Krause. Learning programs from noisy data. In POPL, 2016.V. Raychev, P. Bielik, M. Vechev, and A. Krause. Learn- ing programs from noisy data. In Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, POPL 2016, pages 761-774, New York, NY...
https://deepmind.com/blog/article/learning-explanatory-rules-noisy-data Suppose you are playing football. The ball arrives at your feet, and you decide to pass it to the unmarked striker. What seems…
In data-driven control, a central question is how to handle noisy data. In this work, we consider the problem of designing a stabilizing controller for an unknown linear system using only a finite set of noisy data collected from the system. For this problem, many recent works have considere...
Niyogi P, Smale S, Weinberger S (2011) A topological view of unsupervised learning from noisy data. SIAM J Comput 40(3):646–663. https://doi.org/10.1137/090762932 Article MathSciNet Google Scholar Pandove D, Goel S, Rani R (2018) Systematic review of clustering high-dimensional and ...
Distant supervision for relation extraction without labeled data Modern models of relation extraction for tasks like ACE are based on supervised learning of relations from small hand-labeled corpora. We investigate an alternative paradigm that does not require labeled corpora, avoiding the domain depe.....
(PCA) and autoencoders reduce the number of variables or features -- dimensions -- within the data sets so that the focus can be given to the relevant features for various objectives. Some experts explain this by saying that dimensionality reduction removesnoisy data. ML engineers often use ...
(PDEs) from scarce and noisy data for nonlinear spatiotemporal systems. In particular, this symbolic reasoning approach seamlessly integrates the strengths of deep neural networks for rich representation learning, physics embedding, automatic differentiation and sparse regression to (1) approximate the ...
Despite great progress in simulating multiphysics problems using the numerical discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate noisy data into existing algorithms, mesh generation remains complex, and high-dimensional problems governed by parameterized PDEs cannot...
Machine learning systems automatically learn programs from data. This is often a very attractive alternative to manually constructing them, and in the last decade the use of machine learning has spread rapidly throughout computer science and beyond. Machine learning is used in Web search, spam filte...
Many inductive logic programming approaches struggle to learn programs from noisy data. To overcome this limitation, we introduce an approach that learns minimal description length programs from noisy data, including recursive programs. Our experiments on several domains, including drug design, game ...