no code implementations • 27 Jan 2024 • Haleema Sheraz, Stefan C. Kremer, Joshua August Skorburg, Graham Taylor, Walter Sinnott-Armstrong, Kyle Boerstler
In response to the pressing challenge of kidney allocation, characterized by growing demands for organs, this research sets out to develop a data-driven solution to this problem, which also incorporates stakeholder values.
1 code implementation • ECCV 2020 • Shivam Kalra, Mohammed Adnan, Graham Taylor, Hamid Tizhoosh
Many real-world tasks such as classification of digital histopathology images and 3D object detection involve learning from a set of instances.
no code implementations • 3 Apr 2018 • Shamak Dutta, Bryan Tripp, Graham Taylor
Neurons in the visual cortex are correlated in their variability.
no code implementations • ICLR 2018 • Daniel Jiwoong Im, He Ma, Graham Taylor, Kristin Branson
Generative adversarial networks (GANs) have been extremely effective in approximating complex distributions of high-dimensional, input data samples, and substantial progress has been made in understanding and improving GAN performance in terms of both theory and application.
no code implementations • 16 Aug 2017 • Aswin Raghavan, Mohamed Amer, Sek Chai, Graham Taylor
The parameters of neural networks are usually unconstrained and have a dynamic range dispersed over all real values.
no code implementations • 13 Dec 2016 • Daniel Jiwoong Im, He Ma, Chris Dongjoo Kim, Graham Taylor
Generative Adversarial Networks have become one of the most studied frameworks for unsupervised learning due to their intuitive formulation.
1 code implementation • 30 Sep 2016 • Roberto DiCecco, Griffin Lacey, Jasmina Vasiljevic, Paul Chow, Graham Taylor, Shawki Areibi
Convolutional Neural Networks (CNNs) have gained significant traction in the field of machine learning, particularly due to their high accuracy in visual recognition.
no code implementations • 24 Feb 2016 • Weiguang Ding, Graham Taylor
Monitoring the number of insect pests is a crucial component in pheromone-based pest management systems.
no code implementations • 20 Nov 2015 • Natalia Neverova, Christian Wolf, Florian Nebout, Graham Taylor
We propose a method for hand pose estimation based on a deep regressor trained on two different kinds of input.
no code implementations • 12 Nov 2015 • Natalia Neverova, Christian Wolf, Griffin Lacey, Lex Fridman, Deepak Chandra, Brandon Barbello, Graham Taylor
We present a large-scale study exploring the capability of temporal deep neural networks to interpret natural human kinematics and introduce the first method for active biometric authentication with mobile inertial sensors.
no code implementations • 1 Jun 2015 • Yasser Roudi, Graham Taylor
Learning and inferring features that generate sensory input is a task continuously performed by cortex.
no code implementations • 22 Dec 2014 • Jan Rudy, Graham Taylor
Recent work by Bengio et al. (2013) proposes a sampling procedure for denoising autoencoders which involves learning the transition operator of a Markov chain.
2 code implementations • 7 Dec 2014 • Weiguang Ding, Ruoyan Wang, Fei Mao, Graham Taylor
In this report, we describe a Theano-based AlexNet (Krizhevsky et al., 2012) implementation and its naive data parallelism on multiple GPUs.