Jamie Ryan Kiros
PhD Student
Machine Learning Group
Department of Computer Science
University of Toronto
Advisors: Dr. Ruslan Salakhutdinov and Dr. Richard Zemel.
Contact: infer from the URL
Google scholar profile
GitHub profile
Twitter
Research
My research interests are in Statistical Machine Learning, Computer Vision and Natural Language Processing.
Education
- PhD, University of Toronto (2013 - present)
- MSc, University of Alberta (2011 - 2013)
- BSc, Dalhousie University (2006 - 2011)
Publications
2016
2015
Action Recognition using Visual Attention
Shikhar Sharma, Ryan Kiros, Ruslan Salakhutdinov. NIPS Time Series Workshop, 2015.
[arXiv]
[project page]
A first attempt at using soft visual attention for action recognition.
Exploratory Visual Analysis and Interactive Pattern Extraction from Semi-Structured Data
Axel J. Soto, Ryan Kiros, Vlado Keselj, Evangelos Milios. ACM Transactions on Interactive and Intelligent Systems, 2015.
[link]
Introducing ViTA-SSD, a visual text analytics tool for semi-structured data that utilizes a new deep learning algorithm for visualization.
Exploring Models and Data for Image Question Answering
Mengye Ren, Ryan Kiros, Richard Zemel. NIPS, 2015.
[arXiv]
[code]
[full results]
[COCO-QA Dataset]
Combining CNNs with LSTM for image question answering and a new dataset derived from Microsoft COCO captions.
Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books
Yukun Zhu, 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, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md. Mostofa Ali Patwary, Prabhat, Ryan P. Adams. ICML, 2015.
[arXiv]
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.
[arXiv]
[project page]
[media]
An interpretable model for generating image descriptions.
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
Ryan Kiros, Ruslan Salakhutdinov, Richard Zemel. TACL, 2015. (to appear)
[arXiv]
[demo]
[INTERESTING.JPG]
[code]
An encoder-decoder architecture for ranking and generating image descriptions.
(Twitter account INTERESTING.JPG is authored and maintained by link, independent of us)
2014
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations
Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov. NIPS, 2014.
[arXiv (old)]
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.
[link]
The VESSEL12 segmentation study that subsumes our 2013 technical report below.
Multimodal Neural Language Models
Ryan Kiros, Ruslan Salakhutdinov, Richard Zemel. ICML, 2014.
[pdf]
[project page and results]
Two new neural language models that can be conditioned on other modalities (such as images).
2013
Learning Deep Representations, Embeddings and Codes from the Pixel Level of Natural and Medical Images
Ryan Kiros. Master's Thesis, University of Alberta, 2013.
[link]
MSc Outstanding Thesis Award.
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.
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.
2012
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.
Talks
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.