Machine Learning Group
Department of Computer Science
University of Toronto
My research interests are in Statistical Machine Learning, Computer Vision and Natural Language Processing.
Multimodal Neural Language Models
Ryan Kiros, Ruslan Salakhutdinov, Richard Zemel. ICML, 2014.
[pdf coming soon][code and multimodal word representations coming soon]
Two new neural language models that can be conditioned on other modalities (such as images).
A previous version appeared at the NIPS 2013 Deep Learning Workshop: [pdf]
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.
Learning Deep Representations, Embeddings and Codes from the Pixel Level of Natural and Medical Images
Ryan Kiros. University of Alberta, 2013.
Nominated for MSc Outstanding Thesis Award.
A Simple Baseline for Segmenting Medical Images. CIFAR Neural Computation and Adaptive Perception Summer School, University of Toronto, 2013.
Deep Representations and Codes for Image Auto-Annotation. University of Guelph, 2012.
On Linear Embeddings and Unsupervised Feature Learning. ICML Representation Learning Workshop, University of Edinburgh, 2012.
Copy Graphs: Compression, Reconstruction and Seed Identification. Canadian Undergraduate Mathematics Conference (CUMC), University of Waterloo, 2010.