Machine Learning Group
Department of Computer Science
University of Toronto
Contact: kirosjamie [at] gmail [dot] com
I will be a research scientist at Google Brain as of November 2017.
Joint Embeddings of Scene Graphs and Images
Eugene Belilovsky, Matthew Blaschko, Jamie Ryan Kiros, Raquel Urtasun, Richard Zemel. ICLR Workshop, 2017.
A collection of baselines for embedding scene graphs and images.
Towards Generalizable Sentence Embeddings
Eleni Triantafillou, Jamie Ryan Kiros, Raquel Urtasun, Richard Zemel. ACL Workshop on Representation Learning for NLP, 2016.
Exploiting a small amount of supervision for unsupervised sentence representations.
Exploratory Visual Analysis and Interactive Pattern Extraction from Semi-Structured Data
Axel J. Soto, Jamie Ryan Kiros, Vlado Keselj, Evangelos Milios. ACM Transactions on Interactive and Intelligent Systems, 2015.
Introducing ViTA-SSD, a visual text analytics tool for semi-structured data that utilizes a new deep learning algorithm for visualization.
Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books
Yukun Zhu, Jamie Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler. ICCV, 2015. (Oral)
[arXiv] [project page]
Aligning movies and books for story-like captioning.
Scalable Bayesian Optimization Using Deep Neural Networks
Jasper Snoek, Oren Rippel, Kevin Swersky, Jamie 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, Jamie Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, Yoshua Bengio. ICML, 2015.
[arXiv] [project page] [media]
An interpretable model for generating image descriptions.
An encoder-decoder architecture for ranking and generating image descriptions.
(Twitter account INTERESTING.JPG is authored and maintained by link, independent of us)
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations
Jamie 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.
Learning Deep Representations, Embeddings and Codes from the Pixel Level of Natural and Medical Images
Jamie Ryan Kiros. Master's Thesis, University of Alberta, 2013.
MSc Outstanding Thesis Award.
Unsupervised Learning of Distributed Sentence Representations. NYU and Facebook, NYC, 2015.
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.