12 citations · 38 across the 5 of their papers we have counts for
6 papers
Snowflake: A Model Agnostic Accelerator for Deep Convolutional Neural Networks
Vinayak Gokhale, Aliasger Zaidy, Andre Xian Ming Chang +1
Deep convolutional neural networks (CNNs) are the deep learning model of choice for performing object detection, classification, semantic segmentation and natural language processi…
Compiling Deep Learning Models for Custom Hardware Accelerators
Andre Xian Ming Chang, Aliasger Zaidy, Vinayak Gokhale +1
Convolutional neural networks (CNNs) are the core of most state-of-the-art deep learning algorithms specialized for object detection and classification. CNNs are both computational…
CortexNet: a Generic Network Family for Robust Visual Temporal Representations
Alfredo Canziani, Eugenio Culurciello
In the past five years we have observed the rise of incredibly well performing feed-forward neural networks trained supervisedly for vision related tasks. These models have achieve…
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
Adam Paszke, Abhishek Chaurasia, Sangpil Kim +1
The ability to perform pixel-wise semantic segmentation in real-time is of paramount importance in mobile applications. Recent deep neural networks aimed at this task have the disa…
Clustering Learning for Robotic Vision
Eugenio Culurciello, Jordan Bates, Aysegul Dundar +2
We present the clustering learning technique applied to multi-layer feedforward deep neural networks. We show that this unsupervised learning technique can compute network filters…
Visual Tracking with Similarity Matching Ratio
Aysegul Dundar, Jonghoon Jin, Eugenio Culurciello
This paper presents a novel approach to visual tracking: Similarity Matching Ratio (SMR). The traditional approach of tracking is minimizing some measures of the difference between…