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ISMM 2020
Tue 16 Jun 2020
co-located with PLDI 2020
PLDI 2020 (series) / ISMM 2020 (series) / Online! /

Room information: MAPL live stream

VenueOnline!
Room nameMAPL live stream
Floor0
Room Information

Slack channel: #mapl

YouTube live stream

Program

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Tue 16 Jun

Displayed time zone: Pacific Time (US & Canada) change

08:00 - 10:00
Graph Neural Networks for Program ReasoningMAPL at MAPL live stream
Chair(s): Ke Wang Visa Research
08:00
30m
Talk
Hoppity: Learning Graph Transformations to Detect and Fix Bugs in Programs
MAPL
Elizabeth Dinella University of Pennsylvania
08:30
60m
Tutorial
A Gentle Tutorial on Graph Neural Networks and Its Application to Programming Languages
MAPL
Yizhou Sun UCLA
09:30
30m
Talk
LambdaNet: Probabilistic Type Inference using Graph Neural Networks
MAPL
Işıl Dillig University of Texas at Austin, USA
10:00 - 10:30
BreakMAPL at MAPL live stream
10:30 - 11:30
Deep Learning and Program VerificationMAPL at MAPL live stream
Chair(s): Xujie Si McGill University, Canada
10:30
30m
Talk
An Abstraction-Based Framework for Neural Network Verification
MAPL
Guy Katz Hebrew University
11:00
30m
Talk
Generating Correctness Proofs with Neural Networks
MAPL
Alex Sanchez-Stern University of California, San Diego, Yousef Alhessi University of California, San Diego, Lawrence Saul University of California, San Diego, Sorin Lerner University of California at San Diego, USA
11:30 - 12:30
Compilers for Deep Learning FrameworksMAPL at MAPL live stream
Chair(s): Charles Sutton Google Research
11:30
30m
Talk
On the Challenges in Programming Mixed-Precision Deep Neural Networks
MAPL
Ruizhe Zhao Imperial College London, Wayne Luk Imperial College London, Chao Xiong Corerain Technologies, Xinyu Niu Corerain Technologies, Kuen Hung Tsoi Corerain Technologies
12:00
30m
Talk
Semi-static Type, Shape and Symbolic Shape Inference for Dynamic Computation Graphs
MAPL
Momoko Hattori The University of Tokyo, Shimpei Sawada Preferred Networks, Shinichiro Hamaji Preferred Networks, Masahiro Sakai Preferred Networks, Shunsuke Shimizu Preferred Networks
12:30 - 13:00
BreakMAPL at MAPL live stream
13:00 - 14:00
Keynote TalkMAPL at MAPL live stream
Chair(s): Justin Gottschlich Intel Labs / Penn
13:00
60m
Talk
Program Optimization for Machine Learning
MAPL
Alex Aiken Stanford University, USA
14:00 - 15:00
Formal Methods and Reinforcement LearningMAPL at MAPL live stream
Chair(s): Aws Albarghouthi University of Wisconsin-Madison, USA
14:00
30m
Talk
Learned Garbage Collection
MAPL
Lujing Cen MIT CSAIL, Ryan Marcus MIT CSAIL / Intel Labs, Hongzi Mao MIT CSAIL, Justin Gottschlich Intel Labs / Penn, Mohammad Alizadeh MIT CSAIL, Tim Kraska MIT CSAIL
14:30
30m
Talk
Trustworthy Autonomy through Program Synthesis
MAPL
Swarat Chaudhuri Rice University
15:00 - 15:30
BreakMAPL at MAPL live stream
15:30 - 17:00
Program SynthesisMAPL at MAPL live stream
Chair(s): Satish Chandra Facebook
15:30
30m
Talk
Neurosymbolic Reasoning and the Third Wave of Program Synthesis
MAPL
Armando Solar-Lezama Massachusetts Institute of Technology, USA
16:00
30m
Talk
Learning Quantitative Representation Synthesis
MAPL
Mayur Patil University of California, Riverside, Farzin Houshmand University of California, Riverside, Mohsen Lesani University of California, Riverside
16:30
30m
Talk
Towards Human-Like Program Synthesis
MAPL
Rishabh Singh Google Brain
17:00 - 17:30
BreakMAPL at MAPL live stream

Tue 16 Jun

Displayed time zone: Pacific Time (US & Canada) change

Room8:00309:003010:003011:003012:003013:003014:003015:003016:003017:0030
MAPL live stream

MAPL

Graph Neural Networks for Program Reasoning

MAPL

Break

MAPL

Deep Learning and Program Verification

MAPL

Compilers for Deep Learning Frameworks

MAPL

Break

MAPL

Keynote Talk

MAPL

Formal Methods and Reinforcement Learning

MAPL

Break

MAPL

Program Synthesis

MAPL

Break

Tue 16 Jun

Displayed time zone: Pacific Time (US & Canada) change

Room8:001530459:0015304510:0015304511:0015304512:0015304513:0015304514:0015304515:0015304516:00153045
MAPL live stream
MAPL
Hoppity: Learning Graph Transformations to Detect and Fix Bugs in Programs
08:00 - 08:30
MAPL
A Gentle Tutorial on Graph Neural Networks and Its Application to Progr ...
08:30 - 09:30
MAPL
LambdaNet: Probabilistic Type Inference using Graph Neural Networks
09:30 - 10:00
MAPL
An Abstraction-Based Framework for Neural Network Verification
10:30 - 11:00
MAPL
Generating Correctness Proofs with Neural Networks
11:00 - 11:30
MAPL
On the Challenges in Programming Mixed-Precision Deep Neural Networks
11:30 - 12:00
MAPL
Semi-static Type, Shape and Symbolic Shape Inference for Dynamic Comput ...
12:00 - 12:30
MAPL
Program Optimization for Machine Learning
13:00 - 14:00
MAPL
Learned Garbage Collection
14:00 - 14:30
MAPL
Trustworthy Autonomy through Program Synthesis
14:30 - 15:00
MAPL
Neurosymbolic Reasoning and the Third Wave of Program Synthesis
15:30 - 16:00
MAPL
Learning Quantitative Representation Synthesis
16:00 - 16:30
MAPL
Towards Human-Like Program Synthesis
16:30 - 17:00