5 citations · 14 across the 15 of their papers we have counts for
15 papers
Distribution and Clusters Approximations as Abstract Domains in Probabilistic Abstract Interpretation to Neural Network Analysis
Zhuofan Zhang, Herbert Wiklicky
The probabilistic abstract interpretation framework of neural network analysis analyzes a neural network by analyzing its density distribution flow of all possible inputs. The grid…
Probabilistic Abstract Interpretation on Neural Networks via Grids Approximation
Zhuofan Zhang, Herbert Wiklicky
Probabilistic abstract interpretation is a theory used to extract particular properties of a computer program when it is infeasible to test every single inputs. In this paper we ap…
Exact Learning with Tunable Quantum Neural Networks and a Quantum Example Oracle
Viet Pham Ngoc, Herbert Wiklicky
In this paper, we study the tunable quantum neural network architecture in the quantum exact learning framework with access to a uniform quantum example oracle. We present an appro…
Tunable Quantum Neural Networks in the QPAC-Learning Framework
Viet Pham Ngoc, David Tuckey, Herbert Wiklicky
In this paper, we investigate the performances of tunable quantum neural networks in the Quantum Probably Approximately Correct (QPAC) learning framework. Tunable neural networks a…
Tunable Quantum Neural Networks for Boolean Functions
Viet Pham Ngoc, Herbert Wiklicky
In this paper we propose a new approach to quantum neural networks. Our multi-layer architecture avoids the use of measurements that usually emulate the non-linear activation funct…
Comparison of Syntactic and Semantic Representations of Programs in Neural Embeddings
Austin P. Wright, Herbert Wiklicky
Neural approaches to program synthesis and understanding have proliferated widely in the last few years; at the same time graph based neural networks have become a promising new to…