188 citations · 217 across the 8 of their papers we have counts for
8 papers
Six Layers Less: Encoder Pruning for Whisper with Label-Free Recovery
Rasmus Aagaard, Nicki Skafte Detlefsen
Pruning large pre-trained transformer-based ASR models such as OpenAI's Whisper has seen great adoption, as pruning the decoder led to significant end-to-end transcription speedups…
Real-Time EEG Cap Electrode Detection for Guided Point-of-Care Placement
William Lehn-Schiøler, Mads Sverker Nilsson, Nicki Skafte Detlefsen
We present a two-stage vision system that detects EEG cap electrodes in a live webcam stream and validates their anatomical placement in real time. A single-class YOLO detector loc…
Exploring different approaches to customize language models for domain-specific text-to-code generation
Luís Freire, Fernanda A. Andaló, Nicki Skafte Detlefsen
Large language models (LLMs) have demonstrated strong capabilities in generating executable code from natural language descriptions. However, general-purpose models often struggle…
Laplacian Autoencoders for Learning Stochastic Representations
Marco Miani, Frederik Warburg, Pablo Moreno-Muñoz +2
Established methods for unsupervised representation learning such as variational autoencoders produce none or poorly calibrated uncertainty estimates making it difficult to evaluat…
Is an encoder within reach?
Helene Hauschultz, Rasmus Berg Palm. Pablo Moreno-Muños, Nicki Skafte Detlefsen +2
The encoder network of an autoencoder is an approximation of the nearest point projection onto the manifold spanned by the decoder. A concern with this approximation is that, while…
What is a meaningful representation of protein sequences?
Nicki Skafte Detlefsen, Søren Hauberg, Wouter Boomsma
How we choose to represent our data has a fundamental impact on our ability to subsequently extract information from them. Machine learning promises to automatically determine effi…
Explicit Disentanglement of Appearance and Perspective in Generative Models
Nicki Skafte Detlefsen, Søren Hauberg
Disentangled representation learning finds compact, independent and easy-to-interpret factors of the data. Learning such has been shown to require an inductive bias, which we expli…
Reliable training and estimation of variance networks
Nicki S. Detlefsen, Martin Jørgensen, Søren Hauberg
We propose and investigate new complementary methodologies for estimating predictive variance networks in regression neural networks. We derive a locally aware mini-batching scheme…