Deep Learning

Background Speech enhancement, the process of improving the quality speech signals, can not only improve quality of experience for listeners and the quality of communication, it can also aid the performance of machine-and deep-learning models in downstream tasks. However, the challenge of the trade-off between noise removal and artifact incorporation is ongoing [1]. The project aims to investigate the factors influencing noise reduction preferences and develop a technical framework around it....

Background We are seeking students interested advancing speech technology in low-resource environments. The project is sufficiently open-ended and will be focused on developing machine learning models and algorithms tailored to address the unique challenges posed by limited data and computational resources in speech processing, also in high-stakes applications like healthcare and education. Objective(s) Potential directions are: Research and develop novel machine learning techniques optimized for low-resource speech technology applications. Design and implement efficient algorithms for speech recognition, synthesis, and understanding in resource-constrained settings....

Background Modern deep neural networks, especially those in the overparameterized regime with a very high number of parameters, perform impressively well. Traditional learning theories contradict these empirical results and fail to explain this phenomenon, leading to new approaches that aim to understand why deep learning generalizes. A common belief is that flat minima [1] in the parameter space lead to models with good generalization characteristics. For instance, these models may learn to extract high-quality features from the data, known as representations....

Background Transcriptomics provide insights into gene expression and with it the ability to analyze one of the fundamental processes of life - the translation from gene to protein. Single Cell RNA sequencing (scRNAseq) is a technology that measures transcriptomics on the single cell level. However, biological data is highly complex, variability and noisy, making it challenging to analyze and work with. The goal of the project is to evaluate if deep learning can infer gene expression profiles of specific conditions (exposures) by only receiving prior information about an exposure, such as a drug’s known gene targets as well as a general protein-protein interaction network....

Background Transcriptomics provide insights into gene expression and with it the ability to analyse one of the fundamental processes of life - the translation from gene to protein. single Cell RNA sequencing (scRNAseq) is a technology that measures transcriptomics on the single cell level. However, biological data is highly complex, variability and noisy, making it challenging to analyse and wo. By building on pre-trained general scRNAseq deep learning model we want to fine-tune and train the model task specific....