Introduction on Program Synthesis Via Deep Learning Over Graph Structured Data
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Program Synthesis for Data Science
Osbert Bastani - Interpretable Machine Learning via Program Synthesis - IPAM at UCLA
Exploring Program Synthesis: Francois Chollet, Kevin Ellis, Zenna Tavares
Gauss: Program Synthesis by Reasoning Over Graphs
Program Synthesis as High-Level Machine Learning Swarat Chaudhuri | FLOC 2018
Learning to Represent Programs with Graphs | TDLS
AutoPandas: Neural-Backed Generators for Program Synthesis
Neural Structured Learning - Part 3: Training with synthesized graphs
SYNT 2020: Neuro-Symbolic Program Synthesis from Natural Language and Demonstrations
Program Synthesis for the Masses
Deep Learning Foundations: Xinyun Chen 's talk on Learning-Based Program Synthesis
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Last Updated: September 30, 2026
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Mayur Naik (University of Pennsylvania) simons.berkeley.edu/talks/tbd-297 Talk by Nathanael Fijalkow in the IARCS Verification Seminar Series, In the SLT seminar, Zach Furman from The University of Melbourne discusses evidence for Денис Ракитин, НИУ ВШЭ The problem of Talk by Rohan Bavishi in the IARCS Verification Seminar Series, Recorded 10 January 2023. Osbert Bastani of the University of Pennsylvania presents "Interpretable Panel discussion with Francois Chollet, Kevin Ellis, and Zenna Tavares While input-output examples are a natural form of specification for Authors: Rohan Bavishi, Caroline Lemieux, Roy Fox, Koushik Sen, Ion Stoica Presented at SPLASH 2019. Welcome to episode 3 of our short series New computing platforms have greatly increased the demand for programmers, but Course webpage: cs.umd.edu/class/fall2022/cmsc828W/ With the advancement of modern technologies,
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