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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.
Updated: January 18 2021
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
Location: Digitally
09:00 Opening
09:20 Keynote 1: Lars Kai Hansen
10:05 Coffee break
10:30 Talk: An Empirical Study on the Robustness of Layerwise Relevance Propagation, Kristine Hein
10:50 Talk: Robust Deep Interpretable Features for Binary Image Classification, Robert Hu
11:10 Talk: Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series, Kristoffer Wickstrøm
11:30 Lunch
12:30 Keynote 2: Laura Leal-Taixé
13:15 Short break
13:30 Talk: Seafloor Pipeline Detection With Deep LearningVemund Sigmundson Schøyen, Narada Dilp Warakagoda
13:50 Talk: A Tomographic Reconstruction Method using Coordinate-based Neural Network with Spatial Regularization, Jakeoung Koo
14:10 Talk: Consistent and accurate estimation of stellar parameters from HARPS-N Spectroscopy using Deep Learning, Frederik Boe Hüttel
14:30 Coffee break
15:00 Keynote 3: Elsa D. Angelini
15:50 Social activity
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
See https://www.nldl.org/ for more info.