2 code implementations • 19 Nov 2015 • Ivan Vendrov, Ryan Kiros, Sanja Fidler, Raquel Urtasun
Hypernymy, textual entailment, and image captioning can be seen as special cases of a single visual-semantic hierarchy over words, sentences, and images.
Ranked #88 on Natural Language Inference on SNLI
2 code implementations • 12 Nov 2015 • Shikhar Sharma, Ryan Kiros, Ruslan Salakhutdinov
We propose a soft attention based model for the task of action recognition in videos.
3 code implementations • ICCV 2015 • Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler
Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story.
16 code implementations • NeurIPS 2015 • Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Antonio Torralba, Raquel Urtasun, Sanja Fidler
The end result is an off-the-shelf encoder that can produce highly generic sentence representations that are robust and perform well in practice.
Ranked #2 on Semantic Similarity on SICK
3 code implementations • NeurIPS 2015 • Mengye Ren, Ryan Kiros, Richard Zemel
A suite of baseline results on this new dataset are also presented.
Ranked #5 on Video Question Answering on SUTD-TrafficQA
4 code implementations • 19 Feb 2015 • Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md. Mostofa Ali Patwary, Prabhat, Ryan P. Adams
Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations.
Ranked #156 on Image Classification on CIFAR-10
88 code implementations • 10 Feb 2015 • Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, Yoshua Bengio
Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images.
3 code implementations • 10 Nov 2014 • Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel
Inspired by recent advances in multimodal learning and machine translation, we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space.
no code implementations • NeurIPS 2014 • Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov
In this paper we propose a general framework for learning distributed representations of attributes: characteristics of text whose representations can be jointly learned with word embeddings.
no code implementations • 16 Jan 2013 • Ryan Kiros
Hessian-free (HF) optimization has been successfully used for training deep autoencoders and recurrent networks.
no code implementations • NeurIPS 2012 • Ryan Kiros, Csaba Szepesvári
The task of assigning a set of relevant tags to an image is challenging due to the size and variability of tag vocabularies.