Building Neural Network Models That Can Reason
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 networks that support memory, attention, composition, and reasoning. Our MACnet and NSM designs provide a strong prior for explicitly iterative reasoning, enabling them to learn explainable, structured reasoning, as well as achieve good generalization from a modest amount of data. The Neural State Machine (NSM) design also emphasizes the use of a more symbolic form of internal computation, represented as attention over symbols, which have distributed representations. Such designs impose structural priors on the operation of networks and encourage certain kinds of modularity and generalization. We demonstrate the models’ strength, robustness, and data efficiency on the CLEVR dataset for visual reasoning (Johnson et al. 2016), VQA-CP, which emphasizes disentanglement (Agrawal et al. 2018), and our own GQA (Hudson and Manning 2019). Joint work with Drew Hudson.
- Date:
- Speakers:
- Christopher Manning
- Affiliation:
- Stanford University
Series: MSR AI Distinguished Lectures and Fireside Chats
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Frontiers in Machine Learning: Fireside Chat
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- Peter Lee,
- Sandy Blyth
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Learning over sets, subgraphs, and streams: How to accurately incorporate graph context
Speakers:- Debadeepta Dey,
- Paul Bennett,
- Sean Andrist
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Fireside Chat with Anca Dragan
Speakers:- Anca Dragan and Eric Horvitz
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Conversations Based on Search Engine Result Pages
Speakers:- Maarten de Rijke
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The Ethical Algorithm
Speakers:- Michael Kearns
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Fireside Chat with Stefanie Jegelka
Speakers:- Alekh Agarwal
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Fireside Chat with Peter Stone
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Efficient Robot Skill Learning: Grounded Simulation Learning and Imitation Learning from Observation
Speakers:- Debadeepta Dey
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Building Neural Network Models That Can Reason
Speakers:- Christopher Manning
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Fireside Chat with David Blei
Speakers: -
The Blessings of Multiple Causes
Speakers:- David Blei
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As We May Program
Speakers:- Peter Norvig
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Fireside Chat with Peter Norvig
Speakers:- Eric Horvitz,
- Peter Norvig
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An Optimization-Based Theory of Mind for Human-Robot Interaction
Speakers:- Anca Dragan
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Fireside Chat with Manuel Blum
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Towards a Conscious AI: A Computer Architecture inspired by Neuroscience
Speakers:- Adith Swaminathan
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Fireside Chat with Dario Amodei
Speakers: -
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Super-Human AI for Strategic Reasoning
Speakers:- Adith Swaminathan