Our paper "Learning to Learn from Weak Supervision by Full Supervision", with Sascha Rothe, and Jaap Kamps, has been accepted at NIPS2017 Workshop on Meta-Learning (MetaLearn 2017). \o/ Using weak or noisy supervision is a straightforward approach to increase the size of the training data and it has been shown that the output of heuristic […]
This post is about the project I've done in collaboration with Aliaksei Severyn, Sascha Rothe, and Jaap Kamps, during my internship at Google Research. Deep neural networks have shown impressive results in a lot of tasks in computer vision, natural language processing, and information retrieval. However, their success is conditioned on the availability of exhaustive […]
Document understanding for the purpose of assessing the relevance of a document or passage to a query based only on the document content appears to be a familiar goal for information retrieval community, however, this problem has remained largely intractable, despite repeated attacks over many years. This is while people are able to assess the relevance […]
With the emergence of deep learning, representation learning has been a hot topic in recent years. There are a large variety of approaches to representation learning in general, but the basic idea is to learn some set of features from data and then using these features for some task. Generating representation for documents is still one the […]
A couple weeks a go, I attended MILLA Deep Learning summer school (DLSS) from June 26th to July 1st and Reinforcement Learning summer school (RLSS) from July 3rd to 5th, 2017, organized by Yoshua Bengio and Aaron Courville. You can find information about the lectures here. In the following, I will share some of "my" highlights from […]
Our paper "Learning to Attend, Copy, and Generate for Session-Based Query Suggestion", with Sascha Rothe, Enrique Alfonseca, and Pascal Fleury, has been accepted as a long paper at the international Conference on Information and Knowledge Management (CIKM'17). This paper is on the outcome of my internship at Google Research. \o/ Users interact with search engines […]
Our paper "Share your Model instead of your Data: Privacy Preserving Mimic Learning for Ranking", with Hosein Azarbonyad, Jaap Kamps, and Maarten de Rijke, has been accepted at Neu-IR: SIGIR Workshop on Neural Information Retrieval (NeuIR'17). \o/ Deep neural networks demonstrate undeniable success in several fields and employing them is taking o for information retrieval […]
Our paper "On Search Powered Navigation", with Glorianna Jagfeld, Hosein Azarbonyad, Alex Olieman, Jaap Kamps, Maarten Marx, has been accepted as a short paper at The 3rd ACM International Conference on the Theory of Information Retrieval (ICTIR2017). \o/ Knowledge graphs and other hierarchical domain ontologies hold great promise for complex information seeking tasks, yet their […]
Our paper "Neural Ranking Models with Weak Supervision", with Hamed Zamani, Aliaksei Severyn, Jaap Kamps, and W. Bruce Croft, has been accepted as a long paper at The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR2017). \o/ This paper is on the outcome of my pet project during my internship […]
We will be giving a full day tutorial on "Neural Networks for Information Retrieval", with Tom Kenter, Alexey Borisov, Christophe Van Gysel, Maarten de Rijke, Bhaskar Mitra at The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR2017). \o/ The aim of this full-day tutorial is to give a clear overview […]