14 citations · 22 across the 4 of their papers we have counts for
4 papers
MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning
Krishnateja Killamsetty, Alexandre V. Evfimievski, Tejaswini Pedapati +3
Training deep networks and tuning hyperparameters on large datasets is computationally intensive. One of the primary research directions for efficient training is to reduce trainin…
AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning
Krishnateja Killamsetty, Guttu Sai Abhishek, Aakriti +4
Deep neural networks have seen great success in recent years; however, training a deep model is often challenging as its performance heavily depends on the hyper-parameters used. I…
On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML
Matthias Boehm, Berthold Reinwald, Dylan Hutchison +2
Many large-scale machine learning (ML) systems allow specifying custom ML algorithms by means of linear algebra programs, and then automatically generate efficient execution plans.…
Declarative Machine Learning - A Classification of Basic Properties and Types
Matthias Boehm, Alexandre V. Evfimievski, Niketan Pansare +1
Declarative machine learning (ML) aims at the high-level specification of ML tasks or algorithms, and automatic generation of optimized execution plans from these specifications. T…