19 citations · 19 across the 6 of their papers we have counts for
6 papers
Inference Optimizations for Large Language Models: Effects, Challenges, and Practical Considerations
Leo Donisch, Sigurd Schacht, Carsten Lanquillon
Large language models are ubiquitous in natural language processing because they can adapt to new tasks without retraining. However, their sheer scale and complexity present unique…
An approach to optimize inference of the DIART speaker diarization pipeline
Roman Aperdannier, Sigurd Schacht, Alexander Piazza
Speaker diarization answers the question "who spoke when" for an audio file. In some diarization scenarios, low latency is required for transcription. Speaker diarization with low…
Systematic Evaluation of Online Speaker Diarization Systems Regarding their Latency
Roman Aperdannier, Sigurd Schacht, Alexander Piazza
In this paper, different online speaker diarization systems are evaluated on the same hardware with the same test data with regard to their latency. The latency is the time span fr…
A Review of Common Online Speaker Diarization Methods
Roman Aperdannier, Sigurd Schacht, Alexander Piazza
Speaker diarization provides the answer to the question "who spoke when?" for an audio file. This information can be used to complete audio transcripts for further processing steps…
PHOENIX: Open-Source Language Adaption for Direct Preference Optimization
Matthias Uhlig, Sigurd Schacht, Sudarshan Kamath Barkur
Large language models have gained immense importance in recent years and have demonstrated outstanding results in solving various tasks. However, despite these achievements, many q…
Towards energy-efficient Deep Learning: An overview of energy-efficient approaches along the Deep Learning Lifecycle
Vanessa Mehlin, Sigurd Schacht, Carsten Lanquillon
Deep Learning has enabled many advances in machine learning applications in the last few years. However, since current Deep Learning algorithms require much energy for computations…