25,075 research outputs found
Human-Guided Learning of Column Networks: Augmenting Deep Learning with Advice
Recently, deep models have been successfully applied in several applications,
especially with low-level representations. However, sparse, noisy samples and
structured domains (with multiple objects and interactions) are some of the
open challenges in most deep models. Column Networks, a deep architecture, can
succinctly capture such domain structure and interactions, but may still be
prone to sub-optimal learning from sparse and noisy samples. Inspired by the
success of human-advice guided learning in AI, especially in data-scarce
domains, we propose Knowledge-augmented Column Networks that leverage human
advice/knowledge for better learning with noisy/sparse samples. Our experiments
demonstrate that our approach leads to either superior overall performance or
faster convergence (i.e., both effective and efficient).Comment: Under Review at 'Machine Learning Journal' (MLJ
Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems
Despite its great success, machine learning can have its limits when dealing
with insufficient training data. A potential solution is the additional
integration of prior knowledge into the training process which leads to the
notion of informed machine learning. In this paper, we present a structured
overview of various approaches in this field. We provide a definition and
propose a concept for informed machine learning which illustrates its building
blocks and distinguishes it from conventional machine learning. We introduce a
taxonomy that serves as a classification framework for informed machine
learning approaches. It considers the source of knowledge, its representation,
and its integration into the machine learning pipeline. Based on this taxonomy,
we survey related research and describe how different knowledge representations
such as algebraic equations, logic rules, or simulation results can be used in
learning systems. This evaluation of numerous papers on the basis of our
taxonomy uncovers key methods in the field of informed machine learning.Comment: Accepted at IEEE Transactions on Knowledge and Data Engineering:
https://ieeexplore.ieee.org/document/942998
Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence
In this work, we propose a new Gaussian process regression (GPR)-based
multifidelity method: physics-informed CoKriging (CoPhIK). In CoKriging-based
multifidelity methods, the quantities of interest are modeled as linear
combinations of multiple parameterized stationary Gaussian processes (GPs), and
the hyperparameters of these GPs are estimated from data via optimization. In
CoPhIK, we construct a GP representing low-fidelity data using physics-informed
Kriging (PhIK), and model the discrepancy between low- and high-fidelity data
using a parameterized GP with hyperparameters identified via optimization. Our
approach reduces the cost of optimization for inferring hyperparameters by
incorporating partial physical knowledge. We prove that the physical
constraints in the form of deterministic linear operators are satisfied up to
an error bound. Furthermore, we combine CoPhIK with a greedy active learning
algorithm for guiding the selection of additional observation locations. The
efficiency and accuracy of CoPhIK are demonstrated for reconstructing the
partially observed modified Branin function, reconstructing the sparsely
observed state of a steady state heat transport problem, and learning a
conservative tracer distribution from sparse tracer concentration measurements
Sparse identification of nonlinear dynamics for model predictive control in the low-data limit
The data-driven discovery of dynamics via machine learning is currently
pushing the frontiers of modeling and control efforts, and it provides a
tremendous opportunity to extend the reach of model predictive control.
However, many leading methods in machine learning, such as neural networks,
require large volumes of training data, may not be interpretable, do not easily
include known constraints and symmetries, and often do not generalize beyond
the attractor where models are trained. These factors limit the use of these
techniques for the online identification of a model in the low-data limit, for
example following an abrupt change to the system dynamics. In this work, we
extend the recent sparse identification of nonlinear dynamics (SINDY) modeling
procedure to include the effects of actuation and demonstrate the ability of
these models to enhance the performance of model predictive control (MPC),
based on limited, noisy data. SINDY models are parsimonious, identifying the
fewest terms in the model needed to explain the data, making them
interpretable, generalizable, and reducing the burden of training data. We show
that the resulting SINDY-MPC framework has higher performance, requires
significantly less data, and is more computationally efficient and robust to
noise than neural network models, making it viable for online training and
execution in response to rapid changes to the system. SINDY-MPC also shows
improved performance over linear data-driven models, although linear models may
provide a stopgap until enough data is available for SINDY. SINDY-MPC is
demonstrated on a variety of dynamical systems with different challenges,
including the chaotic Lorenz system, a simple model for flight control of an F8
aircraft, and an HIV model incorporating drug treatment.Comment: 24 pages, 12 figure
Explainable Machine Learning for Scientific Insights and Discoveries
Machine learning methods have been remarkably successful for a wide range of
application areas in the extraction of essential information from data. An
exciting and relatively recent development is the uptake of machine learning in
the natural sciences, where the major goal is to obtain novel scientific
insights and discoveries from observational or simulated data. A prerequisite
for obtaining a scientific outcome is domain knowledge, which is needed to gain
explainability, but also to enhance scientific consistency. In this article we
review explainable machine learning in view of applications in the natural
sciences and discuss three core elements which we identified as relevant in
this context: transparency, interpretability, and explainability. With respect
to these core elements, we provide a survey of recent scientific works that
incorporate machine learning and the way that explainable machine learning is
used in combination with domain knowledge from the application areas
Verification for Machine Learning, Autonomy, and Neural Networks Survey
This survey presents an overview of verification techniques for autonomous
systems, with a focus on safety-critical autonomous cyber-physical systems
(CPS) and subcomponents thereof. Autonomy in CPS is enabling by recent advances
in artificial intelligence (AI) and machine learning (ML) through approaches
such as deep neural networks (DNNs), embedded in so-called learning enabled
components (LECs) that accomplish tasks from classification to control.
Recently, the formal methods and formal verification community has developed
methods to characterize behaviors in these LECs with eventual goals of formally
verifying specifications for LECs, and this article presents a survey of many
of these recent approaches
Large-scale Collaborative Filtering with Product Embeddings
The application of machine learning techniques to large-scale personalized
recommendation problems is a challenging task. Such systems must make sense of
enormous amounts of implicit feedback in order to understand user preferences
across numerous product categories. This paper presents a deep learning based
solution to this problem within the collaborative filtering with implicit
feedback framework. Our approach combines neural attention mechanisms, which
allow for context dependent weighting of past behavioral signals, with
representation learning techniques to produce models which obtain extremely
high coverage, can easily incorporate new information as it becomes available,
and are computationally efficient. Offline experiments demonstrate significant
performance improvements when compared to several alternative methods from the
literature. Results from an online setting show that the approach compares
favorably with current production techniques used to produce personalized
product recommendations.Comment: 15 pages, 5 figure
Using Human Brain Activity to Guide Machine Learning
Machine learning is a field of computer science that builds algorithms that
learn. In many cases, machine learning algorithms are used to recreate a human
ability like adding a caption to a photo, driving a car, or playing a game.
While the human brain has long served as a source of inspiration for machine
learning, little effort has been made to directly use data collected from
working brains as a guide for machine learning algorithms. Here we demonstrate
a new paradigm of "neurally-weighted" machine learning, which takes fMRI
measurements of human brain activity from subjects viewing images, and infuses
these data into the training process of an object recognition learning
algorithm to make it more consistent with the human brain. After training,
these neurally-weighted classifiers are able to classify images without
requiring any additional neural data. We show that our neural-weighting
approach can lead to large performance gains when used with traditional machine
vision features, as well as to significant improvements with already
high-performing convolutional neural network features. The effectiveness of
this approach points to a path forward for a new class of hybrid machine
learning algorithms which take both inspiration and direct constraints from
neuronal data.Comment: Supplemental material can be downloaded here:
http://www.wjscheirer.com/misc/activity_weights/fong-et-al-supplementary.pd
Case studies in network community detection
Community structure describes the organization of a network into subgraphs
that contain a prevalence of edges within each subgraph and relatively few
edges across boundaries between subgraphs. The development of
community-detection methods has occurred across disciplines, with numerous and
varied algorithms proposed to find communities. As we present in this Chapter
via several case studies, community detection is not just an "end game" unto
itself, but rather a step in the analysis of network data which is then useful
for furthering research in the disciplinary domain of interest. These
case-study examples arise from diverse applications, ranging from social and
political science to neuroscience and genetics, and we have chosen them to
demonstrate key aspects of community detection and to highlight that community
detection, in practice, should be directed by the application at hand.Comment: 21 pages, 5 figure
Solving Nonlinear and High-Dimensional Partial Differential Equations via Deep Learning
In this work we apply the Deep Galerkin Method (DGM) described in Sirignano
and Spiliopoulos (2018) to solve a number of partial differential equations
that arise in quantitative finance applications including option pricing,
optimal execution, mean field games, etc. The main idea behind DGM is to
represent the unknown function of interest using a deep neural network. A key
feature of this approach is the fact that, unlike other commonly used numerical
approaches such as finite difference methods, it is mesh-free. As such, it does
not suffer (as much as other numerical methods) from the curse of
dimensionality associated with highdimensional PDEs and PDE systems. The main
goals of this paper are to elucidate the features, capabilities and limitations
of DGM by analyzing aspects of its implementation for a number of different
PDEs and PDE systems. Additionally, we present: (1) a brief overview of PDEs in
quantitative finance along with numerical methods for solving them; (2) a brief
overview of deep learning and, in particular, the notion of neural networks;
(3) a discussion of the theoretical foundations of DGM with a focus on the
justification of why this method is expected to perform well
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