL combines SQL, the grammar of relational database systems, with a grammar…

cs.HC202320 cited

Angler: Helping Machine Translation Practitioners Prioritize Model Improvements

Samantha Robertson, Zijie J. Wang, Dominik Moritz +2

Machine learning (ML) models can fail in unexpected ways in the real world, but not all model failures are equal. With finite time and resources, ML practitioners are forced to pri…

cs.HC2022

VegaFusion: Automatic Server-Side Scaling for Interactive Vega Visualizations

Nicolas Kruchten, Jon Mease, Dominik Moritz

The Vega grammar has been broadly adopted by a growing ecosystem of browser-based visualization tools. However, the reference Vega renderer does not scale well to large datasets (e…