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800 | Towards Machine Intelligence | cs.AI | There exists a theory of a single general-purpose learning algorithm which
could explain the principles of its operation. This theory assumes that the
brain has some initial rough architecture, a small library of simple innate
circuits which are prewired at birth and proposes that all significant mental
algorithms can ... | computer science |
801 | Dynamic Frame skip Deep Q Network | cs.LG | Deep Reinforcement Learning methods have achieved state of the art
performance in learning control policies for the games in the Atari 2600
domain. One of the important parameters in the Arcade Learning Environment
(ALE) is the frame skip rate. It decides the granularity at which agents can
control game play. A frame s... | computer science |
802 | Programming with a Differentiable Forth Interpreter | cs.NE | Given that in practice training data is scarce for all but a small set of
problems, a core question is how to incorporate prior knowledge into a model.
In this paper, we consider the case of prior procedural knowledge for neural
networks, such as knowing how a program should traverse a sequence, but not
what local acti... | computer science |
803 | Generative Choreography using Deep Learning | cs.AI | Recent advances in deep learning have enabled the extraction of high-level
features from raw sensor data which has opened up new possibilities in many
different fields, including computer generated choreography. In this paper we
present a system chor-rnn for generating novel choreographic material in the
nuanced choreo... | computer science |
804 | Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and
Knowledge | cs.AI | We propose Logic Tensor Networks: a uniform framework for integrating
automatic learning and reasoning. A logic formalism called Real Logic is
defined on a first-order language whereby formulas have truth-value in the
interval [0,1] and semantics defined concretely on the domain of real numbers.
Logical constants are i... | computer science |
805 | Identifying and Harnessing the Building Blocks of Machine Learning
Pipelines for Sensible Initialization of a Data Science Automation Tool | cs.NE | As data science continues to grow in popularity, there will be an increasing
need to make data science tools more scalable, flexible, and accessible. In
particular, automated machine learning (AutoML) systems seek to automate the
process of designing and optimizing machine learning pipelines. In this
chapter, we presen... | computer science |
806 | Neuroevolution-Based Inverse Reinforcement Learning | cs.NE | The problem of Learning from Demonstration is targeted at learning to perform
tasks based on observed examples. One approach to Learning from Demonstration
is Inverse Reinforcement Learning, in which actions are observed to infer
rewards. This work combines a feature based state evaluation approach to
Inverse Reinforce... | computer science |
807 | TerpreT: A Probabilistic Programming Language for Program Induction | cs.LG | We study machine learning formulations of inductive program synthesis; given
input-output examples, we try to synthesize source code that maps inputs to
corresponding outputs. Our aims are to develop new machine learning approaches
based on neural networks and graphical models, and to understand the
capabilities of mac... | computer science |
808 | Multi-Label Classification Method Based on Extreme Learning Machines | cs.LG | In this paper, an Extreme Learning Machine (ELM) based technique for
Multi-label classification problems is proposed and discussed. In multi-label
classification, each of the input data samples belongs to one or more than one
class labels. The traditional binary and multi-class classification problems
are the subset of... | computer science |
809 | A Novel Online Real-time Classifier for Multi-label Data Streams | cs.LG | In this paper, a novel extreme learning machine based online multi-label
classifier for real-time data streams is proposed. Multi-label classification
is one of the actively researched machine learning paradigm that has gained
much attention in the recent years due to its rapidly increasing real world
applications. In ... | computer science |
810 | A Novel Progressive Learning Technique for Multi-class Classification | cs.LG | In this paper, a progressive learning technique for multi-class
classification is proposed. This newly developed learning technique is
independent of the number of class constraints and it can learn new classes
while still retaining the knowledge of previous classes. Whenever a new class
(non-native to the knowledge le... | computer science |
811 | A novel online multi-label classifier for high-speed streaming data
applications | cs.LG | In this paper, a high-speed online neural network classifier based on extreme
learning machines for multi-label classification is proposed. In multi-label
classification, each of the input data sample belongs to one or more than one
of the target labels. The traditional binary and multi-class classification
where each ... | computer science |
812 | Ternary Neural Networks for Resource-Efficient AI Applications | cs.LG | The computation and storage requirements for Deep Neural Networks (DNNs) are
usually high. This issue limits their deployability on ubiquitous computing
devices such as smart phones, wearables and autonomous drones. In this paper,
we propose ternary neural networks (TNNs) in order to make deep learning more
resource-ef... | computer science |
813 | Fitted Learning: Models with Awareness of their Limits | cs.AI | Though deep learning has pushed the boundaries of classification forward, in
recent years hints of the limits of standard classification have begun to
emerge. Problems such as fooling, adding new classes over time, and the need to
retrain learning models only for small changes to the original problem all
point to a pot... | computer science |
814 | Learning to learn with backpropagation of Hebbian plasticity | cs.NE | Hebbian plasticity is a powerful principle that allows biological brains to
learn from their lifetime experience. By contrast, artificial neural networks
trained with backpropagation generally have fixed connection weights that do
not change once training is complete. While recent methods can endow neural
networks with... | computer science |
815 | Learning by Stimulation Avoidance: A Principle to Control Spiking Neural
Networks Dynamics | cs.NE | Learning based on networks of real neurons, and by extension biologically
inspired models of neural networks, has yet to find general learning rules
leading to widespread applications. In this paper, we argue for the existence
of a principle allowing to steer the dynamics of a biologically inspired neural
network. Usin... | computer science |
816 | Surprisal-Driven Zoneout | cs.LG | We propose a novel method of regularization for recurrent neural networks
called suprisal-driven zoneout. In this method, states zoneout (maintain their
previous value rather than updating), when the suprisal (discrepancy between
the last state's prediction and target) is small. Thus regularization is
adaptive and inpu... | computer science |
817 | Neural Architecture Search with Reinforcement Learning | cs.LG | Neural networks are powerful and flexible models that work well for many
difficult learning tasks in image, speech and natural language understanding.
Despite their success, neural networks are still hard to design. In this paper,
we use a recurrent network to generate the model descriptions of neural
networks and trai... | computer science |
818 | Emergence of foveal image sampling from learning to attend in visual
scenes | cs.NE | We describe a neural attention model with a learnable retinal sampling
lattice. The model is trained on a visual search task requiring the
classification of an object embedded in a visual scene amidst background
distractors using the smallest number of fixations. We explore the tiling
properties that emerge in the mode... | computer science |
819 | Long Timescale Credit Assignment in NeuralNetworks with External Memory | cs.AI | Credit assignment in traditional recurrent neural networks usually involves
back-propagating through a long chain of tied weight matrices. The length of
this chain scales linearly with the number of time-steps as the same network is
run at each time-step. This creates many problems, such as vanishing gradients,
that ha... | computer science |
820 | Energy Saving Additive Neural Network | cs.NE | In recent years, machine learning techniques based on neural networks for
mobile computing become increasingly popular. Classical multi-layer neural
networks require matrix multiplications at each stage. Multiplication operation
is not an energy efficient operation and consequently it drains the battery of
the mobile d... | computer science |
821 | Learning to Repeat: Fine Grained Action Repetition for Deep
Reinforcement Learning | cs.LG | Reinforcement Learning algorithms can learn complex behavioral patterns for
sequential decision making tasks wherein an agent interacts with an environment
and acquires feedback in the form of rewards sampled from it. Traditionally,
such algorithms make decisions, i.e., select actions to execute, at every
single time s... | computer science |
822 | Survey of reasoning using Neural networks | cs.LG | Reason and inference require process as well as memory skills by humans.
Neural networks are able to process tasks like image recognition (better than
humans) but in memory aspects are still limited (by attention mechanism, size).
Recurrent Neural Network (RNN) and it's modified version LSTM are able to solve
small mem... | computer science |
823 | One-Shot Imitation Learning | cs.AI | Imitation learning has been commonly applied to solve different tasks in
isolation. This usually requires either careful feature engineering, or a
significant number of samples. This is far from what we desire: ideally, robots
should be able to learn from very few demonstrations of any given task, and
instantly general... | computer science |
824 | Deep Learning for Explicitly Modeling Optimization Landscapes | cs.NE | In all but the most trivial optimization problems, the structure of the
solutions exhibit complex interdependencies between the input parameters.
Decades of research with stochastic search techniques has shown the benefit of
explicitly modeling the interactions between sets of parameters and the overall
quality of the ... | computer science |
825 | Stochastic Neural Networks for Hierarchical Reinforcement Learning | cs.AI | Deep reinforcement learning has achieved many impressive results in recent
years. However, tasks with sparse rewards or long horizons continue to pose
significant challenges. To tackle these important problems, we propose a
general framework that first learns useful skills in a pre-training
environment, and then levera... | computer science |
826 | Batch Reinforcement Learning on the Industrial Benchmark: First
Experiences | cs.LG | The Particle Swarm Optimization Policy (PSO-P) has been recently introduced
and proven to produce remarkable results on interacting with academic
reinforcement learning benchmarks in an off-policy, batch-based setting. To
further investigate the properties and feasibility on real-world applications,
this paper investig... | computer science |
827 | End-to-End Differentiable Proving | cs.NE | We introduce neural networks for end-to-end differentiable proving of queries
to knowledge bases by operating on dense vector representations of symbols.
These neural networks are constructed recursively by taking inspiration from
the backward chaining algorithm as used in Prolog. Specifically, we replace
symbolic unif... | computer science |
828 | Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments | cs.LG | We explore deep reinforcement learning methods for multi-agent domains. We
begin by analyzing the difficulty of traditional algorithms in the multi-agent
case: Q-learning is challenged by an inherent non-stationarity of the
environment, while policy gradient suffers from a variance that increases as
the number of agent... | computer science |
829 | Getting deep recommenders fit: Bloom embeddings for sparse binary
input/output networks | cs.LG | Recommendation algorithms that incorporate techniques from deep learning are
becoming increasingly popular. Due to the structure of the data coming from
recommendation domains (i.e., one-hot-encoded vectors of item preferences),
these algorithms tend to have large input and output dimensionalities that
dominate their o... | computer science |
830 | Beyond Monte Carlo Tree Search: Playing Go with Deep Alternative Neural
Network and Long-Term Evaluation | cs.AI | Monte Carlo tree search (MCTS) is extremely popular in computer Go which
determines each action by enormous simulations in a broad and deep search tree.
However, human experts select most actions by pattern analysis and careful
evaluation rather than brute search of millions of future nteractions. In this
paper, we pro... | computer science |
831 | Hindsight Experience Replay | cs.LG | Dealing with sparse rewards is one of the biggest challenges in Reinforcement
Learning (RL). We present a novel technique called Hindsight Experience Replay
which allows sample-efficient learning from rewards which are sparse and binary
and therefore avoid the need for complicated reward engineering. It can be
combined... | computer science |
832 | Trial without Error: Towards Safe Reinforcement Learning via Human
Intervention | cs.AI | AI systems are increasingly applied to complex tasks that involve interaction
with humans. During training, such systems are potentially dangerous, as they
haven't yet learned to avoid actions that could cause serious harm. How can an
AI system explore and learn without making a single mistake that harms humans
or othe... | computer science |
833 | Reverse Curriculum Generation for Reinforcement Learning | cs.AI | Many relevant tasks require an agent to reach a certain state, or to
manipulate objects into a desired configuration. For example, we might want a
robot to align and assemble a gear onto an axle or insert and turn a key in a
lock. These goal-oriented tasks present a considerable challenge for
reinforcement learning, si... | computer science |
834 | Ideological Sublations: Resolution of Dialectic in Population-based
Optimization | cs.LG | A population-based optimization algorithm was designed, inspired by two main
thinking modes in philosophy, both based on dialectic concept and
thesis-antithesis paradigm. They impose two different kinds of dialectics.
Idealistic and materialistic antitheses are formulated as optimization models.
Based on the models, th... | computer science |
835 | ProjectionNet: Learning Efficient On-Device Deep Networks Using Neural
Projections | cs.LG | Deep neural networks have become ubiquitous for applications related to
visual recognition and language understanding tasks. However, it is often
prohibitive to use typical neural networks on devices like mobile phones or
smart watches since the model sizes are huge and cannot fit in the limited
memory available on suc... | computer science |
836 | A Flow Model of Neural Networks | cs.LG | Based on a natural connection between ResNet and transport equation or its
characteristic equation, we propose a continuous flow model for both ResNet and
plain net. Through this continuous model, a ResNet can be explicitly
constructed as a refinement of a plain net. The flow model provides an
alternative perspective t... | computer science |
837 | Multimodal Content Analysis for Effective Advertisements on YouTube | cs.AI | The rapid advances in e-commerce and Web 2.0 technologies have greatly
increased the impact of commercial advertisements on the general public. As a
key enabling technology, a multitude of recommender systems exists which
analyzes user features and browsing patterns to recommend appealing
advertisements to users. In th... | computer science |
838 | Overcoming Exploration in Reinforcement Learning with Demonstrations | cs.LG | Exploration in environments with sparse rewards has been a persistent problem
in reinforcement learning (RL). Many tasks are natural to specify with a sparse
reward, and manually shaping a reward function can result in suboptimal
performance. However, finding a non-zero reward is exponentially more difficult
with incre... | computer science |
839 | Lattice Recurrent Unit: Improving Convergence and Statistical Efficiency
for Sequence Modeling | cs.LG | Recurrent neural networks have shown remarkable success in modeling
sequences. However low resource situations still adversely affect the
generalizability of these models. We introduce a new family of models, called
Lattice Recurrent Units (LRU), to address the challenge of learning deep
multi-layer recurrent models wi... | computer science |
840 | Scalable Recollections for Continual Lifelong Learning | cs.LG | Given the recent success of Deep Learning applied to a variety of single
tasks, it is natural to consider more human-realistic settings. Perhaps the
most difficult of these settings is that of continual lifelong learning, where
the model must learn online over a continuous stream of non-stationary data. A
continual lif... | computer science |
841 | Hidden Tree Markov Networks: Deep and Wide Learning for Structured Data | cs.LG | The paper introduces the Hidden Tree Markov Network (HTN), a
neuro-probabilistic hybrid fusing the representation power of generative models
for trees with the incremental and discriminative learning capabilities of
neural networks. We put forward a modular architecture in which multiple
generative models of limited co... | computer science |
842 | Hierarchical Actor-Critic | cs.AI | The ability to learn at different resolutions in time may help overcome one
of the main challenges in deep reinforcement learning -- sample efficiency.
Hierarchical agents that operate at different levels of temporal abstraction
can learn tasks more quickly because they can divide the work of learning
behaviors among m... | computer science |
843 | Proximodistal Exploration in Motor Learning as an Emergent Property of
Optimization | cs.NE | To harness the complexity of their high-dimensional bodies during
sensorimotor development, infants are guided by patterns of freezing and
freeing of degrees of freedom. For instance, when learning to reach, infants
free the degrees of freedom in their arm proximodistally, i.e. from joints that
are closer to the body t... | computer science |
844 | Null Dynamical State Models of Human Cognitive Dysfunction | cs.AI | The hard problem in artificial intelligence asks how the shuffling of
syntactical symbols in a program can lead to systems which experience semantics
and qualia. We address this question in three stages. First, we introduce a new
class of human semantic symbols which appears when unexpected and drastic
environmental ch... | computer science |
845 | Accelerating Deep Learning with Memcomputing | cs.LG | Restricted Boltzmann machines (RBMs) and their extensions, called
'deep-belief networks', are powerful neural networks that have found
applications in the fields of machine learning and big data. The standard way
to training these models resorts to an iterative unsupervised procedure based
on Gibbs sampling, called 'co... | computer science |
846 | mvn2vec: Preservation and Collaboration in Multi-View Network Embedding | cs.SI | Multi-view networks are ubiquitous in real-world applications. In order to
extract knowledge or business value, it is of interest to transform such
networks into representations that are easily machine-actionable. Meanwhile,
network embedding has emerged as an effective approach to generate distributed
network represen... | computer science |
847 | Granger-causal Attentive Mixtures of Experts | cs.LG | Several methods have recently been proposed to detect salient input features
for outputs of neural networks. Those methods offer a qualitative glimpse at
feature importance, but they fall short of providing quantifiable attributions
that can be compared across decisions and measures of the expected quality of
their exp... | computer science |
848 | Memorize or generalize? Searching for a compositional RNN in a haystack | cs.AI | Neural networks are very powerful learning systems, but they do not readily
generalize from one task to the other. This is partly due to the fact that they
do not learn in a compositional way, that is, by discovering skills that are
shared by different tasks, and recombining them to solve new problems. In this
paper, w... | computer science |
849 | Continual Reinforcement Learning with Complex Synapses | cs.AI | Unlike humans, who are capable of continual learning over their lifetimes,
artificial neural networks have long been known to suffer from a phenomenon
known as catastrophic forgetting, whereby new learning can lead to abrupt
erasure of previously acquired knowledge. Whereas in a neural network the
parameters are typica... | computer science |
850 | Meta-Reinforcement Learning of Structured Exploration Strategies | cs.LG | Exploration is a fundamental challenge in reinforcement learning (RL). Many
of the current exploration methods for deep RL use task-agnostic objectives,
such as information gain or bonuses based on state visitation. However, many
practical applications of RL involve learning more than a single task, and
prior tasks can... | computer science |
851 | Approximation Algorithms for Cascading Prediction Models | cs.LG | We present an approximation algorithm that takes a pool of pre-trained models
as input and produces from it a cascaded model with similar accuracy but lower
average-case cost. Applied to state-of-the-art ImageNet classification models,
this yields up to a 2x reduction in floating point multiplications, and up to a
6x r... | computer science |
852 | Coloring black boxes: visualization of neural network decisions | cs.NE | Neural networks are commonly regarded as black boxes performing
incomprehensible functions. For classification problems networks provide maps
from high dimensional feature space to K-dimensional image space. Images of
training vector are projected on polygon vertices, providing visualization of
network function. Such v... | computer science |
853 | Relational Neural Expectation Maximization: Unsupervised Discovery of
Objects and their Interactions | cs.LG | Common-sense physical reasoning is an essential ingredient for any
intelligent agent operating in the real-world. For example, it can be used to
simulate the environment, or to infer the state of parts of the world that are
currently unobserved. In order to match real-world conditions this causal
knowledge must be lear... | computer science |
854 | A Bayesian Model for Activities Recommendation and Event Structure
Optimization Using Visitors Tracking | cs.NE | In events that are composed by many activities, there is a problem that
involves retrieve and management the information of visitors that are visiting
the activities. This management is crucial to find some activities that are
drawing attention of visitors; identify an ideal positioning for activities;
which path is mo... | computer science |
855 | The Lottery Ticket Hypothesis: Training Pruned Neural Networks | cs.LG | Recent work on neural network pruning indicates that, at training time,
neural networks need to be significantly larger in size than is necessary to
represent the eventual functions that they learn. This paper articulates a new
hypothesis to explain this phenomenon. This conjecture, which we term the
"lottery ticket hy... | computer science |
856 | Learning recurrent dynamics in spiking networks | cs.AI | Spiking activity of neurons engaged in learning and performing a task show
complex spatiotemporal dynamics. While the output of recurrent network models
can learn to perform various tasks, the possible range of recurrent dynamics
that emerge after learning remains unknown. Here we show that modifying the
recurrent conn... | computer science |
857 | Principal Graphs and Manifolds | cs.LG | In many physical, statistical, biological and other investigations it is
desirable to approximate a system of points by objects of lower dimension
and/or complexity. For this purpose, Karl Pearson invented principal component
analysis in 1901 and found 'lines and planes of closest fit to system of
points'. The famous k... | computer science |
858 | Sparse Penalty in Deep Belief Networks: Using the Mixed Norm Constraint | cs.NE | Deep Belief Networks (DBN) have been successfully applied on popular machine
learning tasks. Specifically, when applied on hand-written digit recognition,
DBNs have achieved approximate accuracy rates of 98.8%. In an effort to
optimize the data representation achieved by the DBN and maximize their
descriptive power, re... | computer science |
859 | Understanding Dropout: Training Multi-Layer Perceptrons with Auxiliary
Independent Stochastic Neurons | cs.NE | In this paper, a simple, general method of adding auxiliary stochastic
neurons to a multi-layer perceptron is proposed. It is shown that the proposed
method is a generalization of recently successful methods of dropout (Hinton et
al., 2012), explicit noise injection (Vincent et al., 2010; Bishop, 1995) and
semantic has... | computer science |
860 | Locally Imposing Function for Generalized Constraint Neural Networks - A
Study on Equality Constraints | cs.NE | This work is a further study on the Generalized Constraint Neural Network
(GCNN) model [1], [2]. Two challenges are encountered in the study, that is, to
embed any type of prior information and to select its imposing schemes. The
work focuses on the second challenge and studies a new constraint imposing
scheme for equa... | computer science |
861 | Evolution of Covariance Functions for Gaussian Process Regression using
Genetic Programming | cs.NE | In this contribution we describe an approach to evolve composite covariance
functions for Gaussian processes using genetic programming. A critical aspect
of Gaussian processes and similar kernel-based models such as SVM is, that the
covariance function should be adapted to the modeled data. Frequently, the
squared expo... | computer science |
862 | Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image
Statistics | cs.NE | We present a theoretical analysis of Gaussian-binary restricted Boltzmann
machines (GRBMs) from the perspective of density models. The key aspect of this
analysis is to show that GRBMs can be formulated as a constrained mixture of
Gaussians, which gives a much better insight into the model's capabilities and
limitation... | computer science |
863 | Training Restricted Boltzmann Machine by Perturbation | cs.NE | A new approach to maximum likelihood learning of discrete graphical models
and RBM in particular is introduced. Our method, Perturb and Descend (PD) is
inspired by two ideas (I) perturb and MAP method for sampling (II) learning by
Contrastive Divergence minimization. In contrast to perturb and MAP, PD
leverages trainin... | computer science |
864 | Multilayer bootstrap networks | cs.LG | Multilayer bootstrap network builds a gradually narrowed multilayer nonlinear
network from bottom up for unsupervised nonlinear dimensionality reduction.
Each layer of the network is a nonparametric density estimator. It consists of
a group of k-centroids clusterings. Each clustering randomly selects data
points with r... | computer science |
865 | Invariant backpropagation: how to train a transformation-invariant
neural network | stat.ML | In many classification problems a classifier should be robust to small
variations in the input vector. This is a desired property not only for
particular transformations, such as translation and rotation in image
classification problems, but also for all others for which the change is small
enough to retain the object ... | computer science |
866 | Shared latent subspace modelling within Gaussian-Binary Restricted
Boltzmann Machines for NIST i-Vector Challenge 2014 | cs.LG | This paper presents a novel approach to speaker subspace modelling based on
Gaussian-Binary Restricted Boltzmann Machines (GRBM). The proposed model is
based on the idea of shared factors as in the Probabilistic Linear Discriminant
Analysis (PLDA). GRBM hidden layer is divided into speaker and channel factors,
herein t... | computer science |
867 | A Neural Transfer Function for a Smooth and Differentiable Transition
Between Additive and Multiplicative Interactions | stat.ML | Existing approaches to combine both additive and multiplicative neural units
either use a fixed assignment of operations or require discrete optimization to
determine what function a neuron should perform. This leads either to an
inefficient distribution of computational resources or an extensive increase in
the comput... | computer science |
868 | A Probabilistic Framework for Deep Learning | stat.ML | We develop a probabilistic framework for deep learning based on the Deep
Rendering Mixture Model (DRMM), a new generative probabilistic model that
explicitly capture variations in data due to latent task nuisance variables. We
demonstrate that max-sum inference in the DRMM yields an algorithm that exactly
reproduces th... | computer science |
869 | Neurogenesis Deep Learning | cs.NE | Neural machine learning methods, such as deep neural networks (DNN), have
achieved remarkable success in a number of complex data processing tasks. These
methods have arguably had their strongest impact on tasks such as image and
audio processing - data processing domains in which humans have long held clear
advantages... | computer science |
870 | Deep learning for neuroimaging: a validation study | cs.NE | Deep learning methods have recently made notable advances in the tasks of
classification and representation learning. These tasks are important for brain
imaging and neuroscience discovery, making the methods attractive for porting
to a neuroimager's toolbox. Success of these methods is, in part, explained by
the flexi... | computer science |
871 | Improving Deep Neural Networks with Probabilistic Maxout Units | stat.ML | We present a probabilistic variant of the recently introduced maxout unit.
The success of deep neural networks utilizing maxout can partly be attributed
to favorable performance under dropout, when compared to rectified linear
units. It however also depends on the fact that each maxout unit performs a
pooling operation... | computer science |
872 | How Many Dissimilarity/Kernel Self Organizing Map Variants Do We Need? | stat.ML | In numerous applicative contexts, data are too rich and too complex to be
represented by numerical vectors. A general approach to extend machine learning
and data mining techniques to such data is to really on a dissimilarity or on a
kernel that measures how different or similar two objects are. This approach
has been ... | computer science |
873 | Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures | cs.LG | Model-based methods and deep neural networks have both been tremendously
successful paradigms in machine learning. In model-based methods, problem
domain knowledge can be built into the constraints of the model, typically at
the expense of difficulties during inference. In contrast, deterministic deep
neural networks a... | computer science |
874 | Learning deep dynamical models from image pixels | stat.ML | Modeling dynamical systems is important in many disciplines, e.g., control,
robotics, or neurotechnology. Commonly the state of these systems is not
directly observed, but only available through noisy and potentially
high-dimensional observations. In these cases, system identification, i.e.,
finding the measurement map... | computer science |
875 | From neural PCA to deep unsupervised learning | stat.ML | A network supporting deep unsupervised learning is presented. The network is
an autoencoder with lateral shortcut connections from the encoder to decoder at
each level of the hierarchy. The lateral shortcut connections allow the higher
levels of the hierarchy to focus on abstract invariant features. While standard
auto... | computer science |
876 | Qualitatively characterizing neural network optimization problems | cs.NE | Training neural networks involves solving large-scale non-convex optimization
problems. This task has long been believed to be extremely difficult, with fear
of local minima and other obstacles motivating a variety of schemes to improve
optimization, such as unsupervised pretraining. However, modern neural networks
are... | computer science |
877 | Why does Deep Learning work? - A perspective from Group Theory | cs.LG | Why does Deep Learning work? What representations does it capture? How do
higher-order representations emerge? We study these questions from the
perspective of group theory, thereby opening a new approach towards a theory of
Deep learning.
One factor behind the recent resurgence of the subject is a key algorithmic
st... | computer science |
878 | ADASECANT: Robust Adaptive Secant Method for Stochastic Gradient | cs.LG | Stochastic gradient algorithms have been the main focus of large-scale
learning problems and they led to important successes in machine learning. The
convergence of SGD depends on the careful choice of learning rate and the
amount of the noise in stochastic estimates of the gradients. In this paper, we
propose a new ad... | computer science |
879 | A Unified Perspective on Multi-Domain and Multi-Task Learning | stat.ML | In this paper, we provide a new neural-network based perspective on
multi-task learning (MTL) and multi-domain learning (MDL). By introducing the
concept of a semantic descriptor, this framework unifies MDL and MTL as well as
encompassing various classic and recent MTL/MDL algorithms by interpreting them
as different w... | computer science |
880 | A Neural Network Anomaly Detector Using the Random Cluster Model | cs.LG | The random cluster model is used to define an upper bound on a distance
measure as a function of the number of data points to be classified and the
expected value of the number of classes to form in a hybrid K-means and
regression classification methodology, with the intent of detecting anomalies.
Conditions are given ... | computer science |
881 | A Group Theoretic Perspective on Unsupervised Deep Learning | cs.LG | Why does Deep Learning work? What representations does it capture? How do
higher-order representations emerge? We study these questions from the
perspective of group theory, thereby opening a new approach towards a theory of
Deep learning.
One factor behind the recent resurgence of the subject is a key algorithmic
st... | computer science |
882 | A Generative Model for Deep Convolutional Learning | stat.ML | A generative model is developed for deep (multi-layered) convolutional
dictionary learning. A novel probabilistic pooling operation is integrated into
the deep model, yielding efficient bottom-up (pretraining) and top-down
(refinement) probabilistic learning. Experimental results demonstrate powerful
capabilities of th... | computer science |
883 | Knowledge Transfer Pre-training | cs.LG | Pre-training is crucial for learning deep neural networks. Most of existing
pre-training methods train simple models (e.g., restricted Boltzmann machines)
and then stack them layer by layer to form the deep structure. This layer-wise
pre-training has found strong theoretical foundation and broad empirical
support. Howe... | computer science |
884 | Stacked What-Where Auto-encoders | stat.ML | We present a novel architecture, the "stacked what-where auto-encoders"
(SWWAE), which integrates discriminative and generative pathways and provides a
unified approach to supervised, semi-supervised and unsupervised learning
without relying on sampling during training. An instantiation of SWWAE uses a
convolutional ne... | computer science |
885 | Training recurrent networks online without backtracking | cs.NE | We introduce the "NoBackTrack" algorithm to train the parameters of dynamical
systems such as recurrent neural networks. This algorithm works in an online,
memoryless setting, thus requiring no backpropagation through time, and is
scalable, avoiding the large computational and memory cost of maintaining the
full gradie... | computer science |
886 | Deep clustering: Discriminative embeddings for segmentation and
separation | cs.NE | We address the problem of acoustic source separation in a deep learning
framework we call "deep clustering." Rather than directly estimating signals or
masking functions, we train a deep network to produce spectrogram embeddings
that are discriminative for partition labels given in training data. Previous
deep network ... | computer science |
887 | Scalable Out-of-Sample Extension of Graph Embeddings Using Deep Neural
Networks | stat.ML | Several popular graph embedding techniques for representation learning and
dimensionality reduction rely on performing computationally expensive
eigendecompositions to derive a nonlinear transformation of the input data
space. The resulting eigenvectors encode the embedding coordinates for the
training samples only, an... | computer science |
888 | Model Accuracy and Runtime Tradeoff in Distributed Deep Learning:A
Systematic Study | stat.ML | This paper presents Rudra, a parameter server based distributed computing
framework tuned for training large-scale deep neural networks. Using variants
of the asynchronous stochastic gradient descent algorithm we study the impact
of synchronization protocol, stale gradient updates, minibatch size, learning
rates, and n... | computer science |
889 | Convolutional Networks on Graphs for Learning Molecular Fingerprints | cs.LG | We introduce a convolutional neural network that operates directly on graphs.
These networks allow end-to-end learning of prediction pipelines whose inputs
are graphs of arbitrary size and shape. The architecture we present generalizes
standard molecular feature extraction methods based on circular fingerprints.
We sho... | computer science |
890 | Population-Contrastive-Divergence: Does Consistency help with RBM
training? | cs.LG | Estimating the log-likelihood gradient with respect to the parameters of a
Restricted Boltzmann Machine (RBM) typically requires sampling using Markov
Chain Monte Carlo (MCMC) techniques. To save computation time, the Markov
chains are only run for a small number of steps, which leads to a biased
estimate. This bias ca... | computer science |
891 | AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction
in Structure-based Drug Discovery | cs.LG | Deep convolutional neural networks comprise a subclass of deep neural
networks (DNN) with a constrained architecture that leverages the spatial and
temporal structure of the domain they model. Convolutional networks achieve the
best predictive performance in areas such as speech and image recognition by
hierarchically ... | computer science |
892 | Distillation as a Defense to Adversarial Perturbations against Deep
Neural Networks | cs.CR | Deep learning algorithms have been shown to perform extremely well on many
classical machine learning problems. However, recent studies have shown that
deep learning, like other machine learning techniques, is vulnerable to
adversarial samples: inputs crafted to force a deep neural network (DNN) to
provide adversary-se... | computer science |
893 | The Variational Gaussian Process | stat.ML | Variational inference is a powerful tool for approximate inference, and it
has been recently applied for representation learning with deep generative
models. We develop the variational Gaussian process (VGP), a Bayesian
nonparametric variational family, which adapts its shape to match complex
posterior distributions. T... | computer science |
894 | Partial Reinitialisation for Optimisers | stat.ML | Heuristic optimisers which search for an optimal configuration of variables
relative to an objective function often get stuck in local optima where the
algorithm is unable to find further improvement. The standard approach to
circumvent this problem involves periodically restarting the algorithm from
random initial con... | computer science |
895 | Efficient Representation of Low-Dimensional Manifolds using Deep
Networks | cs.NE | We consider the ability of deep neural networks to represent data that lies
near a low-dimensional manifold in a high-dimensional space. We show that deep
networks can efficiently extract the intrinsic, low-dimensional coordinates of
such data. We first show that the first two layers of a deep network can
exactly embed... | computer science |
896 | Enhanced perceptrons using contrastive biclusters | cs.NE | Perceptrons are neuronal devices capable of fully discriminating linearly
separable classes. Although straightforward to implement and train, their
applicability is usually hindered by non-trivial requirements imposed by
real-world classification problems. Therefore, several approaches, such as
kernel perceptrons, have... | computer science |
897 | Alternating optimization method based on nonnegative matrix
factorizations for deep neural networks | cs.LG | The backpropagation algorithm for calculating gradients has been widely used
in computation of weights for deep neural networks (DNNs). This method requires
derivatives of objective functions and has some difficulties finding
appropriate parameters such as learning rate. In this paper, we propose a novel
approach for c... | computer science |
898 | Robust Large Margin Deep Neural Networks | stat.ML | The generalization error of deep neural networks via their classification
margin is studied in this work. Our approach is based on the Jacobian matrix of
a deep neural network and can be applied to networks with arbitrary
non-linearities and pooling layers, and to networks with different
architectures such as feed forw... | computer science |
899 | No bad local minima: Data independent training error guarantees for
multilayer neural networks | stat.ML | We use smoothed analysis techniques to provide guarantees on the training
loss of Multilayer Neural Networks (MNNs) at differentiable local minima.
Specifically, we examine MNNs with piecewise linear activation functions,
quadratic loss and a single output, under mild over-parametrization. We prove
that for a MNN with ... | computer science |
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