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33,002 | Deep Primal-Dual Reinforcement Learning: Accelerating Actor-Critic using
Bellman Duality | cs.LG | We develop a parameterized Primal-Dual $\pi$ Learning method based on deep
neural networks for Markov decision process with large state space and
off-policy reinforcement learning. In contrast to the popular Q-learning and
actor-critic methods that are based on successive approximations to the
nonlinear Bellman equatio... | computer science |
33,003 | Semi-Supervised Learning with IPM-based GANs: an Empirical Study | cs.LG | We present an empirical investigation of a recent class of Generative
Adversarial Networks (GANs) using Integral Probability Metrics (IPM) and their
performance for semi-supervised learning. IPM-based GANs like Wasserstein GAN,
Fisher GAN and Sobolev GAN have desirable properties in terms of theoretical
understanding, ... | computer science |
33,004 | Nonconvex Sparse Spectral Clustering by Alternating Direction Method of
Multipliers and Its Convergence Analysis | cs.LG | Spectral Clustering (SC) is a widely used data clustering method which first
learns a low-dimensional embedding $U$ of data by computing the eigenvectors of
the normalized Laplacian matrix, and then performs k-means on $U^\top$ to get
the final clustering result. The Sparse Spectral Clustering (SSC) method
extends SC w... | computer science |
33,005 | Artificial Neural Networks that Learn to Satisfy Logic Constraints | cs.LG | Logic-based problems such as planning, theorem proving, or puzzles, typically
involve combinatoric search and structured knowledge representation. Artificial
neural networks are very successful statistical learners, however, for many
years, they have been criticized for their weaknesses in representing and in
processin... | computer science |
33,006 | A General Memory-Bounded Learning Algorithm | cs.LG | In an era of big data there is a growing need for memory-bounded learning
algorithms. In the last few years researchers have investigated what cannot be
learned under memory constraints. In this paper we focus on the complementary
question of what can be learned under memory constraints. We show that if a
hypothesis cl... | computer science |
33,007 | Deep Reinforcement Learning Boosted by External Knowledge | cs.LG | Recent improvements in deep reinforcement learning have allowed to solve
problems in many 2D domains such as Atari games. However, in complex 3D
environments, numerous learning episodes are required which may be too time
consuming or even impossible especially in real-world scenarios. We present a
new architecture to c... | computer science |
33,008 | Learning From Noisy Singly-labeled Data | cs.LG | Supervised learning depends on annotated examples, which are taken to be the
\emph{ground truth}. But these labels often come from noisy crowdsourcing
platforms, like Amazon Mechanical Turk. Practitioners typically collect
multiple labels per example and aggregate the results to mitigate noise (the
classic crowdsourcin... | computer science |
33,009 | Deep Neural Networks as 0-1 Mixed Integer Linear Programs: A Feasibility
Study | cs.LG | Deep Neural Networks (DNNs) are very popular these days, and are the subject
of a very intense investigation. A DNN is made by layers of internal units (or
neurons), each of which computes an affine combination of the output of the
units in the previous layer, applies a nonlinear operator, and outputs the
corresponding... | computer science |
33,010 | Squeezed Convolutional Variational AutoEncoder for Unsupervised Anomaly
Detection in Edge Device Industrial Internet of Things | cs.LG | In this paper, we propose Squeezed Convolutional Variational AutoEncoder
(SCVAE) for anomaly detection in time series data for Edge Computing in
Industrial Internet of Things (IIoT). The proposed model is applied to labeled
time series data from UCI datasets for exact performance evaluation, and
applied to real world d... | computer science |
33,011 | A Shapelet Transform for Multivariate Time Series Classification | cs.LG | Shapelets are phase independent subsequences designed for time series
classification. We propose three adaptations to the Shapelet Transform (ST) to
capture multivariate features in multivariate time series classification. We
create a unified set of data to benchmark our work on, and compare with three
other algorithms... | computer science |
33,012 | On Wasserstein Reinforcement Learning and the Fokker-Planck equation | cs.LG | Policy gradients methods often achieve better performance when the change in
policy is limited to a small Kullback-Leibler divergence. We derive policy
gradients where the change in policy is limited to a small Wasserstein distance
(or trust region). This is done in the discrete and continuous multi-armed
bandit settin... | computer science |
33,013 | Linear Time Clustering for High Dimensional Mixtures of Gaussian Clouds | cs.LG | Clustering mixtures of Gaussian distributions is a fundamental and
challenging problem that is ubiquitous in various high-dimensional data
processing tasks. While state-of-the-art work on learning Gaussian mixture
models has focused primarily on improving separation bounds and their
generalization to arbitrary classes ... | computer science |
33,014 | DropMax: Adaptive Variational Softmax | cs.LG | We propose DropMax, a stochastic version of softmax classifier which at each
iteration drops non-target classes according to dropout probabilities
adaptively decided for each instance. Specifically, we overlay binary masking
variables over class output probabilities, which are input-adaptively learned
via variational i... | computer science |
33,015 | Estimating the Success of Unsupervised Image to Image Translation | cs.LG | While in supervised learning, the validation error is an unbiased estimator
of the generalization (test) error and complexity-based generalization bounds
are abundant, no such bounds exist for learning a mapping in an unsupervised
way. As a result, when training GANs and specifically when using GANs for
learning to map... | computer science |
33,016 | Learning the Kernel for Classification and Regression | cs.LG | We investigate a series of learning kernel problems with polynomial
combinations of base kernels, which will help us solve regression and
classification problems. We also perform some numerical experiments of
polynomial kernels with regression and classification tasks on different
datasets. | computer science |
33,017 | A short variational proof of equivalence between policy gradients and
soft Q learning | cs.LG | Two main families of reinforcement learning algorithms, Q-learning and policy
gradients, have recently been proven to be equivalent when using a softmax
relaxation on one part, and an entropic regularization on the other. We relate
this result to the well-known convex duality of Shannon entropy and the softmax
function... | computer science |
33,018 | Online Forecasting Matrix Factorization | cs.LG | In this paper the problem of forecasting high dimensional time series is
considered. Such time series can be modeled as matrices where each column
denotes a measurement. In addition, when missing values are present, low rank
matrix factorization approaches are suitable for predicting future values. This
paper formally ... | computer science |
33,019 | Transfer Regression via Pairwise Similarity Regularization | cs.LG | Transfer learning methods address the situation where little labeled training
data from the "target" problem exists, but much training data from a related
"source" domain is available. However, the overwhelming majority of transfer
learning methods are designed for simple settings where the source and target
predictive... | computer science |
33,020 | Learning to Run with Actor-Critic Ensemble | cs.LG | We introduce an Actor-Critic Ensemble(ACE) method for improving the
performance of Deep Deterministic Policy Gradient(DDPG) algorithm. At inference
time, our method uses a critic ensemble to select the best action from
proposals of multiple actors running in parallel. By having a larger candidate
set, our method can av... | computer science |
33,021 | Strongly Hierarchical Factorization Machines and ANOVA Kernel Regression | cs.LG | High-order parametric models that include terms for feature interactions are
applied to various data mining tasks, where ground truth depends on
interactions of features. However, with sparse data, the high- dimensional
parameters for feature interactions often face three issues: expensive
computation, difficulty in pa... | computer science |
33,022 | Differentially Private Matrix Completion, Revisited | cs.LG | We study the problem of privacy-preserving collaborative filtering where the
objective is to reconstruct the entire users-items preference matrix using a
few observed preferences of users for some of the items. Furthermore, the
collaborative filtering algorithm should reconstruct the preference matrix
while preserving ... | computer science |
33,023 | Gradient Regularization Improves Accuracy of Discriminative Models | cs.LG | Regularizing the gradient norm of the output of a neural network with respect
to its inputs is a powerful technique, first proposed by Drucker & LeCun (1991)
who named it Double Backpropagation. The idea has been independently
rediscovered several times since then, most often with the goal of making
models robust again... | computer science |
33,024 | The Multilinear Structure of ReLU Networks | cs.LG | We study the loss surface of neural networks equipped with a hinge loss
criterion and ReLU or leaky ReLU nonlinearities. Any such network defines a
piecewise multilinear form in parameter space, and as a consequence, optima of
such networks generically occur in non-differentiable regions of parameter
space. Any underst... | computer science |
33,025 | Boosting the Actor with Dual Critic | cs.LG | This paper proposes a new actor-critic-style algorithm called Dual
Actor-Critic or Dual-AC. It is derived in a principled way from the Lagrangian
dual form of the Bellman optimality equation, which can be viewed as a
two-player game between the actor and a critic-like function, which is named as
dual critic. Compared t... | computer science |
33,026 | Smoothed Dual Embedding Control | cs.LG | We revisit the Bellman optimality equation with Nesterov's smoothing
technique and provide a unique saddle-point optimization perspective of the
policy optimization problem in reinforcement learning based on Fenchel duality.
A new reinforcement learning algorithm, called Smoothed Dual Embedding Control
or SDEC, is deri... | computer science |
33,027 | Theory of Deep Learning III: explaining the non-overfitting puzzle | cs.LG | A main puzzle of deep networks revolves around the absence of overfitting
despite large overparametrization and despite the large capacity demonstrated
by zero training error on randomly labeled data. In this note, we show that the
dynamics associated to gradient descent minimization of nonlinear networks is
topologica... | computer science |
33,028 | Error-Robust Multi-View Clustering | cs.LG | In the era of big data, data may come from multiple sources, known as
multi-view data. Multi-view clustering aims at generating better clusters by
exploiting complementary and consistent information from multiple views rather
than relying on the individual view. Due to inevitable system errors caused by
data-captured s... | computer science |
33,029 | Polynomial-based rotation invariant features | cs.LG | One of basic difficulties of machine learning is handling unknown rotations
of objects, for example in image recognition. A related problem is evaluation
of similarity of shapes, for example of two chemical molecules, for which
direct approach requires costly pairwise rotation alignment and comparison.
Rotation invaria... | computer science |
33,030 | Predicting Chronic Disease Hospitalizations from Electronic Health
Records: An Interpretable Classification Approach | cs.LG | Urban living in modern large cities has significant adverse effects on
health, increasing the risk of several chronic diseases. We focus on the two
leading clusters of chronic disease, heart disease and diabetes, and develop
data-driven methods to predict hospitalizations due to these conditions. We
base these predicti... | computer science |
33,031 | Learning $3$D-FilterMap for Deep Convolutional Neural Networks | cs.LG | We present a novel and compact architecture for deep Convolutional Neural
Networks (CNNs) in this paper, termed $3$D-FilterMap Convolutional Neural
Networks ($3$D-FM-CNNs). The convolution layer of $3$D-FM-CNN learns a compact
representation of the filters, named $3$D-FilterMap, instead of a set of
independent filters ... | computer science |
33,032 | Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for
Network-wide Traffic Speed Prediction | cs.LG | Short-term traffic forecasting based on deep learning methods, especially
long short-term memory (LSTM) neural networks, has received much attention in
recent years. However, the potential of deep learning methods in traffic
forecasting has not yet fully been exploited in terms of the depth of the model
architecture, t... | computer science |
33,033 | Covariant Compositional Networks For Learning Graphs | cs.LG | Most existing neural networks for learning graphs address permutation
invariance by conceiving of the network as a message passing scheme, where each
node sums the feature vectors coming from its neighbors. We argue that this
imposes a limitation on their representation power, and instead propose a new
general architec... | computer science |
33,034 | Theory of Deep Learning IIb: Optimization Properties of SGD | cs.LG | In Theory IIb we characterize with a mix of theory and experiments the
optimization of deep convolutional networks by Stochastic Gradient Descent. The
main new result in this paper is theoretical and experimental evidence for the
following conjecture about SGD: SGD concentrates in probability -- like the
classical Lang... | computer science |
33,035 | A Machine Learning Framework for Register Placement Optimization in
Digital Circuit Design | cs.LG | In modern digital circuit back-end design, designers heavily rely on
electronic-design-automoation (EDA) tool to close timing. However, the
heuristic algorithms used in the place and route tool usually does not result
in optimal solution. Thus, significant design effort is used to tune parameters
or provide user constr... | computer science |
33,036 | Graph Memory Networks for Molecular Activity Prediction | cs.LG | Molecular activity prediction is critical in drug design. Machine learning
techniques such as kernel methods and random forests have been successful for
this task. These models require fixed-size feature vectors as input while the
molecules are variable in size and structure. As a result, fixed-size
fingerprint represe... | computer science |
33,037 | Modeling urbanization patterns with generative adversarial networks | cs.LG | In this study we propose a new method to simulate hyper-realistic urban
patterns using Generative Adversarial Networks trained with a global urban
land-use inventory. We generated a synthetic urban "universe" that
qualitatively reproduces the complex spatial organization observed in global
urban patterns, while being a... | computer science |
33,038 | Compressing Deep Neural Networks: A New Hashing Pipeline Using Kac's
Random Walk Matrices | cs.LG | The popularity of deep learning is increasing by the day. However, despite
the recent advancements in hardware, deep neural networks remain
computationally intensive. Recent work has shown that by preserving the angular
distance between vectors, random feature maps are able to reduce dimensionality
without introducing ... | computer science |
33,039 | eCommerceGAN : A Generative Adversarial Network for E-commerce | cs.LG | E-commerce companies such as Amazon, Alibaba and Flipkart process billions of
orders every year. However, these orders represent only a small fraction of all
plausible orders. Exploring the space of all plausible orders could help us
better understand the relationships between the various entities in an
e-commerce ecos... | computer science |
33,040 | Blessing of dimensionality: mathematical foundations of the statistical
physics of data | cs.LG | The concentration of measure phenomena were discovered as the mathematical
background of statistical mechanics at the end of the XIX - beginning of the XX
century and were then explored in mathematics of the XX-XXI centuries. At the
beginning of the XXI century, it became clear that the proper utilisation of
these phen... | computer science |
33,041 | A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual
Bandit Problem | cs.LG | Bandit learning is characterized by the tension between long-term exploration
and short-term exploitation. However, as has recently been noted, in settings
in which the choices of the learning algorithm correspond to important
decisions about individual people (such as criminal recidivism prediction,
lending, and seque... | computer science |
33,042 | A Hardware-Friendly Algorithm for Scalable Training and Deployment of
Dimensionality Reduction Models on FPGA | cs.LG | With ever-increasing application of machine learning models in various
domains such as image classification, speech recognition and synthesis, and
health care, designing efficient hardware for these models has gained a lot of
popularity. While the majority of researches in this area focus on efficient
deployment of mac... | computer science |
33,043 | Cost-Sensitive Convolution based Neural Networks for Imbalanced
Time-Series Classification | cs.LG | Some deep convolutional neural networks were proposed for time-series
classification and class imbalanced problems. However, those models performed
degraded and even failed to recognize the minority class of an imbalanced
temporal sequences dataset. Minority samples would bring troubles for temporal
deep learning class... | computer science |
33,044 | Towards a more efficient representation of imputation operators in TPOT | cs.LG | Automated Machine Learning encompasses a set of meta-algorithms intended to
design and apply machine learning techniques (e.g., model selection,
hyperparameter tuning, model assessment, etc.). TPOT, a software for optimizing
machine learning pipelines based on genetic programming (GP), is a novel
example of this kind o... | computer science |
33,045 | Unsupervised Cipher Cracking Using Discrete GANs | cs.LG | This work details CipherGAN, an architecture inspired by CycleGAN used for
inferring the underlying cipher mapping given banks of unpaired ciphertext and
plaintext. We demonstrate that CipherGAN is capable of cracking language data
enciphered using shift and Vigenere ciphers to a high degree of fidelity and
for vocabul... | computer science |
33,046 | Rank Selection of CP-decomposed Convolutional Layers with Variational
Bayesian Matrix Factorization | cs.LG | Convolutional Neural Networks (CNNs) is one of successful method in many
areas such as image classification tasks. However, the amount of memory and
computational cost needed for CNNs inference obstructs them to run efficiently
in mobile devices because of memory and computational ability limitation. One
of the method ... | computer science |
33,047 | ALE: Additive Latent Effect Models for Grade Prediction | cs.LG | The past decade has seen a growth in the development and deployment of
educational technologies for assisting college-going students in choosing
majors, selecting courses and acquiring feedback based on past academic
performance. Grade prediction methods seek to estimate a grade that a student
may achieve in a course t... | computer science |
33,048 | On the Reduction of Biases in Big Data Sets for the Detection of
Irregular Power Usage | cs.LG | In machine learning, a bias occurs whenever training sets are not
representative for the test data, which results in unreliable models. The most
common biases in data are arguably class imbalance and covariate shift. In this
work, we aim to shed light on this topic in order to increase the overall
attention to this iss... | computer science |
33,049 | An Overview of Machine Teaching | cs.LG | In this paper we try to organize machine teaching as a coherent set of ideas.
Each idea is presented as varying along a dimension. The collection of
dimensions then form the problem space of machine teaching, such that existing
teaching problems can be characterized in this space. We hope this organization
allows us to... | computer science |
33,050 | An Iterative Closest Point Method for Unsupervised Word Translation | cs.LG | Unsupervised word translation from non-parallel inter-lingual corpora has
attracted much research interest. Very recently, neural network methods trained
with adversarial loss functions achieved high accuracy on this task. Despite
the impressive success of the recent techniques, they suffer from the typical
drawbacks o... | computer science |
33,051 | Latitude: A Model for Mixed Linear-Tropical Matrix Factorization | cs.LG | Nonnegative matrix factorization (NMF) is one of the most frequently-used
matrix factorization models in data analysis. A significant reason to the
popularity of NMF is its interpretability and the `parts of whole'
interpretation of its components. Recently, max-times, or subtropical, matrix
factorization (SMF) has bee... | computer science |
33,052 | Tractable Learning and Inference for Large-Scale Probabilistic Boolean
Networks | cs.LG | Probabilistic Boolean Networks (PBNs) have been previously proposed so as to
gain insights into complex dy- namical systems. However, identification of
large networks and of the underlying discrete Markov Chain which describes
their temporal evolution, still remains a challenge. In this paper, we
introduce an equivalen... | computer science |
33,053 | Investigating the Effects of Dynamic Precision Scaling on Neural Network
Training | cs.LG | Training neural networks is a time- and compute-intensive operation. This is
mainly due to the large amount of floating point tensor operations that are
required during training. These constraints limit the scope of design space
explorations (in terms of hyperparameter search) for data scientists and
researchers. Recen... | computer science |
33,054 | Recasting Gradient-Based Meta-Learning as Hierarchical Bayes | cs.LG | Meta-learning allows an intelligent agent to leverage prior learning episodes
as a basis for quickly improving performance on a novel task. Bayesian
hierarchical modeling provides a theoretical framework for formalizing
meta-learning as inference for a set of parameters that are shared across
tasks. Here, we reformulat... | computer science |
33,055 | Approximate Inference via Weighted Rademacher Complexity | cs.LG | Rademacher complexity is often used to characterize the learnability of a
hypothesis class and is known to be related to the class size. We leverage this
observation and introduce a new technique for estimating the size of an
arbitrary weighted set, defined as the sum of weights of all elements in the
set. Our techniqu... | computer science |
33,056 | Certified Defenses against Adversarial Examples | cs.LG | While neural networks have achieved high accuracy on standard image
classification benchmarks, their accuracy drops to nearly zero in the presence
of small adversarial perturbations to test inputs. Defenses based on
regularization and adversarial training have been proposed, but often followed
by new, stronger attacks ... | computer science |
33,057 | Learning Combinations of Activation Functions | cs.LG | In the last decade, an active area of research has been devoted to design
novel activation functions that are able to help deep neural networks to
converge, obtaining better performance. The training procedure of these
architectures usually involves optimization of the weights of their layers
only, while non-linearitie... | computer science |
33,058 | Learning the Reward Function for a Misspecified Model | cs.LG | In model-based reinforcement learning it is typical to treat the problems of
learning the dynamics model and learning the reward function separately.
However, when the dynamics model is flawed, it may generate erroneous states
that would never occur in the true environment. A reward function trained only
to map environ... | computer science |
33,059 | Learning to Emulate an Expert Projective Cone Scheduler | cs.LG | Projective cone scheduling defines a large class of rate-stabilizing policies
for queueing models relevant to several applications. While there exists
considerable theory on the properties of projective cone schedulers, there is
little practical guidance on choosing the parameters that define them. In this
paper, we pr... | computer science |
33,060 | FastGCN: Fast Learning with Graph Convolutional Networks via Importance
Sampling | cs.LG | The graph convolutional networks (GCN) recently proposed by Kipf and Welling
are an effective graph model for semi-supervised learning. This model, however,
was originally designed to be learned with the presence of both training and
test data. Moreover, the recursive neighborhood expansion across layers poses
time and... | computer science |
33,061 | Deep Learning of Constrained Autoencoders for Enhanced Understanding of
Data | cs.LG | Unsupervised feature extractors are known to perform an efficient and
discriminative representation of data. Insight into the mappings they perform
and human ability to understand them, however, remain very limited. This is
especially prominent when multilayer deep learning architectures are used. This
paper demonstrat... | computer science |
33,062 | A New Backpropagation Algorithm without Gradient Descent | cs.LG | The backpropagation algorithm, which had been originally introduced in the
1970s, is the workhorse of learning in neural networks. This backpropagation
algorithm makes use of the famous machine learning algorithm known as Gradient
Descent, which is a first-order iterative optimization algorithm for finding
the minimum ... | computer science |
33,063 | Rethinking the Smaller-Norm-Less-Informative Assumption in Channel
Pruning of Convolution Layers | cs.LG | Model pruning has become a useful technique that improves the computational
efficiency of deep learning, making it possible to deploy solutions in
resource-limited scenarios. A widely-used practice in relevant work assumes
that a smaller-norm parameter or feature plays a less informative role at the
inference time. In ... | computer science |
33,064 | Bootstrapping and Multiple Imputation Ensemble Approaches for Missing
Data | cs.LG | Presence of missing values in a dataset can adversely affect the performance
of a classifier. Single and Multiple Imputation are normally performed to fill
in the missing values. In this paper, we present several variants of combining
single and multiple imputation with bootstrapping to create ensembles that can
model ... | computer science |
33,065 | Augmented Space Linear Model | cs.LG | The linear model uses the space defined by the input to project the target or
desired signal and find the optimal set of model parameters. When the problem
is nonlinear, the adaption requires nonlinear models for good performance, but
it becomes slower and more cumbersome. In this paper, we propose a linear model
calle... | computer science |
33,066 | Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep
Auto-Encoder Representations | cs.LG | Clustering is essential to many tasks in pattern recognition and computer
vision. With the advent of deep learning, there is an increasing interest in
learning deep unsupervised representations for clustering analysis. Many works
on this domain rely on variants of auto-encoders and use the encoder outputs as
representa... | computer science |
33,067 | Training Neural Networks by Using Power Linear Units (PoLUs) | cs.LG | In this paper, we introduce "Power Linear Unit" (PoLU) which increases the
nonlinearity capacity of a neural network and thus helps improving its
performance. PoLU adopts several advantages of previously proposed activation
functions. First, the output of PoLU for positive inputs is designed to be
identity to avoid the... | computer science |
33,068 | Analysis of Fast Alternating Minimization for Structured Dictionary
Learning | cs.LG | Methods exploiting sparsity have been popular in imaging and signal
processing applications including compression, denoising, and imaging inverse
problems. Data-driven approaches such as dictionary learning and transform
learning enable one to discover complex image features from datasets and
provide promising performa... | computer science |
33,069 | GeniePath: Graph Neural Networks with Adaptive Receptive Paths | cs.LG | We present, GeniePath, a scalable approach for learning adaptive receptive
fields of neural networks defined on permutation invariant graph data. In
GeniePath, we propose an adaptive path layer consists of two functions designed
for breadth and depth exploration respectively, where the former learns the
importance of d... | computer science |
33,070 | Online Compact Convexified Factorization Machine | cs.LG | Factorization Machine (FM) is a supervised learning approach with a powerful
capability of feature engineering. It yields state-of-the-art performance in
various batch learning tasks where all the training data is made available
prior to the training. However, in real-world applications where the data
arrives sequentia... | computer science |
33,071 | Explicit Inductive Bias for Transfer Learning with Convolutional
Networks | cs.LG | In inductive transfer learning, fine-tuning pre-trained convolutional
networks substantially outperforms training from scratch. When using
fine-tuning, the underlying assumption is that the pre-trained model extracts
generic features, which are at least partially relevant for solving the target
task, but would be diffi... | computer science |
33,072 | Selective Sampling and Mixture Models in Generative Adversarial Networks | cs.LG | In this paper, we propose a multi-generator extension to the adversarial
training framework, in which the objective of each generator is to represent a
unique component of a target mixture distribution. In the training phase, the
generators cooperate to represent, as a mixture, the target distribution while
maintaining... | computer science |
33,073 | MotifNet: a motif-based Graph Convolutional Network for directed graphs | cs.LG | Deep learning on graphs and in particular, graph convolutional neural
networks, have recently attracted significant attention in the machine learning
community. Many of such techniques explore the analogy between the graph
Laplacian eigenvectors and the classical Fourier basis, allowing to formulate
the convolution as ... | computer science |
33,074 | Re-Weighted Learning for Sparsifying Deep Neural Networks | cs.LG | This paper addresses the topic of sparsifying deep neural networks (DNN's).
While DNN's are powerful models that achieve state-of-the-art performance on a
large number of tasks, the large number of model parameters poses serious
storage and computational challenges. To combat these difficulties, a growing
line of work ... | computer science |
33,075 | Mixed Link Networks | cs.LG | Basing on the analysis by revealing the equivalence of modern networks, we
find that both ResNet and DenseNet are essentially derived from the same "dense
topology", yet they only differ in the form of connection -- addition (dubbed
"inner link") vs. concatenation (dubbed "outer link"). However, both two forms
of conne... | computer science |
33,076 | Directly and Efficiently Optimizing Prediction Error and AUC of Linear
Classifiers | cs.LG | The predictive quality of machine learning models is typically measured in
terms of their (approximate) expected prediction error or the so-called Area
Under the Curve (AUC) for a particular data distribution. However, when the
models are constructed by the means of empirical risk minimization, surrogate
functions such... | computer science |
33,077 | Improving the Universality and Learnability of Neural
Programmer-Interpreters with Combinator Abstraction | cs.LG | To overcome the limitations of Neural Programmer-Interpreters (NPI) in its
universality and learnability, we propose the incorporation of combinator
abstraction into neural programing and a new NPI architecture to support this
abstraction, which we call Combinatory Neural Programmer-Interpreter (CNPI).
Combinator abstr... | computer science |
33,078 | Online Learning: A Comprehensive Survey | cs.LG | Online learning represents an important family of machine learning
algorithms, in which a learner attempts to resolve an online prediction (or any
type of decision-making) task by learning a model/hypothesis from a sequence of
data instances one at a time. The goal of online learning is to ensure that the
online learne... | computer science |
33,079 | Learning and Querying Fast Generative Models for Reinforcement Learning | cs.LG | A key challenge in model-based reinforcement learning (RL) is to synthesize
computationally efficient and accurate environment models. We show that
carefully designed generative models that learn and operate on compact state
representations, so-called state-space models, substantially reduce the
computational costs for... | computer science |
33,080 | Make the Minority Great Again: First-Order Regret Bound for Contextual
Bandits | cs.LG | Regret bounds in online learning compare the player's performance to $L^*$,
the optimal performance in hindsight with a fixed strategy. Typically such
bounds scale with the square root of the time horizon $T$. The more refined
concept of first-order regret bound replaces this with a scaling $\sqrt{L^*}$,
which may be m... | computer science |
33,081 | Learning Local Metrics and Influential Regions for Classification | cs.LG | The performance of distance-based classifiers heavily depends on the
underlying distance metric, so it is valuable to learn a suitable metric from
the data. To address the problem of multimodality, it is desirable to learn
local metrics. In this short paper, we define a new intuitive distance with
local metrics and inf... | computer science |
33,082 | Metric Learning via Maximizing the Lipschitz Margin Ratio | cs.LG | In this paper, we propose the Lipschitz margin ratio and a new metric
learning framework for classification through maximizing the ratio. This
framework enables the integration of both the inter-class margin and the
intra-class dispersion, as well as the enhancement of the generalization
ability of a classifier. To int... | computer science |
33,083 | A Continuation Method for Discrete Optimization and its Application to
Nearest Neighbor Classification | cs.LG | The continuation method is a popular approach in non-convex optimization and
computer vision. The main idea is to start from a simple function that can be
minimized efficiently, and gradually transform it to the more complicated
original objective function. The solution of the simpler problem is used as the
starting po... | computer science |
33,084 | Disturbance Grassmann Kernels for Subspace-Based Learning | cs.LG | In this paper, we focus on subspace-based learning problems, where data
elements are linear subspaces instead of vectors. To handle this kind of data,
Grassmann kernels were proposed to measure the space structure and used with
classifiers, e.g., Support Vector Machines (SVMs). However, the existing
discriminative algo... | computer science |
33,085 | Low-Norm Graph Embedding | cs.LG | Learning distributed representations for nodes in graphs has become an
important problem that underpins a wide spectrum of applications. Existing
methods to this problem learn representations by optimizing a softmax objective
while constraining the dimension of embedding vectors. We argue that the
generalization perfor... | computer science |
33,086 | Tips, guidelines and tools for managing multi-label datasets: the
mldr.datasets R package and the Cometa data repository | cs.LG | New proposals in the field of multi-label learning algorithms have been
growing in number steadily over the last few years. The experimentation
associated with each of them always goes through the same phases: selection of
datasets, partitioning, training, analysis of results and, finally, comparison
with existing meth... | computer science |
33,087 | Deep Meta-Learning: Learning to Learn in the Concept Space | cs.LG | Few-shot learning remains challenging for meta-learning that learns a
learning algorithm (meta-learner) from many related tasks. In this work, we
argue that this is due to the lack of a good representation for meta-learning,
and propose deep meta-learning to integrate the representation power of deep
learning into meta... | computer science |
33,088 | Curriculum Learning by Transfer Learning: Theory and Experiments with
Deep Networks | cs.LG | Our first contribution in this paper is a theoretical investigation of
curriculum learning in the context of stochastic gradient descent when
optimizing the least squares loss function. We prove that the rate of
convergence of an ideal curriculum learning method in monotonically increasing
with the difficulty of the ex... | computer science |
33,089 | PRIL: Perceptron Ranking Using Interval Labeled Data | cs.LG | In this paper, we propose an online learning algorithm PRIL for learning
ranking classifiers using interval labeled data and show its correctness. We
show its convergence in finite number of steps if there exists an ideal
classifier such that the rank given by it for an example always lies in its
label interval. We the... | computer science |
33,090 | On the Needs for Rotations in Hypercubic Quantization Hashing | cs.LG | The aim of this paper is to endow the well-known family of hypercubic
quantization hashing methods with theoretical guarantees. In hypercubic
quantization, applying a suitable (random or learned) rotation after
dimensionality reduction has been experimentally shown to improve the results
accuracy in the nearest neighbo... | computer science |
33,091 | Policy Gradients for Contextual Bandits | cs.LG | We study a generalized contextual-bandits problem, where there is a state
that decides the distribution of contexts of arms and affects the immediate
reward when choosing an arm. The problem applies to a wide range of realistic
settings such as personalized recommender systems and natural language
generations. We put f... | computer science |
33,092 | Electric Vehicle Driver Clustering using Statistical Model and Machine
Learning | cs.LG | Electric Vehicle (EV) is playing a significant role in the distribution
energy management systems since the power consumption level of the EVs is much
higher than the other regular home appliances. The randomness of the EV driver
behaviors make the optimal charging or discharging scheduling even more
difficult due to t... | computer science |
33,093 | Sparse and Robust Reject Option Classifier using Successive Linear
Programming | cs.LG | In this paper, we propose a new sparse and robust reject option classifier
based on minimization of $l_1$ regularized risk under double ramp loss
$L_{dr,\rho}$. We use DC programming to find the risk minimizer. The algorithm
solves a sequence of linear programs to learn the reject option classifier.
Moreover, we show t... | computer science |
33,094 | Multi-Armed Bandits on Unit Interval Graphs | cs.LG | An online learning problem with side information on the similarity and
dissimilarity across different actions is considered. The problem is formulated
as a stochastic multi-armed bandit problem with a graph-structured learning
space. Each node in the graph represents an arm in the bandit problem and an
edge between two... | computer science |
33,095 | Classification from Pairwise Similarity and Unlabeled Data | cs.LG | One of the biggest bottlenecks in supervised learning is its high labeling
cost. To overcome this problem, we propose a new weakly-supervised learning
setting called SU classification, where only similar (S) data pairs (two
examples belong to the same class) and unlabeled (U) data are needed, instead
of fully-supervise... | computer science |
33,096 | Neural Tensor Factorization | cs.LG | Neural collaborative filtering (NCF) and recurrent recommender systems (RRN)
have been successful in modeling user-item relational data. However, they are
also limited in their assumption of static or sequential modeling of relational
data as they do not account for evolving users' preference over time as well as
chang... | computer science |
33,097 | Learning Inverse Mappings with Adversarial Criterion | cs.LG | We propose a flipped-Adversarial AutoEncoder (FAAE) that simultaneously
trains a generative model G that maps an arbitrary latent code distribution to
a data distribution and an encoder E that embodies an "inverse mapping" that
encodes a data sample into a latent code vector. Unlike previous hybrid
approaches that leve... | computer science |
33,098 | Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks
by Backdooring | cs.LG | Deep Neural Networks have recently gained lots of success after enabling
several breakthroughs in notoriously challenging problems. Training these
networks is computationally expensive and requires vast amounts of training
data. Selling such pre-trained models can, therefore, be a lucrative business
model. Unfortunatel... | computer science |
33,099 | Training and Inference with Integers in Deep Neural Networks | cs.LG | Researches on deep neural networks with discrete parameters and their
deployment in embedded systems have been active and promising topics. Although
previous works have successfully reduced precision in inference, transferring
both training and inference processes to low-bitwidth integers has not been
demonstrated simu... | computer science |
33,100 | Predict and Constrain: Modeling Cardinality in Deep Structured
Prediction | cs.LG | Many machine learning problems require the prediction of multi-dimensional
labels. Such structured prediction models can benefit from modeling
dependencies between labels. Recently, several deep learning approaches to
structured prediction have been proposed. Here we focus on capturing
cardinality constraints in such m... | computer science |
33,101 | Identify Susceptible Locations in Medical Records via Adversarial
Attacks on Deep Predictive Models | cs.LG | The surging availability of electronic medical records (EHR) leads to
increased research interests in medical predictive modeling. Recently many deep
learning based predicted models are also developed for EHR data and
demonstrated impressive performance. However, a series of recent studies showed
that these deep models... | computer science |
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