no code implementations • 2 Nov 2022 • Shantanu Mandal, Todd A. Anderson, Javier Turek, Justin Gottschlich, Abdullah Muzahid
In this paper, we present a novel formulation of program synthesis as a continuous optimization problem and use a state-of-the-art evolutionary approach, known as Covariance Matrix Adaptation Evolution Strategy to solve the problem.
no code implementations • 6 Dec 2021 • Alexander Lavin, David Krakauer, Hector Zenil, Justin Gottschlich, Tim Mattson, Johann Brehmer, Anima Anandkumar, Sanjay Choudry, Kamil Rocki, Atılım Güneş Baydin, Carina Prunkl, Brooks Paige, Olexandr Isayev, Erik Peterson, Peter L. McMahon, Jakob Macke, Kyle Cranmer, Jiaxin Zhang, Haruko Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfeffer
We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence.
no code implementations • 26 Apr 2021 • Celine Lee, Justin Gottschlich, Dan Roth
With the growth of natural language processing techniques and demand for improved software engineering efficiency, there is an emerging interest in translating intention from human languages to programming languages.
1 code implementation • 6 Nov 2020 • Niranjan Hasabnis, Justin Gottschlich
Software debugging has been shown to utilize upwards of half of developers' time.
1 code implementation • 12 Oct 2020 • Min Du, Nesime Tatbul, Brian Rivers, Akhilesh Kumar Gupta, Lucas Hu, Wei Wang, Ryan Marcus, Shengtian Zhou, Insup Lee, Justin Gottschlich
Class distribution skews in imbalanced datasets may lead to models with prediction bias towards majority classes, making fair assessment of classifiers a challenging task.
no code implementations • 28 Sep 2020 • Fangke Ye, Shengtian Zhou, Anand Venkat, Ryan Marcus, Nesime Tatbul, Jesmin Jahan Tithi, Niranjan Hasabnis, Paul Petersen, Timothy G Mattson, Tim Kraska, Pradeep Dubey, Vivek Sarkar, Justin Gottschlich
First, MISIM uses a novel context-aware semantic structure (CASS), which is designed to aid in lifting semantic meaning from code syntax.
no code implementations • 5 Jun 2020 • Fangke Ye, Shengtian Zhou, Anand Venkat, Ryan Marcus, Nesime Tatbul, Jesmin Jahan Tithi, Niranjan Hasabnis, Paul Petersen, Timothy Mattson, Tim Kraska, Pradeep Dubey, Vivek Sarkar, Justin Gottschlich
Code semantics similarity can be used for many tasks such as code recommendation, automated software defect correction, and clone detection.
no code implementations • NeurIPS Workshop CAP 2020 • Roshni G. Iyer, Yizhou Sun, Wei Wang, Justin Gottschlich
To continue to advance this research, we present the program-derived semantics graph, a new graphical structure to capture semantics of code.
no code implementations • 24 Mar 2020 • Fangke Ye, Shengtian Zhou, Anand Venkat, Ryan Marcus, Paul Petersen, Jesmin Jahan Tithi, Tim Mattson, Tim Kraska, Pradeep Dubey, Vivek Sarkar, Justin Gottschlich
The simplified parse tree (SPT) presented in Aroma, a state-of-the-art code recommendation system, is a tree-structured representation used to infer code semantics by capturing program \emph{structure} rather than program \emph{syntax}.
1 code implementation • 31 Oct 2019 • Yizhak Yisrael Elboher, Justin Gottschlich, Guy Katz
In this paper, we propose a framework that can enhance neural network verification techniques by using over-approximation to reduce the size of the network - thus making it more amenable to verification.
no code implementations • 22 Aug 2019 • Shantanu Mandal, Todd A. Anderson, Javier S. Turek, Justin Gottschlich, Shengtian Zhou, Abdullah Muzahid
The problem of automatic software generation is known as Machine Programming.
no code implementations • 29 Mar 2019 • Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael. I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar
Machine learning (ML) techniques are enjoying rapidly increasing adoption.
no code implementations • 20 Mar 2018 • Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, Michael Carbin, Martin Rinard, Regina Barzilay, Saman Amarasinghe, Joshua B. Tenenbaum, Tim Mattson
In this position paper, we describe our vision of the future of machine programming through a categorical examination of three pillars of research.
4 code implementations • NeurIPS 2018 • Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Mejbah Alam, Justin Gottschlich
Classical anomaly detection is principally concerned with point-based anomalies, those anomalies that occur at a single point in time.
no code implementations • 18 Jan 2018 • Lindsey Kuper, Guy Katz, Justin Gottschlich, Kyle Julian, Clark Barrett, Mykel Kochenderfer
The increasing use of deep neural networks for safety-critical applications, such as autonomous driving and flight control, raises concerns about their safety and reliability.
no code implementations • 9 Jan 2018 • Tae Jun Lee, Justin Gottschlich, Nesime Tatbul, Eric Metcalf, Stan Zdonik
This short paper describes our ongoing research on Greenhouse - a zero-positive machine learning system for time-series anomaly detection.
no code implementations • 9 Jan 2018 • Tae Jun Lee, Justin Gottschlich, Nesime Tatbul, Eric Metcalf, Stan Zdonik
Classical anomaly detection is principally concerned with point-based anomalies, anomalies that occur at a single data point.
no code implementations • 9 Jan 2018 • Justin Gottschlich
In this paper, we present Paranom, a parallel anomaly dataset generator.
1 code implementation • NeurIPS 2019 • Mejbah Alam, Justin Gottschlich, Nesime Tatbul, Javier Turek, Timothy Mattson, Abdullah Muzahid
This is, in part, due to the emergence of a wide range of novel techniques in machine learning.
1 code implementation • 17 Sep 2017 • Kory Becker, Justin Gottschlich
In this paper, we present the first-of-its-kind machine learning (ML) system, called AI Programmer, that can automatically generate full software programs requiring only minimal human guidance.