33 citations · 36 across the 5 of their papers we have counts for
5 papers
Calibrated Dataset Condensation for Faster Hyperparameter Search
Mucong Ding, Yuancheng Xu, Tahseen Rabbani +5
Dataset condensation can be used to reduce the computational cost of training multiple models on a large dataset by condensing the training dataset into a small synthetic set. Stat…
Accelerating Sparse Tensor Decomposition Using Adaptive Linearized Representation
Jan Laukemann, Ahmed E. Helal, S. Isaac Geronimo Anderson +7
High-dimensional sparse data emerge in many critical application domains such as healthcare and cybersecurity. To extract meaningful insights from massive volumes of these multi-di…
Design of General Purpose Minimal-Auxiliary Ising Machines
Isaac K. Martin, Andrew G. Moore, John T. Daly +2
Ising machines are a form of quantum-inspired processing-in-memory computer which has shown great promise for overcoming the limitations of traditional computing paradigms while op…
Efficient, Out-of-Memory Sparse MTTKRP on Massively Parallel Architectures
Andy Nguyen, Ahmed E. Helal, Fabio Checconi +6
Tensor decomposition (TD) is an important method for extracting latent information from high-dimensional (multi-modal) sparse data. This study presents a novel framework for accele…
ALTO: Adaptive Linearized Storage of Sparse Tensors
Ahmed E. Helal, Jan Laukemann, Fabio Checconi +4
The analysis of high-dimensional sparse data is becoming increasingly popular in many important domains. However, real-world sparse tensors are challenging to process due to their…