[talks] Reminder: Colloquium Speaker Christopher Manning, tomorrow at 12:30

[Emily Lawrence]

Colloquium Speaker

Prof. Christopher Manning, from Stanford University 

Thursday, November 16, 2017 - 12:30pm

Computer Science - Room 105

Host: Dr. Sanjeev Arora

*Please note, arrive early as seating and lunch may run out.

 

Deep learning has had enormous success on perceptual tasks but still
struggles in providing a model for inference. To address this gap, we have
been developing Compositional Attention Networks (CANs). The CAN design
provides a strong prior for explicitly iterative reasoning, enabling it to
support explainable and structured learning, as well as generalization from
a modest amount of data. The model builds on the great success of existing
recurrent cells such as LSTMs: A CAN is a sequence of a single recurrent
Memory, Attention, and Control (MAC) cell, and by careful design imposes
structural constraints on the operation of each cell and the interactions
between them, incorporating explicit control and soft attention mechanisms
into their interfaces. We demonstrate the model's strength and robustness on
the challenging CLEVR dataset for visual reasoning (Johnson et al. 2016),
achieving a new state-of-the-art 98.9% accuracy, halving the error rate of
the previous best model. More importantly, we show that the new model is
more computationally efficient and data-efficient, requiring an order of
magnitude less time and/or data to achieve good results. Joint work with
Drew Arad.

 

Bio: Christopher Manning is the Thomas M. Siebel Professor in Machine
Learning, Linguistics and Computer Science at Stanford University. He works
on software that can intelligently process, understand, and generate human
language material.  He is a leader in applying Deep Learning to Natural
Language Processing, including exploring Tree Recursive Neural Networks,
sentiment analysis, neural network dependency parsing, the GloVe model of
word vectors, neural machine translation, and deep language understanding.
He also focuses on computational linguistic approaches to parsing, robust
textual inference and multilingual language processing, including being a
principal developer of Stanford Dependencies and Universal Dependencies.
Manning is an ACM Fellow, a AAAI Fellow, an ACL Fellow, and a Past President
of ACL. He has coauthored leading textbooks on statistical natural language
processing and information retrieval. He is a member of the Stanford NLP
group (@stanfordnlp) and manages development of the Stanford CoreNLP
software.

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