48 citations · 60 across the 5 of their papers we have counts for
6 papers
Triton for MTIA: Bridging the Programming Model Gaps for Custom AI Accelerators
Haishan Zhu, Domi Yan, Michael Levesque-Dion +40
The rapid growth in machine learning workloads has fueled the proliferation of custom accelerator architectures. Designed from the ground up, these accelerators often expose progra…
COVID-19 Lung Lesion Segmentation Using a Sparsely Supervised Mask R-CNN on Chest X-rays Automatically Computed from Volumetric CTs
Vignav Ramesh, Blaine Rister, Daniel L. Rubin
Chest X-rays of coronavirus disease 2019 (COVID-19) patients are frequently obtained to determine the extent of lung disease and are a valuable source of data for creating artifici…
Probabilistic bounds on neuron death in deep rectifier networks
Blaine Rister, Daniel L. Rubin
Neuron death is a complex phenomenon with implications for model trainability: the deeper the network, the lower the probability of finding a valid initialization. In this work, we…
CT organ segmentation using GPU data augmentation, unsupervised labels and IOU loss
Blaine Rister, Darvin Yi, Kaushik Shivakumar +2
Fully-convolutional neural networks have achieved superior performance in a variety of image segmentation tasks. However, their training requires laborious manual annotation of lar…
Piecewise convexity of artificial neural networks
Blaine Rister, Daniel L Rubin
Although artificial neural networks have shown great promise in applications including computer vision and speech recognition, there remains considerable practical and theoretical…
A Systematic Approach to Blocking Convolutional Neural Networks
Xuan Yang, Jing Pu, Blaine Burton Rister +6
Convolutional Neural Networks (CNNs) are the state of the art solution for many computer vision problems, and many researchers have explored optimized implementations. Most impleme…