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10,001 | TensorLog: A Differentiable Deductive Database | cs.AI | Large knowledge bases (KBs) are useful in many tasks, but it is unclear how
to integrate this sort of knowledge into "deep" gradient-based learning
systems. To address this problem, we describe a probabilistic deductive
database, called TensorLog, in which reasoning uses a differentiable process.
In TensorLog, each cla... | computer science |
10,002 | Learning to Communicate with Deep Multi-Agent Reinforcement Learning | cs.AI | We consider the problem of multiple agents sensing and acting in environments
with the goal of maximising their shared utility. In these environments, agents
must learn communication protocols in order to share information that is needed
to solve the tasks. By embracing deep neural networks, we are able to
demonstrate ... | computer science |
10,003 | Adaptive ADMM with Spectral Penalty Parameter Selection | cs.LG | The alternating direction method of multipliers (ADMM) is a versatile tool
for solving a wide range of constrained optimization problems, with
differentiable or non-differentiable objective functions. Unfortunately, its
performance is highly sensitive to a penalty parameter, which makes ADMM often
unreliable and hard t... | computer science |
10,004 | Internal Guidance for Satallax | cs.LO | We propose a new internal guidance method for automated theorem provers based
on the given-clause algorithm. Our method influences the choice of unprocessed
clauses using positive and negative examples from previous proofs. To this end,
we present an efficient scheme for Naive Bayesian classification by
generalising la... | computer science |
10,005 | Distance Metric Ensemble Learning and the Andrews-Curtis Conjecture | cs.AI | Motivated by the search for a counterexample to the Poincar\'e conjecture in
three and four dimensions, the Andrews-Curtis conjecture was proposed in 1965.
It is now generally suspected that the Andrews-Curtis conjecture is false, but
small potential counterexamples are not so numerous, and previous work has
attempted ... | computer science |
10,006 | DeepMath - Deep Sequence Models for Premise Selection | cs.AI | We study the effectiveness of neural sequence models for premise selection in
automated theorem proving, one of the main bottlenecks in the formalization of
mathematics. We propose a two stage approach for this task that yields good
results for the premise selection task on the Mizar corpus while avoiding the
hand-engi... | computer science |
10,007 | Proceedings First International Workshop on Hammers for Type Theories | cs.LO | This volume of EPTCS contains the proceedings of the First Workshop on
Hammers for Type Theories (HaTT 2016), held on 1 July 2016 as part of the
International Joint Conference on Automated Reasoning (IJCAR 2016) in Coimbra,
Portugal. The proceedings contain four regular papers, as well as abstracts of
the two invited t... | computer science |
10,008 | A Comparative Analysis of classification data mining techniques :
Deriving key factors useful for predicting students performance | cs.LG | Students opting for Engineering as their discipline is increasing rapidly.
But due to various factors and inappropriate primary education in India,
failure rates are high. Students are unable to excel in core engineering
because of complex and mathematical subjects. Hence, they fail in such
subjects. With the help of d... | computer science |
10,009 | The VGLC: The Video Game Level Corpus | cs.HC | Levels are a key component of many different video games, and a large body of
work has been produced on how to procedurally generate game levels. Recently,
Machine Learning techniques have been applied to video game level generation
towards the purpose of automatically generating levels that have the properties
of the ... | computer science |
10,010 | Neural Network Based Next-Song Recommendation | cs.IR | Recently, the next-item/basket recommendation system, which considers the
sequential relation between bought items, has drawn attention of researchers.
The utilization of sequential patterns has boosted performance on several kinds
of recommendation tasks. Inspired by natural language processing (NLP)
techniques, we pr... | computer science |
10,011 | A Reduction for Optimizing Lattice Submodular Functions with Diminishing
Returns | cs.DS | A function $f: \mathbb{Z}_+^E \rightarrow \mathbb{R}_+$ is DR-submodular if
it satisfies $f(\bx + \chi_i) -f (\bx) \ge f(\by + \chi_i) - f(\by)$ for all
$\bx\le \by, i\in E$. Recently, the problem of maximizing a DR-submodular
function $f: \mathbb{Z}_+^E \rightarrow \mathbb{R}_+$ subject to a budget
constraint $\|\bx\|... | computer science |
10,012 | Technical Report: Towards a Universal Code Formatter through Machine
Learning | cs.PL | There are many declarative frameworks that allow us to implement code
formatters relatively easily for any specific language, but constructing them
is cumbersome. The first problem is that "everybody" wants to format their code
differently, leading to either many formatter variants or a ridiculous number
of configurati... | computer science |
10,013 | subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs
from Large Graphs | cs.LG | In this paper, we present subgraph2vec, a novel approach for learning latent
representations of rooted subgraphs from large graphs inspired by recent
advancements in Deep Learning and Graph Kernels. These latent representations
encode semantic substructure dependencies in a continuous vector space, which
is easily expl... | computer science |
10,014 | Performance Based Evaluation of Various Machine Learning Classification
Techniques for Chronic Kidney Disease Diagnosis | cs.LG | Areas where Artificial Intelligence (AI) & related fields are finding their
applications are increasing day by day, moving from core areas of computer
science they are finding their applications in various other domains.In recent
times Machine Learning i.e. a sub-domain of AI has been widely used in order to
assist med... | computer science |
10,015 | Fractal Dimension Pattern Based Multiresolution Analysis for Rough
Estimator of Person-Dependent Audio Emotion Recognition | cs.AI | As a general means of expression, audio analysis and recognition has
attracted much attentions for its wide applications in real-life world. Audio
emotion recognition (AER) attempts to understand emotional states of human with
the given utterance signals, and has been studied abroad for its further
development on frien... | computer science |
10,016 | Adaptive Neighborhood Graph Construction for Inference in
Multi-Relational Networks | cs.SI | A neighborhood graph, which represents the instances as vertices and their
relations as weighted edges, is the basis of many semi-supervised and
relational models for node labeling and link prediction. Most methods employ a
sequential process to construct the neighborhood graph. This process often
consists of generatin... | computer science |
10,017 | Application of Statistical Relational Learning to Hybrid Recommendation
Systems | cs.AI | Recommendation systems usually involve exploiting the relations among known
features and content that describe items (content-based filtering) or the
overlap of similar users who interacted with or rated the target item
(collaborative filtering). To combine these two filtering approaches, current
model-based hybrid rec... | computer science |
10,018 | CaR-FOREST: Joint Classification-Regression Decision Forests for
Overlapping Audio Event Detection | cs.SD | This report describes our submissions to Task2 and Task3 of the DCASE 2016
challenge. The systems aim at dealing with the detection of overlapping audio
events in continuous streams, where the detectors are based on random decision
forests. The proposed forests are jointly trained for classification and
regression simu... | computer science |
10,019 | Explaining Deep Convolutional Neural Networks on Music Classification | cs.LG | Deep convolutional neural networks (CNNs) have been actively adopted in the
field of music information retrieval, e.g. genre classification, mood
detection, and chord recognition. However, the process of learning and
prediction is little understood, particularly when it is applied to
spectrograms. We introduce auralisa... | computer science |
10,020 | DeepQA: Improving the estimation of single protein model quality with
deep belief networks | cs.AI | Protein quality assessment (QA) by ranking and selecting protein models has
long been viewed as one of the major challenges for protein tertiary structure
prediction. Especially, estimating the quality of a single protein model, which
is important for selecting a few good models out of a large model pool
consisting of ... | computer science |
10,021 | The Price of Anarchy in Auctions | cs.GT | This survey outlines a general and modular theory for proving approximation
guarantees for equilibria of auctions in complex settings. This theory
complements traditional economic techniques, which generally focus on exact and
optimal solutions and are accordingly limited to relatively stylized settings.
We highlight... | computer science |
10,022 | Discovering Latent States for Model Learning: Applying Sensorimotor
Contingencies Theory and Predictive Processing to Model Context | cs.RO | Autonomous robots need to be able to adapt to unforeseen situations and to
acquire new skills through trial and error. Reinforcement learning in principle
offers a suitable methodological framework for this kind of autonomous
learning. However current computational reinforcement learning agents mostly
learn each indivi... | computer science |
10,023 | Learning Transferable Policies for Monocular Reactive MAV Control | cs.RO | The ability to transfer knowledge gained in previous tasks into new contexts
is one of the most important mechanisms of human learning. Despite this,
adapting autonomous behavior to be reused in partially similar settings is
still an open problem in current robotics research. In this paper, we take a
small step in this... | computer science |
10,024 | Context Discovery for Model Learning in Partially Observable
Environments | cs.RO | The ability to learn a model is essential for the success of autonomous
agents. Unfortunately, learning a model is difficult in partially observable
environments, where latent environmental factors influence what the agent
observes. In the absence of a supervisory training signal, autonomous agents
therefore require a ... | computer science |
10,025 | ZaliQL: A SQL-Based Framework for Drawing Causal Inference from Big Data | cs.DB | Causal inference from observational data is a subject of active research and
development in statistics and computer science. Many toolkits have been
developed for this purpose that depends on statistical software. However, these
toolkits do not scale to large datasets. In this paper we describe a suite of
techniques fo... | computer science |
10,026 | A Formal Solution to the Grain of Truth Problem | cs.AI | A Bayesian agent acting in a multi-agent environment learns to predict the
other agents' policies if its prior assigns positive probability to them (in
other words, its prior contains a \emph{grain of truth}). Finding a reasonably
large class of policies that contains the Bayes-optimal policies with respect
to this cla... | computer science |
10,027 | Recognizing Detailed Human Context In-the-Wild from Smartphones and
Smartwatches | cs.AI | The ability to automatically recognize a person's behavioral context can
contribute to health monitoring, aging care and many other domains. Validating
context recognition in-the-wild is crucial to promote practical applications
that work in real-life settings. We collected over 300k minutes of sensor data
with context... | computer science |
10,028 | Semiring Programming: A Framework for Search, Inference and Learning | cs.AI | To solve hard problems, AI relies on a variety of disciplines such as logic,
probabilistic reasoning, machine learning and mathematical programming.
Although it is widely accepted that solving real-world problems requires an
integration amongst these, contemporary representation methodologies offer
little support for t... | computer science |
10,029 | Discovering Sound Concepts and Acoustic Relations In Text | cs.SD | In this paper we describe approaches for discovering acoustic concepts and
relations in text. The first major goal is to be able to identify text phrases
which contain a notion of audibility and can be termed as a sound or an
acoustic concept. We also propose a method to define an acoustic scene through
a set of sound ... | computer science |
10,030 | Learning from the Hindsight Plan -- Episodic MPC Improvement | cs.RO | Model predictive control (MPC) is a popular control method that has proved
effective for robotics, among other fields. MPC performs re-planning at every
time step. Re-planning is done with a limited horizon per computational and
real-time constraints and often also for robustness to potential model errors.
However, the... | computer science |
10,031 | Deep Reinforcement Learning for Robotic Manipulation with Asynchronous
Off-Policy Updates | cs.RO | Reinforcement learning holds the promise of enabling autonomous robots to
learn large repertoires of behavioral skills with minimal human intervention.
However, robotic applications of reinforcement learning often compromise the
autonomy of the learning process in favor of achieving training times that are
practical fo... | computer science |
10,032 | Collective Robot Reinforcement Learning with Distributed Asynchronous
Guided Policy Search | cs.LG | In principle, reinforcement learning and policy search methods can enable
robots to learn highly complex and general skills that may allow them to
function amid the complexity and diversity of the real world. However, training
a policy that generalizes well across a wide range of real-world conditions
requires far grea... | computer science |
10,033 | Micro-Data Learning: The Other End of the Spectrum | cs.AI | Many fields are now snowed under with an avalanche of data, which raises
considerable challenges for computer scientists. Meanwhile, robotics (among
other fields) can often only use a few dozen data points because acquiring them
involves a process that is expensive or time-consuming. How can an algorithm
learn with onl... | computer science |
10,034 | EPOpt: Learning Robust Neural Network Policies Using Model Ensembles | cs.LG | Sample complexity and safety are major challenges when learning policies with
reinforcement learning for real-world tasks, especially when the policies are
represented using rich function approximators like deep neural networks.
Model-based methods where the real-world target domain is approximated using a
simulated so... | computer science |
10,035 | Ranking academic institutions on potential paper acceptance in upcoming
conferences | cs.AI | The crux of the problem in KDD Cup 2016 involves developing data mining
techniques to rank research institutions based on publications. Rank importance
of research institutions are derived from predictions on the number of full
research papers that would potentially get accepted in upcoming top-tier
conferences, utiliz... | computer science |
10,036 | Heuristic Approaches for Generating Local Process Models through Log
Projections | cs.LG | Local Process Model (LPM) discovery is focused on the mining of a set of
process models where each model describes the behavior represented in the event
log only partially, i.e. subsets of possible events are taken into account to
create so-called local process models. Often such smaller models provide
valuable insight... | computer science |
10,037 | Transfer from Simulation to Real World through Learning Deep Inverse
Dynamics Model | cs.RO | Developing control policies in simulation is often more practical and safer
than directly running experiments in the real world. This applies to policies
obtained from planning and optimization, and even more so to policies obtained
from reinforcement learning, which is often very data demanding. However, a
policy that... | computer science |
10,038 | An Information Theoretic Feature Selection Framework for Big Data under
Apache Spark | cs.AI | With the advent of extremely high dimensional datasets, dimensionality
reduction techniques are becoming mandatory. Among many techniques, feature
selection has been growing in interest as an important tool to identify
relevant features on huge datasets --both in number of instances and
features--. The purpose of this ... | computer science |
10,039 | Quantum-enhanced machine learning | cs.AI | The emerging field of quantum machine learning has the potential to
substantially aid in the problems and scope of artificial intelligence. This is
only enhanced by recent successes in the field of classical machine learning.
In this work we propose an approach for the systematic treatment of machine
learning, from the... | computer science |
10,040 | Synthesis of Shared Control Protocols with Provable Safety and
Performance Guarantees | cs.RO | We formalize synthesis of shared control protocols with correctness
guarantees for temporal logic specifications. More specifically, we introduce a
modeling formalism in which both a human and an autonomy protocol can issue
commands to a robot towards performing a certain task. These commands are
blended into a joint i... | computer science |
10,041 | Learning-Theoretic Foundations of Algorithm Configuration for
Combinatorial Partitioning Problems | cs.DS | Max-cut, clustering, and many other partitioning problems that are of
significant importance to machine learning and other scientific fields are
NP-hard, a reality that has motivated researchers to develop a wealth of
approximation algorithms and heuristics. Although the best algorithm to use
typically depends on the s... | computer science |
10,042 | Composing Music with Grammar Argumented Neural Networks and Note-Level
Encoding | cs.LG | Creating aesthetically pleasing pieces of art, including music, has been a
long-term goal for artificial intelligence research. Despite recent successes
of long-short term memory (LSTM) recurrent neural networks (RNNs) in sequential
learning, LSTM neural networks have not, by themselves, been able to generate
natural-s... | computer science |
10,043 | Monte Carlo Connection Prover | cs.LO | Monte Carlo Tree Search (MCTS) is a technique to guide search in a large
decision space by taking random samples and evaluating their outcome. In this
work, we study MCTS methods in the context of the connection calculus and
implement them on top of the leanCoP prover. This includes proposing useful
proof-state evaluat... | computer science |
10,044 | Structural Causal Models: Cycles, Marginalizations, Exogenous
Reparametrizations and Reductions | stat.ME | Structural causal models (SCMs), also known as non-parametric structural
equation models (NP-SEMs), are widely used for causal modeling purposes. In
this paper, we give a rigorous treatment of structural causal models, dealing
with measure-theoretic complications that arise in the presence of cyclic
relations. The cent... | computer science |
10,045 | A Survey of Credit Card Fraud Detection Techniques: Data and Technique
Oriented Perspective | cs.CR | Credit card plays a very important rule in today's economy. It becomes an
unavoidable part of household, business and global activities. Although using
credit cards provides enormous benefits when used carefully and
responsibly,significant credit and financial damages may be caused by
fraudulent activities. Many techni... | computer science |
10,046 | Training an Interactive Humanoid Robot Using Multimodal Deep
Reinforcement Learning | cs.LG | Training robots to perceive, act and communicate using multiple modalities
still represents a challenging problem, particularly if robots are expected to
learn efficiently from small sets of example interactions. We describe a
learning approach as a step in this direction, where we teach a humanoid robot
how to play th... | computer science |
10,047 | BliStrTune: Hierarchical Invention of Theorem Proving Strategies | cs.LO | Inventing targeted proof search strategies for specific problem sets is a
difficult task. State-of-the-art automated theorem provers (ATPs) such as E
allow a large number of user-specified proof search strategies described in a
rich domain specific language. Several machine learning methods that invent
strategies autom... | computer science |
10,048 | SeDMiD for Confusion Detection: Uncovering Mind State from Time Series
Brain Wave Data | cs.AI | Understanding how brain functions has been an intriguing topic for years.
With the recent progress on collecting massive data and developing advanced
technology, people have become interested in addressing the challenge of
decoding brain wave data into meaningful mind states, with many machine
learning models and algor... | computer science |
10,049 | On the Usability of Probably Approximately Correct Implication Bases | cs.AI | We revisit the notion of probably approximately correct implication bases
from the literature and present a first formulation in the language of formal
concept analysis, with the goal to investigate whether such bases represent a
suitable substitute for exact implication bases in practical use-cases. To this
end, we qu... | computer science |
10,050 | Toward negotiable reinforcement learning: shifting priorities in Pareto
optimal sequential decision-making | cs.AI | Existing multi-objective reinforcement learning (MORL) algorithms do not
account for objectives that arise from players with differing beliefs.
Concretely, consider two players with different beliefs and utility functions
who may cooperate to build a machine that takes actions on their behalf. A
representation is neede... | computer science |
10,051 | Learning local trajectories for high precision robotic tasks :
application to KUKA LBR iiwa Cartesian positioning | cs.AI | To ease the development of robot learning in industry, two conditions need to
be fulfilled. Manipulators must be able to learn high accuracy and precision
tasks while being safe for workers in the factory. In this paper, we extend
previously submitted work which consists in rapid learning of local high
accuracy behavio... | computer science |
10,052 | Real-Time Bidding by Reinforcement Learning in Display Advertising | cs.LG | The majority of online display ads are served through real-time bidding (RTB)
--- each ad display impression is auctioned off in real-time when it is just
being generated from a user visit. To place an ad automatically and optimally,
it is critical for advertisers to devise a learning algorithm to cleverly bid
an ad im... | computer science |
10,053 | Real-time eSports Match Result Prediction | stat.AP | In this paper, we try to predict the winning team of a match in the
multiplayer eSports game Dota 2. To address the weaknesses of previous work, we
consider more aspects of prior (pre-match) features from individual players'
match history, as well as real-time (during-match) features at each minute as
the match progres... | computer science |
10,054 | Residual LSTM: Design of a Deep Recurrent Architecture for Distant
Speech Recognition | cs.LG | In this paper, a novel architecture for a deep recurrent neural network,
residual LSTM is introduced. A plain LSTM has an internal memory cell that can
learn long term dependencies of sequential data. It also provides a temporal
shortcut path to avoid vanishing or exploding gradients in the temporal domain.
The residua... | computer science |
10,055 | Achieving Privacy in the Adversarial Multi-Armed Bandit | cs.LG | In this paper, we improve the previously best known regret bound to achieve
$\epsilon$-differential privacy in oblivious adversarial bandits from
$\mathcal{O}{(T^{2/3}/\epsilon)}$ to $\mathcal{O}{(\sqrt{T} \ln T /\epsilon)}$.
This is achieved by combining a Laplace Mechanism with EXP3. We show that
though EXP3 is alrea... | computer science |
10,056 | Label Propagation on K-partite Graphs with Heterophily | cs.LG | In this paper, for the first time, we study label propagation in
heterogeneous graphs under heterophily assumption. Homophily label propagation
(i.e., two connected nodes share similar labels) in homogeneous graph (with
same types of vertices and relations) has been extensively studied before.
Unfortunately, real-life ... | computer science |
10,057 | ENIGMA: Efficient Learning-based Inference Guiding Machine | cs.LO | ENIGMA is a learning-based method for guiding given clause selection in
saturation-based theorem provers. Clauses from many proof searches are
classified as positive and negative based on their participation in the proofs.
An efficient classification model is trained on this data, using fast
feature-based characterizat... | computer science |
10,058 | Deep Network Guided Proof Search | cs.AI | Deep learning techniques lie at the heart of several significant AI advances
in recent years including object recognition and detection, image captioning,
machine translation, speech recognition and synthesis, and playing the game of
Go. Automated first-order theorem provers can aid in the formalization and
verificatio... | computer science |
10,059 | Fast Exact k-Means, k-Medians and Bregman Divergence Clustering in 1D | cs.DS | The $k$-Means clustering problem on $n$ points is NP-Hard for any dimension
$d\ge 2$, however, for the 1D case there exist exact polynomial time
algorithms. Previous literature reported an $O(kn^2)$ time dynamic programming
algorithm that uses $O(kn)$ space. We present a new algorithm computing the
optimal clustering i... | computer science |
10,060 | Click Through Rate Prediction for Contextual Advertisment Using Linear
Regression | cs.IR | This research presents an innovative and unique way of solving the
advertisement prediction problem which is considered as a learning problem over
the past several years. Online advertising is a multi-billion-dollar industry
and is growing every year with a rapid pace. The goal of this research is to
enhance click thro... | computer science |
10,061 | Towards "AlphaChem": Chemical Synthesis Planning with Tree Search and
Deep Neural Network Policies | cs.AI | Retrosynthesis is a technique to plan the chemical synthesis of organic
molecules, for example drugs, agro- and fine chemicals. In retrosynthesis, a
search tree is built by analysing molecules recursively and dissecting them
into simpler molecular building blocks until one obtains a set of known
building blocks. The se... | computer science |
10,062 | Traffic Lights with Auction-Based Controllers: Algorithms and Real-World
Data | cs.AI | Real-time optimization of traffic flow addresses important practical
problems: reducing a driver's wasted time, improving city-wide efficiency,
reducing gas emissions and improving air quality. Much of the current research
in traffic-light optimization relies on extending the capabilities of traffic
lights to either co... | computer science |
10,063 | A Theoretical Analysis of First Heuristics of Crowdsourced Entity
Resolution | cs.DB | Entity resolution (ER) is the task of identifying all records in a database
that refer to the same underlying entity, and are therefore duplicates of each
other. Due to inherent ambiguity of data representation and poor data quality,
ER is a challenging task for any automated process. As a remedy, human-powered
ER via ... | computer science |
10,064 | Graph Based Relational Features for Collective Classification | cs.IR | Statistical Relational Learning (SRL) methods have shown that classification
accuracy can be improved by integrating relations between samples. Techniques
such as iterative classification or relaxation labeling achieve this by
propagating information between related samples during the inference process.
When only a few... | computer science |
10,065 | Multi-agent Reinforcement Learning in Sequential Social Dilemmas | cs.MA | Matrix games like Prisoner's Dilemma have guided research on social dilemmas
for decades. However, they necessarily treat the choice to cooperate or defect
as an atomic action. In real-world social dilemmas these choices are temporally
extended. Cooperativeness is a property that applies to policies, not
elementary act... | computer science |
10,066 | Revisiting Distributed Synchronous SGD | cs.DC | Distributed training of deep learning models on large-scale training data is
typically conducted with asynchronous stochastic optimization to maximize the
rate of updates, at the cost of additional noise introduced from asynchrony. In
contrast, the synchronous approach is often thought to be impractical due to
idle tim... | computer science |
10,067 | Hemingway: Modeling Distributed Optimization Algorithms | cs.DC | Distributed optimization algorithms are widely used in many industrial
machine learning applications. However choosing the appropriate algorithm and
cluster size is often difficult for users as the performance and convergence
rate of optimization algorithms vary with the size of the cluster. In this
paper we make the c... | computer science |
10,068 | Towards a Common Implementation of Reinforcement Learning for Multiple
Robotic Tasks | cs.AI | Mobile robots are increasingly being employed for performing complex tasks in
dynamic environments. Reinforcement learning (RL) methods are recognized to be
promising for specifying such tasks in a relatively simple manner. However, the
strong dependency between the learning method and the task to learn is a
well-known... | computer science |
10,069 | DeepCloak: Masking Deep Neural Network Models for Robustness Against
Adversarial Samples | cs.LG | Recent studies have shown that deep neural networks (DNN) are vulnerable to
adversarial samples: maliciously-perturbed samples crafted to yield incorrect
model outputs. Such attacks can severely undermine DNN systems, particularly in
security-sensitive settings. It was observed that an adversary could easily
generate a... | computer science |
10,070 | Strongly-Typed Agents are Guaranteed to Interact Safely | cs.LG | As artificial agents proliferate, it is becoming increasingly important to
ensure that their interactions with one another are well-behaved. In this
paper, we formalize a common-sense notion of when algorithms are well-behaved:
an algorithm is safe if it does no harm. Motivated by recent progress in deep
learning, we f... | computer science |
10,071 | Analysis of Agent Expertise in Ms. Pac-Man using
Value-of-Information-based Policies | cs.LG | Conventional reinforcement learning methods for Markov decision processes
rely on weakly-guided, stochastic searches to drive the learning process. It
can therefore be difficult to predict what agent behaviors might emerge. In
this paper, we consider an information-theoretic cost function for performing
constrained sto... | computer science |
10,072 | Stabilising Experience Replay for Deep Multi-Agent Reinforcement
Learning | cs.AI | Many real-world problems, such as network packet routing and urban traffic
control, are naturally modeled as multi-agent reinforcement learning (RL)
problems. However, existing multi-agent RL methods typically scale poorly in
the problem size. Therefore, a key challenge is to translate the success of
deep learning on s... | computer science |
10,073 | Virtual-to-real Deep Reinforcement Learning: Continuous Control of
Mobile Robots for Mapless Navigation | cs.RO | We present a learning-based mapless motion planner by taking the sparse
10-dimensional range findings and the target position with respect to the
mobile robot coordinate frame as input and the continuous steering commands as
output. Traditional motion planners for mobile ground robots with a laser range
sensor mostly d... | computer science |
10,074 | Truth and Regret in Online Scheduling | cs.GT | We consider a scheduling problem where a cloud service provider has multiple
units of a resource available over time. Selfish clients submit jobs, each with
an arrival time, deadline, length, and value. The service provider's goal is to
implement a truthful online mechanism for scheduling jobs so as to maximize the
soc... | computer science |
10,075 | Metric Learning for Generalizing Spatial Relations to New Objects | cs.RO | Human-centered environments are rich with a wide variety of spatial relations
between everyday objects. For autonomous robots to operate effectively in such
environments, they should be able to reason about these relations and
generalize them to objects with different shapes and sizes. For example, having
learned to pl... | computer science |
10,076 | Towards Generalization and Simplicity in Continuous Control | cs.LG | This work shows that policies with simple linear and RBF parameterizations
can be trained to solve a variety of continuous control tasks, including the
OpenAI gym benchmarks. The performance of these trained policies are
competitive with state of the art results, obtained with more elaborate
parameterizations such as f... | computer science |
10,077 | Robust Adversarial Reinforcement Learning | cs.LG | Deep neural networks coupled with fast simulation and improved computation
have led to recent successes in the field of reinforcement learning (RL).
However, most current RL-based approaches fail to generalize since: (a) the gap
between simulation and real world is so large that policy-learning approaches
fail to trans... | computer science |
10,078 | An Integrated and Scalable Platform for Proactive Event-Driven Traffic
Management | cs.AI | Traffic on freeways can be managed by means of ramp meters from Road Traffic
Control rooms. Human operators cannot efficiently manage a network of ramp
meters. To support them, we present an intelligent platform for traffic
management which includes a new ramp metering coordination scheme in the
decision making module,... | computer science |
10,079 | Learning a Unified Control Policy for Safe Falling | cs.RO | Being able to fall safely is a necessary motor skill for humanoids performing
highly dynamic tasks, such as running and jumping. We propose a new method to
learn a policy that minimizes the maximal impulse during the fall. The
optimization solves for both a discrete contact planning problem and a
continuous optimal con... | computer science |
10,080 | Efficient Simulation of Financial Stress Testing Scenarios with
Suppes-Bayes Causal Networks | cs.LG | The most recent financial upheavals have cast doubt on the adequacy of some
of the conventional quantitative risk management strategies, such as VaR (Value
at Risk), in many common situations. Consequently, there has been an increasing
need for verisimilar financial stress testings, namely simulating and analyzing
fina... | computer science |
10,081 | Real-Time Machine Learning: The Missing Pieces | cs.DC | Machine learning applications are increasingly deployed not only to serve
predictions using static models, but also as tightly-integrated components of
feedback loops involving dynamic, real-time decision making. These applications
pose a new set of requirements, none of which are difficult to achieve in
isolation, but... | computer science |
10,082 | Weighted Voting Via No-Regret Learning | cs.GT | Voting systems typically treat all voters equally. We argue that perhaps they
should not: Voters who have supported good choices in the past should be given
higher weight than voters who have supported bad ones. To develop a formal
framework for desirable weighting schemes, we draw on no-regret learning.
Specifically, ... | computer science |
10,083 | Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under
Partial Observability | cs.LG | Many real-world tasks involve multiple agents with partial observability and
limited communication. Learning is challenging in these settings due to local
viewpoints of agents, which perceive the world as non-stationary due to
concurrently-exploring teammates. Approaches that learn specialized policies
for individual t... | computer science |
10,084 | Information-theoretic Model Identification and Policy Search using
Physics Engines with Application to Robotic Manipulation | cs.RO | We consider the problem of a robot learning the mechanical properties of
objects through physical interaction with the object, and introduce a
practical, data-efficient approach for identifying the motion models of these
objects. The proposed method utilizes a physics engine, where the robot seeks
to identify the inert... | computer science |
10,085 | Learning Visual Servoing with Deep Features and Fitted Q-Iteration | cs.LG | Visual servoing involves choosing actions that move a robot in response to
observations from a camera, in order to reach a goal configuration in the
world. Standard visual servoing approaches typically rely on manually designed
features and analytical dynamics models, which limits their generalization
capability and of... | computer science |
10,086 | Brief Notes on Hard Takeoff, Value Alignment, and Coherent Extrapolated
Volition | cs.AI | I make some basic observations about hard takeoff, value alignment, and
coherent extrapolated volition, concepts which have been central in analyses of
superintelligent AI systems. | computer science |
10,087 | Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders | cs.LG | Generative models in vision have seen rapid progress due to algorithmic
improvements and the availability of high-quality image datasets. In this
paper, we offer contributions in both these areas to enable similar progress in
audio modeling. First, we detail a powerful new WaveNet-style autoencoder model
that condition... | computer science |
10,088 | Embodied Artificial Intelligence through Distributed Adaptive Control:
An Integrated Framework | cs.AI | In this paper, we argue that the future of Artificial Intelligence research
resides in two keywords: integration and embodiment. We support this claim by
analyzing the recent advances of the field. Regarding integration, we note that
the most impactful recent contributions have been made possible through the
integratio... | computer science |
10,089 | Fully Distributed and Asynchronized Stochastic Gradient Descent for
Networked Systems | cs.LG | This paper considers a general data-fitting problem over a networked system,
in which many computing nodes are connected by an undirected graph. This kind
of problem can find many real-world applications and has been studied
extensively in the literature. However, existing solutions either need a
central controller for... | computer science |
10,090 | Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds | math.OC | Monte Carlo Tree Search (MCTS), most famously used in game-play artificial
intelligence (e.g., the game of Go), is a well-known strategy for constructing
approximate solutions to sequential decision problems. Its primary innovation
is the use of a heuristic, known as a default policy, to obtain Monte Carlo
estimates of... | computer science |
10,091 | Learning of Human-like Algebraic Reasoning Using Deep Feedforward Neural
Networks | cs.AI | There is a wide gap between symbolic reasoning and deep learning. In this
research, we explore the possibility of using deep learning to improve symbolic
reasoning. Briefly, in a reasoning system, a deep feedforward neural network is
used to guide rewriting processes after learning from algebraic reasoning
examples pro... | computer science |
10,092 | Maximum Resilience of Artificial Neural Networks | cs.LG | The deployment of Artificial Neural Networks (ANNs) in safety-critical
applications poses a number of new verification and certification challenges.
In particular, for ANN-enabled self-driving vehicles it is important to
establish properties about the resilience of ANNs to noisy or even maliciously
manipulated sensory ... | computer science |
10,093 | Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks | cs.LO | We present an approach for the verification of feed-forward neural networks
in which all nodes have a piece-wise linear activation function. Such networks
are often used in deep learning and have been shown to be hard to verify for
modern satisfiability modulo theory (SMT) and integer linear programming (ILP)
solvers.
... | computer science |
10,094 | SLDR-DL: A Framework for SLD-Resolution with Deep Learning | cs.AI | This paper introduces an SLD-resolution technique based on deep learning.
This technique enables neural networks to learn from old and successful
resolution processes and to use learnt experiences to guide new resolution
processes. An implementation of this technique is named SLDR-DL. It includes a
Prolog library of de... | computer science |
10,095 | Data Readiness Levels | cs.DB | Application of models to data is fraught. Data-generating collaborators often
only have a very basic understanding of the complications of collating,
processing and curating data. Challenges include: poor data collection
practices, missing values, inconvenient storage mechanisms, intellectual
property, security and pri... | computer science |
10,096 | Probabilistically Safe Policy Transfer | cs.RO | Although learning-based methods have great potential for robotics, one
concern is that a robot that updates its parameters might cause large amounts
of damage before it learns the optimal policy. We formalize the idea of safe
learning in a probabilistic sense by defining an optimization problem: we
desire to maximize t... | computer science |
10,097 | Learning to Represent Haptic Feedback for Partially-Observable Tasks | cs.RO | The sense of touch, being the earliest sensory system to develop in a human
body [1], plays a critical part of our daily interaction with the environment.
In order to successfully complete a task, many manipulation interactions
require incorporating haptic feedback. However, manually designing a feedback
mechanism can ... | computer science |
10,098 | Automatic Goal Generation for Reinforcement Learning Agents | cs.LG | Reinforcement learning is a powerful technique to train an agent to perform a
task. However, an agent that is trained using reinforcement learning is only
capable of achieving the single task that is specified via its reward function.
Such an approach does not scale well to settings in which an agent needs to
perform a... | computer science |
10,099 | Atari games and Intel processors | cs.DC | The asynchronous nature of the state-of-the-art reinforcement learning
algorithms such as the Asynchronous Advantage Actor-Critic algorithm, makes
them exceptionally suitable for CPU computations. However, given the fact that
deep reinforcement learning often deals with interpreting visual information, a
large part of ... | computer science |
10,100 | Detection Algorithms for Communication Systems Using Deep Learning | cs.LG | The design and analysis of communication systems typically rely on the
development of mathematical models that describe the underlying communication
channel, which dictates the relationship between the transmitted and the
received signals. However, in some systems, such as molecular communication
systems where chemical... | computer science |
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