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700 | Distribution of the search of evolutionary product unit neural networks
for classification | cs.NE | This paper deals with the distributed processing in the search for an optimum
classification model using evolutionary product unit neural networks. For this
distributed search we used a cluster of computers. Our objective is to obtain a
more efficient design than those net architectures which do not use a
distributed p... | computer science |
701 | Correlation Alignment for Unsupervised Domain Adaptation | cs.CV | In this chapter, we present CORrelation ALignment (CORAL), a simple yet
effective method for unsupervised domain adaptation. CORAL minimizes domain
shift by aligning the second-order statistics of source and target
distributions, without requiring any target labels. In contrast to subspace
manifold methods, it aligns t... | computer science |
702 | CITlab ARGUS for historical handwritten documents | cs.CV | We describe CITlab's recognition system for the HTRtS competition attached to
the 13. International Conference on Document Analysis and Recognition, ICDAR
2015. The task comprises the recognition of historical handwritten documents.
The core algorithms of our system are based on multi-dimensional recurrent
neural netwo... | computer science |
703 | Generalized Haar Filter based Deep Networks for Real-Time Object
Detection in Traffic Scene | cs.CV | Vision-based object detection is one of the fundamental functions in numerous
traffic scene applications such as self-driving vehicle systems and advance
driver assistance systems (ADAS). However, it is also a challenging task due to
the diversity of traffic scene and the storage, power and computing source
limitations... | computer science |
704 | Autoencoder Regularized Network For Driving Style Representation
Learning | cs.CV | In this paper, we study learning generalized driving style representations
from automobile GPS trip data. We propose a novel Autoencoder Regularized deep
neural Network (ARNet) and a trip encoding framework trip2vec to learn drivers'
driving styles directly from GPS records, by combining supervised and
unsupervised fea... | computer science |
705 | Fashioning with Networks: Neural Style Transfer to Design Clothes | cs.CV | Convolutional Neural Networks have been highly successful in performing a
host of computer vision tasks such as object recognition, object detection,
image segmentation and texture synthesis. In 2015, Gatys et. al [7] show how
the style of a painter can be extracted from an image of the painting and
applied to another ... | computer science |
706 | GlobeNet: Convolutional Neural Networks for Typhoon Eye Tracking from
Remote Sensing Imagery | cs.NE | Advances in remote sensing technologies have made it possible to use
high-resolution visual data for weather observation and forecasting tasks. We
propose the use of multi-layer neural networks for understanding complex
atmospheric dynamics based on multichannel satellite images. The capability of
our model was evaluat... | computer science |
707 | Improving Efficiency in Convolutional Neural Network with Multilinear
Filters | cs.CV | The excellent performance of deep neural networks has enabled us to solve
several automatization problems, opening an era of autonomous devices. However,
current deep net architectures are heavy with millions of parameters and
require billions of floating point operations. Several works have been
developed to compress ... | computer science |
708 | Discovery Radiomics with CLEAR-DR: Interpretable Computer Aided
Diagnosis of Diabetic Retinopathy | cs.AI | Objective: Radiomics-driven Computer Aided Diagnosis (CAD) has shown
considerable promise in recent years as a potential tool for improving clinical
decision support in medical oncology, particularly those based around the
concept of Discovery Radiomics, where radiomic sequencers are discovered
through the analysis of ... | computer science |
709 | HP-GAN: Probabilistic 3D human motion prediction via GAN | cs.CV | Predicting and understanding human motion dynamics has many applications,
such as motion synthesis, augmented reality, security, and autonomous vehicles.
Due to the recent success of generative adversarial networks (GAN), there has
been much interest in probabilistic estimation and synthetic data generation
using deep ... | computer science |
710 | Report: Dynamic Eye Movement Matching and Visualization Tool in Neuro
Gesture | cs.NE | In the research of the impact of gestures using by a lecturer, one
challenging task is to infer the attention of a group of audiences. Two
important measurements that can help infer the level of attention are eye
movement data and Electroencephalography (EEG) data. Under the fundamental
assumption that a group of peopl... | computer science |
711 | Nature vs. Nurture: The Role of Environmental Resources in Evolutionary
Deep Intelligence | cs.NE | Evolutionary deep intelligence synthesizes highly efficient deep neural
networks architectures over successive generations. Inspired by the nature
versus nurture debate, we propose a study to examine the role of external
factors on the network synthesis process by varying the availability of
simulated environmental res... | computer science |
712 | A stochastic model of human visual attention with a dynamic Bayesian
network | cs.CV | Recent studies in the field of human vision science suggest that the human
responses to the stimuli on a visual display are non-deterministic. People may
attend to different locations on the same visual input at the same time. Based
on this knowledge, we propose a new stochastic model of visual attention by
introducing... | computer science |
713 | Smart Content Recognition from Images Using a Mixture of Convolutional
Neural Networks | stat.ML | With rapid development of the Internet, web contents become huge. Most of the
websites are publicly available, and anyone can access the contents from
anywhere such as workplace, home and even schools. Nevertheless, not all the
web contents are appropriate for all users, especially children. An example of
these content... | computer science |
714 | Cortical spatio-temporal dimensionality reduction for visual grouping | cs.CV | The visual systems of many mammals, including humans, is able to integrate
the geometric information of visual stimuli and to perform cognitive tasks
already at the first stages of the cortical processing. This is thought to be
the result of a combination of mechanisms, which include feature extraction at
single cell l... | computer science |
715 | Visual Sentiment Prediction with Deep Convolutional Neural Networks | cs.CV | Images have become one of the most popular types of media through which users
convey their emotions within online social networks. Although vast amount of
research is devoted to sentiment analysis of textual data, there has been very
limited work that focuses on analyzing sentiment of image data. In this work,
we propo... | computer science |
716 | Correntropy Maximization via ADMM - Application to Robust Hyperspectral
Unmixing | stat.ML | In hyperspectral images, some spectral bands suffer from low signal-to-noise
ratio due to noisy acquisition and atmospheric effects, thus requiring robust
techniques for the unmixing problem. This paper presents a robust supervised
spectral unmixing approach for hyperspectral images. The robustness is achieved
by writi... | computer science |
717 | Identifying individual facial expressions by deconstructing a neural
network | cs.CV | This paper focuses on the problem of explaining predictions of psychological
attributes such as attractiveness, happiness, confidence and intelligence from
face photographs using deep neural networks. Since psychological attribute
datasets typically suffer from small sample sizes, we apply transfer learning
with two ba... | computer science |
718 | Object Boundary Detection and Classification with Image-level Labels | cs.CV | Semantic boundary and edge detection aims at simultaneously detecting object
edge pixels in images and assigning class labels to them. Systematic training
of predictors for this task requires the labeling of edges in images which is a
particularly tedious task. We propose a novel strategy for solving this task,
when pi... | computer science |
719 | Evolving Spatially Aggregated Features from Satellite Imagery for
Regional Modeling | stat.ML | Satellite imagery and remote sensing provide explanatory variables at
relatively high resolutions for modeling geospatial phenomena, yet regional
summaries are often desirable for analysis and actionable insight. In this
paper, we propose a novel method of inducing spatial aggregations as a
component of the machine lea... | computer science |
720 | Pillar Networks++: Distributed non-parametric deep and wide networks | cs.CV | In recent work, it was shown that combining multi-kernel based support vector
machines (SVMs) can lead to near state-of-the-art performance on an action
recognition dataset (HMDB-51 dataset). This was 0.4\% lower than frameworks
that used hand-crafted features in addition to the deep convolutional feature
extractors. I... | computer science |
721 | Market-Based Reinforcement Learning in Partially Observable Worlds | cs.AI | Unlike traditional reinforcement learning (RL), market-based RL is in
principle applicable to worlds described by partially observable Markov
Decision Processes (POMDPs), where an agent needs to learn short-term memories
of relevant previous events in order to execute optimal actions. Most previous
work, however, has f... | computer science |
722 | Controlled hierarchical filtering: Model of neocortical sensory
processing | cs.NE | A model of sensory information processing is presented. The model assumes
that learning of internal (hidden) generative models, which can predict the
future and evaluate the precision of that prediction, is of central importance
for information extraction. Furthermore, the model makes a bridge to
goal-oriented systems ... | computer science |
723 | When Do Differences Matter? On-Line Feature Extraction Through Cognitive
Economy | cs.LG | For an intelligent agent to be truly autonomous, it must be able to adapt its
representation to the requirements of its task as it interacts with the world.
Most current approaches to on-line feature extraction are ad hoc; in contrast,
this paper presents an algorithm that bases judgments of state compatibility
and sta... | computer science |
724 | Applying Policy Iteration for Training Recurrent Neural Networks | cs.AI | Recurrent neural networks are often used for learning time-series data. Based
on a few assumptions we model this learning task as a minimization problem of a
nonlinear least-squares cost function. The special structure of the cost
function allows us to build a connection to reinforcement learning. We exploit
this conne... | computer science |
725 | A Neural-Network Technique to Learn Concepts from Electroencephalograms | cs.NE | A new technique is presented developed to learn multi-class concepts from
clinical electroencephalograms. A desired concept is represented as a neuronal
computational model consisting of the input, hidden, and output neurons. In
this model the hidden neurons learn independently to classify the
electroencephalogram segm... | computer science |
726 | Empirical learning aided by weak domain knowledge in the form of feature
importance | cs.LG | Standard hybrid learners that use domain knowledge require stronger knowledge
that is hard and expensive to acquire. However, weaker domain knowledge can
benefit from prior knowledge while being cost effective. Weak knowledge in the
form of feature relative importance (FRI) is presented and explained. Feature
relative ... | computer science |
727 | Evolutionary Algorithms for Reinforcement Learning | cs.LG | There are two distinct approaches to solving reinforcement learning problems,
namely, searching in value function space and searching in policy space.
Temporal difference methods and evolutionary algorithms are well-known examples
of these approaches. Kaelbling, Littman and Moore recently provided an
informative survey... | computer science |
728 | On Training Deep Boltzmann Machines | cs.NE | The deep Boltzmann machine (DBM) has been an important development in the
quest for powerful "deep" probabilistic models. To date, simultaneous or joint
training of all layers of the DBM has been largely unsuccessful with existing
training methods. We introduce a simple regularization scheme that encourages
the weight ... | computer science |
729 | Memristive fuzzy edge detector | cs.NE | Fuzzy inference systems always suffer from the lack of efficient structures
or platforms for their hardware implementation. In this paper, we tried to
overcome this problem by proposing new method for the implementation of those
fuzzy inference systems which use fuzzy rule base to make inference. To achieve
this goal, ... | computer science |
730 | Echo State Queueing Network: a new reservoir computing learning tool | cs.NE | In the last decade, a new computational paradigm was introduced in the field
of Machine Learning, under the name of Reservoir Computing (RC). RC models are
neural networks which a recurrent part (the reservoir) that does not
participate in the learning process, and the rest of the system where no
recurrence (no neural ... | computer science |
731 | The Predictron: End-To-End Learning and Planning | cs.LG | One of the key challenges of artificial intelligence is to learn models that
are effective in the context of planning. In this document we introduce the
predictron architecture. The predictron consists of a fully abstract model,
represented by a Markov reward process, that can be rolled forward multiple
"imagined" plan... | computer science |
732 | Quadratically constrained quadratic programming for classification using
particle swarms and applications | cs.AI | Particle swarm optimization is used in several combinatorial optimization
problems. In this work, particle swarms are used to solve quadratic programming
problems with quadratic constraints. The approach of particle swarms is an
example for interior point methods in optimization as an iterative technique.
This approach... | computer science |
733 | Learning to Execute | cs.NE | Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTM) are
widely used because they are expressive and are easy to train. Our interest
lies in empirically evaluating the expressiveness and the learnability of LSTMs
in the sequence-to-sequence regime by training them to evaluate short computer
program... | computer science |
734 | Bitwise Neural Networks | cs.LG | Based on the assumption that there exists a neural network that efficiently
represents a set of Boolean functions between all binary inputs and outputs, we
propose a process for developing and deploying neural networks whose weight
parameters, bias terms, input, and intermediate hidden layer output signals,
are all bin... | computer science |
735 | Graying the black box: Understanding DQNs | cs.LG | In recent years there is a growing interest in using deep representations for
reinforcement learning. In this paper, we present a methodology and tools to
analyze Deep Q-networks (DQNs) in a non-blind matter. Moreover, we propose a
new model, the Semi Aggregated Markov Decision Process (SAMDP), and an
algorithm that le... | computer science |
736 | Evaluation of a Tree-based Pipeline Optimization Tool for Automating
Data Science | cs.NE | As the field of data science continues to grow, there will be an
ever-increasing demand for tools that make machine learning accessible to
non-experts. In this paper, we introduce the concept of tree-based pipeline
optimization for automating one of the most tedious parts of machine
learning---pipeline design. We imple... | computer science |
737 | Probabilistic Reasoning via Deep Learning: Neural Association Models | cs.AI | In this paper, we propose a new deep learning approach, called neural
association model (NAM), for probabilistic reasoning in artificial
intelligence. We propose to use neural networks to model association between
any two events in a domain. Neural networks take one event as input and compute
a conditional probability ... | computer science |
738 | Deep Reinforcement Learning With Macro-Actions | cs.LG | Deep reinforcement learning has been shown to be a powerful framework for
learning policies from complex high-dimensional sensory inputs to actions in
complex tasks, such as the Atari domain. In this paper, we explore output
representation modeling in the form of temporal abstraction to improve
convergence and reliabil... | computer science |
739 | RETAIN: An Interpretable Predictive Model for Healthcare using Reverse
Time Attention Mechanism | cs.LG | Accuracy and interpretability are two dominant features of successful
predictive models. Typically, a choice must be made in favor of complex black
box models such as recurrent neural networks (RNN) for accuracy versus less
accurate but more interpretable traditional models such as logistic regression.
This tradeoff po... | computer science |
740 | A High Speed Multi-label Classifier based on Extreme Learning Machines | cs.LG | In this paper a high speed neural network classifier based on extreme
learning machines for multi-label classification problem is proposed and
dis-cussed. Multi-label classification is a superset of traditional binary and
multi-class classification problems. The proposed work extends the extreme
learning machine techni... | computer science |
741 | An Online Universal Classifier for Binary, Multi-class and Multi-label
Classification | cs.LG | Classification involves the learning of the mapping function that associates
input samples to corresponding target label. There are two major categories of
classification problems: Single-label classification and Multi-label
classification. Traditional binary and multi-class classifications are
sub-categories of single... | computer science |
742 | Adaptive Online Sequential ELM for Concept Drift Tackling | cs.AI | A machine learning method needs to adapt to over time changes in the
environment. Such changes are known as concept drift. In this paper, we propose
concept drift tackling method as an enhancement of Online Sequential Extreme
Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by
adding adaptive ca... | computer science |
743 | Adaptive Convolutional ELM For Concept Drift Handling in Online Stream
Data | cs.AI | In big data era, the data continuously generated and its distribution may
keep changes overtime. These challenges in online stream of data are known as
concept drift. In this paper, we proposed the Adaptive Convolutional ELM method
(ACNNELM) as enhancement of Convolutional Neural Network (CNN) with a hybrid
Extreme Lea... | computer science |
744 | Particle Swarm Optimization for Generating Interpretable Fuzzy
Reinforcement Learning Policies | cs.NE | Fuzzy controllers are efficient and interpretable system controllers for
continuous state and action spaces. To date, such controllers have been
constructed manually or trained automatically either using expert-generated
problem-specific cost functions or incorporating detailed knowledge about the
optimal control strat... | computer science |
745 | A Growing Long-term Episodic & Semantic Memory | cs.AI | The long-term memory of most connectionist systems lies entirely in the
weights of the system. Since the number of weights is typically fixed, this
bounds the total amount of knowledge that can be learned and stored. Though
this is not normally a problem for a neural network designed for a specific
task, such a bound i... | computer science |
746 | Cognitive Discriminative Mappings for Rapid Learning | cs.AI | Humans can learn concepts or recognize items from just a handful of examples,
while machines require many more samples to perform the same task. In this
paper, we build a computational model to investigate the possibility of this
kind of rapid learning. The proposed method aims to improve the learning task
of input fro... | computer science |
747 | Towards a Mathematical Understanding of the Difficulty in Learning with
Feedforward Neural Networks | cs.LG | Training deep neural networks for solving machine learning problems is one
great challenge in the field, mainly due to its associated optimisation problem
being highly non-convex. Recent developments have suggested that many training
algorithms do not suffer from undesired local minima under certain scenario,
and conse... | computer science |
748 | An effective algorithm for hyperparameter optimization of neural
networks | cs.AI | A major challenge in designing neural network (NN) systems is to determine
the best structure and parameters for the network given the data for the
machine learning problem at hand. Examples of parameters are the number of
layers and nodes, the learning rates, and the dropout rates. Typically, these
parameters are chos... | computer science |
749 | Evolutionary Training of Sparse Artificial Neural Networks: A Network
Science Perspective | cs.NE | Through the success of deep learning, Artificial Neural Networks (ANNs) are
among the most used artificial intelligence methods nowadays. ANNs have led to
major breakthroughs in various domains, such as particle physics, reinforcement
learning, speech recognition, computer vision, and so on. Taking inspiration
from the... | computer science |
750 | Attend and Predict: Understanding Gene Regulation by Selective Attention
on Chromatin | cs.LG | The past decade has seen a revolution in genomic technologies that enable a
flood of genome-wide profiling of chromatin marks. Recent literature tried to
understand gene regulation by predicting gene expression from large-scale
chromatin measurements. Two fundamental challenges exist for such learning
tasks: (1) genome... | computer science |
751 | Parallelizing Linear Recurrent Neural Nets Over Sequence Length | cs.NE | Recurrent neural networks (RNNs) are widely used to model sequential data but
their non-linear dependencies between sequence elements prevent parallelizing
training over sequence length. We show the training of RNNs with only linear
sequential dependencies can be parallelized over the sequence length using the
parallel... | computer science |
752 | Feature learning in feature-sample networks using multi-objective
optimization | cs.AI | Data and knowledge representation are fundamental concepts in machine
learning. The quality of the representation impacts the performance of the
learning model directly. Feature learning transforms or enhances raw data to
structures that are effectively exploited by those models. In recent years,
several works have bee... | computer science |
753 | Meta-Learning and Universality: Deep Representations and Gradient
Descent can Approximate any Learning Algorithm | cs.LG | Learning to learn is a powerful paradigm for enabling models to learn from
data more effectively and efficiently. A popular approach to meta-learning is
to train a recurrent model to read in a training dataset as input and output
the parameters of a learned model, or output predictions for new test inputs.
Alternativel... | computer science |
754 | Hindsight policy gradients | cs.LG | Goal-conditional policies allow reinforcement learning agents to pursue
specific goals during different episodes. In addition to their potential to
generalize desired behavior to unseen goals, such policies may also help in
defining options for arbitrary subgoals, enabling higher-level planning. While
trying to achieve... | computer science |
755 | SquishedNets: Squishing SqueezeNet further for edge device scenarios via
deep evolutionary synthesis | cs.NE | While deep neural networks have been shown in recent years to outperform
other machine learning methods in a wide range of applications, one of the
biggest challenges with enabling deep neural networks for widespread deployment
on edge devices such as mobile and other consumer devices is high computational
and memory r... | computer science |
756 | Autonomous development and learning in artificial intelligence and
robotics: Scaling up deep learning to human--like learning | cs.AI | Autonomous lifelong development and learning is a fundamental capability of
humans, differentiating them from current deep learning systems. However, other
branches of artificial intelligence have designed crucial ingredients towards
autonomous learning: curiosity and intrinsic motivation, social learning and
natural i... | computer science |
757 | Learning from Scarce Experience | cs.AI | Searching the space of policies directly for the optimal policy has been one
popular method for solving partially observable reinforcement learning
problems. Typically, with each change of the target policy, its value is
estimated from the results of following that very policy. This requires a large
number of interacti... | computer science |
758 | Fitness inheritance in the Bayesian optimization algorithm | cs.NE | This paper describes how fitness inheritance can be used to estimate fitness
for a proportion of newly sampled candidate solutions in the Bayesian
optimization algorithm (BOA). The goal of estimating fitness for some candidate
solutions is to reduce the number of fitness evaluations for problems where
fitness evaluatio... | computer science |
759 | The Combined Technique for Detection of Artifacts in Clinical
Electroencephalograms of Sleeping Newborns | cs.NE | In this paper we describe a new method combining the polynomial neural
network and decision tree techniques in order to derive comprehensible
classification rules from clinical electroencephalograms (EEGs) recorded from
sleeping newborns. These EEGs are heavily corrupted by cardiac, eye movement,
muscle and noise artif... | computer science |
760 | Evolving Classifiers: Methods for Incremental Learning | cs.LG | The ability of a classifier to take on new information and classes by
evolving the classifier without it having to be fully retrained is known as
incremental learning. Incremental learning has been successfully applied to
many classification problems, where the data is changing and is not all
available at once. In this... | computer science |
761 | Automatic Pattern Classification by Unsupervised Learning Using
Dimensionality Reduction of Data with Mirroring Neural Networks | cs.LG | This paper proposes an unsupervised learning technique by using Multi-layer
Mirroring Neural Network and Forgy's clustering algorithm. Multi-layer
Mirroring Neural Network is a neural network that can be trained with
generalized data inputs (different categories of image patterns) to perform
non-linear dimensionality r... | computer science |
762 | Improving the Performance of PieceWise Linear Separation Incremental
Algorithms for Practical Hardware Implementations | cs.NE | In this paper we shall review the common problems associated with Piecewise
Linear Separation incremental algorithms. This kind of neural models yield poor
performances when dealing with some classification problems, due to the
evolving schemes used to construct the resulting networks. So as to avoid this
undesirable b... | computer science |
763 | A Novel Rough Set Reduct Algorithm for Medical Domain Based on Bee
Colony Optimization | cs.LG | Feature selection refers to the problem of selecting relevant features which
produce the most predictive outcome. In particular, feature selection task is
involved in datasets containing huge number of features. Rough set theory has
been one of the most successful methods used for feature selection. However,
this metho... | computer science |
764 | Automated Query Learning with Wikipedia and Genetic Programming | cs.AI | Most of the existing information retrieval systems are based on bag of words
model and are not equipped with common world knowledge. Work has been done
towards improving the efficiency of such systems by using intelligent
algorithms to generate search queries, however, not much research has been done
in the direction o... | computer science |
765 | Scaling Up Estimation of Distribution Algorithms For Continuous
Optimization | cs.NE | Since Estimation of Distribution Algorithms (EDA) were proposed, many
attempts have been made to improve EDAs' performance in the context of global
optimization. So far, the studies or applications of multivariate probabilistic
model based continuous EDAs are still restricted to rather low dimensional
problems (smaller... | computer science |
766 | Transfer Learning, Soft Distance-Based Bias, and the Hierarchical BOA | cs.NE | An automated technique has recently been proposed to transfer learning in the
hierarchical Bayesian optimization algorithm (hBOA) based on distance-based
statistics. The technique enables practitioners to improve hBOA efficiency by
collecting statistics from probabilistic models obtained in previous hBOA runs
and using... | computer science |
767 | Discrete Dynamical Genetic Programming in XCS | cs.AI | A number of representation schemes have been presented for use within
Learning Classifier Systems, ranging from binary encodings to neural networks.
This paper presents results from an investigation into using a discrete
dynamical system representation within the XCS Learning Classifier System. In
particular, asynchron... | computer science |
768 | Fuzzy Dynamical Genetic Programming in XCSF | cs.AI | A number of representation schemes have been presented for use within
Learning Classifier Systems, ranging from binary encodings to Neural Networks,
and more recently Dynamical Genetic Programming (DGP). This paper presents
results from an investigation into using a fuzzy DGP representation within the
XCSF Learning Cla... | computer science |
769 | Learning-Based Procedural Content Generation | cs.AI | Procedural content generation (PCG) has recently become one of the hottest
topics in computational intelligence and AI game researches. Among a variety of
PCG techniques, search-based approaches overwhelmingly dominate PCG development
at present. While SBPCG leads to promising results and successful applications,
it po... | computer science |
770 | Systematic N-tuple Networks for Position Evaluation: Exceeding 90% in
the Othello League | cs.NE | N-tuple networks have been successfully used as position evaluation functions
for board games such as Othello or Connect Four. The effectiveness of such
networks depends on their architecture, which is determined by the placement of
constituent n-tuples, sequences of board locations, providing input to the
network. The... | computer science |
771 | Towards a Self-Organized Agent-Based Simulation Model for Exploration of
Human Synaptic Connections | cs.NE | In this paper, the early design of our self-organized agent-based simulation
model for exploration of synaptic connections that faithfully generates what is
observed in natural situation is given. While we take inspiration from
neuroscience, our intent is not to create a veridical model of processes in
neurodevelopment... | computer science |
772 | Motion Planning Of an Autonomous Mobile Robot Using Artificial Neural
Network | cs.RO | The paper presents the electronic design and motion planning of a robot based
on decision making regarding its straight motion and precise turn using
Artificial Neural Network (ANN). The ANN helps in learning of robot so that it
performs motion autonomously. The weights calculated are implemented in
microcontroller. Th... | computer science |
773 | Learning Bayesian Network Equivalence Classes with Ant Colony
Optimization | cs.NE | Bayesian networks are a useful tool in the representation of uncertain
knowledge. This paper proposes a new algorithm called ACO-E, to learn the
structure of a Bayesian network. It does this by conducting a search through
the space of equivalence classes of Bayesian networks using Ant Colony
Optimization (ACO). To this... | computer science |
774 | Probabilistic Neural Programs | cs.NE | We present probabilistic neural programs, a framework for program induction
that permits flexible specification of both a computational model and inference
algorithm while simultaneously enabling the use of deep neural networks.
Probabilistic neural programs combine a computation graph for specifying a
neural network w... | computer science |
775 | Cognitive Deep Machine Can Train Itself | cs.LG | Machine learning is making substantial progress in diverse applications. The
success is mostly due to advances in deep learning. However, deep learning can
make mistakes and its generalization abilities to new tasks are questionable.
We ask when and how one can combine network outputs, when (i) details of the
observati... | computer science |
776 | Summary - TerpreT: A Probabilistic Programming Language for Program
Induction | cs.LG | We study machine learning formulations of inductive program synthesis; that
is, given input-output examples, synthesize source code that maps inputs to
corresponding outputs. Our key contribution is TerpreT, a domain-specific
language for expressing program synthesis problems. A TerpreT model is composed
of a specifica... | computer science |
777 | Learning in the Machine: Random Backpropagation and the Deep Learning
Channel | cs.LG | Random backpropagation (RBP) is a variant of the backpropagation algorithm
for training neural networks, where the transpose of the forward matrices are
replaced by fixed random matrices in the calculation of the weight updates. It
is remarkable both because of its effectiveness, in spite of using random
matrices to co... | computer science |
778 | Highway and Residual Networks learn Unrolled Iterative Estimation | cs.NE | The past year saw the introduction of new architectures such as Highway
networks and Residual networks which, for the first time, enabled the training
of feedforward networks with dozens to hundreds of layers using simple gradient
descent. While depth of representation has been posited as a primary reason for
their suc... | computer science |
779 | Deep neural heart rate variability analysis | cs.NE | Despite of the pain and limited accuracy of blood tests for early recognition
of cardiovascular disease, they dominate risk screening and triage. On the
other hand, heart rate variability is non-invasive and cheap, but not
considered accurate enough for clinical practice. Here, we tackle heart beat
interval based class... | computer science |
780 | A neural network approach to ordinal regression | cs.LG | Ordinal regression is an important type of learning, which has properties of
both classification and regression. Here we describe a simple and effective
approach to adapt a traditional neural network to learn ordinal categories. Our
approach is a generalization of the perceptron method for ordinal regression.
On severa... | computer science |
781 | Computational Model of Music Sight Reading: A Reinforcement Learning
Approach | cs.AI | Although the Music Sight Reading process has been studied from the cognitive
psychology view points, but the computational learning methods like the
Reinforcement Learning have not yet been used to modeling of such processes. In
this paper, with regards to essential properties of our specific problem, we
consider the v... | computer science |
782 | Using Artificial Bee Colony Algorithm for MLP Training on Earthquake
Time Series Data Prediction | cs.NE | Nowadays, computer scientists have shown the interest in the study of social
insect's behaviour in neural networks area for solving different combinatorial
and statistical problems. Chief among these is the Artificial Bee Colony (ABC)
algorithm. This paper investigates the use of ABC algorithm that simulates the
intell... | computer science |
783 | Multiple chaotic central pattern generators with learning for legged
locomotion and malfunction compensation | cs.AI | An originally chaotic system can be controlled into various periodic
dynamics. When it is implemented into a legged robot's locomotion control as a
central pattern generator (CPG), sophisticated gait patterns arise so that the
robot can perform various walking behaviors. However, such a single chaotic CPG
controller ha... | computer science |
784 | Teaching Deep Convolutional Neural Networks to Play Go | cs.AI | Mastering the game of Go has remained a long standing challenge to the field
of AI. Modern computer Go systems rely on processing millions of possible
future positions to play well, but intuitively a stronger and more 'humanlike'
way to play the game would be to rely on pattern recognition abilities rather
then brute f... | computer science |
785 | Polyphonic Music Generation by Modeling Temporal Dependencies Using a
RNN-DBN | cs.LG | In this paper, we propose a generic technique to model temporal dependencies
and sequences using a combination of a recurrent neural network and a Deep
Belief Network. Our technique, RNN-DBN, is an amalgamation of the memory state
of the RNN that allows it to provide temporal information and a multi-layer DBN
that help... | computer science |
786 | Massively Parallel Methods for Deep Reinforcement Learning | cs.LG | We present the first massively distributed architecture for deep
reinforcement learning. This architecture uses four main components: parallel
actors that generate new behaviour; parallel learners that are trained from
stored experience; a distributed neural network to represent the value function
or behaviour policy; ... | computer science |
787 | A genetic algorithm for autonomous navigation in partially observable
domain | cs.LG | The problem of autonomous navigation is one of the basic problems for
robotics. Although, in general, it may be challenging when an autonomous
vehicle is placed into partially observable domain. In this paper we consider
simplistic environment model and introduce a navigation algorithm based on
Learning Classifier Syst... | computer science |
788 | Distributed Deep Q-Learning | cs.LG | We propose a distributed deep learning model to successfully learn control
policies directly from high-dimensional sensory input using reinforcement
learning. The model is based on the deep Q-network, a convolutional neural
network trained with a variant of Q-learning. Its input is raw pixels and its
output is a value ... | computer science |
789 | Lifted Relational Neural Networks | cs.AI | We propose a method combining relational-logic representations with neural
network learning. A general lifted architecture, possibly reflecting some
background domain knowledge, is described through relational rules which may be
handcrafted or learned. The relational rule-set serves as a template for
unfolding possibly... | computer science |
790 | Giraffe: Using Deep Reinforcement Learning to Play Chess | cs.AI | This report presents Giraffe, a chess engine that uses self-play to discover
all its domain-specific knowledge, with minimal hand-crafted knowledge given by
the programmer. Unlike previous attempts using machine learning only to perform
parameter-tuning on hand-crafted evaluation functions, Giraffe's learning
system al... | computer science |
791 | Attention with Intention for a Neural Network Conversation Model | cs.NE | In a conversation or a dialogue process, attention and intention play
intrinsic roles. This paper proposes a neural network based approach that
models the attention and intention processes. It essentially consists of three
recurrent networks. The encoder network is a word-level model representing
source side sentences.... | computer science |
792 | Deep Reinforcement Learning in Parameterized Action Space | cs.AI | Recent work has shown that deep neural networks are capable of approximating
both value functions and policies in reinforcement learning domains featuring
continuous state and action spaces. However, to the best of our knowledge no
previous work has succeeded at using deep neural networks in structured
(parameterized) ... | computer science |
793 | MazeBase: A Sandbox for Learning from Games | cs.LG | This paper introduces MazeBase: an environment for simple 2D games, designed
as a sandbox for machine learning approaches to reasoning and planning. Within
it, we create 10 simple games embodying a range of algorithmic tasks (e.g.
if-then statements or set negation). A variety of neural models (fully
connected, convolu... | computer science |
794 | On Learning to Think: Algorithmic Information Theory for Novel
Combinations of Reinforcement Learning Controllers and Recurrent Neural World
Models | cs.AI | This paper addresses the general problem of reinforcement learning (RL) in
partially observable environments. In 2013, our large RL recurrent neural
networks (RNNs) learned from scratch to drive simulated cars from
high-dimensional video input. However, real brains are more powerful in many
ways. In particular, they le... | computer science |
795 | An Empirical Comparison of Neural Architectures for Reinforcement
Learning in Partially Observable Environments | cs.NE | This paper explores the performance of fitted neural Q iteration for
reinforcement learning in several partially observable environments, using
three recurrent neural network architectures: Long Short-Term Memory, Gated
Recurrent Unit and MUT1, a recurrent neural architecture evolved from a pool of
several thousands ca... | computer science |
796 | Predicting Clinical Events by Combining Static and Dynamic Information
Using Recurrent Neural Networks | cs.LG | In clinical data sets we often find static information (e.g. patient gender,
blood type, etc.) combined with sequences of data that are recorded during
multiple hospital visits (e.g. medications prescribed, tests performed, etc.).
Recurrent Neural Networks (RNNs) have proven to be very successful for
modelling sequence... | computer science |
797 | Weight Normalization: A Simple Reparameterization to Accelerate Training
of Deep Neural Networks | cs.LG | We present weight normalization: a reparameterization of the weight vectors
in a neural network that decouples the length of those weight vectors from
their direction. By reparameterizing the weights in this way we improve the
conditioning of the optimization problem and we speed up convergence of
stochastic gradient d... | computer science |
798 | Bounded Rational Decision-Making in Feedforward Neural Networks | cs.AI | Bounded rational decision-makers transform sensory input into motor output
under limited computational resources. Mathematically, such decision-makers can
be modeled as information-theoretic channels with limited transmission rate.
Here, we apply this formalism for the first time to multilayer feedforward
neural networ... | computer science |
799 | Lie Access Neural Turing Machine | cs.NE | Following the recent trend in explicit neural memory structures, we present a
new design of an external memory, wherein memories are stored in an Euclidean
key space $\mathbb R^n$. An LSTM controller performs read and write via
specialized read and write heads. It can move a head by either providing a new
address in th... | computer science |
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