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The rising demand for electricity and its essential nature in today's world
calls for intelligent home energy management (HEM) systems that can reduce
energy usage. This involves scheduling of loads from peak hours of the day when
energy consumption is at its highest to leaner off-peak periods of the day when
energy consumption is relatively lower thereby reducing the system's peak load
demand, which would consequently result in lesser energy bills, and improved
load demand profile. This work introduces a novel way to develop a learning
system that can learn from experience to shift loads from one time instance to
another and achieve the goal of minimizing the aggregate peak load. This paper
proposes a Deep Reinforcement Learning (DRL) model for demand response where
the virtual agent learns the task like humans do. The agent gets feedback for
every action it takes in the environment; these feedbacks will drive the agent
to learn about the environment and take much smarter steps later in its
learning stages. Our method outperformed the state of the art mixed integer
linear programming (MILP) for load peak reduction. The authors have also
designed an agent to learn to minimize both consumers' electricity bills and
utilities' system peak load demand simultaneously. The proposed model was
analyzed with loads from five different residential consumers; the proposed
method increases the monthly savings of each consumer by reducing their
electricity bill drastically along with minimizing the peak load on the system
when time shiftable loads are handled by the proposed method. | [
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] |
Serverless computing has emerged as a compelling new paradigm of cloud
computing models in recent years. It promises the user services at large scale
and low cost while eliminating the need for infrastructure management. On cloud
provider side, flexible resource management is required to meet fluctuating
demand. It can be enabled through automated provisioning and deprovisioning of
resources. A common approach among both commercial and open source serverless
computing platforms is workload-based auto-scaling, where a designated
algorithm scales instances according to the number of incoming requests. In the
recently evolving serverless framework Knative a request-based policy is
proposed, where the algorithm scales resources by a configured maximum number
of requests that can be processed in parallel per instance, the so-called
concurrency. As we show in a baseline experiment, this predefined concurrency
level can strongly influence the performance of a serverless application.
However, identifying the concurrency configuration that yields the highest
possible quality of service is a challenging task due to various factors, e.g.
varying workload and complex infrastructure characteristics, influencing
throughput and latency. While there has been considerable research into
intelligent techniques for optimizing auto-scaling for virtual machine
provisioning, this topic has not yet been discussed in the area of serverless
computing. For this reason, we investigate the applicability of a reinforcement
learning approach, which has been proven on dynamic virtual machine
provisioning, to request-based auto-scaling in a serverless framework. Our
results show that within a limited number of iterations our proposed model
learns an effective scaling policy per workload, improving the performance
compared to the default auto-scaling configuration. | [
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] |
We consider a living organism as an observer of the evolution of its
environment recording sensory information about the state space X of the
environment in real time. Sensory information is sampled and then processed on
two levels. On the biological level, the organism serves as an evaluation
mechanism of the subjective relevance of the incoming data to the observer: the
observer assigns excitation values to events in X it could recognize using its
sensory equipment. On the algorithmic level, sensory input is used for updating
a database, the memory of the observer whose purpose is to serve as a
geometric/combinatorial model of X, whose nodes are weighted by the excitation
values produced by the evaluation mechanism. These values serve as a guidance
system for deciding how the database should transform as observation data
mounts. We define a searching problem for the proposed model and discuss the
model's flexibility and its computational efficiency, as well as the
possibility of implementing it as a dynamic network of neuron-like units. We
show how various easily observable properties of the human memory and thought
process can be explained within the framework of this model. These include:
reasoning (with efficiency bounds), errors, temporary and permanent loss of
information. We are also able to define general learning problems in terms of
the new model, such as the language acquisition problem. | [
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] |
In many applications, an anomaly detection system presents the most anomalous
data instance to a human analyst, who then must determine whether the instance
is truly of interest (e.g. a threat in a security setting). Unfortunately, most
anomaly detectors provide no explanation about why an instance was considered
anomalous, leaving the analyst with no guidance about where to begin the
investigation. To address this issue, we study the problems of computing and
evaluating sequential feature explanations (SFEs) for anomaly detectors. An SFE
of an anomaly is a sequence of features, which are presented to the analyst one
at a time (in order) until the information contained in the highlighted
features is enough for the analyst to make a confident judgement about the
anomaly. Since analyst effort is related to the amount of information that they
consider in an investigation, an explanation's quality is related to the number
of features that must be revealed to attain confidence. One of our main
contributions is to present a novel framework for large scale quantitative
evaluations of SFEs, where the quality measure is based on analyst effort. To
do this we construct anomaly detection benchmarks from real data sets along
with artificial experts that can be simulated for evaluation. Our second
contribution is to evaluate several novel explanation approaches within the
framework and on traditional anomaly detection benchmarks, offering several
insights into the approaches. | [
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] |
The global influence of Big Data is not only growing but seemingly endless.
The trend is leaning towards knowledge that is attained easily and quickly from
massive pools of Big Data. Today we are living in the technological world that
Dr. Usama Fayyad and his distinguished research fellows discussed in the
introductory explanations of Knowledge Discovery in Databases (KDD) predicted
nearly two decades ago. Indeed, they were precise in their outlook on Big Data
analytics. In fact, the continued improvement of the interoperability of
machine learning, statistics, database building and querying fused to create
this increasingly popular science- Data Mining and Knowledge Discovery. The
next generation computational theories are geared towards helping to extract
insightful knowledge from even larger volumes of data at higher rates of speed.
As the trend increases in popularity, the need for a highly adaptive solution
for knowledge discovery will be necessary. In this research paper, we are
introducing the investigation and development of 23 bit-questions for a
Metaknowledge template for Big Data Processing and clustering purposes. This
research aims to demonstrate the construction of this methodology and proves
the validity and the beneficial utilization that brings Knowledge Discovery
from Big Data. | [
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] |
This article provides a thorough meta-analysis of the anomaly detection
problem. To accomplish this we first identify approaches to benchmarking
anomaly detection algorithms across the literature and produce a large corpus
of anomaly detection benchmarks that vary in their construction across several
dimensions we deem important to real-world applications: (a) point difficulty,
(b) relative frequency of anomalies, (c) clusteredness of anomalies, and (d)
relevance of features. We apply a representative set of anomaly detection
algorithms to this corpus, yielding a very large collection of experimental
results. We analyze these results to understand many phenomena observed in
previous work. First we observe the effects of experimental design on
experimental results. Second, results are evaluated with two metrics, ROC Area
Under the Curve and Average Precision. We employ statistical hypothesis testing
to demonstrate the value (or lack thereof) of our benchmarks. We then offer
several approaches to summarizing our experimental results, drawing several
conclusions about the impact of our methodology as well as the strengths and
weaknesses of some algorithms. Last, we compare results against a trivial
solution as an alternate means of normalizing the reported performance of
algorithms. The intended contributions of this article are many; in addition to
providing a large publicly-available corpus of anomaly detection benchmarks, we
provide an ontology for describing anomaly detection contexts, a methodology
for controlling various aspects of benchmark creation, guidelines for future
experimental design and a discussion of the many potential pitfalls of trying
to measure success in this field. | [
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] |
We introduce a new representation and feature extraction method for
biological sequences. Named bio-vectors (BioVec) to refer to biological
sequences in general with protein-vectors (ProtVec) for proteins (amino-acid
sequences) and gene-vectors (GeneVec) for gene sequences, this representation
can be widely used in applications of deep learning in proteomics and genomics.
In the present paper, we focus on protein-vectors that can be utilized in a
wide array of bioinformatics investigations such as family classification,
protein visualization, structure prediction, disordered protein identification,
and protein-protein interaction prediction. In this method, we adopt artificial
neural network approaches and represent a protein sequence with a single dense
n-dimensional vector. To evaluate this method, we apply it in classification of
324,018 protein sequences obtained from Swiss-Prot belonging to 7,027 protein
families, where an average family classification accuracy of 93%+-0.06% is
obtained, outperforming existing family classification methods. In addition, we
use ProtVec representation to predict disordered proteins from structured
proteins. Two databases of disordered sequences are used: the DisProt database
as well as a database featuring the disordered regions of nucleoporins rich
with phenylalanine-glycine repeats (FG-Nups). Using support vector machine
classifiers, FG-Nup sequences are distinguished from structured protein
sequences found in Protein Data Bank (PDB) with a 99.8% accuracy, and
unstructured DisProt sequences are differentiated from structured DisProt
sequences with 100.0% accuracy. These results indicate that by only providing
sequence data for various proteins into this model, accurate information about
protein structure can be determined. | [
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] |
While there is currently a lot of enthusiasm about "big data", useful data is
usually "small" and expensive to acquire. In this paper, we present a new
paradigm of learning partial differential equations from {\em small} data. In
particular, we introduce \emph{hidden physics models}, which are essentially
data-efficient learning machines capable of leveraging the underlying laws of
physics, expressed by time dependent and nonlinear partial differential
equations, to extract patterns from high-dimensional data generated from
experiments. The proposed methodology may be applied to the problem of
learning, system identification, or data-driven discovery of partial
differential equations. Our framework relies on Gaussian processes, a powerful
tool for probabilistic inference over functions, that enables us to strike a
balance between model complexity and data fitting. The effectiveness of the
proposed approach is demonstrated through a variety of canonical problems,
spanning a number of scientific domains, including the Navier-Stokes,
Schr\"odinger, Kuramoto-Sivashinsky, and time dependent linear fractional
equations. The methodology provides a promising new direction for harnessing
the long-standing developments of classical methods in applied mathematics and
mathematical physics to design learning machines with the ability to operate in
complex domains without requiring large quantities of data. | [
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] |
Recent studies have shown that attackers can force deep learning models to
misclassify so-called "adversarial examples": maliciously generated images
formed by making imperceptible modifications to pixel values. With growing
interest in deep learning for security applications, it is important for
security experts and users of machine learning to recognize how learning
systems may be attacked. Due to the complex nature of deep learning, it is
challenging to understand how deep models can be fooled by adversarial
examples. Thus, we present a web-based visualization tool,
Adversarial-Playground, to demonstrate the efficacy of common adversarial
methods against a convolutional neural network (CNN) system.
Adversarial-Playground is educational, modular and interactive. (1) It enables
non-experts to compare examples visually and to understand why an adversarial
example can fool a CNN-based image classifier. (2) It can help security experts
explore more vulnerability of deep learning as a software module. (3) Building
an interactive visualization is challenging in this domain due to the large
feature space of image classification (generating adversarial examples is slow
in general and visualizing images are costly). Through multiple novel design
choices, our tool can provide fast and accurate responses to user requests.
Empirically, we find that our client-server division strategy reduced the
response time by an average of 1.5 seconds per sample. Our other innovation, a
faster variant of JSMA evasion algorithm, empirically performed twice as fast
as JSMA and yet maintains a comparable evasion rate.
Project source code and data from our experiments available at:
https://github.com/QData/AdversarialDNN-Playground | [
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] |
As deep neural networks become more complex and input datasets grow larger,
it can take days or even weeks to train a deep neural network to the desired
accuracy. Therefore, distributed Deep Learning at a massive scale is a critical
capability, since it offers the potential to reduce the training time from
weeks to hours. In this paper, we present a software-hardware co-optimized
distributed Deep Learning system that can achieve near-linear scaling up to
hundreds of GPUs. The core algorithm is a multi-ring communication pattern that
provides a good tradeoff between latency and bandwidth and adapts to a variety
of system configurations. The communication algorithm is implemented as a
library for easy use. This library has been integrated into Tensorflow, Caffe,
and Torch. We train Resnet-101 on Imagenet 22K with 64 IBM Power8 S822LC
servers (256 GPUs) in about 7 hours to an accuracy of 33.8 % validation
accuracy. Microsoft's ADAM and Google's DistBelief results did not reach 30 %
validation accuracy for Imagenet 22K. Compared to Facebook AI Research's recent
paper on 256 GPU training, we use a different communication algorithm, and our
combined software and hardware system offers better communication overhead for
Resnet-50. A PowerAI DDL enabled version of Torch completed 90 epochs of
training on Resnet 50 for 1K classes in 50 minutes using 64 IBM Power8 S822LC
servers (256 GPUs). | [
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] |
From medicines to materials, small organic molecules are indispensable for
human well-being. To plan their syntheses, chemists employ a problem solving
technique called retrosynthesis. In retrosynthesis, target molecules are
recursively transformed into increasingly simpler precursor compounds until a
set of readily available starting materials is obtained. Computer-aided
retrosynthesis would be a highly valuable tool, however, past approaches were
slow and provided results of unsatisfactory quality. Here, we employ Monte
Carlo Tree Search (MCTS) to efficiently discover retrosynthetic routes. MCTS
was combined with an expansion policy network that guides the search, and an
"in-scope" filter network to pre-select the most promising retrosynthetic
steps. These deep neural networks were trained on 12 million reactions, which
represents essentially all reactions ever published in organic chemistry. Our
system solves almost twice as many molecules and is 30 times faster in
comparison to the traditional search method based on extracted rules and
hand-coded heuristics. Finally after a 60 year history of computer-aided
synthesis planning, chemists can no longer distinguish between routes generated
by a computer system and real routes taken from the scientific literature. We
anticipate that our method will accelerate drug and materials discovery by
assisting chemists to plan better syntheses faster, and by enabling fully
automated robot synthesis. | [
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] |
Missing data and noisy observations pose significant challenges for reliably
predicting events from irregularly sampled multivariate time series
(longitudinal) data. Imputation methods, which are typically used for
completing the data prior to event prediction, lack a principled mechanism to
account for the uncertainty due to missingness. Alternatively, state-of-the-art
joint modeling techniques can be used for jointly modeling the longitudinal and
event data and compute event probabilities conditioned on the longitudinal
observations. These approaches, however, make strong parametric assumptions and
do not easily scale to multivariate signals with many observations. Our
proposed approach consists of several key innovations. First, we develop a
flexible and scalable joint model based upon sparse multiple-output Gaussian
processes. Unlike state-of-the-art joint models, the proposed model can explain
highly challenging structure including non-Gaussian noise while scaling to
large data. Second, we derive an optimal policy for predicting events using the
distribution of the event occurrence estimated by the joint model. The derived
policy trades-off the cost of a delayed detection versus incorrect assessments
and abstains from making decisions when the estimated event probability does
not satisfy the derived confidence criteria. Experiments on a large dataset
show that the proposed framework significantly outperforms state-of-the-art
techniques in event prediction. | [
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] |
Music is usually highly structured and it is still an open question how to
design models which can successfully learn to recognize and represent musical
structure. A fundamental problem is that structurally related patterns can have
very distinct appearances, because the structural relationships are often based
on transformations of musical material, like chromatic or diatonic
transposition, inversion, retrograde, or rhythm change. In this preliminary
work, we study the potential of two unsupervised learning techniques -
Restricted Boltzmann Machines (RBMs) and Gated Autoencoders (GAEs) - to capture
pre-defined transformations from constructed data pairs. We evaluate the models
by using the learned representations as inputs in a discriminative task where
for a given type of transformation (e.g. diatonic transposition), the specific
relation between two musical patterns must be recognized (e.g. an upward
transposition of diatonic steps). Furthermore, we measure the reconstruction
error of models when reconstructing musical transformed patterns. Lastly, we
test the models in an analogy-making task. We find that it is difficult to
learn musical transformations with the RBM and that the GAE is much more
adequate for this task, since it is able to learn representations of specific
transformations that are largely content-invariant. We believe these results
show that models such as GAEs may provide the basis for more encompassing music
analysis systems, by endowing them with a better understanding of the
structures underlying music. | [
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] |
Transfer learning borrows knowledge from a source domain to facilitate
learning in a target domain. Two primary issues to be addressed in transfer
learning are what and how to transfer. For a pair of domains, adopting
different transfer learning algorithms results in different knowledge
transferred between them. To discover the optimal transfer learning algorithm
that maximally improves the learning performance in the target domain,
researchers have to exhaustively explore all existing transfer learning
algorithms, which is computationally intractable. As a trade-off, a sub-optimal
algorithm is selected, which requires considerable expertise in an ad-hoc way.
Meanwhile, it is widely accepted in educational psychology that human beings
improve transfer learning skills of deciding what to transfer through
meta-cognitive reflection on inductive transfer learning practices. Motivated
by this, we propose a novel transfer learning framework known as Learning to
Transfer (L2T) to automatically determine what and how to transfer are the best
by leveraging previous transfer learning experiences. We establish the L2T
framework in two stages: 1) we first learn a reflection function encrypting
transfer learning skills from experiences; and 2) we infer what and how to
transfer for a newly arrived pair of domains by optimizing the reflection
function. Extensive experiments demonstrate the L2T's superiority over several
state-of-the-art transfer learning algorithms and its effectiveness on
discovering more transferable knowledge. | [
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] |
Recent advances in Deep Neural Networks (DNNs) have led to the development of
DNN-driven autonomous cars that, using sensors like camera, LiDAR, etc., can
drive without any human intervention. Most major manufacturers including Tesla,
GM, Ford, BMW, and Waymo/Google are working on building and testing different
types of autonomous vehicles. The lawmakers of several US states including
California, Texas, and New York have passed new legislation to fast-track the
process of testing and deployment of autonomous vehicles on their roads.
However, despite their spectacular progress, DNNs, just like traditional
software, often demonstrate incorrect or unexpected corner case behaviors that
can lead to potentially fatal collisions. Several such real-world accidents
involving autonomous cars have already happened including one which resulted in
a fatality. Most existing testing techniques for DNN-driven vehicles are
heavily dependent on the manual collection of test data under different driving
conditions which become prohibitively expensive as the number of test
conditions increases.
In this paper, we design, implement and evaluate DeepTest, a systematic
testing tool for automatically detecting erroneous behaviors of DNN-driven
vehicles that can potentially lead to fatal crashes. First, our tool is
designed to automatically generated test cases leveraging real-world changes in
driving conditions like rain, fog, lighting conditions, etc. DeepTest
systematically explores different parts of the DNN logic by generating test
inputs that maximize the numbers of activated neurons. DeepTest found thousands
of erroneous behaviors under different realistic driving conditions (e.g.,
blurring, rain, fog, etc.) many of which lead to potentially fatal crashes in
three top performing DNNs in the Udacity self-driving car challenge. | [
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] |
With the widespread use of machine learning (ML) techniques, ML as a service
has become increasingly popular. In this setting, an ML model resides on a
server and users can query it with their data via an API. However, if the
user's input is sensitive, sending it to the server is undesirable and
sometimes even legally not possible. Equally, the service provider does not
want to share the model by sending it to the client for protecting its
intellectual property and pay-per-query business model.
In this paper, we propose MLCapsule, a guarded offline deployment of machine
learning as a service. MLCapsule executes the model locally on the user's side
and therefore the data never leaves the client. Meanwhile, MLCapsule offers the
service provider the same level of control and security of its model as the
commonly used server-side execution. In addition, MLCapsule is applicable to
offline applications that require local execution. Beyond protecting against
direct model access, we couple the secure offline deployment with defenses
against advanced attacks on machine learning models such as model stealing,
reverse engineering, and membership inference. | [
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While it is relatively easy to start an online advertising campaign,
obtaining a high Key Performance Indicator (KPI) can be challenging. A large
body of work on this subject has already been performed and platforms known as
DSPs are available on the market that deal with such an optimization. From the
advertiser's point of view, each DSP is a different black box, with its pros
and cons, that needs to be configured. In order to take advantage of the pros
of every DSP, advertisers are well-advised to use a combination of them when
setting up their campaigns. In this paper, we propose an algorithm for
advertisers to add an optimization layer on top of DSPs. The algorithm we
introduce, called SKOTT, maximizes the chosen KPI by optimally configuring the
DSPs and putting them in competition with each other. SKOTT is a highly
specialized iterative algorithm loosely based on gradient descent that is made
up of three independent sub-routines, each dealing with a different problem:
partitioning the budget, setting the desired average bid, and preventing
under-delivery. In particular, one of the novelties of our approach lies in our
taking the perspective of the advertisers rather than the DSPs. Synthetic
market data is used to evaluate the efficiency of SKOTT against other
state-of-the-art approaches adapted from similar problems. The results
illustrate the benefits of our proposals, which greatly outperforms the other
methods. | [
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] |
In a wide variety of applications, including personalization, we want to
measure the difference in outcome due to an intervention and thus have to deal
with counterfactual inference. The feedback from a customer in any of these
situations is only 'bandit feedback' - that is, a partial feedback based on
whether we chose to intervene or not. Typically randomized experiments are
carried out to understand whether an intervention is overall better than no
intervention. Here we present a feature learning algorithm to learn from a
randomized experiment where the intervention in consideration is most effective
and where it is least effective rather than only focusing on the overall
impact, thus adding a context to our learning mechanism and extract more
information. From the randomized experiment, we learn the feature
representations which divide the population into subpopulations where we
observe statistically significant difference in average customer feedback
between those who were subjected to the intervention and those who were not,
with a level of significance l, where l is a configurable parameter in our
model. We use this information to derive the value of the intervention in
consideration for each instance in the population. With experiments, we show
that using this additional learning, in future interventions, the context for
each instance could be leveraged to decide whether to intervene or not. | [
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] |
The fifth generation of cellular networks (5G) will rely on edge cloud
deployments to satisfy the ultra-low latency demand of future applications. In
this paper, we argue that such deployments can also be used to enable advanced
data-driven and Machine Learning (ML) applications in mobile networks. We
propose an edge-controller-based architecture for cellular networks and
evaluate its performance with real data from hundreds of base stations of a
major U.S. operator. In this regard, we will provide insights on how to
dynamically cluster and associate base stations and controllers, according to
the global mobility patterns of the users. Then, we will describe how the
controllers can be used to run ML algorithms to predict the number of users in
each base station, and a use case in which these predictions are exploited by a
higher-layer application to route vehicular traffic according to network Key
Performance Indicators (KPIs). We show that the prediction accuracy improves
when based on machine learning algorithms that rely on the controllers' view
and, consequently, on the spatial correlation introduced by the user mobility,
with respect to when the prediction is based only on the local data of each
single base station. | [
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] |
For any autonomous driving vehicle, control module determines its road
performance and safety, i.e. its precision and stability should stay within a
carefully-designed range. Nonetheless, control algorithms require vehicle
dynamics (such as longitudinal dynamics) as inputs, which, unfortunately, are
obscure to calibrate in real time. As a result, to achieve reasonable
performance, most, if not all, research-oriented autonomous vehicles do manual
calibrations in a one-by-one fashion. Since manual calibration is not
sustainable once entering into mass production stage for industrial purposes,
we here introduce a machine-learning based auto-calibration system for
autonomous driving vehicles. In this paper, we will show how we build a
data-driven longitudinal calibration procedure using machine learning
techniques. We first generated offline calibration tables from human driving
data. The offline table serves as an initial guess for later uses and it only
needs twenty-minutes data collection and process. We then used an
online-learning algorithm to appropriately update the initial table (the
offline table) based on real-time performance analysis. This longitudinal
auto-calibration system has been deployed to more than one hundred Baidu Apollo
self-driving vehicles (including hybrid family vehicles and electronic
delivery-only vehicles) since April 2018. By August 27, 2018, it had been
tested for more than two thousands hours, ten thousands kilometers (6,213
miles) and yet proven to be effective. | [
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] |
In this paper, through a very intuitive vanilla proximal method perspective,
we derive accelerated high-order optimization algorithms for minimizing a
convex function that has H\"{o}lder continuous derivatives. In this general
convex setting, we propose a concise unified acceleration framework (UAF),
which reconciles the two different high-order acceleration approaches, one by
Nesterov and Baes [29, 3, 33] and one by Monteiro and Svaiter [25]. As result,
the UAF unifies the high-order acceleration instances [29, 3, 33, 15, 16, 25,
19, 6, 14] of the two approaches by only two problem-related parameters and two
additional parameters for framework design. Furthermore, the UAF (and its
analysis) is the first approach to make high-order methods applicable for
high-order smoothness conditions with respect to non-Euclidean norms. If the
function is further uniformly convex, we propose a general restart scheme for
the UAF. The iteration complexities of instances of both the UAF and the
restarted UAF match existing lower bounds in most important cases [2, 16]. For
practical implementation, we introduce a new and effective heuristic that
significantly simplifies the binary search procedure required by the framework.
We use experiments to verify the effectiveness of the heuristic and demonstrate
clear and consistent advantages of high-order acceleration methods over
first-order ones, in terms of run-time complexity. Finally, the UAF is proposed
directly in the general composite convex setting, thus show that the existing
high-order algorithms [29, 3, 33, 16, 6, 14] can be naturally extended to the
general composite convex setting. | [
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] |
The current standalone deep learning framework tends to result in overfitting
and low utility. This problem can be addressed by either a centralized
framework that deploys a central server to train a global model on the joint
data from all parties, or a distributed framework that leverages a parameter
server to aggregate local model updates. Server-based solutions are prone to
the problem of a single-point-of-failure. In this respect, collaborative
learning frameworks, such as federated learning (FL), are more robust. Existing
federated learning frameworks overlook an important aspect of participation:
fairness. All parties are given the same final model without regard to their
contributions. To address these issues, we propose a decentralized Fair and
Privacy-Preserving Deep Learning (FPPDL) framework to incorporate fairness into
federated deep learning models. In particular, we design a local credibility
mutual evaluation mechanism to guarantee fairness, and a three-layer
onion-style encryption scheme to guarantee both accuracy and privacy. Different
from existing FL paradigm, under FPPDL, each participant receives a different
version of the FL model with performance commensurate with his contributions.
Experiments on benchmark datasets demonstrate that FPPDL balances fairness,
privacy and accuracy. It enables federated learning ecosystems to detect and
isolate low-contribution parties, thereby promoting responsible participation. | [
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] |
Our interest in this paper is in meeting a rapidly growing industrial demand
for information extraction from images of documents such as invoices, bills,
receipts etc. In practice users are able to provide a very small number of
example images labeled with the information that needs to be extracted. We
adopt a novel two-level neuro-deductive, approach where (a) we use pre-trained
deep neural networks to populate a relational database with facts about each
document-image; and (b) we use a form of deductive reasoning, related to
meta-interpretive learning of transition systems to learn extraction programs:
Given task-specific transitions defined using the entities and relations
identified by the neural detectors and a small number of instances (usually 1,
sometimes 2) of images and the desired outputs, a resource-bounded
meta-interpreter constructs proofs for the instance(s) via logical deduction; a
set of logic programs that extract each desired entity is easily synthesized
from such proofs. In most cases a single training example together with a
noisy-clone of itself suffices to learn a program-set that generalizes well on
test documents, at which time the value of each entity is determined by a
majority vote across its program-set. We demonstrate our two-level
neuro-deductive approach on publicly available datasets ("Patent" and "Doctor's
Bills") and also describe its use in a real-life industrial problem. | [
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] |
Cognitive and metacognitive strategy had demonstrated a significant role in
self-regulated learning (SRL), and an appropriate use of strategies is
beneficial to effective learning or question-solving tasks during a
human-computer interaction process. This paper proposes a novel method
combining Knowledge Map (KM) based data mining technique with Thinking Map (TM)
to detect learner's cognitive and metacognitive strategy in the
question-solving scenario. In particular, a graph-based mining algorithm is
designed to facilitate our proposed method, which can automatically map
cognitive strategy to metacognitive strategy with raising abstraction level,
and make the cognitive and metacognitive process viewable, which acts like a
reverse engineering engine to explain how a learner thinks when solving a
question. Additionally, we develop an online learning environment system for
participants to learn and record their behaviors. To corroborate the
effectiveness of our approach and algorithm, we conduct experiments recruiting
173 postgraduate and undergraduate students, and they were asked to complete a
question-solving task, such as "What are similarities and differences between
array and pointer?" from "The C Programming Language" course and "What are
similarities and differences between packet switching and circuit switching?"
from "Computer Network Principle" course. The mined strategies patterns results
are encouraging and supported well our proposed method. | [
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] |
Climate change is one of the greatest challenges facing humanity, and we, as
machine learning experts, may wonder how we can help. Here we describe how
machine learning can be a powerful tool in reducing greenhouse gas emissions
and helping society adapt to a changing climate. From smart grids to disaster
management, we identify high impact problems where existing gaps can be filled
by machine learning, in collaboration with other fields. Our recommendations
encompass exciting research questions as well as promising business
opportunities. We call on the machine learning community to join the global
effort against climate change. | [
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] |
A remarkable time of human promise has been ushered in by the convergence of
the ever-expanding availability of big data, the soaring speed and stretch of
cloud computing platforms, and the advancement of increasingly sophisticated
machine learning algorithms. Innovations in AI are already leaving a mark on
government by improving the provision of essential social goods and services
from healthcare, education, and transportation to food supply, energy, and
environmental management. These bounties are likely just the start. The
prospect that progress in AI will help government to confront some of its most
urgent challenges is exciting, but legitimate worries abound. As with any new
and rapidly evolving technology, a steep learning curve means that mistakes and
miscalculations will be made and that both unanticipated and harmful impacts
will occur.
This guide, written for department and delivery leads in the UK public sector
and adopted by the British Government in its publication, 'Using AI in the
Public Sector,' identifies the potential harms caused by AI systems and
proposes concrete, operationalisable measures to counteract them. It stresses
that public sector organisations can anticipate and prevent these potential
harms by stewarding a culture of responsible innovation and by putting in place
governance processes that support the design and implementation of ethical,
fair, and safe AI systems. It also highlights the need for algorithmically
supported outcomes to be interpretable by their users and made understandable
to decision subjects in clear, non-technical, and accessible ways. Finally, it
builds out a vision of human-centred and context-sensitive implementation that
gives a central role to communication, evidence-based reasoning, situational
awareness, and moral justifiability. | [
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Reinforcement Learning (RL) of contact-rich manipulation tasks has yielded
impressive results in recent years. While many studies in RL focus on varying
the observation space or reward model, few efforts focused on the choice of
action space (e.g. joint or end-effector space, position, velocity, etc.).
However, studies in robot motion control indicate that choosing an action space
that conforms to the characteristics of the task can simplify exploration and
improve robustness to disturbances. This paper studies the effect of different
action spaces in deep RL and advocates for Variable Impedance Control in
End-effector Space (VICES) as an advantageous action space for constrained and
contact-rich tasks. We evaluate multiple action spaces on three prototypical
manipulation tasks: Path Following (task with no contact), Door Opening (task
with kinematic constraints), and Surface Wiping (task with continuous contact).
We show that VICES improves sample efficiency, maintains low energy
consumption, and ensures safety across all three experimental setups. Further,
RL policies learned with VICES can transfer across different robot models in
simulation, and from simulation to real for the same robot. Further information
is available at https://stanfordvl.github.io/vices. | [
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] |
A honeynet is a promising active cyber defense mechanism. It reveals the
fundamental Indicators of Compromise (IoCs) by luring attackers to conduct
adversarial behaviors in a controlled and monitored environment. The active
interaction at the honeynet brings a high reward but also introduces high
implementation costs and risks of adversarial honeynet exploitation. In this
work, we apply infinite-horizon Semi-Markov Decision Process (SMDP) to
characterize a stochastic transition and sojourn time of attackers in the
honeynet and quantify the reward-risk trade-off. In particular, we design
adaptive long-term engagement policies shown to be risk-averse, cost-effective,
and time-efficient. Numerical results have demonstrated that our adaptive
engagement policies can quickly attract attackers to the target honeypot and
engage them for a sufficiently long period to obtain worthy threat information.
Meanwhile, the penetration probability is kept at a low level. The results show
that the expected utility is robust against attackers of a large range of
persistence and intelligence. Finally, we apply reinforcement learning to the
SMDP to solve the curse of modeling. Under a prudent choice of the learning
rate and exploration policy, we achieve a quick and robust convergence of the
optimal policy and value. | [
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] |
In this paper, we study large-scale convex optimization algorithms based on
the Newton method applied to regularized generalized self-concordant losses,
which include logistic regression and softmax regression. We first prove that
our new simple scheme based on a sequence of problems with decreasing
regularization parameters is provably globally convergent, that this
convergence is linear with a constant factor which scales only logarithmically
with the condition number. In the parametric setting, we obtain an algorithm
with the same scaling than regular first-order methods but with an improved
behavior, in particular in ill-conditioned problems. Second, in the non
parametric machine learning setting, we provide an explicit algorithm combining
the previous scheme with Nystr{\"o}m projection techniques, and prove that it
achieves optimal generalization bounds with a time complexity of order O(ndf
$\lambda$), a memory complexity of order O(df 2 $\lambda$) and no dependence on
the condition number, generalizing the results known for least-squares
regression. Here n is the number of observations and df $\lambda$ is the
associated degrees of freedom. In particular, this is the first large-scale
algorithm to solve logistic and softmax regressions in the non-parametric
setting with large condition numbers and theoretical guarantees. | [
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The decision-making process of many state-of-the-art machine learning models
is inherently inscrutable to the extent that it is impossible for a human to
interpret the model directly: they are black box models. This has led to a call
for research on explaining black box models, for which there are two main
approaches. Global explanations that aim to explain a model's decision making
process in general, and local explanations that aim to explain a single
prediction. Since it remains challenging to establish fidelity to black box
models in globally interpretable approximations, much attention is put on local
explanations. However, whether local explanations are able to reliably
represent the black box model and provide useful insights remains an open
question. We present Global Aggregations of Local Explanations (GALE) with the
objective to provide insights in a model's global decision making process.
Overall, our results reveal that the choice of aggregation matters. We find
that the global importance introduced by Local Interpretable Model-agnostic
Explanations (LIME) does not reliably represent the model's global behavior.
Our proposed aggregations are better able to represent how features affect the
model's predictions, and to provide global insights by identifying
distinguishing features. | [
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] |
This paper addresses two challenges facing sampling-based kinodynamic motion
planning: a way to identify good candidate states for local transitions and the
subsequent computationally intractable steering between these candidate states.
Through the combination of sampling-based planning, a Rapidly Exploring
Randomized Tree (RRT) and an efficient kinodynamic motion planner through
machine learning, we propose an efficient solution to long-range planning for
kinodynamic motion planning. First, we use deep reinforcement learning to learn
an obstacle-avoiding policy that maps a robot's sensor observations to actions,
which is used as a local planner during planning and as a controller during
execution. Second, we train a reachability estimator in a supervised manner,
which predicts the RL policy's time to reach a state in the presence of
obstacles. Lastly, we introduce RL-RRT that uses the RL policy as a local
planner, and the reachability estimator as the distance function to bias
tree-growth towards promising regions. We evaluate our method on three
kinodynamic systems, including physical robot experiments. Results across all
three robots tested indicate that RL-RRT outperforms state of the art
kinodynamic planners in efficiency, and also provides a shorter path finish
time than a steering function free method. The learned local planner policy and
accompanying reachability estimator demonstrate transferability to the
previously unseen experimental environments, making RL-RRT fast because the
expensive computations are replaced with simple neural network inference.
Video: https://youtu.be/dDMVMTOI8KY | [
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] |
This paper describes Motion Planning Networks (MPNet), a computationally
efficient, learning-based neural planner for solving motion planning problems.
MPNet uses neural networks to learn general near-optimal heuristics for path
planning in seen and unseen environments. It takes environment information such
as raw point-cloud from depth sensors, as well as a robot's initial and desired
goal configurations and recursively calls itself to bidirectionally generate
connectable paths. In addition to finding directly connectable and near-optimal
paths in a single pass, we show that worst-case theoretical guarantees can be
proven if we merge this neural network strategy with classical sample-based
planners in a hybrid approach while still retaining significant computational
and optimality improvements. To train the MPNet models, we present an active
continual learning approach that enables MPNet to learn from streaming data and
actively ask for expert demonstrations when needed, drastically reducing data
for training. We validate MPNet against gold-standard and state-of-the-art
planning methods in a variety of problems from 2D to 7D robot configuration
spaces in challenging and cluttered environments, with results showing
significant and consistently stronger performance metrics, and motivating
neural planning in general as a modern strategy for solving motion planning
problems efficiently. | [
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] |
We present new techniques for automatically constructing probabilistic
programs for data analysis, interpretation, and prediction. These techniques
work with probabilistic domain-specific data modeling languages that capture
key properties of a broad class of data generating processes, using Bayesian
inference to synthesize probabilistic programs in these modeling languages
given observed data. We provide a precise formulation of Bayesian synthesis for
automatic data modeling that identifies sufficient conditions for the resulting
synthesis procedure to be sound. We also derive a general class of synthesis
algorithms for domain-specific languages specified by probabilistic
context-free grammars and establish the soundness of our approach for these
languages. We apply the techniques to automatically synthesize probabilistic
programs for time series data and multivariate tabular data. We show how to
analyze the structure of the synthesized programs to compute, for key
qualitative properties of interest, the probability that the underlying data
generating process exhibits each of these properties. Second, we translate
probabilistic programs in the domain-specific language into probabilistic
programs in Venture, a general-purpose probabilistic programming system. The
translated Venture programs are then executed to obtain predictions of new time
series data and new multivariate data records. Experimental results show that
our techniques can accurately infer qualitative structure in multiple
real-world data sets and outperform standard data analysis methods in
forecasting and predicting new data. | [
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] |
The presumed data owners' right to explanations brought about by the General
Data Protection Regulation in Europe has shed light on the social challenges of
explainable artificial intelligence (XAI). In this paper, we present a case
study with Deep Learning (DL) experts from a research and development
laboratory focused on the delivery of industrial-strength AI technologies. Our
aim was to investigate the social meaning (i.e. meaning to others) that DL
experts assign to what they do, given a richly contextualized and familiar
domain of application. Using qualitative research techniques to collect and
analyze empirical data, our study has shown that participating DL experts did
not spontaneously engage into considerations about the social meaning of
machine learning models that they build. Moreover, when explicitly stimulated
to do so, these experts expressed expectations that, with real-world DL
application, there will be available mediators to bridge the gap between
technical meanings that drive DL work, and social meanings that AI technology
users assign to it. We concluded that current research incentives and values
guiding the participants' scientific interests and conduct are at odds with
those required to face some of the scientific challenges involved in advancing
XAI, and thus responding to the alleged data owners' right to explanations or
similar societal demands emerging from current debates. As a concrete
contribution to mitigate what seems to be a more general problem, we propose
three preliminary XAI Mediation Challenges with the potential to bring together
technical and social meanings of DL applications, as well as to foster much
needed interdisciplinary collaboration among AI and the Social Sciences
researchers. | [
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] |
Personalized Route Recommendation (PRR) aims to generate user-specific route
suggestions in response to users' route queries. Early studies cast the PRR
task as a pathfinding problem on graphs, and adopt adapted search algorithms by
integrating heuristic strategies. Although these methods are effective to some
extent, they require setting the cost functions with heuristics. In addition,
it is difficult to utilize useful context information in the search procedure.
To address these issues, we propose using neural networks to automatically
learn the cost functions of a classic heuristic algorithm, namely A* algorithm,
for the PRR task. Our model consists of two components. First, we employ
attention-based Recurrent Neural Networks (RNN) to model the cost from the
source to the candidate location by incorporating useful context information.
Instead of learning a single cost value, the RNN component is able to learn a
time-varying vectorized representation for the moving state of a user. Second,
we propose to use a value network for estimating the cost from a candidate
location to the destination. For capturing structural characteristics, the
value network is built on top of improved graph attention networks by
incorporating the moving state of a user and other context information. The two
components are integrated in a principled way for deriving a more accurate cost
of a candidate location. Extensive experiment results on three real-world
datasets have shown the effectiveness and robustness of the proposed model. | [
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Many animals, and an increasing number of artificial agents, display
sophisticated capabilities to perceive and manipulate objects. But human beings
remain distinctive in their capacity for flexible, creative tool use -- using
objects in new ways to act on the world, achieve a goal, or solve a problem. To
study this type of general physical problem solving, we introduce the Virtual
Tools game. In this game, people solve a large range of challenging physical
puzzles in just a handful of attempts. We propose that the flexibility of human
physical problem solving rests on an ability to imagine the effects of
hypothesized actions, while the efficiency of human search arises from rich
action priors which are updated via observations of the world. We instantiate
these components in the "Sample, Simulate, Update" (SSUP) model and show that
it captures human performance across 30 levels of the Virtual Tools game. More
broadly, this model provides a mechanism for explaining how people condense
general physical knowledge into actionable, task-specific plans to achieve
flexible and efficient physical problem-solving. | [
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] |
In spite of the rapidly increasing number of applications of machine learning
in various domains, a principled and systematic approach to the incorporation
of domain knowledge in the engineering process is still lacking and ad hoc
solutions that are difficult to validate are still the norm in practice, which
is of growing concern not only in mission-critical applications.
In this note, we introduce Probabilistic Approximate Logic (PALO) as a logic
based on the notion of mean approximate probability to overcome conceptual and
computational difficulties inherent to strictly probabilistic logics. The logic
is approximate in several dimensions. Logical independence assumptions are used
to obtain approximate probabilities, but by averaging over many instances of
formulas a useful estimate of mean probability with known confidence can
usually be obtained. To enable efficient computational inference, the logic has
a continuous semantics that reflects only a subset of the structural properties
of classical logic, but this imprecision can be partly compensated by richer
theories obtained by classical inference or other means. Computational
inference, which refers to the construction of models and validation of logical
properties, is based on Stochastic Gradient Descent (SGD) and Markov Chain
Monte Carlo (MCMC) techniques and hence another dimension where approximations
are involved.
We also present the Logical Imagination Engine (LIME), a prototypical
implementation of PALO based on TensorFlow. Albeit not limited to the
biological domain, we illustrate its operation in a quite substantial
bioinformatics machine learning application concerned with network synthesis
and analysis in a recent DARPA project. | [
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] |
While most Bayesian nonparametric models in machine learning have focused on
the Dirichlet process, the beta process, or their variants, the gamma process
has recently emerged as a useful nonparametric prior in its own right. Current
inference schemes for models involving the gamma process are restricted to
MCMC-based methods, which limits their scalability. In this paper, we present a
variational inference framework for models involving gamma process priors. Our
approach is based on a novel stick-breaking constructive definition of the
gamma process. We prove correctness of this stick-breaking process by using the
characterization of the gamma process as a completely random measure (CRM), and
we explicitly derive the rate measure of our construction using Poisson process
machinery. We also derive error bounds on the truncation of the infinite
process required for variational inference, similar to the truncation analyses
for other nonparametric models based on the Dirichlet and beta processes. Our
representation is then used to derive a variational inference algorithm for a
particular Bayesian nonparametric latent structure formulation known as the
infinite Gamma-Poisson model, where the latent variables are drawn from a gamma
process prior with Poisson likelihoods. Finally, we present results for our
algorithms on nonnegative matrix factorization tasks on document corpora, and
show that we compare favorably to both sampling-based techniques and
variational approaches based on beta-Bernoulli priors. | [
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Recently, there has been significant progress in understanding reinforcement
learning in discounted infinite-horizon Markov decision processes (MDPs) by
deriving tight sample complexity bounds. However, in many real-world
applications, an interactive learning agent operates for a fixed or bounded
period of time, for example tutoring students for exams or handling customer
service requests. Such scenarios can often be better treated as episodic
fixed-horizon MDPs, for which only looser bounds on the sample complexity
exist. A natural notion of sample complexity in this setting is the number of
episodes required to guarantee a certain performance with high probability (PAC
guarantee). In this paper, we derive an upper PAC bound $\tilde
O(\frac{|\mathcal S|^2 |\mathcal A| H^2}{\epsilon^2} \ln\frac 1 \delta)$ and a
lower PAC bound $\tilde \Omega(\frac{|\mathcal S| |\mathcal A| H^2}{\epsilon^2}
\ln \frac 1 {\delta + c})$ that match up to log-terms and an additional linear
dependency on the number of states $|\mathcal S|$. The lower bound is the first
of its kind for this setting. Our upper bound leverages Bernstein's inequality
to improve on previous bounds for episodic finite-horizon MDPs which have a
time-horizon dependency of at least $H^3$. | [
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] |
Variational autoencoders (VAEs) learn representations of data by jointly
training a probabilistic encoder and decoder network. Typically these models
encode all features of the data into a single variable. Here we are interested
in learning disentangled representations that encode distinct aspects of the
data into separate variables. We propose to learn such representations using
model architectures that generalise from standard VAEs, employing a general
graphical model structure in the encoder and decoder. This allows us to train
partially-specified models that make relatively strong assumptions about a
subset of interpretable variables and rely on the flexibility of neural
networks to learn representations for the remaining variables. We further
define a general objective for semi-supervised learning in this model class,
which can be approximated using an importance sampling procedure. We evaluate
our framework's ability to learn disentangled representations, both by
qualitative exploration of its generative capacity, and quantitative evaluation
of its discriminative ability on a variety of models and datasets. | [
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] |
This study investigates how adequate coordination among the different
cognitive processes of a humanoid robot can be developed through end-to-end
learning of direct perception of visuomotor stream. We propose a deep dynamic
neural network model built on a dynamic vision network, a motor generation
network, and a higher-level network. The proposed model was designed to process
and to integrate direct perception of dynamic visuomotor patterns in a
hierarchical model characterized by different spatial and temporal constraints
imposed on each level. We conducted synthetic robotic experiments in which a
robot learned to read human's intention through observing the gestures and then
to generate the corresponding goal-directed actions. Results verify that the
proposed model is able to learn the tutored skills and to generalize them to
novel situations. The model showed synergic coordination of perception, action
and decision making, and it integrated and coordinated a set of cognitive
skills including visual perception, intention reading, attention switching,
working memory, action preparation and execution in a seamless manner. Analysis
reveals that coherent internal representations emerged at each level of the
hierarchy. Higher-level representation reflecting actional intention developed
by means of continuous integration of the lower-level visuo-proprioceptive
stream. | [
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] |
This study presents a dynamic neural network model based on the predictive
coding framework for perceiving and predicting the dynamic visuo-proprioceptive
patterns. In our previous study [1], we have shown that the deep dynamic neural
network model was able to coordinate visual perception and action generation in
a seamless manner. In the current study, we extended the previous model under
the predictive coding framework to endow the model with a capability of
perceiving and predicting dynamic visuo-proprioceptive patterns as well as a
capability of inferring intention behind the perceived visuomotor information
through minimizing prediction error. A set of synthetic experiments were
conducted in which a robot learned to imitate the gestures of another robot in
a simulation environment. The experimental results showed that with given
intention states, the model was able to mentally simulate the possible incoming
dynamic visuo-proprioceptive patterns in a top-down process without the inputs
from the external environment. Moreover, the results highlighted the role of
minimizing prediction error in inferring underlying intention of the perceived
visuo-proprioceptive patterns, supporting the predictive coding account of the
mirror neuron systems. The results also revealed that minimizing prediction
error in one modality induced the recall of the corresponding representation of
another modality acquired during the consolidative learning of raw-level
visuo-proprioceptive patterns. | [
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] |
Designing an incentive compatible auction that maximizes expected revenue is
an intricate task. The single-item case was resolved in a seminal piece of work
by Myerson in 1981, but more than 40 years later a full analytical
understanding of the optimal design still remains elusive for settings with two
or more items. In this work, we initiate the exploration of the use of tools
from deep learning for the automated design of optimal auctions. We model an
auction as a multi-layer neural network, frame optimal auction design as a
constrained learning problem, and show how it can be solved using standard
machine learning pipelines. In addition to providing generalization bounds, we
present extensive experimental results, recovering essentially all known
solutions that come from the theoretical analysis of optimal auction design
problems and obtaining novel mechanisms for settings in which the optimal
mechanism is unknown. | [
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] |
Note that a newer expanded version of this paper is now available at:
arXiv:1802.03888
It is critical in many applications to understand what features are important
for a model, and why individual predictions were made. For tree ensemble
methods these questions are usually answered by attributing importance values
to input features, either globally or for a single prediction. Here we show
that current feature attribution methods are inconsistent, which means changing
the model to rely more on a given feature can actually decrease the importance
assigned to that feature. To address this problem we develop fast exact
solutions for SHAP (SHapley Additive exPlanation) values, which were recently
shown to be the unique additive feature attribution method based on conditional
expectations that is both consistent and locally accurate. We integrate these
improvements into the latest version of XGBoost, demonstrate the
inconsistencies of current methods, and show how using SHAP values results in
significantly improved supervised clustering performance. Feature importance
values are a key part of understanding widely used models such as gradient
boosting trees and random forests, so improvements to them have broad practical
implications. | [
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] |
In Acoustic Scene Classification (ASC) two major approaches have been
followed . While one utilizes engineered features such as
mel-frequency-cepstral-coefficients (MFCCs), the other uses learned features
that are the outcome of an optimization algorithm. I-vectors are the result of
a modeling technique that usually takes engineered features as input. It has
been shown that standard MFCCs extracted from monaural audio signals lead to
i-vectors that exhibit poor performance, especially on indoor acoustic scenes.
At the same time, Convolutional Neural Networks (CNNs) are well known for their
ability to learn features by optimizing their filters. They have been applied
on ASC and have shown promising results. In this paper, we first propose a
novel multi-channel i-vector extraction and scoring scheme for ASC, improving
their performance on indoor and outdoor scenes. Second, we propose a CNN
architecture that achieves promising ASC results. Further, we show that
i-vectors and CNNs capture complementary information from acoustic scenes.
Finally, we propose a hybrid system for ASC using multi-channel i-vectors and
CNNs by utilizing a score fusion technique. Using our method, we participated
in the ASC task of the DCASE-2016 challenge. Our hybrid approach achieved 1 st
rank among 49 submissions, substantially improving the previous state of the
art. | [
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] |
We present an approach for mobile robots to learn to navigate in dynamic
environments with pedestrians via raw depth inputs, in a socially compliant
manner. To achieve this, we adopt a generative adversarial imitation learning
(GAIL) strategy, which improves upon a pre-trained behavior cloning policy. Our
approach overcomes the disadvantages of previous methods, as they heavily
depend on the full knowledge of the location and velocity information of nearby
pedestrians, which not only requires specific sensors, but also the extraction
of such state information from raw sensory input could consume much computation
time. In this paper, our proposed GAIL-based model performs directly on raw
depth inputs and plans in real-time. Experiments show that our GAIL-based
approach greatly improves the safety and efficiency of the behavior of mobile
robots from pure behavior cloning. The real-world deployment also shows that
our method is capable of guiding autonomous vehicles to navigate in a socially
compliant manner directly through raw depth inputs. In addition, we release a
simulation plugin for modeling pedestrian behaviors based on the social force
model. | [
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] |
This paper presents a deep learning framework that is capable of solving
partially observable locomotion tasks based on our novel interpretation of
Recurrent Deterministic Policy Gradient (RDPG). We study on bias of sampled
error measure and its variance induced by the partial observability of
environment and subtrajectory sampling, respectively. Three major improvements
are introduced in our RDPG based learning framework: tail-step bootstrap of
interpolated temporal difference, initialisation of hidden state using past
trajectory scanning, and injection of external experiences learned by other
agents. The proposed learning framework was implemented to solve the
Bipedal-Walker challenge in OpenAI's gym simulation environment where only
partial state information is available. Our simulation study shows that the
autonomous behaviors generated by the RDPG agent are highly adaptive to a
variety of obstacles and enables the agent to effectively traverse rugged
terrains for long distance with higher success rate than leading contenders. | [
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] |
Deep neural networks have enabled progress in a wide variety of applications.
Growing the size of the neural network typically results in improved accuracy.
As model sizes grow, the memory and compute requirements for training these
models also increases. We introduce a technique to train deep neural networks
using half precision floating point numbers. In our technique, weights,
activations and gradients are stored in IEEE half-precision format.
Half-precision floating numbers have limited numerical range compared to
single-precision numbers. We propose two techniques to handle this loss of
information. Firstly, we recommend maintaining a single-precision copy of the
weights that accumulates the gradients after each optimizer step. This
single-precision copy is rounded to half-precision format during training.
Secondly, we propose scaling the loss appropriately to handle the loss of
information with half-precision gradients. We demonstrate that this approach
works for a wide variety of models including convolution neural networks,
recurrent neural networks and generative adversarial networks. This technique
works for large scale models with more than 100 million parameters trained on
large datasets. Using this approach, we can reduce the memory consumption of
deep learning models by nearly 2x. In future processors, we can also expect a
significant computation speedup using half-precision hardware units. | [
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] |
We present PRM-RL, a hierarchical method for long-range navigation task
completion that combines sampling based path planning with reinforcement
learning (RL). The RL agents learn short-range, point-to-point navigation
policies that capture robot dynamics and task constraints without knowledge of
the large-scale topology. Next, the sampling-based planners provide roadmaps
which connect robot configurations that can be successfully navigated by the RL
agent. The same RL agents are used to control the robot under the direction of
the planning, enabling long-range navigation. We use the Probabilistic Roadmaps
(PRMs) for the sampling-based planner. The RL agents are constructed using
feature-based and deep neural net policies in continuous state and action
spaces. We evaluate PRM-RL, both in simulation and on-robot, on two navigation
tasks with non-trivial robot dynamics: end-to-end differential drive indoor
navigation in office environments, and aerial cargo delivery in urban
environments with load displacement constraints. Our results show improvement
in task completion over both RL agents on their own and traditional
sampling-based planners. In the indoor navigation task, PRM-RL successfully
completes up to 215 m long trajectories under noisy sensor conditions, and the
aerial cargo delivery completes flights over 1000 m without violating the task
constraints in an environment 63 million times larger than used in training. | [
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Deep neural networks are widely used for classification. These deep models
often suffer from a lack of interpretability -- they are particularly difficult
to understand because of their non-linear nature. As a result, neural networks
are often treated as "black box" models, and in the past, have been trained
purely to optimize the accuracy of predictions. In this work, we create a novel
network architecture for deep learning that naturally explains its own
reasoning for each prediction. This architecture contains an autoencoder and a
special prototype layer, where each unit of that layer stores a weight vector
that resembles an encoded training input. The encoder of the autoencoder allows
us to do comparisons within the latent space, while the decoder allows us to
visualize the learned prototypes. The training objective has four terms: an
accuracy term, a term that encourages every prototype to be similar to at least
one encoded input, a term that encourages every encoded input to be close to at
least one prototype, and a term that encourages faithful reconstruction by the
autoencoder. The distances computed in the prototype layer are used as part of
the classification process. Since the prototypes are learned during training,
the learned network naturally comes with explanations for each prediction, and
the explanations are loyal to what the network actually computes. | [
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] |
Social network analysis provides meaningful information about behavior of
network members that can be used for diverse applications such as
classification, link prediction. However, network analysis is computationally
expensive because of feature learning for different applications. In recent
years, many researches have focused on feature learning methods in social
networks. Network embedding represents the network in a lower dimensional
representation space with the same properties which presents a compressed
representation of the network. In this paper, we introduce a novel algorithm
named "CARE" for network embedding that can be used for different types of
networks including weighted, directed and complex. Current methods try to
preserve local neighborhood information of nodes, whereas the proposed method
utilizes local neighborhood and community information of network nodes to cover
both local and global structure of social networks. CARE builds customized
paths, which are consisted of local and global structure of network nodes, as a
basis for network embedding and uses the Skip-gram model to learn
representation vector of nodes. Subsequently, stochastic gradient descent is
applied to optimize our objective function and learn the final representation
of nodes. Our method can be scalable when new nodes are appended to network
without information loss. Parallelize generation of customized random walks is
also used for speeding up CARE. We evaluate the performance of CARE on multi
label classification and link prediction tasks. Experimental results on various
networks indicate that the proposed method outperforms others in both Micro and
Macro-f1 measures for different size of training data. | [
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] |
In order for robots to perform mission-critical tasks, it is essential that
they are able to quickly adapt to changes in their environment as well as to
injuries and or other bodily changes. Deep reinforcement learning has been
shown to be successful in training robot control policies for operation in
complex environments. However, existing methods typically employ only a single
policy. This can limit the adaptability since a large environmental
modification might require a completely different behavior compared to the
learning environment. To solve this problem, we propose Map-based Multi-Policy
Reinforcement Learning (MMPRL), which aims to search and store multiple
policies that encode different behavioral features while maximizing the
expected reward in advance of the environment change. Thanks to these policies,
which are stored into a multi-dimensional discrete map according to its
behavioral feature, adaptation can be performed within reasonable time without
retraining the robot. An appropriate pre-trained policy from the map can be
recalled using Bayesian optimization. Our experiments show that MMPRL enables
robots to quickly adapt to large changes without requiring any prior knowledge
on the type of injuries that could occur. A highlight of the learned behaviors
can be found here: https://youtu.be/QwInbilXNOE . | [
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] |
Black-box risk scoring models permeate our lives, yet are typically
proprietary or opaque. We propose Distill-and-Compare, a model distillation and
comparison approach to audit such models. To gain insight into black-box
models, we treat them as teachers, training transparent student models to mimic
the risk scores assigned by black-box models. We compare the student model
trained with distillation to a second un-distilled transparent model trained on
ground-truth outcomes, and use differences between the two models to gain
insight into the black-box model. Our approach can be applied in a realistic
setting, without probing the black-box model API. We demonstrate the approach
on four public data sets: COMPAS, Stop-and-Frisk, Chicago Police, and Lending
Club. We also propose a statistical test to determine if a data set is missing
key features used to train the black-box model. Our test finds that the
ProPublica data is likely missing key feature(s) used in COMPAS. | [
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] |
Due to the lack of enough generalization in the state-space, common methods
in Reinforcement Learning (RL) suffer from slow learning speed especially in
the early learning trials. This paper introduces a model-based method in
discrete state-spaces for increasing learning speed in terms of required
experience (but not required computational time) by exploiting generalization
in the experiences of the subspaces. A subspace is formed by choosing a subset
of features in the original state representation (full-space). Generalization
and faster learning in a subspace are due to many-to-one mapping of experiences
from the full-space to each state in the subspace. Nevertheless, due to
inherent perceptual aliasing in the subspaces, the policy suggested by each
subspace does not generally converge to the optimal policy. Our approach,
called Model Based Learning with Subspaces (MoBLeS), calculates confidence
intervals of the estimated Q-values in the full-space and in the subspaces.
These confidence intervals are used in the decision making, such that the agent
benefits the most from the possible generalization while avoiding from
detriment of the perceptual aliasing in the subspaces. Convergence of MoBLeS to
the optimal policy is theoretically investigated. Additionally, we show through
several experiments that MoBLeS improves the learning speed in the early
trials. | [
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] |
This paper presents a method for identifying mechanical parameters of robots
or objects, such as their mass and friction coefficients. Key features are the
use of off-the-shelf physics engines and the adaptation of a Bayesian
optimization technique towards minimizing the number of real-world experiments
needed for model-based reinforcement learning. The proposed framework
reproduces in a physics engine experiments performed on a real robot and
optimizes the model's mechanical parameters so as to match real-world
trajectories. The optimized model is then used for learning a policy in
simulation, before real-world deployment. It is well understood, however, that
it is hard to exactly reproduce real trajectories in simulation. Moreover, a
near-optimal policy can be frequently found with an imperfect model. Therefore,
this work proposes a strategy for identifying a model that is just good enough
to approximate the value of a locally optimal policy with a certain confidence,
instead of wasting effort on identifying the most accurate model. Evaluations,
performed both in simulation and on a real robotic manipulation task, indicate
that the proposed strategy results in an overall time-efficient, integrated
model identification and learning solution, which significantly improves the
data-efficiency of existing policy search algorithms. | [
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Graphs (networks) are ubiquitous and allow us to model entities (nodes) and
the dependencies (edges) between them. Learning a useful feature representation
from graph data lies at the heart and success of many machine learning tasks
such as classification, anomaly detection, link prediction, among many others.
Many existing techniques use random walks as a basis for learning features or
estimating the parameters of a graph model for a downstream prediction task.
Examples include recent node embedding methods such as DeepWalk, node2vec, as
well as graph-based deep learning algorithms. However, the simple random walk
used by these methods is fundamentally tied to the identity of the node. This
has three main disadvantages. First, these approaches are inherently
transductive and do not generalize to unseen nodes and other graphs. Second,
they are not space-efficient as a feature vector is learned for each node which
is impractical for large graphs. Third, most of these approaches lack support
for attributed graphs.
To make these methods more generally applicable, we propose a framework for
inductive network representation learning based on the notion of attributed
random walk that is not tied to node identity and is instead based on learning
a function $\Phi : \mathrm{\rm \bf x} \rightarrow w$ that maps a node attribute
vector $\mathrm{\rm \bf x}$ to a type $w$. This framework serves as a basis for
generalizing existing methods such as DeepWalk, node2vec, and many other
previous methods that leverage traditional random walks. | [
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] |
We consider the problem of performing inverse reinforcement learning when the
trajectory of the expert is not perfectly observed by the learner. Instead, a
noisy continuous-time observation of the trajectory is provided to the learner.
This problem exhibits wide-ranging applications and the specific application we
consider here is the scenario in which the learner seeks to penetrate a
perimeter patrolled by a robot. The learner's field of view is limited due to
which it cannot observe the patroller's complete trajectory. Instead, we allow
the learner to listen to the expert's movement sound, which it can also use to
estimate the expert's state and action using an observation model. We treat the
expert's state and action as hidden data and present an algorithm based on
expectation maximization and maximum entropy principle to solve the non-linear,
non-convex problem. Related work considers discrete-time observations and an
observation model that does not include actions. In contrast, our technique
takes expectations over both state and action of the expert, enabling learning
even in the presence of extreme noise and broader applications. | [
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] |
Deep learning models require extensive architecture design exploration and
hyperparameter optimization to perform well on a given task. The exploration of
the model design space is often made by a human expert, and optimized using a
combination of grid search and search heuristics over a large space of possible
choices. Neural Architecture Search (NAS) is a Reinforcement Learning approach
that has been proposed to automate architecture design. NAS has been
successfully applied to generate Neural Networks that rival the best
human-designed architectures. However, NAS requires sampling, constructing, and
training hundreds to thousands of models to achieve well-performing
architectures. This procedure needs to be executed from scratch for each new
task. The application of NAS to a wide set of tasks currently lacks a way to
transfer generalizable knowledge across tasks. In this paper, we present the
Multitask Neural Model Search (MNMS) controller. Our goal is to learn a
generalizable framework that can condition model construction on successful
model searches for previously seen tasks, thus significantly speeding up the
search for new tasks. We demonstrate that MNMS can conduct an automated
architecture search for multiple tasks simultaneously while still learning
well-performing, specialized models for each task. We then show that
pre-trained MNMS controllers can transfer learning to new tasks. By leveraging
knowledge from previous searches, we find that pre-trained MNMS models start
from a better location in the search space and reduce search time on unseen
tasks, while still discovering models that outperform published human-designed
models. | [
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Recurrent neural networks (RNNs) are important class of architectures among
neural networks useful for language modeling and sequential prediction.
However, optimizing RNNs is known to be harder compared to feed-forward neural
networks. A number of techniques have been proposed in literature to address
this problem. In this paper we propose a simple technique called fraternal
dropout that takes advantage of dropout to achieve this goal. Specifically, we
propose to train two identical copies of an RNN (that share parameters) with
different dropout masks while minimizing the difference between their
(pre-softmax) predictions. In this way our regularization encourages the
representations of RNNs to be invariant to dropout mask, thus being robust. We
show that our regularization term is upper bounded by the expectation-linear
dropout objective which has been shown to address the gap due to the difference
between the train and inference phases of dropout. We evaluate our model and
achieve state-of-the-art results in sequence modeling tasks on two benchmark
datasets - Penn Treebank and Wikitext-2. We also show that our approach leads
to performance improvement by a significant margin in image captioning
(Microsoft COCO) and semi-supervised (CIFAR-10) tasks. | [
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] |
In meta-learning an agent extracts knowledge from observed tasks, aiming to
facilitate learning of novel future tasks. Under the assumption that future
tasks are 'related' to previous tasks, the accumulated knowledge should be
learned in a way which captures the common structure across learned tasks,
while allowing the learner sufficient flexibility to adapt to novel aspects of
new tasks. We present a framework for meta-learning that is based on
generalization error bounds, allowing us to extend various PAC-Bayes bounds to
meta-learning. Learning takes place through the construction of a distribution
over hypotheses based on the observed tasks, and its utilization for learning a
new task. Thus, prior knowledge is incorporated through setting an
experience-dependent prior for novel tasks. We develop a gradient-based
algorithm which minimizes an objective function derived from the bounds and
demonstrate its effectiveness numerically with deep neural networks. In
addition to establishing the improved performance available through
meta-learning, we demonstrate the intuitive way by which prior information is
manifested at different levels of the network. | [
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] |
In robotics, it is essential to be able to plan efficiently in
high-dimensional continuous state-action spaces for long horizons. For such
complex planning problems, unguided uniform sampling of actions until a path to
a goal is found is hopelessly inefficient, and gradient-based approaches often
fall short when the optimization manifold of a given problem is not smooth. In
this paper we present an approach that guides the search of a state-space
planner, such as A*, by learning an action-sampling distribution that can
generalize across different instances of a planning problem. The motivation is
that, unlike typical learning approaches for planning for continuous action
space that estimate a policy, an estimated action sampler is more robust to
error since it has a planner to fall back on. We use a Generative Adversarial
Network (GAN), and address an important issue: search experience consists of a
relatively large number of actions that are not on a solution path and a
relatively small number of actions that actually are on a solution path. We
introduce a new technique, based on an importance-ratio estimation method, for
using samples from a non-target distribution to make GAN learning more
data-efficient. We provide theoretical guarantees and empirical evaluation in
three challenging continuous robot planning problems to illustrate the
effectiveness of our algorithm. | [
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The performance of many parallel applications depends on loop-level
parallelism. However, manually parallelizing all loops may result in degrading
parallel performance, as some of them cannot scale desirably to a large number
of threads. In addition, the overheads of manually tuning loop parameters might
prevent an application from reaching its maximum parallel performance. We
illustrate how machine learning techniques can be applied to address these
challenges. In this research, we develop a framework that is able to
automatically capture the static and dynamic information of a loop. Moreover,
we advocate a novel method by introducing HPX smart executors for determining
the execution policy, chunk size, and prefetching distance of an HPX loop to
achieve higher possible performance by feeding static information captured
during compilation and runtime-based dynamic information to our learning model.
Our evaluated execution results show that using these smart executors can speed
up the HPX execution process by around 12%-35% for the Matrix Multiplication,
Stream and $2D$ Stencil benchmarks compared to setting their HPX loop's
execution policy/parameters manually or using HPX auto-parallelization
techniques. | [
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] |
RoboCup is an international scientific robot competition in which teams of
multiple robots compete against each other. Its different leagues provide many
sources of robotics data, that can be used for further analysis and application
of machine learning. This paper describes a large dataset from games of some of
the top teams (from 2016 and 2017) in RoboCup Soccer Simulation League (2D),
where teams of 11 robots (agents) compete against each other. Overall, we used
10 different teams to play each other, resulting in 45 unique pairings. For
each pairing, we ran 25 matches (of 10mins), leading to 1125 matches or more
than 180 hours of game play. The generated CSV files are 17GB of data (zipped),
or 229GB (unzipped). The dataset is unique in the sense that it contains both
the ground truth data (global, complete, noise-free information of all objects
on the field), as well as the noisy, local and incomplete percepts of each
robot. These data are made available as CSV files, as well as in the original
soccer simulator formats. | [
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] |
A primary goal of computational phenotype research is to conduct medical
diagnosis. In hospital, physicians rely on massive clinical data to make
diagnosis decisions, among which laboratory tests are one of the most important
resources. However, the longitudinal and incomplete nature of laboratory test
data casts a significant challenge on its interpretation and usage, which may
result in harmful decisions by both human physicians and automatic diagnosis
systems. In this work, we take advantage of deep generative models to deal with
the complex laboratory tests. Specifically, we propose an end-to-end
architecture that involves a deep generative variational recurrent neural
networks (VRNN) to learn robust and generalizable features, and a
discriminative neural network (NN) model to learn diagnosis decision making,
and the two models are trained jointly. Our experiments are conducted on a
dataset involving 46,252 patients, and the 50 most frequent tests are used to
predict the 50 most common diagnoses. The results show that our model, VRNN+NN,
significantly (p<0.001) outperforms other baseline models. Moreover, we
demonstrate that the representations learned by the joint training are more
informative than those learned by pure generative models. Finally, we find that
our model offers a surprisingly good imputation for missing values. | [
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] |
Recovering images from undersampled linear measurements typically leads to an
ill-posed linear inverse problem, that asks for proper statistical priors.
Building effective priors is however challenged by the low train and test
overhead dictated by real-time tasks; and the need for retrieving visually
"plausible" and physically "feasible" images with minimal hallucination. To
cope with these challenges, we design a cascaded network architecture that
unrolls the proximal gradient iterations by permeating benefits from generative
residual networks (ResNet) to modeling the proximal operator. A mixture of
pixel-wise and perceptual costs is then deployed to train proximals. The
overall architecture resembles back-and-forth projection onto the intersection
of feasible and plausible images. Extensive computational experiments are
examined for a global task of reconstructing MR images of pediatric patients,
and a more local task of superresolving CelebA faces, that are insightful to
design efficient architectures. Our observations indicate that for MRI
reconstruction, a recurrent ResNet with a single residual block effectively
learns the proximal. This simple architecture appears to significantly
outperform the alternative deep ResNet architecture by 2dB SNR, and the
conventional compressed-sensing MRI by 4dB SNR with 100x faster inference. For
image superresolution, our preliminary results indicate that modeling the
denoising proximal demands deep ResNets. | [
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] |
The literature on Inverse Reinforcement Learning (IRL) typically assumes that
humans take actions in order to minimize the expected value of a cost function,
i.e., that humans are risk neutral. Yet, in practice, humans are often far from
being risk neutral. To fill this gap, the objective of this paper is to devise
a framework for risk-sensitive IRL in order to explicitly account for a human's
risk sensitivity. To this end, we propose a flexible class of models based on
coherent risk measures, which allow us to capture an entire spectrum of risk
preferences from risk-neutral to worst-case. We propose efficient
non-parametric algorithms based on linear programming and semi-parametric
algorithms based on maximum likelihood for inferring a human's underlying risk
measure and cost function for a rich class of static and dynamic
decision-making settings. The resulting approach is demonstrated on a simulated
driving game with ten human participants. Our method is able to infer and mimic
a wide range of qualitatively different driving styles from highly risk-averse
to risk-neutral in a data-efficient manner. Moreover, comparisons of the
Risk-Sensitive (RS) IRL approach with a risk-neutral model show that the RS-IRL
framework more accurately captures observed participant behavior both
qualitatively and quantitatively, especially in scenarios where catastrophic
outcomes such as collisions can occur. | [
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] |
A prediscretisation of numerical attributes which is required by some rule
learning algorithms is a source of inefficiencies. This paper describes new
rule tuning steps that aim to recover lost information in the discretisation
and new pruning techniques that may further reduce the size of rule models and
improve their accuracy. The proposed QCBA method was initially developed to
postprocess quantitative attributes in models generated by the Classification
based on associations (CBA) algorithm, but it can also be applied to the
results of other rule learning approaches. We demonstrate the effectiveness on
the postprocessing of models generated by five association rule classification
algorithms (CBA, CMAR, CPAR, IDS, SBRL) and two first-order logic rule learners
(FOIL2 and PRM). Benchmarks on 22 datasets from the UCI repository show smaller
size and the overall best predictive performance for FOIL2+QCBA compared to all
seven baselines. Postoptimised CBA models have a better predictive performance
compared to the state-of-the-art rule learner CORELS in this benchmark. The
article contains an ablation study for the individual postprocessing steps and
a scalability analysis on the KDD'99 Anomaly detection dataset. | [
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] |
We propose a novel computational strategy for de novo design of molecules
with desired properties termed ReLeaSE (Reinforcement Learning for Structural
Evolution). Based on deep and reinforcement learning approaches, ReLeaSE
integrates two deep neural networks - generative and predictive - that are
trained separately but employed jointly to generate novel targeted chemical
libraries. ReLeaSE employs simple representation of molecules by their SMILES
strings only. Generative models are trained with stack-augmented memory network
to produce chemically feasible SMILES strings, and predictive models are
derived to forecast the desired properties of the de novo generated compounds.
In the first phase of the method, generative and predictive models are trained
separately with a supervised learning algorithm. In the second phase, both
models are trained jointly with the reinforcement learning approach to bias the
generation of new chemical structures towards those with the desired physical
and/or biological properties. In the proof-of-concept study, we have employed
the ReLeaSE method to design chemical libraries with a bias toward structural
complexity or biased toward compounds with either maximal, minimal, or specific
range of physical properties such as melting point or hydrophobicity, as well
as to develop novel putative inhibitors of JAK2. The approach proposed herein
can find a general use for generating targeted chemical libraries of novel
compounds optimized for either a single desired property or multiple
properties. | [
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] |
Gaussian processes are rich distributions over functions, with generalization
properties determined by a kernel function. When used for long-range
extrapolation, predictions are particularly sensitive to the choice of kernel
parameters. It is therefore critical to account for kernel uncertainty in our
predictive distributions. We propose a distribution over kernels formed by
modelling a spectral mixture density with a L\'evy process. The resulting
distribution has support for all stationary covariances--including the popular
RBF, periodic, and Mat\'ern kernels--combined with inductive biases which
enable automatic and data efficient learning, long-range extrapolation, and
state of the art predictive performance. The proposed model also presents an
approach to spectral regularization, as the L\'evy process introduces a
sparsity-inducing prior over mixture components, allowing automatic selection
over model order and pruning of extraneous components. We exploit the algebraic
structure of the proposed process for $\mathcal{O}(n)$ training and
$\mathcal{O}(1)$ predictions. We perform extrapolations having reasonable
uncertainty estimates on several benchmarks, show that the proposed model can
recover flexible ground truth covariances and that it is robust to errors in
initialization. | [
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] |
In the last years many accurate decision support systems have been
constructed as black boxes, that is as systems that hide their internal logic
to the user. This lack of explanation constitutes both a practical and an
ethical issue. The literature reports many approaches aimed at overcoming this
crucial weakness sometimes at the cost of scarifying accuracy for
interpretability. The applications in which black box decision systems can be
used are various, and each approach is typically developed to provide a
solution for a specific problem and, as a consequence, delineating explicitly
or implicitly its own definition of interpretability and explanation. The aim
of this paper is to provide a classification of the main problems addressed in
the literature with respect to the notion of explanation and the type of black
box system. Given a problem definition, a black box type, and a desired
explanation this survey should help the researcher to find the proposals more
useful for his own work. The proposed classification of approaches to open
black box models should also be useful for putting the many research open
questions in perspective. | [
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] |
Scientific disciplines, such as Behavioural Psychology, Anthropology and
recently Social Signal Processing are concerned with the systematic exploration
of human behaviour. A typical work-flow includes the manual annotation (also
called coding) of social signals in multi-modal corpora of considerable size.
For the involved annotators this defines an exhausting and time-consuming task.
In the article at hand we present a novel method and also provide the tools to
speed up the coding procedure. To this end, we suggest and evaluate the use of
Cooperative Machine Learning (CML) techniques to reduce manual labelling
efforts by combining the power of computational capabilities and human
intelligence. The proposed CML strategy starts with a small number of labelled
instances and concentrates on predicting local parts first. Afterwards, a
session-independent classification model is created to finish the remaining
parts of the database. Confidence values are computed to guide the manual
inspection and correction of the predictions. To bring the proposed approach
into application we introduce NOVA - an open-source tool for collaborative and
machine-aided annotations. In particular, it gives labellers immediate access
to CML strategies and directly provides visual feedback on the results. Our
experiments show that the proposed method has the potential to significantly
reduce human labelling efforts. | [
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] |
Recently, a new multi-step temporal learning algorithm, called $Q(\sigma)$,
unifies $n$-step Tree-Backup (when $\sigma=0$) and $n$-step Sarsa (when
$\sigma=1$) by introducing a sampling parameter $\sigma$. However, similar to
other multi-step temporal-difference learning algorithms, $Q(\sigma)$ needs
much memory consumption and computation time. Eligibility trace is an important
mechanism to transform the off-line updates into efficient on-line ones which
consume less memory and computation time. In this paper, we further develop the
original $Q(\sigma)$, combine it with eligibility traces and propose a new
algorithm, called $Q(\sigma ,\lambda)$, in which $\lambda$ is trace-decay
parameter. This idea unifies Sarsa$(\lambda)$ (when $\sigma =1$) and
$Q^{\pi}(\lambda)$ (when $\sigma =0$). Furthermore, we give an upper error
bound of $Q(\sigma ,\lambda)$ policy evaluation algorithm. We prove that
$Q(\sigma,\lambda)$ control algorithm can converge to the optimal value
function exponentially. We also empirically compare it with conventional
temporal-difference learning methods. Results show that, with an intermediate
value of $\sigma$, $Q(\sigma ,\lambda)$ creates a mixture of the existing
algorithms that can learn the optimal value significantly faster than the
extreme end ($\sigma=0$, or $1$). | [
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] |
We study the sparse entropy-regularized reinforcement learning (ERL) problem
in which the entropy term is a special form of the Tsallis entropy. The optimal
policy of this formulation is sparse, i.e.,~at each state, it has non-zero
probability for only a small number of actions. This addresses the main
drawback of the standard Shannon entropy-regularized RL (soft ERL) formulation,
in which the optimal policy is softmax, and thus, may assign a non-negligible
probability mass to non-optimal actions. This problem is aggravated as the
number of actions is increased. In this paper, we follow the work of Nachum et
al. (2017) in the soft ERL setting, and propose a class of novel path
consistency learning (PCL) algorithms, called {\em sparse PCL}, for the sparse
ERL problem that can work with both on-policy and off-policy data. We first
derive a {\em sparse consistency} equation that specifies a relationship
between the optimal value function and policy of the sparse ERL along any
system trajectory. Crucially, a weak form of the converse is also true, and we
quantify the sub-optimality of a policy which satisfies sparse consistency, and
show that as we increase the number of actions, this sub-optimality is better
than that of the soft ERL optimal policy. We then use this result to derive the
sparse PCL algorithms. We empirically compare sparse PCL with its soft
counterpart, and show its advantage, especially in problems with a large number
of actions. | [
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] |
Program translation is an important tool to migrate legacy code in one
language into an ecosystem built in a different language. In this work, we are
the first to employ deep neural networks toward tackling this problem. We
observe that program translation is a modular procedure, in which a sub-tree of
the source tree is translated into the corresponding target sub-tree at each
step. To capture this intuition, we design a tree-to-tree neural network to
translate a source tree into a target one. Meanwhile, we develop an attention
mechanism for the tree-to-tree model, so that when the decoder expands one
non-terminal in the target tree, the attention mechanism locates the
corresponding sub-tree in the source tree to guide the expansion of the
decoder. We evaluate the program translation capability of our tree-to-tree
model against several state-of-the-art approaches. Compared against other
neural translation models, we observe that our approach is consistently better
than the baselines with a margin of up to 15 points. Further, our approach can
improve the previous state-of-the-art program translation approaches by a
margin of 20 points on the translation of real-world projects. | [
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] |
We study reinforcement learning (RL) in high dimensional episodic Markov
decision processes (MDP). We consider value-based RL when the optimal Q-value
is a linear function of d-dimensional state-action feature representation. For
instance, in deep-Q networks (DQN), the Q-value is a linear function of the
feature representation layer (output layer). We propose two algorithms, one
based on optimism, LINUCB, and another based on posterior sampling, LINPSRL. We
guarantee frequentist and Bayesian regret upper bounds of O(d sqrt{T}) for
these two algorithms, where T is the number of episodes. We extend these
methods to deep RL and propose Bayesian deep Q-networks (BDQN), which uses an
efficient Thompson sampling algorithm for high dimensional RL. We deploy the
double DQN (DDQN) approach, and instead of learning the last layer of Q-network
using linear regression, we use Bayesian linear regression, resulting in an
approximated posterior over Q-function. This allows us to directly incorporate
the uncertainty over the Q-function and deploy Thompson sampling on the learned
posterior distribution resulting in efficient exploration/exploitation
trade-off. We empirically study the behavior of BDQN on a wide range of Atari
games. Since BDQN carries out more efficient exploration and exploitation, it
is able to reach higher return substantially faster compared to DDQN. | [
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] |
In this paper, we aim to develop a unified view of causal and non-causal
feature selection methods. The unified view will fill in the gap in the
research of the relation between the two types of methods. Based on the
Bayesian network framework and information theory, we first show that causal
and non-causal feature selection methods share the same objective. That is to
find the Markov blanket of a class attribute, the theoretically optimal feature
set for classification. We then examine the assumptions made by causal and
non-causal feature selection methods when searching for the optimal feature
set, and unify the assumptions by mapping them to the restrictions on the
structure of the Bayesian network model of the studied problem. We further
analyze in detail how the structural assumptions lead to the different levels
of approximations employed by the methods in their search, which then result in
the approximations in the feature sets found by the methods with respect to the
optimal feature set. With the unified view, we are able to interpret the output
of non-causal methods from a causal perspective and derive the error bounds of
both types of methods. Finally, we present practical understanding of the
relation between causal and non-causal methods using extensive experiments with
synthetic data and various types of real-word data. | [
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] |
Online optimization has been a successful framework for solving large-scale
problems under computational constraints and partial information. Current
methods for online convex optimization require either a projection or exact
gradient computation at each step, both of which can be prohibitively expensive
for large-scale applications. At the same time, there is a growing trend of
non-convex optimization in machine learning community and a need for online
methods. Continuous DR-submodular functions, which exhibit a natural
diminishing returns condition, have recently been proposed as a broad class of
non-convex functions which may be efficiently optimized. Although online
methods have been introduced, they suffer from similar problems. In this work,
we propose Meta-Frank-Wolfe, the first online projection-free algorithm that
uses stochastic gradient estimates. The algorithm relies on a careful sampling
of gradients in each round and achieves the optimal $O( \sqrt{T})$ adversarial
regret bounds for convex and continuous submodular optimization. We also
propose One-Shot Frank-Wolfe, a simpler algorithm which requires only a single
stochastic gradient estimate in each round and achieves an $O(T^{2/3})$
stochastic regret bound for convex and continuous submodular optimization. We
apply our methods to develop a novel "lifting" framework for the online
discrete submodular maximization and also see that they outperform current
state-of-the-art techniques on various experiments. | [
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] |
We study the task of finding good local optima in combinatorial optimization
problems. Although combinatorial optimization is NP-hard in general, locally
optimal solutions are frequently used in practice. Local search methods however
typically converge to a limited set of optima that depend on their
initialization. Sampling methods on the other hand can access any valid
solution, and thus can be used either directly or alongside methods of the
former type as a way for finding good local optima. Since the effectiveness of
this strategy depends on the sampling distribution, we derive a robust learning
algorithm that adapts sampling distributions towards good local optima of
arbitrary objective functions. As a first use case, we empirically study the
efficiency in which sampling methods can recover locally maximal cliques in
undirected graphs. Not only do we show how our adaptive sampler outperforms
related methods, we also show how it can even approach the performance of
established clique algorithms. As a second use case, we consider how greedy
algorithms can be combined with our adaptive sampler, and we demonstrate how
this leads to superior performance in k-medoid clustering. Together, these
findings suggest that our adaptive sampler can provide an effective strategy to
combinatorial optimization problems that arise in practice. | [
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] |
Neural networks have become an increasingly popular solution for network
intrusion detection systems (NIDS). Their capability of learning complex
patterns and behaviors make them a suitable solution for differentiating
between normal traffic and network attacks. However, a drawback of neural
networks is the amount of resources needed to train them. Many network gateways
and routers devices, which could potentially host an NIDS, simply do not have
the memory or processing power to train and sometimes even execute such models.
More importantly, the existing neural network solutions are trained in a
supervised manner. Meaning that an expert must label the network traffic and
update the model manually from time to time.
In this paper, we present Kitsune: a plug and play NIDS which can learn to
detect attacks on the local network, without supervision, and in an efficient
online manner. Kitsune's core algorithm (KitNET) uses an ensemble of neural
networks called autoencoders to collectively differentiate between normal and
abnormal traffic patterns. KitNET is supported by a feature extraction
framework which efficiently tracks the patterns of every network channel. Our
evaluations show that Kitsune can detect various attacks with a performance
comparable to offline anomaly detectors, even on a Raspberry PI. This
demonstrates that Kitsune can be a practical and economic NIDS. | [
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] |
Synthesizing user-intended programs from a small number of input-output
examples is a challenging problem with several important applications like
spreadsheet manipulation, data wrangling and code refactoring. Existing
synthesis systems either completely rely on deductive logic techniques that are
extensively hand-engineered or on purely statistical models that need massive
amounts of data, and in general fail to provide real-time synthesis on
challenging benchmarks. In this work, we propose Neural Guided Deductive Search
(NGDS), a hybrid synthesis technique that combines the best of both symbolic
logic techniques and statistical models. Thus, it produces programs that
satisfy the provided specifications by construction and generalize well on
unseen examples, similar to data-driven systems. Our technique effectively
utilizes the deductive search framework to reduce the learning problem of the
neural component to a simple supervised learning setup. Further, this allows us
to both train on sparingly available real-world data and still leverage
powerful recurrent neural network encoders. We demonstrate the effectiveness of
our method by evaluating on real-world customer scenarios by synthesizing
accurate programs with up to 12x speed-up compared to state-of-the-art systems. | [
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] |
A longstanding goal in character animation is to combine data-driven
specification of behavior with a system that can execute a similar behavior in
a physical simulation, thus enabling realistic responses to perturbations and
environmental variation. We show that well-known reinforcement learning (RL)
methods can be adapted to learn robust control policies capable of imitating a
broad range of example motion clips, while also learning complex recoveries,
adapting to changes in morphology, and accomplishing user-specified goals. Our
method handles keyframed motions, highly-dynamic actions such as
motion-captured flips and spins, and retargeted motions. By combining a
motion-imitation objective with a task objective, we can train characters that
react intelligently in interactive settings, e.g., by walking in a desired
direction or throwing a ball at a user-specified target. This approach thus
combines the convenience and motion quality of using motion clips to define the
desired style and appearance, with the flexibility and generality afforded by
RL methods and physics-based animation. We further explore a number of methods
for integrating multiple clips into the learning process to develop
multi-skilled agents capable of performing a rich repertoire of diverse skills.
We demonstrate results using multiple characters (human, Atlas robot, bipedal
dinosaur, dragon) and a large variety of skills, including locomotion,
acrobatics, and martial arts. | [
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] |
Data clustering is a common unsupervised learning method frequently used in
exploratory data analysis. However, identifying relevant structures in
unlabeled, high-dimensional data is nontrivial, requiring iterative
experimentation with clustering parameters as well as data features and
instances. The number of possible clusterings for a typical dataset is vast,
and navigating in this vast space is also challenging. The absence of
ground-truth labels makes it impossible to define an optimal solution, thus
requiring user judgment to establish what can be considered a satisfiable
clustering result. Data scientists need adequate interactive tools to
effectively explore and navigate the large clustering space so as to improve
the effectiveness of exploratory clustering analysis. We introduce
\textit{Clustrophile~2}, a new interactive tool for guided clustering analysis.
\textit{Clustrophile~2} guides users in clustering-based exploratory analysis,
adapts user feedback to improve user guidance, facilitates the interpretation
of clusters, and helps quickly reason about differences between clusterings. To
this end, \textit{Clustrophile~2} contributes a novel feature, the Clustering
Tour, to help users choose clustering parameters and assess the quality of
different clustering results in relation to current analysis goals and user
expectations. We evaluate \textit{Clustrophile~2} through a user study with 12
data scientists, who used our tool to explore and interpret sub-cohorts in a
dataset of Parkinson's disease patients. Results suggest that
\textit{Clustrophile~2} improves the speed and effectiveness of exploratory
clustering analysis for both experts and non-experts. | [
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] |
The personnel scheduling problem is a well-known NP-hard combinatorial
problem. Due to the complexity of this problem and the size of the real-world
instances, it is not possible to use exact methods, and thus heuristics,
meta-heuristics, or hyper-heuristics must be employed. The majority of
heuristic approaches are based on iterative search, where the quality of
intermediate solutions must be calculated. Unfortunately, this is
computationally highly expensive because these problems have many constraints
and some are very complex. In this study, we propose a machine learning
technique as a tool to accelerate the evaluation phase in heuristic approaches.
The solution is based on a simple classifier, which is able to determine
whether the changed solution (more precisely, the changed part of the solution)
is better than the original or not. This decision is made much faster than a
standard cost-oriented evaluation process. However, the classification process
cannot guarantee 100% correctness. Therefore, our approach, which is
illustrated using a tabu search algorithm in this study, includes a filtering
mechanism, where the classifier rejects the majority of the potentially bad
solutions and the remaining solutions are then evaluated in a standard manner.
We also show how the boosting algorithms can improve the quality of the final
solution compared with a simple classifier. We verified our proposed approach
and premises, based on standard and real-world benchmark instances, to
demonstrate the significant speedup obtained with comparable solution quality. | [
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] |
This paper presents BigDL (a distributed deep learning framework for Apache
Spark), which has been used by a variety of users in the industry for building
deep learning applications on production big data platforms. It allows deep
learning applications to run on the Apache Hadoop/Spark cluster so as to
directly process the production data, and as a part of the end-to-end data
analysis pipeline for deployment and management. Unlike existing deep learning
frameworks, BigDL implements distributed, data parallel training directly on
top of the functional compute model (with copy-on-write and coarse-grained
operations) of Spark. We also share real-world experience and "war stories" of
users that have adopted BigDL to address their challenges(i.e., how to easily
build end-to-end data analysis and deep learning pipelines for their production
data). | [
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In many sequential decision making tasks, it is challenging to design reward
functions that help an RL agent efficiently learn behavior that is considered
good by the agent designer. A number of different formulations of the
reward-design problem, or close variants thereof, have been proposed in the
literature. In this paper we build on the Optimal Rewards Framework of Singh
et.al. that defines the optimal intrinsic reward function as one that when used
by an RL agent achieves behavior that optimizes the task-specifying or
extrinsic reward function. Previous work in this framework has shown how good
intrinsic reward functions can be learned for lookahead search based planning
agents. Whether it is possible to learn intrinsic reward functions for learning
agents remains an open problem. In this paper we derive a novel algorithm for
learning intrinsic rewards for policy-gradient based learning agents. We
compare the performance of an augmented agent that uses our algorithm to
provide additive intrinsic rewards to an A2C-based policy learner (for Atari
games) and a PPO-based policy learner (for Mujoco domains) with a baseline
agent that uses the same policy learners but with only extrinsic rewards. Our
results show improved performance on most but not all of the domains. | [
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] |
In recent years, a large amount of model-agnostic methods to improve the
transparency, trustability and interpretability of machine learning models have
been developed. We introduce local feature importance as a local version of a
recent model-agnostic global feature importance method. Based on local feature
importance, we propose two visual tools: partial importance (PI) and individual
conditional importance (ICI) plots which visualize how changes in a feature
affect the model performance on average, as well as for individual
observations. Our proposed methods are related to partial dependence (PD) and
individual conditional expectation (ICE) plots, but visualize the expected
(conditional) feature importance instead of the expected (conditional)
prediction. Furthermore, we show that averaging ICI curves across observations
yields a PI curve, and integrating the PI curve with respect to the
distribution of the considered feature results in the global feature
importance. Another contribution of our paper is the Shapley feature
importance, which fairly distributes the overall performance of a model among
the features according to the marginal contributions and which can be used to
compare the feature importance across different models. | [
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] |
The cross-domain recommendation technique is an effective way of alleviating
the data sparse issue in recommender systems by leveraging the knowledge from
relevant domains. Transfer learning is a class of algorithms underlying these
techniques. In this paper, we propose a novel transfer learning approach for
cross-domain recommendation by using neural networks as the base model. In
contrast to the matrix factorization based cross-domain techniques, our method
is deep transfer learning, which can learn complex user-item interaction
relationships. We assume that hidden layers in two base networks are connected
by cross mappings, leading to the collaborative cross networks (CoNet). CoNet
enables dual knowledge transfer across domains by introducing cross connections
from one base network to another and vice versa. CoNet is achieved in
multi-layer feedforward networks by adding dual connections and joint loss
functions, which can be trained efficiently by back-propagation. The proposed
model is thoroughly evaluated on two large real-world datasets. It outperforms
baselines by relative improvements of 7.84\% in NDCG. We demonstrate the
necessity of adaptively selecting representations to transfer. Our model can
reduce tens of thousands training examples comparing with non-transfer methods
and still has the competitive performance with them. | [
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Multiple classifier systems focus on the combination of classifiers to obtain
better performance than a single robust one. These systems unfold three major
phases: pool generation, selection and integration. One of the most promising
MCS approaches is Dynamic Selection (DS), which relies on finding the most
competent classifier or ensemble of classifiers to predict each test sample.
The majority of the DS techniques are based on the K-Nearest Neighbors (K-NN)
definition, and the quality of the neighborhood has a huge impact on the
performance of DS methods. In this paper, we perform an analysis comparing the
classification results of DS techniques and the K-NN classifier under different
conditions. Experiments are performed on 18 state-of-the-art DS techniques over
30 classification datasets and results show that DS methods present a
significant boost in classification accuracy even though they use the same
neighborhood as the K-NN. The reasons behind the outperformance of DS
techniques over the K-NN classifier reside in the fact that DS techniques can
deal with samples with a high degree of instance hardness (samples that are
located close to the decision border) as opposed to the K-NN. In this paper,
not only we explain why DS techniques achieve higher classification performance
than the K-NN but also when DS should be used. | [
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] |
Streaming adaptations of manifold learning based dimensionality reduction
methods, such as Isomap, are based on the assumption that a small initial batch
of observations is enough for exact learning of the manifold, while remaining
streaming data instances can be cheaply mapped to this manifold. However, there
are no theoretical results to show that this core assumption is valid.
Moreover, such methods typically assume that the underlying data distribution
is stationary. Such methods are not equipped to detect, or handle, sudden
changes or gradual drifts in the distribution that may occur when the data is
streaming. We present theoretical results to show that the quality of a
manifold asymptotically converges as the size of data increases. We then show
that a Gaussian Process Regression (GPR) model, that uses a manifold-specific
kernel function and is trained on an initial batch of sufficient size, can
closely approximate the state-of-art streaming Isomap algorithms. The
predictive variance obtained from the GPR prediction is then shown to be an
effective detector of changes in the underlying data distribution. Results on
several synthetic and real data sets show that the resulting algorithm can
effectively learn lower dimensional representation of high dimensional data in
a streaming setting, while identifying shifts in the generative distribution. | [
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] |
This paper presents a solution for persistent monitoring of real-world
stochastic phenomena, where the underlying covariance structure changes sharply
across time, using a small number of mobile robot sensors. We propose an
adaptive solution for the problem where stochastic real-world dynamics are
modeled as a Gaussian Process (GP). The belief on the underlying covariance
structure is learned from recently observed dynamics as a Gaussian Mixture (GM)
in the low-dimensional hyper-parameters space of the GP and adapted across time
using Sequential Monte Carlo methods. Each robot samples a belief point from
the GM and locally optimizes a set of informative regions by greedy
maximization of the submodular entropy function. The key contributions of this
paper are threefold: adapting the belief on the covariance using Markov Chain
Monte Carlo (MCMC) sampling such that particles survive even under sharp
covariance changes across time; exploiting the belief to transform the problem
of entropy maximization into a decentralized one; and developing an
approximation algorithm to maximize entropy on a set of informative regions in
the continuous space. We illustrate the application of the proposed solution
through extensive simulations using an artificial dataset and multiple real
datasets from fixed sensor deployments, and compare it to three competing
state-of-the-art approaches. | [
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In recent years a number of large-scale triple-oriented knowledge graphs have
been generated and various models have been proposed to perform learning in
those graphs. Most knowledge graphs are static and reflect the world in its
current state. In reality, of course, the state of the world is changing: a
healthy person becomes diagnosed with a disease and a new president is
inaugurated. In this paper, we extend models for static knowledge graphs to
temporal knowledge graphs. This enables us to store episodic data and to
generalize to new facts (inductive learning). We generalize leading learning
models for static knowledge graphs (i.e., Tucker, RESCAL, HolE, ComplEx,
DistMult) to temporal knowledge graphs. In particular, we introduce a new
tensor model, ConT, with superior generalization performance. The performances
of all proposed models are analyzed on two different datasets: the Global
Database of Events, Language, and Tone (GDELT) and the database for Integrated
Conflict Early Warning System (ICEWS). We argue that temporal knowledge graph
embeddings might be models also for cognitive episodic memory (facts we
remember and can recollect) and that a semantic memory (current facts we know)
can be generated from episodic memory by a marginalization operation. We
validate this episodic-to-semantic projection hypothesis with the ICEWS
dataset. | [
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] |
This paper is based on a previous publication [29]. Our work extends
exception mining and outlier detection to the case of object-relational data.
Object-relational data represent a complex heterogeneous network [12], which
comprises objects of different types, links among these objects, also of
different types, and attributes of these links. This special structure
prohibits a direct vectorial data representation. We follow the
well-established Exceptional Model Mining framework, which leverages machine
learning models for exception mining: A object is exceptional to the extent
that a model learned for the object data differs from a model learned for the
general population. Exceptional objects can be viewed as outliers. We apply
state of-the-art probabilistic modelling techniques for object-relational data
that construct a graphical model (Bayesian network), which compactly represents
probabilistic associations in the data. A new metric, derived from the learned
object-relational model, quantifies the extent to which the individual
association pattern of a potential outlier deviates from that of the whole
population. The metric is based on the likelihood ratio of two parameter
vectors: One that represents the population associations, and another that
represents the individual associations. Our method is validated on synthetic
datasets and on real-world data sets about soccer matches and movies. Compared
to baseline methods, our novel transformed likelihood ratio achieved the best
detection accuracy on all datasets. | [
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Routing questions in Community Question Answer services (CQAs) such as Stack
Exchange sites is a well-studied problem. Yet, cold-start -- a phenomena
observed when a new question is posted is not well addressed by existing
approaches. Additionally, cold questions posted by new askers present
significant challenges to state-of-the-art approaches. We propose ColdRoute to
address these challenges. ColdRoute is able to handle the task of routing cold
questions posted by new or existing askers to matching experts. Specifically,
we use Factorization Machines on the one-hot encoding of critical features such
as question tags and compare our approach to well-studied techniques such as
CQARank and semantic matching (LDA, BoW, and Doc2Vec). Using data from eight
stack exchange sites, we are able to improve upon the routing metrics
(Precision$@1$, Accuracy, MRR) over the state-of-the-art models such as
semantic matching by $159.5\%$,$31.84\%$, and $40.36\%$ for cold questions
posted by existing askers, and $123.1\%$, $27.03\%$, and $34.81\%$ for cold
questions posted by new askers respectively. | [
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In compressed sensing, a small number of linear measurements can be used to
reconstruct an unknown signal. Existing approaches leverage assumptions on the
structure of these signals, such as sparsity or the availability of a
generative model. A domain-specific generative model can provide a stronger
prior and thus allow for recovery with far fewer measurements. However, unlike
sparsity-based approaches, existing methods based on generative models
guarantee exact recovery only over their support, which is typically only a
small subset of the space on which the signals are defined. We propose
Sparse-Gen, a framework that allows for sparse deviations from the support set,
thereby achieving the best of both worlds by using a domain specific prior and
allowing reconstruction over the full space of signals. Theoretically, our
framework provides a new class of signals that can be acquired using compressed
sensing, reducing classic sparse vector recovery to a special case and avoiding
the restrictive support due to a generative model prior. Empirically, we
observe consistent improvements in reconstruction accuracy over competing
approaches, especially in the more practical setting of transfer compressed
sensing where a generative model for a data-rich, source domain aids sensing on
a data-scarce, target domain. | [
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] |
The fast demographic growth, together with the concentration of the
population in cities and the increasing amount of daily waste, are factors that
push to the limit the ability of waste assimilation by Nature. Therefore, we
need technological means to make an optimal management of the waste collection
process, which represents 70% of the operational cost in waste treatment. In
this article, we present a free intelligent software system, based on
computational learning algorithms, which plans the best routes for waste
collection supported by past (historical) and future (predictions) data.
The objective of the system is the cost reduction of the waste collection
service by means of the minimization in distance traveled by any truck to
collect a container, hence the fuel consumption. At the same time the quality
of service to the citizen is increased avoiding the annoying overflows of
containers thanks to the accurate fill level predictions performed by BIN-CT.
In this article we show the features of our software system, illustrating it
operation with a real case study of a Spanish city. We conclude that the use of
BIN-CT avoids unnecessary visits to containers, reduces the distance traveled
to collect a container and therefore we obtain a reduction of total costs and
harmful emissions thrown to the atmosphere. | [
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] |
Most policy search algorithms require thousands of training episodes to find
an effective policy, which is often infeasible with a physical robot. This
survey article focuses on the extreme other end of the spectrum: how can a
robot adapt with only a handful of trials (a dozen) and a few minutes? By
analogy with the word "big-data", we refer to this challenge as "micro-data
reinforcement learning". We show that a first strategy is to leverage prior
knowledge on the policy structure (e.g., dynamic movement primitives), on the
policy parameters (e.g., demonstrations), or on the dynamics (e.g.,
simulators). A second strategy is to create data-driven surrogate models of the
expected reward (e.g., Bayesian optimization) or the dynamical model (e.g.,
model-based policy search), so that the policy optimizer queries the model
instead of the real system. Overall, all successful micro-data algorithms
combine these two strategies by varying the kind of model and prior knowledge.
The current scientific challenges essentially revolve around scaling up to
complex robots (e.g., humanoids), designing generic priors, and optimizing the
computing time. | [
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Collectively, machine learning (ML) researchers are engaged in the creation
and dissemination of knowledge about data-driven algorithms. In a given paper,
researchers might aspire to any subset of the following goals, among others: to
theoretically characterize what is learnable, to obtain understanding through
empirically rigorous experiments, or to build a working system that has high
predictive accuracy. While determining which knowledge warrants inquiry may be
subjective, once the topic is fixed, papers are most valuable to the community
when they act in service of the reader, creating foundational knowledge and
communicating as clearly as possible.
Recent progress in machine learning comes despite frequent departures from
these ideals. In this paper, we focus on the following four patterns that
appear to us to be trending in ML scholarship: (i) failure to distinguish
between explanation and speculation; (ii) failure to identify the sources of
empirical gains, e.g., emphasizing unnecessary modifications to neural
architectures when gains actually stem from hyper-parameter tuning; (iii)
mathiness: the use of mathematics that obfuscates or impresses rather than
clarifies, e.g., by confusing technical and non-technical concepts; and (iv)
misuse of language, e.g., by choosing terms of art with colloquial connotations
or by overloading established technical terms.
While the causes behind these patterns are uncertain, possibilities include
the rapid expansion of the community, the consequent thinness of the reviewer
pool, and the often-misaligned incentives between scholarship and short-term
measures of success (e.g., bibliometrics, attention, and entrepreneurial
opportunity). While each pattern offers a corresponding remedy (don't do it),
we also discuss some speculative suggestions for how the community might combat
these trends. | [
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] |
Heterogeneous information networks (HINs) are ubiquitous in real-world
applications. In the meantime, network embedding has emerged as a convenient
tool to mine and learn from networked data. As a result, it is of interest to
develop HIN embedding methods. However, the heterogeneity in HINs introduces
not only rich information but also potentially incompatible semantics, which
poses special challenges to embedding learning in HINs. With the intention to
preserve the rich yet potentially incompatible information in HIN embedding, we
propose to study the problem of comprehensive transcription of heterogeneous
information networks. The comprehensive transcription of HINs also provides an
easy-to-use approach to unleash the power of HINs, since it requires no
additional supervision, expertise, or feature engineering. To cope with the
challenges in the comprehensive transcription of HINs, we propose the HEER
algorithm, which embeds HINs via edge representations that are further coupled
with properly-learned heterogeneous metrics. To corroborate the efficacy of
HEER, we conducted experiments on two large-scale real-words datasets with an
edge reconstruction task and multiple case studies. Experiment results
demonstrate the effectiveness of the proposed HEER model and the utility of
edge representations and heterogeneous metrics. The code and data are available
at https://github.com/GentleZhu/HEER. | [
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] |
This paper proposes an approach to detect emotion from human speech employing
majority voting technique over several machine learning techniques. The
contribution of this work is in two folds: firstly it selects those features of
speech which is most promising for classification and secondly it uses the
majority voting technique that selects the exact class of emotion. Here,
majority voting technique has been applied over Neural Network (NN), Decision
Tree (DT), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Input
vector of NN, DT, SVM and KNN consists of various acoustic and prosodic
features like Pitch, Mel-Frequency Cepstral coefficients etc. From speech
signal many feature have been extracted and only promising features have been
selected. To consider a feature as promising, Fast Correlation based feature
selection (FCBF) and Fisher score algorithms have been used and only those
features are selected which are highly ranked by both of them. The proposed
approach has been tested on Berlin dataset of emotional speech [3] and
Electromagnetic Articulography (EMA) dataset [4]. The experimental result shows
that majority voting technique attains better accuracy over individual machine
learning techniques. The employment of the proposed approach can effectively
recognize the emotion of human beings in case of social robot, intelligent chat
client, call-center of a company etc. | [
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Machine learning algorithms have found several applications in the field of
robotics and control systems. The control systems community has started to show
interest towards several machine learning algorithms from the sub-domains such
as supervised learning, imitation learning and reinforcement learning to
achieve autonomous control and intelligent decision making. Amongst many
complex control problems, stable bipedal walking has been the most challenging
problem. In this paper, we present an architecture to design and simulate a
planar bipedal walking robot(BWR) using a realistic robotics simulator, Gazebo.
The robot demonstrates successful walking behaviour by learning through several
of its trial and errors, without any prior knowledge of itself or the world
dynamics. The autonomous walking of the BWR is achieved using reinforcement
learning algorithm called Deep Deterministic Policy Gradient(DDPG). DDPG is one
of the algorithms for learning controls in continuous action spaces. After
training the model in simulation, it was observed that, with a proper shaped
reward function, the robot achieved faster walking or even rendered a running
gait with an average speed of 0.83 m/s. The gait pattern of the bipedal walker
was compared with the actual human walking pattern. The results show that the
bipedal walking pattern had similar characteristics to that of a human walking
pattern. The video presenting our experiment is available at
https://goo.gl/NHXKqR. | [
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