Machine Learning Group
Department of Computer Science
University of Toronto
My research interests are in Statistical Machine Learning, Computer Vision and Natural Language Processing.
Scalable Bayesian Optimization Using Deep Neural Networks
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md. Mostofa Ali Patwary, Prabhat, Ryan P. Adams. ICML, 2015.
Replacing a Gaussian process with a neural network for Bayesian optimization with large-scale parallelism.
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, Yoshua Bengio. ICML, 2015.
An interpretable model for generating image descriptions.
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations
Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov. NIPS, 2014.
A third order model for jointly learning distributed representations of words and document attributes.
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.
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.
MSc Outstanding Thesis Award.
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models. LISA lab, Université de Montréal, 2014.
Generating image captions with neural networks. CIFAR Neural Computation and Adaptive Perception Summer School, University of Toronto, 2014.
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.