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10,101 | Generating Time-Based Label Refinements to Discover More Precise Process
Models | cs.LG | Process mining is a research field focused on the analysis of event data with
the aim of extracting insights related to dynamic behavior. Applying process
mining techniques on data from smart home environments has the potential to
provide valuable insights in (un)healthy habits and to contribute to ambient
assisted liv... | computer science |
10,102 | Anomaly Detection in a Digital Video Broadcasting System Using Timed
Automata | cs.LG | This paper focuses on detecting anomalies in a digital video broadcasting
(DVB) system from providers' perspective. We learn a probabilistic
deterministic real timed automaton profiling benign behavior of encryption
control in the DVB control access system. This profile is used as a one-class
classifier. Anomalous item... | computer science |
10,103 | Mining Process Model Descriptions of Daily Life through Event
Abstraction | cs.LG | Process mining techniques focus on extracting insight in processes from event
logs. Process mining has the potential to provide valuable insights in
(un)healthy habits and to contribute to ambient assisted living solutions when
applied on data from smart home environments. However, events recorded in smart
home environ... | computer science |
10,104 | Learning End-to-end Multimodal Sensor Policies for Autonomous Navigation | cs.RO | Multisensory polices are known to enhance both state estimation and target
tracking. However, in the space of end-to-end sensorimotor control, this
multi-sensor outlook has received limited attention. Moreover, systematic ways
to make policies robust to partial sensor failure are not well explored. In
this work, we pro... | computer science |
10,105 | Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient
Estimation for Deep Reinforcement Learning | cs.LG | Off-policy model-free deep reinforcement learning methods using previously
collected data can improve sample efficiency over on-policy policy gradient
techniques. On the other hand, on-policy algorithms are often more stable and
easier to use. This paper examines, both theoretically and empirically,
approaches to mergi... | computer science |
10,106 | Towards Synthesizing Complex Programs from Input-Output Examples | cs.LG | In recent years, deep learning techniques have been developed to improve the
performance of program synthesis from input-output examples. Albeit its
significant progress, the programs that can be synthesized by state-of-the-art
approaches are still simple in terms of their complexity. In this work, we move
a significan... | computer science |
10,107 | Adversarial-Playground: A Visualization Suite for Adversarial Sample
Generation | cs.CR | With growing interest in adversarial machine learning, it is important for
machine learning practitioners and users to understand how their models may be
attacked. We propose a web-based visualization tool, Adversarial-Playground, to
demonstrate the efficacy of common adversarial methods against a deep neural
network (... | computer science |
10,108 | Seamless Integration and Coordination of Cognitive Skills in Humanoid
Robots: A Deep Learning Approach | cs.AI | 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... | computer science |
10,109 | Predictive Coding-based Deep Dynamic Neural Network for Visuomotor
Learning | cs.AI | 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 sea... | computer science |
10,110 | Stock Trading Using PE ratio: A Dynamic Bayesian Network Modeling on
Behavioral Finance and Fundamental Investment | cs.CE | On a daily investment decision in a security market, the price earnings (PE)
ratio is one of the most widely applied methods being used as a firm valuation
tool by investment experts. Unfortunately, recent academic developments in
financial econometrics and machine learning rarely look at this tool. In
practice, fundam... | computer science |
10,111 | Optimal Auctions through Deep Learning | cs.GT | Designing an auction that maximizes expected revenue is an intricate task.
Indeed, as of today--despite major efforts and impressive progress over the
past few years--only the single-item case is fully understood. In this work, we
initiate the exploration of the use of tools from deep learning on this topic.
The design... | computer science |
10,112 | Generative Models for Learning from Crowds | cs.AI | In this paper, we propose generative probabilistic models for label
aggregation. We use Gibbs sampling and a novel variational inference algorithm
to perform the posterior inference. Empirical results show that our methods
consistently outperform state-of-the-art methods. | computer science |
10,113 | Multi-Label Annotation Aggregation in Crowdsourcing | cs.LG | As a means of human-based computation, crowdsourcing has been widely used to
annotate large-scale unlabeled datasets. One of the obvious challenges is how
to aggregate these possibly noisy labels provided by a set of heterogeneous
annotators. Another challenge stems from the difficulty in evaluating the
annotator relia... | computer science |
10,114 | A Hybrid Approach with Multi-channel I-Vectors and Convolutional Neural
Networks for Acoustic Scene Classification | cs.SD | 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 ta... | computer science |
10,115 | NPGLM: A Non-Parametric Method for Temporal Link Prediction | cs.LG | In this paper, we try to solve the problem of temporal link prediction in
information networks. This implies predicting the time it takes for a link to
appear in the future, given its features that have been extracted at the
current network snapshot. To this end, we introduce a probabilistic
non-parametric approach, ca... | computer science |
10,116 | An approach to reachability analysis for feed-forward ReLU neural
networks | cs.AI | We study the reachability problem for systems implemented as feed-forward
neural networks whose activation function is implemented via ReLU functions. We
draw a correspondence between establishing whether some arbitrary output can
ever be outputed by a neural system and linear problems characterising a neural
system of... | computer science |
10,117 | Neural SLAM: Learning to Explore with External Memory | cs.LG | We present an approach for agents to learn representations of a global map
from sensor data, to aid their exploration in new environments. To achieve
this, we embed procedures mimicking that of traditional Simultaneous
Localization and Mapping (SLAM) into the soft attention based addressing of
external memory architect... | computer science |
10,118 | Graph Based Recommendations: From Data Representation to Feature
Extraction and Application | cs.IR | Modeling users for the purpose of identifying their preferences and then
personalizing services on the basis of these models is a complex task,
primarily due to the need to take into consideration various explicit and
implicit signals, missing or uncertain information, contextual aspects, and
more. In this study, a nov... | computer science |
10,119 | Evaluating race and sex diversity in the world's largest companies using
deep neural networks | cs.CY | Diversity is one of the fundamental properties for the survival of species,
populations, and organizations. Recent advances in deep learning allow for the
rapid and automatic assessment of organizational diversity and possible
discrimination by race, sex, age and other parameters. Automating the process
of assessing th... | computer science |
10,120 | A Fast Integrated Planning and Control Framework for Autonomous Driving
via Imitation Learning | cs.AI | For safe and efficient planning and control in autonomous driving, we need a
driving policy which can achieve desirable driving quality in long-term horizon
with guaranteed safety and feasibility. Optimization-based approaches, such as
Model Predictive Control (MPC), can provide such optimal policies, but their
computa... | computer science |
10,121 | Best-Effort Inductive Logic Programming via Fine-grained Cost-based
Hypothesis Generation | cs.AI | We describe the Inspire system which participated in the first competition on
Inductive Logic Programming (ILP). Inspire is based on Answer Set Programming
(ASP). The distinguishing feature of Inspire is an ASP encoding for hypothesis
space generation: given a set of facts representing the mode bias, and a set of
cost ... | computer science |
10,122 | Vision-Based Multi-Task Manipulation for Inexpensive Robots Using
End-To-End Learning from Demonstration | cs.LG | In this paper, we propose a multi-task learning from demonstration method
that works using raw images as input to autonomously accomplish a wide variety
of tasks in the real world using a low-cost robotic arm. The controller is a
single recurrent neural network that can generate robot arm trajectories to
perform differ... | computer science |
10,123 | Learning Heuristic Search via Imitation | cs.RO | Robotic motion planning problems are typically solved by constructing a
search tree of valid maneuvers from a start to a goal configuration. Limited
onboard computation and real-time planning constraints impose a limit on how
large this search tree can grow. Heuristics play a crucial role in such
situations by guiding ... | computer science |
10,124 | A Survey on Resilient Machine Learning | cs.AI | Machine learning based system are increasingly being used for sensitive tasks
such as security surveillance, guiding autonomous vehicle, taking investment
decisions, detecting and blocking network intrusion and malware etc. However,
recent research has shown that machine learning models are venerable to attacks
by adve... | computer science |
10,125 | Learning like humans with Deep Symbolic Networks | cs.AI | We introduce the Deep Symbolic Network (DSN) model, which aims at becoming
the white-box version of Deep Neural Networks (DNN). The DSN model provides a
simple, universal yet powerful structure, similar to DNN, to represent any
knowledge of the world, which is transparent to humans. The conjecture behind
the DSN model ... | computer science |
10,126 | Predicting Abandonment in Online Coding Tutorials | cs.LG | Learners regularly abandon online coding tutorials when they get bored or
frustrated, but there are few techniques for anticipating this abandonment to
intervene. In this paper, we examine the feasibility of predicting abandonment
with machine-learned classifiers. Using interaction logs from an online
programming game,... | computer science |
10,127 | Lenient Multi-Agent Deep Reinforcement Learning | cs.MA | Much of the success of single agent deep reinforcement learning (DRL) in
recent years can be attributed to the use of experience replay memories (ERM),
which allow Deep Q-Networks (DQNs) to be trained efficiently through sampling
stored state transitions. However, care is required when using ERMs for
multi-agent deep r... | computer science |
10,128 | On-line Building Energy Optimization using Deep Reinforcement Learning | cs.LG | Unprecedented high volumes of data are becoming available with the growth of
the advanced metering infrastructure. These are expected to benefit planning
and operation of the future power system, and to help the customers transition
from a passive to an active role. In this paper, we explore for the first time
in the s... | computer science |
10,129 | Pragmatic-Pedagogic Value Alignment | cs.AI | As intelligent systems gain autonomy and capability, it becomes vital to
ensure that their objectives match those of their human users; this is known as
the value-alignment problem. In robotics, value alignment is key to the design
of collaborative robots that can integrate into human workflows, successfully
inferring ... | computer science |
10,130 | Data-Driven Stochastic Robust Optimization: A General Computational
Framework and Algorithm for Optimization under Uncertainty in the Big Data
Era | cs.LG | A novel data-driven stochastic robust optimization (DDSRO) framework is
proposed for optimization under uncertainty leveraging labeled multi-class
uncertainty data. Uncertainty data in large datasets are often collected from
various conditions, which are encoded by class labels. Machine learning methods
including Diric... | computer science |
10,131 | The Topology of Statistical Verifiability | cs.LG | Topological models of empirical and formal inquiry are increasingly
prevalent. They have emerged in such diverse fields as domain theory [1, 16],
formal learning theory [18], epistemology and philosophy of science [10, 15, 8,
9, 2], statistics [6, 7] and modal logic [17, 4]. In those applications, open
sets are typical... | computer science |
10,132 | Adversarial-Playground: A Visualization Suite Showing How Adversarial
Examples Fool Deep Learning | cs.CR | 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 u... | computer science |
10,133 | PowerAI DDL | cs.DC | 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 hou... | computer science |
10,134 | Neural Network Dynamics for Model-Based Deep Reinforcement Learning with
Model-Free Fine-Tuning | cs.LG | Model-free deep reinforcement learning algorithms have been shown to be
capable of learning a wide range of robotic skills, but typically require a
very large number of samples to achieve good performance. Model-based
algorithms, in principle, can provide for much more efficient learning, but
have proven difficult to e... | computer science |
10,135 | Resilient Linear Classification: An Approach to Deal with Attacks on
Training Data | cs.LG | Data-driven techniques are used in cyber-physical systems (CPS) for
controlling autonomous vehicles, handling demand responses for energy
management, and modeling human physiology for medical devices. These
data-driven techniques extract models from training data, where their
performance is often analyzed with respect ... | computer science |
10,136 | Learning to Plan Chemical Syntheses | cs.AI | 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 av... | computer science |
10,137 | Learning Musical Relations using Gated Autoencoders | cs.SD | 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 tran... | computer science |
10,138 | SafePredict: A Meta-Algorithm for Machine Learning That Uses Refusals to
Guarantee Correctness | cs.LG | SafePredict is a novel meta-algorithm that works with any base prediction
algorithm for online data to guarantee an arbitrarily chosen correctness rate,
$1-\epsilon$, by allowing refusals. Allowing refusals means that the
meta-algorithm may refuse to emit a prediction produced by the base algorithm
on occasion so that ... | computer science |
10,139 | Learning Deep Neural Network Representations for Koopman Operators of
Nonlinear Dynamical Systems | cs.LG | The Koopman operator has recently garnered much attention for its value in
dynamical systems analysis and data-driven model discovery. However, its
application has been hindered by the computational complexity of extended
dynamic mode decomposition; this requires a combinatorially large basis set to
adequately describe... | computer science |
10,140 | DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous
Cars | cs.SE | 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 o... | computer science |
10,141 | Safe Reinforcement Learning via Shielding | cs.LO | Reinforcement learning algorithms discover policies that maximize reward, but
do not necessarily guarantee safety during learning or execution phases. We
introduce a new approach to learn optimal policies while enforcing properties
expressed in temporal logic. To this end, given the temporal logic
specification that is... | computer science |
10,142 | From Query-By-Keyword to Query-By-Example: LinkedIn Talent Search
Approach | cs.IR | One key challenge in talent search is to translate complex criteria of a
hiring position into a search query, while it is relatively easy for a searcher
to list examples of suitable candidates for a given position. To improve search
efficiency, we propose the next generation of talent search at LinkedIn, also
referred ... | computer science |
10,143 | Interacting Attention-gated Recurrent Networks for Recommendation | cs.IR | Capturing the temporal dynamics of user preferences over items is important
for recommendation. Existing methods mainly assume that all time steps in
user-item interaction history are equally relevant to recommendation, which
however does not apply in real-world scenarios where user-item interactions can
often happen a... | computer science |
10,144 | Bayesian Optimisation for Safe Navigation under Localisation Uncertainty | cs.RO | In outdoor environments, mobile robots are required to navigate through
terrain with varying characteristics, some of which might significantly affect
the integrity of the platform. Ideally, the robot should be able to identify
areas that are safe for navigation based on its own percepts about the
environment while avo... | computer science |
10,145 | An Analysis of ISO 26262: Using Machine Learning Safely in Automotive
Software | cs.AI | Machine learning (ML) plays an ever-increasing role in advanced automotive
functionality for driver assistance and autonomous operation; however, its
adequacy from the perspective of safety certification remains controversial. In
this paper, we analyze the impacts that the use of ML as an implementation
approach has on... | computer science |
10,146 | Causality-Aided Falsification | cs.SY | Falsification is drawing attention in quality assurance of heterogeneous
systems whose complexities are beyond most verification techniques'
scalability. In this paper we introduce the idea of causality aid in
falsification: by providing a falsification solver -- that relies on stochastic
optimization of a certain cost... | computer science |
10,147 | Identifying Irregular Power Usage by Turning Predictions into
Holographic Spatial Visualizations | cs.LG | Power grids are critical infrastructure assets that face non-technical losses
(NTL) such as electricity theft or faulty meters. NTL may range up to 40% of
the total electricity distributed in emerging countries. Industrial NTL
detection systems are still largely based on expert knowledge when deciding
whether to carry ... | computer science |
10,148 | MBMF: Model-Based Priors for Model-Free Reinforcement Learning | cs.LG | Reinforcement Learning is divided in two main paradigms: model-free and
model-based. Each of these two paradigms has strengths and limitations, and has
been successfully applied to real world domains that are appropriate to its
corresponding strengths. In this paper, we present a new approach aimed at
bridging the gap ... | computer science |
10,149 | Automated Cloud Provisioning on AWS using Deep Reinforcement Learning | cs.DC | As the use of cloud computing continues to rise, controlling cost becomes
increasingly important. Yet there is evidence that 30\% - 45\% of cloud spend
is wasted. Existing tools for cloud provisioning typically rely on highly
trained human experts to specify what to monitor, thresholds for triggering
action, and action... | computer science |
10,150 | An Empirical Study of AI Population Dynamics with Million-agent
Reinforcement Learning | cs.AI | In this paper, we conduct an empirical study on discovering the ordered
collective dynamics obtained by a population of artificial intelligence (AI)
agents. Our intention is to put AI agents into a simulated natural context, and
then to understand their induced dynamics at the population level. In
particular, we aim to... | computer science |
10,151 | Feedforward and Recurrent Neural Networks Backward Propagation and
Hessian in Matrix Form | cs.LG | In this paper we focus on the linear algebra theory behind feedforward (FNN)
and recurrent (RNN) neural networks. We review backward propagation, including
backward propagation through time (BPTT). Also, we obtain a new exact
expression for Hessian, which represents second order effects. We show that for
$t$ time steps... | computer science |
10,152 | MuseGAN: Multi-track Sequential Generative Adversarial Networks for
Symbolic Music Generation and Accompaniment | eess.AS | Generating music has a few notable differences from generating images and
videos. First, music is an art of time, necessitating a temporal model. Second,
music is usually composed of multiple instruments/tracks with their own
temporal dynamics, but collectively they unfold over time interdependently.
Lastly, musical no... | computer science |
10,153 | Exact Learning of Lightweight Description Logic Ontologies | cs.LG | We study the problem of learning description logic (DL) ontologies in Angluin
et al.'s framework of exact learning via queries. We admit membership queries
("is a given subsumption entailed by the target ontology?") and equivalence
queries ("is a given ontology equivalent to the target ontology?"). We present
three mai... | computer science |
10,154 | Multi-task Learning with Gradient Guided Policy Specialization | cs.RO | We present a method for efficient learning of control policies for multiple
related robotic motor skills. Our approach consists of two stages, joint
training and specialization training. During the joint training stage, a neural
network policy is trained with minimal information to disambiguate the motor
skills. This f... | computer science |
10,155 | Self-supervised learning: When is fusion of the primary and secondary
sensor cue useful? | cs.RO | Self-supervised learning (SSL) is a reliable learning mechanism in which a
robot enhances its perceptual capabilities. Typically, in SSL a trusted,
primary sensor cue provides supervised training data to a secondary sensor cue.
In this article, a theoretical analysis is performed on the fusion of the
primary and second... | computer science |
10,156 | Underwater Multi-Robot Convoying using Visual Tracking by Detection | cs.RO | We present a robust multi-robot convoying approach that relies on visual
detection of the leading agent, thus enabling target following in unstructured
3-D environments. Our method is based on the idea of tracking-by-detection,
which interleaves efficient model-based object detection with temporal
filtering of image-ba... | computer science |
10,157 | Towards continuous control of flippers for a multi-terrain robot using
deep reinforcement learning | cs.RO | In this paper we focus on developing a control algorithm for multi-terrain
tracked robots with flippers using a reinforcement learning (RL) approach. The
work is based on the deep deterministic policy gradient (DDPG) algorithm,
proven to be very successful in simple simulation environments. The algorithm
works in an en... | computer science |
10,158 | Towards automation of data quality system for CERN CMS experiment | cs.AI | Daily operation of a large-scale experiment is a challenging task,
particularly from perspectives of routine monitoring of quality for data being
taken. We describe an approach that uses Machine Learning for the automated
system to monitor data quality, which is based on partial use of data qualified
manually by detect... | computer science |
10,159 | Active Learning amidst Logical Knowledge | cs.AI | Structured prediction is ubiquitous in applications of machine learning such
as knowledge extraction and natural language processing. Structure often can be
formulated in terms of logical constraints. We consider the question of how to
perform efficient active learning in the presence of logical constraints among
varia... | computer science |
10,160 | A Benchmark Environment Motivated by Industrial Control Problems | cs.AI | In the research area of reinforcement learning (RL), frequently novel and
promising methods are developed and introduced to the RL community. However,
although many researchers are keen to apply their methods on real-world
problems, implementing such methods in real industry environments often is a
frustrating and tedi... | computer science |
10,161 | Premise Selection for Theorem Proving by Deep Graph Embedding | cs.AI | We propose a deep learning-based approach to the problem of premise
selection: selecting mathematical statements relevant for proving a given
conjecture. We represent a higher-order logic formula as a graph that is
invariant to variable renaming but still fully preserves syntactic and semantic
information. We then embe... | computer science |
10,162 | Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep
Reinforcement Learning | cs.RO | Developing a safe and efficient collision avoidance policy for multiple
robots is challenging in the decentralized scenarios where each robot generate
its paths without observing other robots' states and intents. While other
distributed multi-robot collision avoidance systems exist, they often require
extracting agent-... | computer science |
10,163 | Learning Complex Dexterous Manipulation with Deep Reinforcement Learning
and Demonstrations | cs.LG | Dexterous multi-fingered hands are extremely versatile and provide a generic
way to perform multiple tasks in human-centric environments. However,
effectively controlling them remains challenging due to their high
dimensionality and large number of potential contacts. Deep reinforcement
learning (DRL) provides a model-... | computer science |
10,164 | Provably Minimally-Distorted Adversarial Examples | cs.LG | The ability to deploy neural networks in real-world, safety-critical systems
is severely limited by the presence of adversarial examples: slightly perturbed
inputs that are misclassified by the network. In recent years, several
techniques have been proposed for increasing robustness to adversarial examples
--- and yet ... | computer science |
10,165 | Self-supervised Deep Reinforcement Learning with Generalized Computation
Graphs for Robot Navigation | cs.LG | Enabling robots to autonomously navigate complex environments is essential
for real-world deployment. Prior methods approach this problem by having the
robot maintain an internal map of the world, and then use a localization and
planning method to navigate through the internal map. However, these approaches
often inclu... | computer science |
10,166 | Rényi Differential Privacy Mechanisms for Posterior Sampling | cs.LG | Using a recently proposed privacy definition of R\'enyi Differential Privacy
(RDP), we re-examine the inherent privacy of releasing a single sample from a
posterior distribution. We exploit the impact of the prior distribution in
mitigating the influence of individual data points. In particular, we focus on
sampling fr... | computer science |
10,167 | Neural Task Programming: Learning to Generalize Across Hierarchical
Tasks | cs.AI | In this work, we propose a novel robot learning framework called Neural Task
Programming (NTP), which bridges the idea of few-shot learning from
demonstration and neural program induction. NTP takes as input a task
specification (e.g., video demonstration of a task) and recursively decomposes
it into finer sub-task spe... | computer science |
10,168 | Socially Compliant Navigation through Raw Depth Inputs with Generative
Adversarial Imitation Learning | cs.RO | 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 over... | computer science |
10,169 | Specification Inference from Demonstrations | cs.LG | Learning from expert demonstrations has received a lot of attention in
artificial intelligence and machine learning. The goal is to infer the
underlying reward function that an agent is optimizing given a set of
observations of the agent's behavior over time in a variety of circumstances,
the system state trajectories,... | computer science |
10,170 | Synkhronos: a Multi-GPU Theano Extension for Data Parallelism | cs.DC | We present Synkhronos, an extension to Theano for multi-GPU computations
leveraging data parallelism. Our framework provides automated execution and
synchronization across devices, allowing users to continue to write serial
programs without risk of race conditions. The NVIDIA Collective Communication
Library is used fo... | computer science |
10,171 | Community Aware Random Walk for Network Embedding | cs.SI | 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 hav... | computer science |
10,172 | Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of
Robots by Deep Reinforcement Learning | cs.RO | 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 environme... | computer science |
10,173 | Asymmetric Actor Critic for Image-Based Robot Learning | cs.RO | Deep reinforcement learning (RL) has proven a powerful technique in many
sequential decision making domains. However, Robotics poses many challenges for
RL, most notably training on a physical system can be expensive and dangerous,
which has sparked significant interest in learning control policies using a
physics simu... | computer science |
10,174 | Model Identification via Physics Engines for Improved Policy Search | cs.RO | This paper presents a practical approach for identifying unknown mechanical
parameters, such as mass and friction models of manipulated rigid objects or
actuated robotic links, in a succinct manner that aims to improve the
performance of policy search algorithms. Key features of this approach are the
use of off-the-she... | computer science |
10,175 | Efficiently Trainable Text-to-Speech System Based on Deep Convolutional
Networks with Guided Attention | cs.SD | This paper describes a novel text-to-speech (TTS) technique based on deep
convolutional neural networks (CNN), without any recurrent units. Recurrent
neural network (RNN) has been a standard technique to model sequential data
recently, and this technique has been used in some cutting-edge neural TTS
techniques. However... | computer science |
10,176 | Inverse Reinforcement Learning Under Noisy Observations | cs.RO | 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
cons... | computer science |
10,177 | Interpretable Apprenticeship Learning with Temporal Logic Specifications | cs.SY | Recent work has addressed using formulas in linear temporal logic (LTL) as
specifications for agents planning in Markov Decision Processes (MDPs). We
consider the inverse problem: inferring an LTL specification from demonstrated
behavior trajectories in MDPs. We formulate this as a multiobjective
optimization problem, ... | computer science |
10,178 | Artificial Intelligence as Structural Estimation: Economic
Interpretations of Deep Blue, Bonanza, and AlphaGo | econ.EM | Artificial intelligence (AI) has achieved superhuman performance in a growing
number of tasks, but understanding and explaining AI remain challenging. This
paper clarifies the connections between machine-learning algorithms to develop
AIs and the econometrics of dynamic structural models through the case studies
of thr... | computer science |
10,179 | Deep Forward and Inverse Perceptual Models for Tracking and Prediction | cs.RO | We consider the problems of learning forward models that map state to
high-dimensional images and inverse models that map high-dimensional images to
state in robotics. Specifically, we present a perceptual model for generating
video frames from state with deep networks, and provide a framework for its use
in tracking a... | computer science |
10,180 | Learning to Represent Programs with Graphs | cs.LG | Learning tasks on source code (i.e., formal languages) have been considered
recently, but most work has tried to transfer natural language methods and does
not capitalize on the unique opportunities offered by code's known syntax. For
example, long-range dependencies induced by using the same variable or function
in di... | computer science |
10,181 | A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning | cs.AI | To achieve general intelligence, agents must learn how to interact with
others in a shared environment: this is the challenge of multiagent
reinforcement learning (MARL). The simplest form is independent reinforcement
learning (InRL), where each agent treats its experience as part of its
(non-stationary) environment. I... | computer science |
10,182 | Provable defenses against adversarial examples via the convex outer
adversarial polytope | cs.LG | We propose a method to learn deep ReLU-based classifiers that are provably
robust against norm-bounded adversarial perturbations on the training data. For
previously unseen examples, the approach is guaranteed to detect all
adversarial examples, though it may flag some non-adversarial examples as well.
The basic idea i... | computer science |
10,183 | Guiding the search in continuous state-action spaces by learning an
action sampling distribution from off-target samples | cs.AI | 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 op... | computer science |
10,184 | HPX Smart Executors | cs.DC | 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 app... | computer science |
10,185 | RoboCupSimData: A RoboCup soccer research dataset | cs.AI | 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 to... | computer science |
10,186 | Un résultat intrigant en commande sans modèle | cs.SY | An elementary mathematical example proves, thanks to the Routh-Hurwitz
criterion, a result that is intriguing with respect to today's practical
understanding of model-free control, i.e., an "intelligent" proportional
controller (iP) may turn to be more difficult to tune than an intelligent
proportional-derivative one (... | computer science |
10,187 | Communicative Capital for Prosthetic Agents | cs.AI | This work presents an overarching perspective on the role that machine
intelligence can play in enhancing human abilities, especially those that have
been diminished due to injury or illness. As a primary contribution, we develop
the hypothesis that assistive devices, and specifically artificial arms and
hands, can and... | computer science |
10,188 | Deep Within-Class Covariance Analysis for Acoustic Scene Classification | cs.LG | Within-Class Covariance Normalization (WCCN) is a powerful post-processing
method for normalizing the within-class covariance of a set of data points.
WCCN projects the observations into a linear sub-space where the within-class
variability is reduced. This property has proven to be beneficial in subsequent
recognition... | computer science |
10,189 | Bayesian Best-Arm Identification for Selecting Influenza Mitigation
Strategies | cs.LG | Pandemic influenza has the epidemic potential to kill millions of people.
While various preventive measures exist (i.a., vaccination and school
closures), deciding on strategies that lead to their most effective and
efficient use, remains challenging. To this end, individual-based
epidemiological models are essential t... | computer science |
10,190 | Fullie and Wiselie: A Dual-Stream Recurrent Convolutional Attention
Model for Activity Recognition | cs.HC | Multimodal features play a key role in wearable sensor based Human Activity
Recognition (HAR). Selecting the most salient features adaptively is a
promising way to maximize the effectiveness of multimodal sensor data. In this
regard, we propose a "collect fully and select wisely (Fullie and Wiselie)"
principle as well ... | computer science |
10,191 | MagNet and "Efficient Defenses Against Adversarial Attacks" are Not
Robust to Adversarial Examples | cs.LG | MagNet and "Efficient Defenses..." were recently proposed as a defense to
adversarial examples. We find that we can construct adversarial examples that
defeat these defenses with only a slight increase in distortion. | computer science |
10,192 | Recurrent Generative Adversarial Networks for Proximal Learning and
Automated Compressive Image Recovery | cs.AI | 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 ph... | computer science |
10,193 | Risk-sensitive Inverse Reinforcement Learning via Semi- and
Non-Parametric Methods | cs.AI | 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 f... | computer science |
10,194 | Homomorphic Parameter Compression for Distributed Deep Learning Training | cs.DC | Distributed training of deep neural networks has received significant
research interest, and its major approaches include implementations on multiple
GPUs and clusters. Parallelization can dramatically improve the efficiency of
training deep and complicated models with large-scale data. A fundamental
barrier against th... | computer science |
10,195 | One-Shot Reinforcement Learning for Robot Navigation with Interactive
Replay | cs.AI | Recently, model-free reinforcement learning algorithms have been shown to
solve challenging problems by learning from extensive interaction with the
environment. A significant issue with transferring this success to the robotics
domain is that interaction with the real world is costly, but training on
limited experienc... | computer science |
10,196 | Backprop as Functor: A compositional perspective on supervised learning | math.CT | A supervised learning algorithm searches over a set of functions $A \to B$
parametrised by a space $P$ to find the best approximation to some ideal
function $f\colon A \to B$. It does this by taking examples $(a,f(a)) \in
A\times B$, and updating the parameter according to some rule. We define a
category where these up... | computer science |
10,197 | Improving Latent User Models in Online Social Media | cs.SI | Modern social platforms are characterized by the presence of rich
user-behavior data associated with the publication, sharing and consumption of
textual content. Users interact with content and with each other in a complex
and dynamic social environment while simultaneously evolving over time. In
order to effectively c... | computer science |
10,198 | MAgent: A Many-Agent Reinforcement Learning Platform for Artificial
Collective Intelligence | cs.LG | We introduce MAgent, a platform to support research and development of
many-agent reinforcement learning. Unlike previous research platforms on single
or multi-agent reinforcement learning, MAgent focuses on supporting the tasks
and the applications that require hundreds to millions of agents. Within the
interactions a... | computer science |
10,199 | Transferring Autonomous Driving Knowledge on Simulated and Real
Intersections | cs.LG | We view intersection handling on autonomous vehicles as a reinforcement
learning problem, and study its behavior in a transfer learning setting. We
show that a network trained on one type of intersection generally is not able
to generalize to other intersections. However, a network that is pre-trained on
one intersecti... | computer science |
10,200 | A Class of Logistic Functions for Approximating State-Inclusive Koopman
Operators | cs.LG | An outstanding challenge in nonlinear systems theory is identification or
learning of a given nonlinear system's Koopman operator directly from data or
models. Advances in extended dynamic mode decomposition approaches and machine
learning methods have enabled data-driven discovery of Koopman operators, for
both contin... | computer science |
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