[Colloquium] REMINDER: 2/1 Talks at TTIC: Jacob Andreas, UC Berkeley

[Mary Marre via Colloquium]

 When:     Thursday, February 1st at *11:00 am*

Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526

Who:       Jacob Andreas, UC Berkeley


Title:        Learning from Language

Abstract: Human language is built from a library of concepts and
compositional operators. These, in turn, provide a rich source of
information about the kinds of abstractions that humans use to navigate the
world. Can this information help us build better machine learning models?
In this talk, we'll explore three different ways of using language to
support learning: as a source of structure for question answering models,
as a scaffold for fast and generalizable reinforcement learning, and as a
tool for understanding the representations computed by general classes of
neural networks.

Bio: Jacob Andreas is a fifth-year PhD student at UC Berkeley. His current
research focuses on using natural language to more effectively train and
understand machine learning models. Jacob received a B.S. from Columbia in
2012 and an M.Phil. from Cambridge in 2013. He received paper awards at
NAACL 2016 and ICML 2017. He was a Churchill scholar from 2012--13, an NSF
graduate fellow from 2013--2016, a Huawei--Berkeley AI fellow from
2016--2017, and is currently supported by a Facebook fellowship.



Host: Kevin Gimpel <kgimpel at ttic.edu>




Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 504*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*

On Wed, Jan 31, 2018 at 3:33 PM, Mary Marre <mmarre at ttic.edu> wrote:

> When:     Thursday, February 1st at *11:00 am*
>
> Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526
>
> Who:       Jacob Andreas, UC Berkeley
>
>
> Title:        Learning from Language
>
> Abstract: Human language is built from a library of concepts and
> compositional operators. These, in turn, provide a rich source of
> information about the kinds of abstractions that humans use to navigate
> the world. Can this information help us build better machine learning
> models? In this talk, we'll explore three different ways of using
> language to support learning: as a source of structure for question
> answering models, as a scaffold for fast and generalizable reinforcement
> learning, and as a tool for understanding the representations computed by
> general classes of neural networks.
>
> Bio: Jacob Andreas is a fifth-year PhD student at UC Berkeley. His
> current research focuses on using natural language to more effectively
> train and understand machine learning models. Jacob received a B.S. from
> Columbia in 2012 and an M.Phil. from Cambridge in 2013. He received paper
> awards at NAACL 2016 and ICML 2017. He was a Churchill scholar from
> 2012--13, an NSF graduate fellow from 2013--2016, a Huawei--Berkeley AI
> fellow from 2016--2017, and is currently supported by a Facebook
> fellowship.
>
>
>
> Host: Kevin Gimpel <kgimpel at ttic.edu>
>
>
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 504*
> *Chicago, IL  60637*
> *p:(773) 834-1757 <(773)%20834-1757>*
> *f: (773) 357-6970 <(773)%20357-6970>*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
> On Thu, Jan 25, 2018 at 5:13 PM, Mary Marre <mmarre at ttic.edu> wrote:
>
>> When:     Thursday, February 1st at *11:00 am*
>>
>> Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526
>>
>> Who:       Jacob Andreas, UC Berkeley
>>
>>
>> Title:        Learning from Language
>>
>> Abstract: Human language is built from a library of concepts and
>> compositional operators. These, in turn, provide a rich source of
>> information about the kinds of abstractions that humans use to navigate
>> the world. Can this information help us build better machine learning
>> models? In this talk, we'll explore three different ways of using
>> language to support learning: as a source of structure for question
>> answering models, as a scaffold for fast and generalizable reinforcement
>> learning, and as a tool for understanding the representations computed
>> by general classes of neural networks.
>>
>> Bio: Jacob Andreas is a fifth-year PhD student at UC Berkeley. His
>> current research focuses on using natural language to more effectively
>> train and understand machine learning models. Jacob received a B.S. from
>> Columbia in 2012 and an M.Phil. from Cambridge in 2013. He received
>> paper awards at NAACL 2016 and ICML 2017. He was a Churchill scholar
>> from 2012--13, an NSF graduate fellow from 2013--2016, a
>> Huawei--Berkeley AI fellow from 2016--2017, and is currently supported
>> by a Facebook fellowship.
>>
>>
>>
>> Host: Kevin Gimpel <kgimpel at ttic.edu>
>>
>>
>> Mary C. Marre
>> Administrative Assistant
>> *Toyota Technological Institute*
>> *6045 S. Kenwood Avenue*
>> *Room 504*
>> *Chicago, IL  60637*
>> *p:(773) 834-1757 <(773)%20834-1757>*
>> *f: (773) 357-6970 <(773)%20357-6970>*
>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>
>
>
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