Northern Lights Deep Learning Workshop 2021

The program will be available shortly. Please check back later.

Northern Lights Deep Learning Workshop 2021

Deep learning is an emerging subfield in machine learning that has in recent years achieved state-of-the-art performance in image classification, object detection, segmentation, time series prediction and speech recognition to name a few. This workshop will gather researchers both on a national and international level to exchange ideas, encourage collaborations and present cutting-edge research.

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NLDL logo


Updated: January 18 2021

Monday January 18, 2021 Mini Deep Learning School

Location: Digitally

10:00 Introduction to Convolutional Neural Networks, Arnt-Børre Salberg

12:30 A brief introduction to generative adversarial networks and practical use cases, Håkon Hukkelås

13:20 Uncertainty Quantification in Deep Neural Networks, Fabian Brickwedde

14:15 Deep Learning in NLP, Lilja Øvrelid, Jeremy Barnes

Tuesday January 19, 2021

Location: Digitally


Keynote 1: Lars Kai Hansen

Coffee break  

Talk: An Empirical Study on the Robustness of Layerwise Relevance Propagation, Kristine Hein

Talk: Robust Deep Interpretable Features for Binary Image Classification, Robert Hu

Talk: Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series, Kristoffer Wickstrøm


Keynote 2: Laura Leal-Taixé

Short break

Talk: Seafloor Pipeline Detection With Deep LearningVemund Sigmundson Schøyen, Narada Dilp Warakagoda

Talk: A Tomographic Reconstruction Method using Coordinate-based Neural Network with Spatial Regularization, Jakeoung Koo

Talk: Consistent and accurate estimation of stellar parameters from HARPS-N Spectroscopy using Deep Learning, Frederik Boe  Hüttel

Coffee break  

Keynote 3: Elsa D. Angelini

Social activity

Wednesday January 20, 2021

Location: Digitally

09:00 Keynote 4: Roland Vollgraf

09:45 Coffee break

10:10 Talk: Deep domain adaptation applied to automatic fish age prediction, Alba Ordonez

10:30 Talk: Reducing Objective Function Mismatch in Deep Clustering with the Unsupervised Companion Objective, Daniel J. Trosten

10:50 Talk: Semi-supervised Semantic Segmentation in Multi-frequency Echosounder Data, Changkyu Choi

11:10 Lunch

12:30 Talk: Extracting Probabilistic Deterministic Finite Automata from a RNN trained on locally sourced traffic-data, Hans Martin

12:50 Talk: Extracting Horn Theories with Queries and Counterexamples, Cosimo Persia

13:10 Coffee break  

13:30 Keynote 5: Arthur Gretton

14:15 Coffee break

14:40 Talk: SCG-Net: Self-Constructing Graph Neural Networks for Semantic Segmentation, Qinghui Liu

15:00 Talk: Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images, Luigi Luppino

15:20 Short break

15:30 NORA Panel Discussion (Moderator: Klas Pettersen, NORA)  

16:30 Closing

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