8,761 research outputs found
On the Generalization Effects of Linear Transformations in Data Augmentation
Data augmentation is a powerful technique to improve performance in
applications such as image and text classification tasks. Yet, there is little
rigorous understanding of why and how various augmentations work. In this work,
we consider a family of linear transformations and study their effects on the
ridge estimator in an over-parametrized linear regression setting. First, we
show that transformations which preserve the labels of the data can improve
estimation by enlarging the span of the training data. Second, we show that
transformations which mix data can improve estimation by playing a
regularization effect. Finally, we validate our theoretical insights on MNIST.
Based on the insights, we propose an augmentation scheme that searches over the
space of transformations by how uncertain the model is about the transformed
data. We validate our proposed scheme on image and text datasets. For example,
our method outperforms RandAugment by 1.24% on CIFAR-100 using
Wide-ResNet-28-10. Furthermore, we achieve comparable accuracy to the SoTA
Adversarial AutoAugment on CIFAR datasets.Comment: International Conference on Machine learning (ICML) 2020. Added
experimental results on ImageNe
Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers
PID control architectures are widely used in industrial applications. Despite
their low number of open parameters, tuning multiple, coupled PID controllers
can become tedious in practice. In this paper, we extend PILCO, a model-based
policy search framework, to automatically tune multivariate PID controllers
purely based on data observed on an otherwise unknown system. The system's
state is extended appropriately to frame the PID policy as a static state
feedback policy. This renders PID tuning possible as the solution of a finite
horizon optimal control problem without further a priori knowledge. The
framework is applied to the task of balancing an inverted pendulum on a seven
degree-of-freedom robotic arm, thereby demonstrating its capabilities of fast
and data-efficient policy learning, even on complex real world problems.Comment: Accepted final version to appear in 2017 IEEE International
Conference on Robotics and Automation (ICRA
HoloDetect: Few-Shot Learning for Error Detection
We introduce a few-shot learning framework for error detection. We show that
data augmentation (a form of weak supervision) is key to training high-quality,
ML-based error detection models that require minimal human involvement. Our
framework consists of two parts: (1) an expressive model to learn rich
representations that capture the inherent syntactic and semantic heterogeneity
of errors; and (2) a data augmentation model that, given a small seed of clean
records, uses dataset-specific transformations to automatically generate
additional training data. Our key insight is to learn data augmentation
policies from the noisy input dataset in a weakly supervised manner. We show
that our framework detects errors with an average precision of ~94% and an
average recall of ~93% across a diverse array of datasets that exhibit
different types and amounts of errors. We compare our approach to a
comprehensive collection of error detection methods, ranging from traditional
rule-based methods to ensemble-based and active learning approaches. We show
that data augmentation yields an average improvement of 20 F1 points while it
requires access to 3x fewer labeled examples compared to other ML approaches.Comment: 18 pages
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