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2020 – today
- 2023
[c5]Marco Casadio
, Luca Arnaboldi, Matthew L. Daggitt, Omri Isac, Tanvi Dinkar, Daniel Kienitz, Verena Rieser, Ekaterina Komendantskaya:
ANTONIO: Towards a Systematic Method of Generating NLP Benchmarks for Verification. FoMLAS@CAV 2023: 59-70- [i4]Marco Casadio, Luca Arnaboldi, Matthew L. Daggitt, Omri Isac, Tanvi Dinkar, Daniel Kienitz, Verena Rieser, Ekaterina Komendantskaya:
ANTONIO: Towards a Systematic Method of Generating NLP Benchmarks for Verification. CoRR abs/2305.04003 (2023) - 2022
- [c4]Daniel Kienitz, Ekaterina Komendantskaya, Michael A. Lones:
The Effect of Manifold Entanglement and Intrinsic Dimensionality on Learning. AAAI 2022: 7160-7167 - [c3]Daniel Kienitz, Ekaterina Komendantskaya, Michael A. Lones:
Comparing Complexities of Decision Boundaries for Robust Training: A Universal Approach. ACCV (6) 2022: 627-645 - [i3]Marco Casadio, Ekaterina Komendantskaya, Verena Rieser, Matthew L. Daggitt, Daniel Kienitz, Luca Arnaboldi, Wen Kokke:
Why Robust Natural Language Understanding is a Challenge. CoRR abs/2206.14575 (2022) - 2021
- [i2]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) - 2020
- [c2]Wen Kokke, Ekaterina Komendantskaya, Daniel Kienitz, Robert Atkey, David Aspinall:
Neural Networks, Secure by Construction - An Exploration of Refinement Types. APLAS 2020: 67-85 - [c1]Ekaterina Komendantskaya, Wen Kokke, Daniel Kienitz:
Continuous Verification of Machine Learning: a Declarative Programming Approach. PPDP 2020: 1:1-1:3
2010 – 2019
- 2019
- [i1]Ekaterina Komendantskaya, Rob Stewart, Kirsty Duncan, Daniel Kienitz, Pierre Le Hen, Pascal Bacchus:
Neural Network Verification for the Masses (of AI graduates). CoRR abs/1907.01297 (2019)
Coauthor Index
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