371 citations · 453 across the 10 of their papers we have counts for
11 papers
MSViT: Dynamic Mixed-Scale Tokenization for Vision Transformers
Jakob Drachmann Havtorn, Amelie Royer, Tijmen Blankevoort +1
The input tokens to Vision Transformers carry little semantic meaning as they are defined as regular equal-sized patches of the input image, regardless of its content. However, pro…
Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study
Joakim Edin, Alexander Junge, Jakob D. Havtorn +4
Medical coding is the task of assigning medical codes to clinical free-text documentation. Healthcare professionals manually assign such codes to track patient diagnoses and treatm…
Self-Supervised Speech Representation Learning: A Review
Abdelrahman Mohamed, Hung-yi Lee, Lasse Borgholt +9
Although supervised deep learning has revolutionized speech and audio processing, it has necessitated the building of specialist models for individual tasks and application scenari…
Model-agnostic out-of-distribution detection using combined statistical tests
Federico Bergamin, Pierre-Alexandre Mattei, Jakob D. Havtorn +5
We present simple methods for out-of-distribution detection using a trained generative model. These techniques, based on classical statistical tests, are model-agnostic in the sens…
A Brief Overview of Unsupervised Neural Speech Representation Learning
Lasse Borgholt, Jakob Drachmann Havtorn, Joakim Edin +2
Unsupervised representation learning for speech processing has matured greatly in the last few years. Work in computer vision and natural language processing has paved the way, but…
Benchmarking Generative Latent Variable Models for Speech
Jakob D. Havtorn, Lasse Borgholt, Søren Hauberg +2
Stochastic latent variable models (LVMs) achieve state-of-the-art performance on natural image generation but are still inferior to deterministic models on speech. In this paper, w…
Do We Still Need Automatic Speech Recognition for Spoken Language Understanding?
Lasse Borgholt, Jakob Drachmann Havtorn, Mostafa Abdou +4
Spoken language understanding (SLU) tasks are usually solved by first transcribing an utterance with automatic speech recognition (ASR) and then feeding the output to a text-based…
Do End-to-End Speech Recognition Models Care About Context?
Lasse Borgholt, Jakob Drachmann Havtorn, Željko Agić +3
The two most common paradigms for end-to-end speech recognition are connectionist temporal classification (CTC) and attention-based encoder-decoder (AED) models. It has been argued…
Hierarchical VAEs Know What They Don't Know
Jakob D. Havtorn, Jes Frellsen, Søren Hauberg +1
Deep generative models have been demonstrated as state-of-the-art density estimators. Yet, recent work has found that they often assign a higher likelihood to data from outside the…
On Scaling Contrastive Representations for Low-Resource Speech Recognition
Lasse Borgholt, Tycho Max Sylvester Tax, Jakob Drachmann Havtorn +2
Recent advances in self-supervised learning through contrastive training have shown that it is possible to learn a competitive speech recognition system with as little as 10 minute…
MultiQT: Multimodal Learning for Real-Time Question Tracking in Speech
Jakob D. Havtorn, Jan Latko, Joakim Edin +6
We address a challenging and practical task of labeling questions in speech in real time during telephone calls to emergency medical services in English, which embeds within a broa…