Learning programs from noisy data. In POPL, pages 761-774. ACM, 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,...
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...
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…
When machine learning programs from data, we ideally want to learn efficient rather than inefficient programs. However, existing inductive logic programmin
(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 ...
Sensitive to Noisy Data:Boosting can be affected because it pays too much attention to outliers or noise in the data. Harder to Tune:Boosting often requires careful tuning of hyperparameters like learning rate and number of iterations. Less Transparent:Like the bagging algorithm, the final model ...
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...
(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 ...
Machine learning is a set of algorithms that define data, learn from the defined data, and then make decisions from the learned data. Here, a number of training instances, with each training instance consisting of inputs and outputs, come up with a model that can classify or find the ...
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 ...