2.7k citations · 4k across the 68 of their papers we have counts for
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Generative Adversarial Parallelization
Daniel Jiwoong Im, He Ma, Chris Dongjoo Kim +1
Generative Adversarial Networks have become one of the most studied frameworks for unsupervised learning due to their intuitive formulation. They have also been shown to be capable…
Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks
Roberto DiCecco, Griffin Lacey, Jasmina Vasiljevic +3
Convolutional Neural Networks (CNNs) have gained significant traction in the field of machine learning, particularly due to their high accuracy in visual recognition. Recent works…
Learning a metric for class-conditional KNN
Daniel Jiwoong Im, Graham W. Taylor
Naive Bayes Nearest Neighbour (NBNN) is a simple and effective framework which addresses many of the pitfalls of K-Nearest Neighbour (KNN) classification. It has yielded competitiv…
Theano-MPI: a Theano-based Distributed Training Framework
He Ma, Fei Mao, Graham W. Taylor
We develop a scalable and extendable training framework that can utilize GPUs across nodes in a cluster and accelerate the training of deep learning models based on data parallelis…
Automatic Moth Detection from Trap Images for Pest Management
Weiguang Ding, Graham Taylor
Monitoring the number of insect pests is a crucial component in pheromone-based pest management systems. In this paper, we propose an automatic detection pipeline based on deep lea…
Deep Learning on FPGAs: Past, Present, and Future
Griffin Lacey, Graham W. Taylor, Shawki Areibi
The rapid growth of data size and accessibility in recent years has instigated a shift of philosophy in algorithm design for artificial intelligence. Instead of engineering algorit…