Dimitrios Vytiniotis — accepted AI-conference papers

2021

Gradient Forward-Propagation for Large-Scale Temporal Video Modelling

Mateusz Malinowski, Dimitrios Vytiniotis, Grzegorz Swirszcz, Viorica Patraucean, Joao Carreira

CVPR 2021poster

How can neural networks be trained on large-volume temporal data efficiently? To compute the gradients required to update parameters, backpropagation blocks computations until the forward and backward passes are completed. For temporal signals, this introduces high latency and hinders real-time lear…

Cited by 9 PDF ↗Scholar ↗

2016

Measuring Neural Net Robustness with Constraints

Osbert Bastani, Yani Ioannou, Leonidas Lampropoulos, Dimitrios Vytiniotis, Aditya Nori, Antonio Criminisi

NeurIPS 2016poster

Despite having high accuracy, neural nets have been shown to be susceptible to adversarial examples, where a small perturbation to an input can cause it to become mislabeled. We propose metrics for measuring the robustness of a neural net and devise a novel algorithm for approximating these metrics…

Cited by 554 Source ↗PDF ↗Scholar ↗