Next week's talks: Vlad Niculae and Andreas Vlachos

[Ekaterina Shutova]

Dear all,

Next week we will have two exciting talks by Vlad Niculae, Instituto de Telecomunicações, Lisbon, Portugal and Andreas Vlachos, University of Cambridge, UK.

Vlad Niculae -- 4pm on Monday, 29 October in F1.15

Andreas Vlachos -- 4pm on Tuesday, 30 October in F1.15.

The abstracts of their talks are below. Hope to see many of you there!

If you'd like to meet with Andreas, please email me. If you'd like to meet with Vlad, please email Miguel Rios (mrio...@gmail.com).

Best,

Katia

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Andreas Vlachos, University of Cambridge, UK

Title: Imitation learning, zero-shot learning and automated fact checking

Abstract: In this talk I will give an overview of my research in machine learning for natural language processing. I will begin by introducing my work on imitation learning, a machine learning paradigm I have used to develop novel algorithms for structure prediction that have been applied successfully to a number of tasks such as semantic parsing, natural language generation and information extraction. Key advantages are the ability to handle large output search spaces and to learn with non-decomposable loss functions. Following this, I will discuss my work on zero-shot learning using neural networks, which enabled us to learn models that can predict labels for which no data was observed during training. I will conclude with my work on automated fact-checking, a challenge we proposed in order to stimulate progress in machine learning, natural language processing and, more broadly, artificial intelligence.

Bio:

Since October 2018, I am a senior lecturer at the Natural Language and Information Processing group at the Department of Computer Science and Technology at the University of Cambridge. Current projects include natural language generation, automated fact checking and imitation learning. I have also worked on semantic parsing, language modelling, information extraction, active learning, clustering and biomedical text mining.

Prior to this I was a lecturer at the University of Sheffield, working on the intersection of Natural Language Processing and Machine Learning. Previously I was a postdoc at the Machine Reading group at UCL working with Sebastian Riedel, at the NLIP group at the University of Cambridge working with Stephen Clark and at the University of Wisconsin-Madison working with Mark Craven. I did my PhD at the University of Cambridge with Ted Briscoe and Zoubin Ghahramani.

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Vlad Niculae, Instituto de Telecomunicações, Lisbon, Portugal.

Title

Learning with Sparse Latent Structure

Abstract

Structured representations are a powerful tool in machine learning, and in particular in natural language processing: The discrete, compositional nature of words and sentences leads to natural combinatorial representations such as trees, sequences, segments, or alignments, among others. At the same time, deep, hierarchical neural networks with latent representations are increasingly widely and successfully applied to language tasks. Deep networks conventionally perform smooth, soft computations resulting in dense hidden representations.

We study deep models with structured and sparse latent representations, without sacrificing differentiability, and thus enabling end-to-end gradient-based training. We demonstrate sparse and structured attention mechanisms, as well as latent computation graph structure learning, with successful empirical results on large scale problems including sentiment analysis, natural language inference, and neural machine translation.

Joint work with Claire Cardie, Mathieu Blondel, and André Martins.

Bio

Vlad is a postdoc in the DeepSpin project at the Instituto de Telecomunicações in Lisbon, Portugal, researching structure and sparsity for machine learning & natural language processing. He earned a PhD in Computer Science from Cornell University in 2018, advised by Claire Cardie. Vlad also maintains the polylearn library for factorization machines and polynomial networks in Python, in addition to being a long time core developer for scikit-learn.

More information at https://vene.ro

Relevant publications

• Vlad Niculae, André F. T. Martins, Mathieu Blondel, Claire Cardie. SparseMAP: Differentiable sparse structured inference. In: Proc. of ICML 2018. [ arXiv ] [ code ] [ slides ]

• Vlad Niculae, André F. T. Martins, Claire Cardie. Towards dynamic computation graphs via sparse latent structure. In: Proc. of EMNLP 2018. [ arXiv ] [ code ]

• Vlad Niculae and Mathieu Blondel. A regularized framework for sparse and structured neural attention. In: Proc. of NIPS 2017. [ arXiv ] [ code ]

[Wilker Aziz]

A reminder that the CLS talk by Vlad Niculae starts in 2 minutes in room F1.15.

Best, 

Wilker.

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Current program: http://www.illc.uva.nl/LaCo/CLS/
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Wilker Aziz

http://wilkeraziz.github.io