13 citations · 17 across the 4 of their papers we have counts for
4 papers
Logic of Differentiable Logics: Towards a Uniform Semantics of DL
Natalia Ślusarz, Ekaterina Komendantskaya, Matthew L. Daggitt +2
Differentiable logics (DL) have recently been proposed as a method of training neural networks to satisfy logical specifications. A DL consists of a syntax in which specifications…
Differentiable Logics for Neural Network Training and Verification
Natalia Slusarz, Ekaterina Komendantskaya, Matthew L. Daggitt +1
The rising popularity of neural networks (NNs) in recent years and their increasing prevalence in real-world applications have drawn attention to the importance of their verificati…
Benchmarking Quantized Neural Networks on FPGAs with FINN
Quentin Ducasse, Pascal Cotret, Loïc Lagadec +1
The ever-growing cost of both training and inference for state-of-the-art neural networks has brought literature to look upon ways to cut off resources used with a minimal impact o…
Neural Network Verification for the Masses (of AI graduates)
Ekaterina Komendantskaya, Rob Stewart, Kirsy Duncan +3
Rapid development of AI applications has stimulated demand for, and has given rise to, the rapidly growing number and diversity of AI MSc degrees. AI and Robotics research communit…