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7,900 | Probabilistic graphs using coupled random variables | cs.LG | Neural network design has utilized flexible nonlinear processes which can
mimic biological systems, but has suffered from a lack of traceability in the
resulting network. Graphical probabilistic models ground network design in
probabilistic reasoning, but the restrictions reduce the expressive capability
of each node m... | computer science |
7,901 | Augmented Neural Networks for Modelling Consumer Indebtness | cs.CE | Consumer Debt has risen to be an important problem of modern societies,
generating a lot of research in order to understand the nature of consumer
indebtness, which so far its modelling has been carried out by statistical
models. In this work we show that Computational Intelligence can offer a more
holistic approach th... | computer science |
7,902 | Building Program Vector Representations for Deep Learning | cs.SE | Deep learning has made significant breakthroughs in various fields of
artificial intelligence. Advantages of deep learning include the ability to
capture highly complicated features, weak involvement of human engineering,
etc. However, it is still virtually impossible to use deep learning to analyze
programs since deep... | computer science |
7,903 | Convolutional Neural Networks over Tree Structures for Programming
Language Processing | cs.LG | Programming language processing (similar to natural language processing) is a
hot research topic in the field of software engineering; it has also aroused
growing interest in the artificial intelligence community. However, different
from a natural language sentence, a program contains rich, explicit, and
complicated st... | computer science |
7,904 | cuDNN: Efficient Primitives for Deep Learning | cs.NE | We present a library of efficient implementations of deep learning
primitives. Deep learning workloads are computationally intensive, and
optimizing their kernels is difficult and time-consuming. As parallel
architectures evolve, kernels must be reoptimized, which makes maintaining
codebases difficult over time. Simila... | computer science |
7,905 | Kickback cuts Backprop's red-tape: Biologically plausible credit
assignment in neural networks | cs.LG | Error backpropagation is an extremely effective algorithm for assigning
credit in artificial neural networks. However, weight updates under Backprop
depend on lengthy recursive computations and require separate output and error
messages -- features not shared by biological neurons, that are perhaps
unnecessary. In this... | computer science |
7,906 | Quantum Deep Learning | cs.LG | In recent years, deep learning has had a profound impact on machine learning
and artificial intelligence. At the same time, algorithms for quantum computers
have been shown to efficiently solve some problems that are intractable on
conventional, classical computers. We show that quantum computing not only
reduces the t... | computer science |
7,907 | Simulating a perceptron on a quantum computer | cs.LG | Perceptrons are the basic computational unit of artificial neural networks,
as they model the activation mechanism of an output neuron due to incoming
signals from its neighbours. As linear classifiers, they play an important role
in the foundations of machine learning. In the context of the emerging field of
quantum m... | computer science |
7,908 | Crypto-Nets: Neural Networks over Encrypted Data | cs.LG | The problem we address is the following: how can a user employ a predictive
model that is held by a third party, without compromising private information.
For example, a hospital may wish to use a cloud service to predict the
readmission risk of a patient. However, due to regulations, the patient's
medical files cannot... | computer science |
7,909 | Detecting Epileptic Seizures from EEG Data using Neural Networks | cs.LG | We explore the use of neural networks trained with dropout in predicting
epileptic seizures from electroencephalographic data (scalp EEG). The input to
the neural network is a 126 feature vector containing 9 features for each of
the 14 EEG channels obtained over 1-second, non-overlapping windows. The models
in our expe... | computer science |
7,910 | Audio Source Separation Using a Deep Autoencoder | cs.SD | This paper proposes a novel framework for unsupervised audio source
separation using a deep autoencoder. The characteristics of unknown source
signals mixed in the mixed input is automatically by properly configured
autoencoders implemented by a network with many layers, and separated by
clustering the coefficient vect... | computer science |
7,911 | Fast Convolutional Nets With fbfft: A GPU Performance Evaluation | cs.LG | We examine the performance profile of Convolutional Neural Network training
on the current generation of NVIDIA Graphics Processing Units. We introduce two
new Fast Fourier Transform convolution implementations: one based on NVIDIA's
cuFFT library, and another based on a Facebook authored FFT implementation,
fbfft, tha... | computer science |
7,912 | Protein Secondary Structure Prediction with Long Short Term Memory
Networks | cs.LG | Prediction of protein secondary structure from the amino acid sequence is a
classical bioinformatics problem. Common methods use feed forward neural
networks or SVMs combined with a sliding window, as these models does not
naturally handle sequential data. Recurrent neural networks are an
generalization of the feed for... | computer science |
7,913 | Stochastic Gradient Based Extreme Learning Machines For Online Learning
of Advanced Combustion Engines | cs.NE | In this article, a stochastic gradient based online learning algorithm for
Extreme Learning Machines (ELM) is developed (SG-ELM). A stability criterion
based on Lyapunov approach is used to prove both asymptotic stability of
estimation error and stability in the estimated parameters suitable for
identification of nonli... | computer science |
7,914 | maxDNN: An Efficient Convolution Kernel for Deep Learning with Maxwell
GPUs | cs.NE | This paper describes maxDNN, a computationally efficient convolution kernel
for deep learning with the NVIDIA Maxwell GPU. maxDNN reaches 96.3%
computational efficiency on typical deep learning network architectures. The
design combines ideas from cuda-convnet2 with the Maxas SGEMM assembly code. We
only address forwar... | computer science |
7,915 | Deep Transform: Cocktail Party Source Separation via Complex Convolution
in a Deep Neural Network | cs.SD | Convolutional deep neural networks (DNN) are state of the art in many
engineering problems but have not yet addressed the issue of how to deal with
complex spectrograms. Here, we use circular statistics to provide a convenient
probabilistic estimate of spectrogram phase in a complex convolutional DNN. In
a typical cock... | computer science |
7,916 | Deep Karaoke: Extracting Vocals from Musical Mixtures Using a
Convolutional Deep Neural Network | cs.SD | Identification and extraction of singing voice from within musical mixtures
is a key challenge in source separation and machine audition. Recently, deep
neural networks (DNN) have been used to estimate 'ideal' binary masks for
carefully controlled cocktail party speech separation problems. However, it is
not yet known ... | computer science |
7,917 | Deep Learning and Music Adversaries | cs.LG | An adversary is essentially an algorithm intent on making a classification
system perform in some particular way given an input, e.g., increase the
probability of a false negative. Recent work builds adversaries for deep
learning systems applied to image object recognition, which exploits the
parameters of the system t... | computer science |
7,918 | Toward a Robust Sparse Data Representation for Wireless Sensor Networks | cs.NI | Compressive sensing has been successfully used for optimized operations in
wireless sensor networks. However, raw data collected by sensors may be neither
originally sparse nor easily transformed into a sparse data representation.
This paper addresses the problem of transforming source data collected by
sensor nodes in... | computer science |
7,919 | Deep Online Convex Optimization by Putting Forecaster to Sleep | cs.LG | Methods from convex optimization such as accelerated gradient descent are
widely used as building blocks for deep learning algorithms. However, the
reasons for their empirical success are unclear, since neural networks are not
convex and standard guarantees do not apply. This paper develops the first
rigorous link betw... | computer science |
7,920 | STDP as presynaptic activity times rate of change of postsynaptic
activity | cs.NE | We introduce a weight update formula that is expressed only in terms of
firing rates and their derivatives and that results in changes consistent with
those associated with spike-timing dependent plasticity (STDP) rules and
biological observations, even though the explicit timing of spikes is not
needed. The new rule c... | computer science |
7,921 | Large-scale Artificial Neural Network: MapReduce-based Deep Learning | cs.DC | Faced with continuously increasing scale of data, original back-propagation
neural network based machine learning algorithm presents two non-trivial
challenges: huge amount of data makes it difficult to maintain both efficiency
and accuracy; redundant data aggravates the system workload. This project is
mainly focused ... | computer science |
7,922 | Adopting Robustness and Optimality in Fitting and Learning | cs.LG | We generalized a modified exponentialized estimator by pushing the
robust-optimal (RO) index $\lambda$ to $-\infty$ for achieving robustness to
outliers by optimizing a quasi-Minimin function. The robustness is realized and
controlled adaptively by the RO index without any predefined threshold.
Optimality is guaranteed... | computer science |
7,923 | Data-driven detrending of nonstationary fractal time series with echo
state networks | cs.LG | In this paper, we propose a novel data-driven approach for removing trends
(detrending) from nonstationary, fractal and multifractal time series. We
consider real-valued time series relative to measurements of an underlying
dynamical system that evolves through time. We assume that such a dynamical
process is predictab... | computer science |
7,924 | Deep Recurrent Neural Networks for Sequential Phenotype Prediction in
Genomics | cs.NE | In analyzing of modern biological data, we are often dealing with ill-posed
problems and missing data, mostly due to high dimensionality and
multicollinearity of the dataset. In this paper, we have proposed a system
based on matrix factorization (MF) and deep recurrent neural networks (DRNNs)
for genotype imputation an... | computer science |
7,925 | Deep Activity Recognition Models with Triaxial Accelerometers | cs.LG | Despite the widespread installation of accelerometers in almost all mobile
phones and wearable devices, activity recognition using accelerometers is still
immature due to the poor recognition accuracy of existing recognition methods
and the scarcity of labeled training data. We consider the problem of human
activity re... | computer science |
7,926 | Automatic Instrument Recognition in Polyphonic Music Using Convolutional
Neural Networks | cs.SD | Traditional methods to tackle many music information retrieval tasks
typically follow a two-step architecture: feature engineering followed by a
simple learning algorithm. In these "shallow" architectures, feature
engineering and learning are typically disjoint and unrelated. Additionally,
feature engineering is diffic... | computer science |
7,927 | Online Batch Selection for Faster Training of Neural Networks | cs.LG | Deep neural networks are commonly trained using stochastic non-convex
optimization procedures, which are driven by gradient information estimated on
fractions (batches) of the dataset. While it is commonly accepted that batch
size is an important parameter for offline tuning, the benefits of online
selection of batches... | computer science |
7,928 | Exponential Natural Particle Filter | cs.LG | Particle Filter algorithm (PF) suffers from some problems such as the loss of
particle diversity, the need for large number of particles, and the costly
selection of the importance density functions. In this paper, a novel
Exponential Natural Particle Filter (xNPF) is introduced to solve the above
problems. In this app... | computer science |
7,929 | Session-based Recommendations with Recurrent Neural Networks | cs.LG | We apply recurrent neural networks (RNN) on a new domain, namely recommender
systems. Real-life recommender systems often face the problem of having to base
recommendations only on short session-based data (e.g. a small sportsware
website) instead of long user histories (as in the case of Netflix). In this
situation th... | computer science |
7,930 | Detecting Road Surface Wetness from Audio: A Deep Learning Approach | cs.LG | We introduce a recurrent neural network architecture for automated road
surface wetness detection from audio of tire-surface interaction. The
robustness of our approach is evaluated on 785,826 bins of audio that span an
extensive range of vehicle speeds, noises from the environment, road surface
types, and pavement con... | computer science |
7,931 | MXNet: A Flexible and Efficient Machine Learning Library for
Heterogeneous Distributed Systems | cs.DC | MXNet is a multi-language machine learning (ML) library to ease the
development of ML algorithms, especially for deep neural networks. Embedded in
the host language, it blends declarative symbolic expression with imperative
tensor computation. It offers auto differentiation to derive gradients. MXNet
is computation and... | computer science |
7,932 | Reinforcement Control with Hierarchical Backpropagated Adaptive Critics | cs.NE | Present incremental learning methods are limited in the ability to achieve
reliable credit assignment over a large number time steps (or events). However,
this situation is typical for cases where the dynamical system to be controlled
requires relatively frequent control updates in order to maintain stability or
robust... | computer science |
7,933 | Mobile Big Data Analytics Using Deep Learning and Apache Spark | cs.DC | The proliferation of mobile devices, such as smartphones and Internet of
Things (IoT) gadgets, results in the recent mobile big data (MBD) era.
Collecting MBD is unprofitable unless suitable analytics and learning methods
are utilized for extracting meaningful information and hidden patterns from
data. This article pre... | computer science |
7,934 | vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient
Neural Network Design | cs.DC | The most widely used machine learning frameworks require users to carefully
tune their memory usage so that the deep neural network (DNN) fits into the
DRAM capacity of a GPU. This restriction hampers a researcher's flexibility to
study different machine learning algorithms, forcing them to either use a less
desirable ... | computer science |
7,935 | Determination of the edge of criticality in echo state networks through
Fisher information maximization | cs.LG | It is a widely accepted fact that the computational capability of recurrent
neural networks is maximized on the so-called "edge of criticality". Once the
network operates in this configuration, it performs efficiently on a specific
application both in terms of (i) low prediction error and (ii) high short-term
memory ca... | computer science |
7,936 | Comparing Time and Frequency Domain for Audio Event Recognition Using
Deep Learning | cs.NE | Recognizing acoustic events is an intricate problem for a machine and an
emerging field of research. Deep neural networks achieve convincing results and
are currently the state-of-the-art approach for many tasks. One advantage is
their implicit feature learning, opposite to an explicit feature extraction of
the input s... | computer science |
7,937 | The Asymptotic Performance of Linear Echo State Neural Networks | cs.LG | In this article, a study of the mean-square error (MSE) performance of linear
echo-state neural networks is performed, both for training and testing tasks.
Considering the realistic setting of noise present at the network nodes, we
derive deterministic equivalents for the aforementioned MSE in the limit where
the numbe... | computer science |
7,938 | DeepLearningKit - an GPU Optimized Deep Learning Framework for Apple's
iOS, OS X and tvOS developed in Metal and Swift | cs.LG | In this paper we present DeepLearningKit - an open source framework that
supports using pretrained deep learning models (convolutional neural networks)
for iOS, OS X and tvOS. DeepLearningKit is developed in Metal in order to
utilize the GPU efficiently and Swift for integration with applications, e.g.
iOS-based mobile... | computer science |
7,939 | DLAU: A Scalable Deep Learning Accelerator Unit on FPGA | cs.LG | As the emerging field of machine learning, deep learning shows excellent
ability in solving complex learning problems. However, the size of the networks
becomes increasingly large scale due to the demands of the practical
applications, which poses significant challenge to construct a high performance
implementations of... | computer science |
7,940 | Ensemble-Compression: A New Method for Parallel Training of Deep Neural
Networks | cs.DC | Parallelization framework has become a necessity to speed up the training of
deep neural networks (DNN) recently. Such framework typically employs the Model
Average approach, denoted as MA-DNN, in which parallel workers conduct
respective training based on their own local data while the parameters of local
models are p... | computer science |
7,941 | Feedforward Initialization for Fast Inference of Deep Generative
Networks is biologically plausible | cs.LG | We consider deep multi-layered generative models such as Boltzmann machines
or Hopfield nets in which computation (which implements inference) is both
recurrent and stochastic, but where the recurrence is not to model sequential
structure, only to perform computation. We find conditions under which a simple
feedforward... | computer science |
7,942 | Adversarial Perturbations Against Deep Neural Networks for Malware
Classification | cs.CR | Deep neural networks, like many other machine learning models, have recently
been shown to lack robustness against adversarially crafted inputs. These
inputs are derived from regular inputs by minor yet carefully selected
perturbations that deceive machine learning models into desired
misclassifications. Existing work ... | computer science |
7,943 | Multi-Modal Hybrid Deep Neural Network for Speech Enhancement | cs.LG | Deep Neural Networks (DNN) have been successful in en- hancing noisy speech
signals. Enhancement is achieved by learning a nonlinear mapping function from
the features of the corrupted speech signal to that of the reference clean
speech signal. The quality of predicted features can be improved by providing
additional s... | computer science |
7,944 | Classifying Variable-Length Audio Files with All-Convolutional Networks
and Masked Global Pooling | cs.NE | We trained a deep all-convolutional neural network with masked global pooling
to perform single-label classification for acoustic scene classification and
multi-label classification for domestic audio tagging in the DCASE-2016
contest. Our network achieved an average accuracy of 84.5% on the four-fold
cross-validation ... | computer science |
7,945 | Automatic Environmental Sound Recognition: Performance versus
Computational Cost | cs.SD | In the context of the Internet of Things (IoT), sound sensing applications
are required to run on embedded platforms where notions of product pricing and
form factor impose hard constraints on the available computing power. Whereas
Automatic Environmental Sound Recognition (AESR) algorithms are most often
developed wit... | computer science |
7,946 | Learning to Decode Linear Codes Using Deep Learning | cs.IT | A novel deep learning method for improving the belief propagation algorithm
is proposed. The method generalizes the standard belief propagation algorithm
by assigning weights to the edges of the Tanner graph. These edges are then
trained using deep learning techniques. A well-known property of the belief
propagation al... | computer science |
7,947 | Horn: A System for Parallel Training and Regularizing of Large-Scale
Neural Networks | cs.DC | I introduce a new distributed system for effective training and regularizing
of Large-Scale Neural Networks on distributed computing architectures. The
experiments demonstrate the effectiveness of flexible model partitioning and
parallelization strategies based on neuron-centric computation model, with an
implementatio... | computer science |
7,948 | SGDR: Stochastic Gradient Descent with Warm Restarts | cs.LG | Restart techniques are common in gradient-free optimization to deal with
multimodal functions. Partial warm restarts are also gaining popularity in
gradient-based optimization to improve the rate of convergence in accelerated
gradient schemes to deal with ill-conditioned functions. In this paper, we
propose a simple wa... | computer science |
7,949 | Convolutional Recurrent Neural Networks for Music Classification | cs.NE | We introduce a convolutional recurrent neural network (CRNN) for music
tagging. CRNNs take advantage of convolutional neural networks (CNNs) for local
feature extraction and recurrent neural networks for temporal summarisation of
the extracted features. We compare CRNN with three CNN structures that have
been used for ... | computer science |
7,950 | A Quantum Implementation Model for Artificial Neural Networks | cs.LG | The learning process for multi layered neural networks with many nodes makes
heavy demands on computational resources. In some neural network models, the
learning formulas, such as the Widrow-Hoff formula, do not change the
eigenvectors of the weight matrix while flatting the eigenvalues. In infinity,
this iterative fo... | computer science |
7,951 | Very Deep Convolutional Neural Networks for Raw Waveforms | cs.SD | Learning acoustic models directly from the raw waveform data with minimal
processing is challenging. Current waveform-based models have generally used
very few (~2) convolutional layers, which might be insufficient for building
high-level discriminative features. In this work, we propose very deep
convolutional neural ... | computer science |
7,952 | Optimizing Memory Efficiency for Deep Convolutional Neural Networks on
GPUs | cs.DC | Leveraging large data sets, deep Convolutional Neural Networks (CNNs) achieve
state-of-the-art recognition accuracy. Due to the substantial compute and
memory operations, however, they require significant execution time. The
massive parallel computing capability of GPUs make them as one of the ideal
platforms to accele... | computer science |
7,953 | Deep Neural Networks for Improved, Impromptu Trajectory Tracking of
Quadrotors | cs.RO | Trajectory tracking control for quadrotors is important for applications
ranging from surveying and inspection, to film making. However, designing and
tuning classical controllers, such as proportional-integral-derivative (PID)
controllers, to achieve high tracking precision can be time-consuming and
difficult, due to ... | computer science |
7,954 | Robustly representing uncertainty in deep neural networks through
sampling | cs.LG | As deep neural networks (DNNs) are applied to increasingly challenging
problems, they will need to be able to represent their own uncertainty.
Modeling uncertainty is one of the key features of Bayesian methods. Using
Bernoulli dropout with sampling at prediction time has recently been proposed
as an efficient and well... | computer science |
7,955 | Generative Multi-Adversarial Networks | cs.LG | Generative adversarial networks (GANs) are a framework for producing a
generative model by way of a two-player minimax game. In this paper, we propose
the \emph{Generative Multi-Adversarial Network} (GMAN), a framework that
extends GANs to multiple discriminators. In previous work, the successful
training of GANs requi... | computer science |
7,956 | DeepSense: A Unified Deep Learning Framework for Time-Series Mobile
Sensing Data Processing | cs.LG | Mobile sensing applications usually require time-series inputs from sensors.
Some applications, such as tracking, can use sensed acceleration and rate of
rotation to calculate displacement based on physical system models. Other
applications, such as activity recognition, extract manually designed features
from sensor i... | computer science |
7,957 | Predicting non-linear dynamics by stable local learning in a recurrent
spiking neural network | cs.LG | Brains need to predict how the body reacts to motor commands. It is an open
question how networks of spiking neurons can learn to reproduce the non-linear
body dynamics caused by motor commands, using local, online and stable learning
rules. Here, we present a supervised learning scheme for the feedforward and
recurren... | computer science |
7,958 | RNN Decoding of Linear Block Codes | cs.IT | Designing a practical, low complexity, close to optimal, channel decoder for
powerful algebraic codes with short to moderate block length is an open
research problem. Recently it has been shown that a feed-forward neural network
architecture can improve on standard belief propagation decoding, despite the
large example... | computer science |
7,959 | Convolutional Gated Recurrent Neural Network Incorporating Spatial
Features for Audio Tagging | cs.SD | Environmental audio tagging is a newly proposed task to predict the presence
or absence of a specific audio event in a chunk. Deep neural network (DNN)
based methods have been successfully adopted for predicting the audio tags in
the domestic audio scene. In this paper, we propose to use a convolutional
neural network ... | computer science |
7,960 | Sample-level Deep Convolutional Neural Networks for Music Auto-tagging
Using Raw Waveforms | cs.SD | Recently, the end-to-end approach that learns hierarchical representations
from raw data using deep convolutional neural networks has been successfully
explored in the image, text and speech domains. This approach was applied to
musical signals as well but has been not fully explored yet. To this end, we
propose sample... | computer science |
7,961 | Multi-Level and Multi-Scale Feature Aggregation Using Pre-trained
Convolutional Neural Networks for Music Auto-tagging | cs.NE | Music auto-tagging is often handled in a similar manner to image
classification by regarding the 2D audio spectrogram as image data. However,
music auto-tagging is distinguished from image classification in that the tags
are highly diverse and have different levels of abstractions. Considering this
issue, we propose a ... | computer science |
7,962 | Sensor Fusion for Robot Control through Deep Reinforcement Learning | cs.RO | Deep reinforcement learning is becoming increasingly popular for robot
control algorithms, with the aim for a robot to self-learn useful feature
representations from unstructured sensory input leading to the optimal
actuation policy. In addition to sensors mounted on the robot, sensors might
also be deployed in the env... | computer science |
7,963 | SEGAN: Speech Enhancement Generative Adversarial Network | cs.LG | Current speech enhancement techniques operate on the spectral domain and/or
exploit some higher-level feature. The majority of them tackle a limited number
of noise conditions and rely on first-order statistics. To circumvent these
issues, deep networks are being increasingly used, thanks to their ability to
learn comp... | computer science |
7,964 | Deep Learning and Quantum Entanglement: Fundamental Connections with
Implications to Network Design | cs.LG | Deep convolutional networks have witnessed unprecedented success in various
machine learning applications. Formal understanding on what makes these
networks so successful is gradually unfolding, but for the most part there are
still significant mysteries to unravel. The inductive bias, which reflects
prior knowledge em... | computer science |
7,965 | Unsupervised prototype learning in an associative-memory network | cs.NE | Unsupervised learning in a generalized Hopfield associative-memory network is
investigated in this work. First, we prove that the (generalized) Hopfield
model is equivalent to a semi-restricted Boltzmann machine with a layer of
visible neurons and another layer of hidden binary neurons, so it could serve
as the buildin... | computer science |
7,966 | A dynamic connectome supports the emergence of stable computational
function of neural circuits through reward-based learning | cs.LG | Synaptic connections between neurons in the brain are dynamic because of
continuously ongoing spine dynamics, axonal sprouting, and other processes. In
fact, it was recently shown that the spontaneous synapse-autonomous component
of spine dynamics is at least as large as the component that depends on the
history of pre... | computer science |
7,967 | In-Datacenter Performance Analysis of a Tensor Processing Unit | cs.AR | Many architects believe that major improvements in cost-energy-performance
must now come from domain-specific hardware. This paper evaluates a custom
ASIC---called a Tensor Processing Unit (TPU)---deployed in datacenters since
2015 that accelerates the inference phase of neural networks (NN). The heart of
the TPU is a ... | computer science |
7,968 | Differential Evolution and Bayesian Optimisation for Hyper-Parameter
Selection in Mixed-Signal Neuromorphic Circuits Applied to UAV Obstacle
Avoidance | cs.NE | The Lobula Giant Movement Detector (LGMD) is a an identified neuron of the
locust that detects looming objects and triggers its escape responses.
Understanding the neural principles and networks that lead to these fast and
robust responses can lead to the design of efficient facilitate obstacle
avoidance strategies in ... | computer science |
7,969 | Maximum Likelihood Estimation based on Random Subspace EDA: Application
to Extrasolar Planet Detection | stat.ME | This paper addresses maximum likelihood (ML) estimation based model fitting
in the context of extrasolar planet detection. This problem is featured by the
following properties: 1) the candidate models under consideration are highly
nonlinear; 2) the likelihood surface has a huge number of peaks; 3) the
parameter space ... | computer science |
7,970 | Making Neural Programming Architectures Generalize via Recursion | cs.LG | Empirically, neural networks that attempt to learn programs from data have
exhibited poor generalizability. Moreover, it has traditionally been difficult
to reason about the behavior of these models beyond a certain level of input
complexity. In order to address these issues, we propose augmenting neural
architectures ... | computer science |
7,971 | Predicting membrane protein contacts from non-membrane proteins by deep
transfer learning | cs.LG | Computational prediction of membrane protein (MP) structures is very
challenging partially due to lack of sufficient solved structures for homology
modeling. Recently direct evolutionary coupling analysis (DCA) sheds some light
on protein contact prediction and accordingly, contact-assisted folding, but
DCA is effectiv... | computer science |
7,972 | Sparse Coding by Spiking Neural Networks: Convergence Theory and
Computational Results | cs.LG | In a spiking neural network (SNN), individual neurons operate autonomously
and only communicate with other neurons sparingly and asynchronously via spike
signals. These characteristics render a massively parallel hardware
implementation of SNN a potentially powerful computer, albeit a non von Neumann
one. But can one g... | computer science |
7,973 | Limited-Memory Matrix Adaptation for Large Scale Black-box Optimization | cs.NE | The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a popular
method to deal with nonconvex and/or stochastic optimization problems when the
gradient information is not available. Being based on the CMA-ES, the recently
proposed Matrix Adaptation Evolution Strategy (MA-ES) provides a rather
surprising resul... | computer science |
7,974 | TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep
Learning | cs.LG | High network communication cost for synchronizing gradients and parameters is
the well-known bottleneck of distributed training. In this work, we propose
TernGrad that uses ternary gradients to accelerate distributed deep learning in
data parallelism. Our approach requires only three numerical levels {-1,0,1},
which ca... | computer science |
7,975 | CATERPILLAR: Coarse Grain Reconfigurable Architecture for Accelerating
the Training of Deep Neural Networks | cs.DC | Accelerating the inference of a trained DNN is a well studied subject. In
this paper we switch the focus to the training of DNNs. The training phase is
compute intensive, demands complicated data communication, and contains
multiple levels of data dependencies and parallelism. This paper presents an
algorithm/architect... | computer science |
7,976 | Dataflow Matrix Machines as a Model of Computations with Linear Streams | cs.NE | We overview dataflow matrix machines as a Turing complete generalization of
recurrent neural networks and as a programming platform. We describe vector
space of finite prefix trees with numerical leaves which allows us to combine
expressive power of dataflow matrix machines with simplicity of traditional
recurrent neur... | computer science |
7,977 | MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU | cs.DC | In this paper, we explore optimizations to run Recurrent Neural Network (RNN)
models locally on mobile devices. RNN models are widely used for Natural
Language Processing, Machine Translation, and other tasks. However, existing
mobile applications that use RNN models do so on the cloud. To address privacy
and efficienc... | computer science |
7,978 | DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems
with a Compressor-Critic Framework | cs.LG | Recent advances in deep learning motivate the use of deep neutral networks in
sensing applications, but their excessive resource needs on constrained
embedded devices remain an important impediment. A recently explored solution
space lies in compressing (approximating or simplifying) deep neural networks
in some manner... | computer science |
7,979 | Transfer entropy-based feedback improves performance in artificial
neural networks | cs.LG | The structure of the majority of modern deep neural networks is characterized
by uni- directional feed-forward connectivity across a very large number of
layers. By contrast, the architecture of the cortex of vertebrates contains
fewer hierarchical levels but many recurrent and feedback connections. Here we
show that a... | computer science |
7,980 | Proceedings of the First International Workshop on Deep Learning and
Music | cs.NE | Proceedings of the First International Workshop on Deep Learning and Music,
joint with IJCNN, Anchorage, US, May 17-18, 2017 | computer science |
7,981 | The Fog of War: A Machine Learning Approach to Forecasting Weather on
Mars | cs.LG | For over a decade, scientists at NASA's Jet Propulsion Laboratory (JPL) have
been recording measurements from the Martian surface as a part of the Mars
Exploration Rovers mission. One quantity of interest has been the opacity of
Mars's atmosphere for its importance in day-to-day estimations of the amount of
power avail... | computer science |
7,982 | Transforming Musical Signals through a Genre Classifying Convolutional
Neural Network | cs.SD | Convolutional neural networks (CNNs) have been successfully applied on both
discriminative and generative modeling for music-related tasks. For a
particular task, the trained CNN contains information representing the decision
making or the abstracting process. One can hope to manipulate existing music
based on this 'in... | computer science |
7,983 | Music Signal Processing Using Vector Product Neural Networks | cs.SD | We propose a novel neural network model for music signal processing using
vector product neurons and dimensionality transformations. Here, the inputs are
first mapped from real values into three-dimensional vectors then fed into a
three-dimensional vector product neural network where the inputs, outputs, and
weights ar... | computer science |
7,984 | Audio Spectrogram Representations for Processing with Convolutional
Neural Networks | cs.SD | One of the decisions that arise when designing a neural network for any
application is how the data should be represented in order to be presented to,
and possibly generated by, a neural network. For audio, the choice is less
obvious than it seems to be for visual images, and a variety of representations
have been used... | computer science |
7,985 | RIDDLE: Race and ethnicity Imputation from Disease history with Deep
LEarning | cs.LG | Anonymized electronic medical records are an increasingly popular source of
research data. However, these datasets often lack race and ethnicity
information. This creates problems for researchers modeling human disease, as
race and ethnicity are powerful confounders for many health exposures and
treatment outcomes; rac... | computer science |
7,986 | Generic Black-Box End-to-End Attack Against State of the Art API Call
Based Malware Classifiers | cs.CR | In this paper, we present a black-box attack against API call based machine
learning malware classifiers, focusing on generating adversarial sequences
combining API calls and static features (e.g., printable strings) that will be
misclassified by the classifier without affecting the malware functionality. We
show that ... | computer science |
7,987 | Learning to Singulate Objects using a Push Proposal Network | cs.RO | Learning to act in unstructured environments, such as cluttered piles of
objects, poses a substantial challenge for manipulation robots. We present a
novel neural network-based approach that separates unknown objects in clutter
by selecting favourable push actions. Our network is trained from data
collected through aut... | computer science |
7,988 | Dragon: A Computation Graph Virtual Machine Based Deep Learning
Framework | cs.SE | Deep Learning has made a great progress for these years. However, it is still
difficult to master the implement of various models because different
researchers may release their code based on different frameworks or interfaces.
In this paper, we proposed a computation graph based framework which only aims
to introduce ... | computer science |
7,989 | SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks | cs.NE | Convolutional Neural Networks (CNNs) have emerged as a fundamental technology
for machine learning. High performance and extreme energy efficiency are
critical for deployments of CNNs in a wide range of situations, especially
mobile platforms such as autonomous vehicles, cameras, and electronic personal
assistants. Thi... | computer science |
7,990 | Self-adaptive node-based PCA encodings | cs.NE | In this paper we propose an algorithm, Simple Hebbian PCA, and prove that it
is able to calculate the principal component analysis (PCA) in a distributed
fashion across nodes. It simplifies existing network structures by removing
intralayer weights, essentially cutting the number of weights that need to be
trained in h... | computer science |
7,991 | Throughput Optimal Decentralized Scheduling of Multi-Hop Networks with
End-to-End Deadline Constraints: II Wireless Networks with Interference | cs.NI | Consider a multihop wireless network serving multiple flows in which wireless
link interference constraints are described by a link interference graph. For
such a network, we design routing-scheduling policies that maximize the
end-to-end timely throughput of the network. Timely throughput of a flow $f$ is
defined as t... | computer science |
7,992 | Phylogenetic Convolutional Neural Networks in Metagenomics | cs.LG | Background: Convolutional Neural Networks can be effectively used only when
data are endowed with an intrinsic concept of neighbourhood in the input space,
as is the case of pixels in images. We introduce here Ph-CNN, a novel deep
learning architecture for the classification of metagenomics data based on the
Convolutio... | computer science |
7,993 | Spatial features of synaptic adaptation affecting learning performance | cs.LG | Recent studies have proposed that the diffusion of messenger molecules, such
as monoamines, can mediate the plastic adaptation of synapses in supervised
learning of neural networks. Based on these findings we developed a model for
neural learning, where the signal for plastic adaptation is assumed to
propagate through ... | computer science |
7,994 | Quantum Autoencoders via Quantum Adders with Genetic Algorithms | cs.LG | The quantum autoencoder is a recent paradigm in the field of quantum machine
learning, which may enable an enhanced use of resources in quantum
technologies. To this end, quantum neural networks with less nodes in the inner
than in the outer layers were considered. Here, we propose a useful connection
between approxima... | computer science |
7,995 | BreathRNNet: Breathing Based Authentication on Resource-Constrained IoT
Devices using RNNs | cs.CR | Recurrent neural networks (RNNs) have shown promising results in audio and
speech processing applications due to their strong capabilities in modelling
sequential data. In many applications, RNNs tend to outperform conventional
models based on GMM/UBMs and i-vectors. Increasing popularity of IoT devices
makes a strong ... | computer science |
7,996 | Deep Learning applied to Road Traffic Speed forecasting | stat.AP | In this paper, we propose deep learning architectures (FNN, CNN and LSTM) to
forecast a regression model for time dependent data. These algorithm's are
designed to handle Floating Car Data (FCD) historic speeds to predict road
traffic data. For this we aggregate the speeds into the network inputs in an
innovative way. ... | computer science |
7,997 | ChainerMN: Scalable Distributed Deep Learning Framework | cs.DC | One of the keys for deep learning to have made a breakthrough in various
fields was to utilize high computing powers centering around GPUs. Enabling the
use of further computing abilities by distributed processing is essential not
only to make the deep learning bigger and faster but also to tackle unsolved
challenges. ... | computer science |
7,998 | Minimum Energy Quantized Neural Networks | cs.NE | This work targets the automated minimum-energy optimization of Quantized
Neural Networks (QNNs) - networks using low precision weights and activations.
These networks are trained from scratch at an arbitrary fixed point precision.
At iso-accuracy, QNNs using fewer bits require deeper and wider network
architectures tha... | computer science |
7,999 | Performance Evaluation of Channel Decoding With Deep Neural Networks | eess.SP | With the demand of high data rate and low latency in fifth generation (5G),
deep neural network decoder (NND) has become a promising candidate due to its
capability of one-shot decoding and parallel computing. In this paper, three
types of NND, i.e., multi-layer perceptron (MLP), convolution neural network
(CNN) and re... | computer science |
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