2021 -22 Conference Publications:
- D. Kienitz, E. Komendantskaya and M. Lones. The Effect of Manifold Entanglement and Intrinsic Dimensionality on Learning. Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI’22), Online.
- AnalyZr: A Python application for zircon grain image segmentation and shape analysis Taryn Scharf, Chris Kirkland, Matthew Daggitt, Milo Barham, Vladimir Puzyrev. Computers & Geosciences 2022
- Alasdair Hill, Ekaterina Komendantskaya, Matthew L. Daggitt, Ronald P. A. Petrick. Actions You Can Handle: Dependent Types for AI Plans. Workshop on Type-driven Development’ 21, at ICFP’21, 2021.
- Marco Casadio, Matthew L. Daggitt, Ekaterina Komendantskaya, Wen Kokke, Daniel Kienitz, Rob Stewart: Property-driven Training: All You (N)Ever Wanted to Know About. CoRR abs/2104.01396 (2021)
- Kirsty Duncan, Robert Stewart and Ekaterina Komendantskaya. Logically Constrained Pruning Towards Robust Neural Networks. FOMLAS’21, Workshop on Formal Methods for ML-Enabled Autonomous Systems, at CAV’21.
2021 BSc and MSc dissertations:
Natalia Slusarz: Mathematical Properties of Neural Networks Trained on Artificial Data Sets. BSc Dissertation at Heriot-Watt University, 2021. Supervisors: E.Komendantskaya and D.Kienitz
Remi Desmartin: Neural Network Verification with Imandra. MSc Dissertation at Heriot-Watt University, 2021. Best Dissertation award. Supervisors: E.Komendantskaya and M.Daggitt
Dan Green: Understanding the Role of Machine Learning in Signed Language Recognition. Supervisors: E.Komendantskaya and M.Daggitt
Michael Pidgeon: Building Trust in Explanations of Machine Learning tools. MSc Dissertation at Heriot-Watt University, 2021. Supervisors: E.Komendantskaya and M.Daggitt
2020 Conference Publications:
- Wen Kokke, Ekaterina Komendantskaya, Daniel Kienitz, Bob Atkey and David Aspinall. Neural Networks, Secure by Construction: An Exploration of Refinement Types. Accepted for Publication at APLAS’20, The 18th Asian Symposium on Programming Languages and Systems.
- Alasdair Hill, Ekaterina Komendantskaya and Ron Petrick. Proof-Carrying Plans: a Resource Logic for AI Planning PPDP’20, the 22nd International Symposium on Principles and Practice of Declarative Programming.
- Ekaterina Komendantskaya, Dmitry Rozplokhas and Henning Basold. The New Normal: We Cannot Eliminate Cuts in Coinductive Sequent Calculi, But We Can Explore Them. ICLP’20, the 36th International Conference on Logic Programming.
- Wen Kokke, Ekaterina Komendantskaya, Daniel Kienitz and David Aspinall. Robustness as a Refinement Type: Verifying Neural Networks in Liquid Haskell and F*. Accepted at ETAPS Workshop LiVe’20: 4th Workshop on Learning in Verification, 25 April 2020, Dublin, Ireland.
- Kirsty Duncan, Ekaterina Komendantskaya, Robert Sewart, Michael Lones Relative Robustness of Quantized Neural Networks Against Adversarial Attacks, accepted to be published and presented at the International Joint Conference on Neural Networks, IJCNN’20, part of the world congress on computational intelligence: https://wcci2020.org/ 19-24 July 2020, Glasgow, Scotland.
- 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.
2020 MSc and PhD Dissertations:
- Alexandre Cardaillac. Explainable AI: Methods to Evaluate Machine Learning Models. MSc Dissertation. 2020. Supervisor: E.Komendantskaya.
- Marco Casadio. Generative Against Logical Training against Adversarial Attacks. MSc Dissertation. 2020. Supervisor: E.Komendantskaya.
- Hugo Cousin. High-performance Data Analysis for Social Networks Using Rust. MSc Dissertation. 2020. Supervisor: H.-W. Loidl.
- Fraser Garrow. Artificial Neural Network Genetic Improvement for Accurate and Robust Image Classification. MSc Dissertation. 2020. Supervisor: M.Lones.
- Dorian Gouzou. Repelling Dual-Swarm Particle Optimisation with Maximisation search. MSc Dissertation. 2020. Supervisor: M.Lones.
- Vincent Larcher. Generation of Adversarial Attacks on Computer Vision Models using Reinforcement Learning. MSc Dissertation. 2020. Supervisor: E.Komendantskaya.
- Bartosz Schatton. Informed Adversarial Examples with Explainable AI and Metaheuristics in Medical Imaging. MSc Dissertation. 2020. Supervisor: E.Komendantskaya.
- Frantisek Farka. Proof-Relevant Resolution: The Foundations of Constructive Proof Automation. PhD Dissertation. 2020. Supervisor: E.Komendantskaya.
2019 Conference papers:
- 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
2019 MSc and PhD Dissertations:
- 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.