A museum object repository using LIDO schema

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Leveraging open access for integrating repository data at Indiana University Libraries

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Repository sustainability

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

My repository is being aggregated: a blessing or a curse?

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

HIMALDOC: A Tool for a Regional Open Repository on Sustainable Mountain Development Issues in the Hindu Kush Himalaya

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

The Human Communication Science Virtual Lab (HCS vLab): A repository microclimate in a rapidly evolving research-ecosystem

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

ReShare and ReCollect: EPrints data repository solutions developed at the UK Data Archive

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Invenio as a platform to implement the SCOAP3 Repository

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

When Metadata Collides: Lessons on Combining Records from Multiple Repository Systems.

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

A geological sketch map of the island of Yesso, Japan

Kartta kuuluu A. E. Nordenskiöldin kokoelmaan

A geological and topographical sketch map of the New York and Westmoreland Gas Coal Company's lands, at Manor Sta., Westm'd Co., Pa.

Kartta kuuluu A. E. Nordenskiöldin kokoelmaan

Geological sketch map of New Zealand constructed from official surveys and the explorations of F. von Hochstetter, Julius Haast and others

Kartta kuuluu A. E. Nordenskiöldin kokoelmaan

Geological map of Europe exhibiting the different systems of rocks according to the most recent researches and inedited materials by Roderick I. Murchison and James Nicol ; constructed by A. Keith Johnston

Kartta kuuluu A. E. Nordenskiöldin kokoelmaan

Deep generative models for facial keypoints detection

A new area of machine learning research called deep learning, has moved machine learning closer to one of its original goals: artificial intelligence and general learning algorithm. The key idea is to pretrain models in completely unsupervised way and finally they can be fine-tuned for the task at hand using supervised learning. In this thesis, a general introduction to deep learning models and algorithms are given and these methods are applied to facial keypoints detection. The task is to predict the positions of 15 keypoints on grayscale face images. Each predicted keypoint is specified by an (x,y) real-valued pair in the space of pixel indices. In experiments, we pretrained deep belief networks (DBN) and finally performed a discriminative fine-tuning. We varied the depth and size of an architecture. We tested both deterministic and sampled hidden activations and the effect of additional unlabeled data on pretraining. The experimental results show that our model provides better results than publicly available benchmarks for the dataset.