Training Neural Networks with Stochastic Hessian-Free Optimization
Ryan Kiros. ICLR, 2013.
[pdf] [code] [openreview discussion]

A stochastic version of Martens' Hessian-Free Optimization for training deep nets.

Deep Representations and Codes for Image Auto-Annotation
Ryan Kiros and Csaba Szepesvari. NIPS, 2012.
[pdf] [supplementary] [code and features]

Learning binary codes for image annotation, using a deep unsupervised net.

Regularizers Versus Losses for Nonlinear Dimensionality Reduction
Yaoliang Yu, James Neufeld, Ryan Kiros, Xinhua Zhang, Dale Schuurmans. ICML, 2012.
[pdf] [supplementary]

A unifying view of non-parametric, nonlinear dimensionality reduction + 2 new convex regularizers.

Representation Learning for Sparse, High Dimensional Multi-Label Classification
Ryan Kiros, Axel J. Soto, Evangelos Milios, Vlado Keselj. RSCTC, 2012.
[pdf] [competition page]

5th place (out of 126) in the JRS 2012 data mining competition.

In-depth Interactive Visual Exploration for Bridging Unstructured and Structured Document Content
Axel J. Soto, Ryan Kiros, Vlado Keselj, Evangelos Milios. SDM Workshop on Exploratory Data Analysis, 2014.
[pdf coming soon]

Introducing ViTA-SSD, a visual text analytics tool for semi-structured data that utilizes a new deep learning algorithm.

Multimodal Neural Language Models
Ryan Kiros, Ruslan Salakhutdinov, Richard Zemel. NIPS Deep Learning Workshop, 2013.
[pdf]

Two new neural language models that can be conditioned on other modalities (such as images).

On Linear Embeddings and Unsupervised Feature Learning
Ryan Kiros and Csaba Szepesvari. ICML Representation Learning Workshop, 2012. (Oral)
[pdf] [code]

Combining k-means feature learning with supervised linear t-distributed embeddings.

Automatic Lung Vessel Segmentation via Stacked Multiscale Feature Learning
Ryan Kiros, Karteek Popuri, Matthew Low, Dana Cobzas, Martin Jagersand. Technical Report, 2013.
[pdf] [code] [competition page] [video] [results viewer]

1st place (out of 24) in the VESSEL12 segmentation challenge.

Learning Deep Representations, Embeddings and Codes from the Pixel Level of Natural and Medical Images
Ryan Kiros. University of Alberta, 2013.
[link]

Nominated for MSc Outstanding Thesis Award.