Pascal Bacchus, Robert Stewart and Ekaterina Komendantskaya. Accuracy, Training Time and Hardware Efficiency Trade-Offs for Quantised Neural Networks on FPGAs. 16th International Symposium on Applied Reconfigurable Computing (ARC2020), 1-3 April Toledo, Spain.
- C.Schwaab, E.Komendantskaya, A.Hill, F.Farka, J.Wells, R.Petrick, K.Hammond. Proof-Carrying Plans. PADL 2019 (21st International Symposium on Practical Aspects of Declarative Languages), 14-15 January 2019, Cascais/Lisbon, Portugal.
- H. Basold, E. Komendantskaya, Y. Li Coinduction in Uniform: Foundations for Corecursive Proof Search with Horn Clauses. ESOP 2019 (28th European Symposium on Programming), 8-11 April 2019, Prague.
- E. Komendantskaya, R. Stewart, K. Duncan, D. Kienitz, P. Le Hen, P. Bacchus. Neural Network Verification for the Masses (of AI graduates). Experience report, June 2019
- P. Bacchus. Performance Metrics for Approximate Deep Learning on Programmable Hardware. MSc Thesis, Heriot-Watt University, 2019. Supervisor: R. Stewart.
- P. Le Hen. Adversarial Attacks on Neural Networks in Image Processing. MSc Thesis, Heriot-Watt University, 2019. Supervisor: E.Komendantskaya.
- D. Kienitz. Robustness of Neural Networks: Understanding the Nature of Adversarial Examples. MSc Thesis, Heriot-Watt University, 2019. Supervisor: E.Komendantskaya.
- Y.Li. A Proof-Theoretic Approach to Coinduction in Horn Clause Logic” . PhD Thesis, Heriot-Watt University, 2019. Supervisor: E.Komendantskaya and M.Lawson.