Machine Learning Group
Department of Computer Science
University of Toronto
I am a 1st year PhD student under the supervision of Dr. Ruslan Salakhutdinov and Dr. Richard Zemel.
Contact: rkiros [at] cs [dot] toronto [dot] edu
Check out my image caption demo!
(New features and improvements to be added over time)
My research interests are in Statistical Machine Learning, Computer Vision and Natural Language Processing.
Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: The VESSEL12 study
Rina D. Rudyanto et al. Medical Image Analysis, 2014.
The VESSEL12 segmentation study that subsumes our 2013 technical report below.
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations
Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov. A previous version was accepted to the ICML KPDLTM workshop, 2014. (Oral)
A third order model for jointly learning distributed representations of words and document attributes.
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.
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. Master's Thesis, University of Alberta, 2013.
Nominated for MSc Outstanding Thesis Award.
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations. ICML KPDLTM Workshop, Beijing, 2014.
Multimodal Neural Language Models. ICML, Beijing, 2014.
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.