61 citations · 67 across the 5 of their papers we have counts for
5 papers
An Embedding-Based Grocery Search Model at Instacart
Yuqing Xie, Taesik Na, Xiao Xiao +7
The key to e-commerce search is how to best utilize the large yet noisy log data. In this paper, we present our embedding-based model for grocery search at Instacart. The system le…
Mixture of Pre-processing Experts Model for Noise Robust Deep Learning on Resource Constrained Platforms
Taesik Na, Minah Lee, Burhan A. Mudassar +3
Deep learning on an edge device requires energy efficient operation due to ever diminishing power budget. Intentional low quality data during the data acquisition for longer batter…
Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platforms
Jong Hwan Ko, Taesik Na, Mohammad Faisal Amir +1
This paper introduces partitioning an inference task of a deep neural network between an edge and a host platform in the IoT environment. We present a DNN as an encoding pipeline,…
NeuroTrainer: An Intelligent Memory Module for Deep Learning Training
Duckhwan Kim, Taesik Na, Sudhakar Yalamanchili +1
This paper presents, NeuroTrainer, an intelligent memory module with in-memory accelerators that forms the building block of a scalable architecture for energy efficient training f…
Cascade Adversarial Machine Learning Regularized with a Unified Embedding
Taesik Na, Jong Hwan Ko, Saibal Mukhopadhyay
Injecting adversarial examples during training, known as adversarial training, can improve robustness against one-step attacks, but not for unknown iterative attacks. To address th…