Search Results for author:
Found 8 papers, 2 papers with code
Simple Regularisation for Uncertainty-Aware Knowledge Distillation
We demonstrate the generality of the approach on combinations of toy data, SVHN/CIFAR-10, simple to complex NN architectures and different tasks.
On Causal Inference for Data-free Structured Pruning
Neural networks (NNs) are making a large impact both on research and industry.
Algorithm and Hardware Co-design for Reconfigurable CNN Accelerator
Recent advances in algorithm-hardware co-design for deep neural networks (DNNs) have demonstrated their potential in automatically designing neural architectures and hardware designs.
Optimizing Bayesian Recurrent Neural Networks on an FPGA-based Accelerator
To further improve the overall algorithmic-hardware performance, a co-design framework is proposed to explore the most fitting algorithmic-hardware configurations for Bayesian RNNs.
High-Performance FPGA-based Accelerator for Bayesian Neural Networks
Neural networks (NNs) have demonstrated their potential in a wide range of applications such as image recognition, decision making or recommendation systems.
ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation
Fully convolutional U-shaped neural networks have largely been the dominant approach for pixel-wise image segmentation.
On the Effects of Quantisation on Model Uncertainty in Bayesian Neural Networks
Bayesian neural networks (BNNs) are making significant progress in many research areas where decision-making needs to be accompanied by uncertainty estimation.
VINNAS: Variational Inference-based Neural Network Architecture Search
In recent years, neural architecture search (NAS) has received intensive scientific and industrial interest due to its capability of finding a neural architecture with high accuracy for various artificial intelligence tasks such as image classification or object detection.
