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Found 14 papers, 5 papers with code
Renate: A Library for Real-World Continual Learning
We discuss requirements for the use of continual learning algorithms in practice, from which we derive design principles for Renate.
YAMLE: Yet Another Machine Learning Environment
YAMLE: Yet Another Machine Learning Environment is an open-source framework that facilitates rapid prototyping and experimentation with machine learning (ML) models and methods.
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.
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.
Navigating Noise: A Study of How Noise Influences Generalisation and Calibration of Neural Networks
Enhancing the generalisation abilities of neural networks (NNs) through integrating noise such as MixUp or Dropout during training has emerged as a powerful and adaptable technique.
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.
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.
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.
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.
On Causal Inference for Data-free Structured Pruning
Neural networks (NNs) are making a large impact both on research and industry.
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.
Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensions
The deployment of large language models (LLMs) raises concerns regarding their cultural misalignment and potential ramifications on individuals from various cultural norms.
SAE: Single Architecture Ensemble Neural Networks
Ensembles of separate neural networks (NNs) have shown superior accuracy and confidence calibration over single NN across tasks.
Large language models surpass human experts in predicting neuroscience results
LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts.
