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7,200 | A Universal Marginalizer for Amortized Inference in Generative Models | cs.LG | We consider the problem of inference in a causal generative model where the
set of available observations differs between data instances. We show how
combining samples drawn from the graphical model with an appropriate masking
function makes it possible to train a single neural network to approximate all
the correspond... | computer science |
7,201 | Network-size independent covering number bounds for deep networks | cs.LG | We give a covering number bound for deep learning networks that is
independent of the size of the network. The key for the simple analysis is that
for linear classifiers, rotating the data doesn't affect the covering number.
Thus, we can ignore the rotation part of each layer's linear transformation,
and get the coveri... | computer science |
7,202 | Oversampling for Imbalanced Learning Based on K-Means and SMOTE | cs.LG | Learning from class-imbalanced data continues to be a common and challenging
problem in supervised learning as standard classification algorithms are
designed to handle balanced class distributions. While different strategies
exist to tackle this problem, methods which generate artificial data to achieve
a balanced cla... | computer science |
7,203 | Generalized Probabilistic Bisection for Stochastic Root-Finding | stat.ML | We consider numerical schemes for root finding of noisy responses through
generalizing the Probabilistic Bisection Algorithm (PBA) to the more practical
context where the sampling distribution is unknown and location-dependent. As
in standard PBA, we rely on a knowledge state for the approximate posterior of
the root l... | computer science |
7,204 | The (Un)reliability of saliency methods | stat.ML | Saliency methods aim to explain the predictions of deep neural networks.
These methods lack reliability when the explanation is sensitive to factors
that do not contribute to the model prediction. We use a simple and common
pre-processing step ---adding a constant shift to the input data--- to show
that a transformatio... | computer science |
7,205 | Beyond normality: Learning sparse probabilistic graphical models in the
non-Gaussian setting | cs.LG | We present an algorithm to identify sparse dependence structure in continuous
and non-Gaussian probability distributions, given a corresponding set of data.
The conditional independence structure of an arbitrary distribution can be
represented as an undirected graph (or Markov random field), but most
algorithms for lea... | computer science |
7,206 | Policy Optimization by Genetic Distillation | stat.ML | Genetic algorithms have been widely used in many practical optimization
problems. Inspired by natural selection, operators, including mutation,
crossover and selection, provide effective heuristics for search and black-box
optimization. However, they have not been shown useful for deep reinforcement
learning, possibly ... | computer science |
7,207 | Structured Variational Inference for Coupled Gaussian Processes | stat.ML | Sparse variational approximations allow for principled and scalable inference
in Gaussian Process (GP) models. In settings where several GPs are part of the
generative model, theses GPs are a posteriori coupled. For many applications
such as regression where predictive accuracy is the quantity of interest, this
couplin... | computer science |
7,208 | Metrics for Deep Generative Models | stat.ML | Neural samplers such as variational autoencoders (VAEs) or generative
adversarial networks (GANs) approximate distributions by transforming samples
from a simple random source---the latent space---to samples from a more complex
distribution represented by a dataset. While the manifold hypothesis implies
that the densit... | computer science |
7,209 | Implicit Weight Uncertainty in Neural Networks | stat.ML | We interpret HyperNetworks within the framework of variational inference
within implicit distributions. Our method, Bayes by Hypernet, is able to model
a richer variational distribution than previous methods. Experiments show that
it achieves comparable predictive performance on the MNIST classification task
while prov... | computer science |
7,210 | Distribution-Preserving k-Anonymity | stat.ML | Preserving the privacy of individuals by protecting their sensitive
attributes is an important consideration during microdata release. However, it
is equally important to preserve the quality or utility of the data for at
least some targeted workloads. We propose a novel framework for privacy
preservation based on the ... | computer science |
7,211 | Wasserstein Auto-Encoders | stat.ML | We propose the Wasserstein Auto-Encoder (WAE)---a new algorithm for building
a generative model of the data distribution. WAE minimizes a penalized form of
the Wasserstein distance between the model distribution and the target
distribution, which leads to a different regularizer than the one used by the
Variational Aut... | computer science |
7,212 | Interpretable Feature Recommendation for Signal Analytics | stat.ML | This paper presents an automated approach for interpretable feature
recommendation for solving signal data analytics problems. The method has been
tested by performing experiments on datasets in the domain of prognostics where
interpretation of features is considered very important. The proposed approach
is based on Wi... | computer science |
7,213 | Deformable Deep Convolutional Generative Adversarial Network in
Microwave Based Hand Gesture Recognition System | stat.ML | Traditional vision-based hand gesture recognition systems is limited under
dark circumstances. In this paper, we build a hand gesture recognition system
based on microwave transceiver and deep learning algorithm. A Doppler radar
sensor with dual receiving channels at 5.8GHz is used to acquire a big database
of hand ges... | computer science |
7,214 | Multi-Player Bandits Revisited | stat.ML | Multi-player Multi-Armed Bandits (MAB) have been extensively studied in the
literature, motivated by applications to Cognitive Radio systems. Driven by
such applications as well, we motivate the introduction of several levels of
feedback for multi-player MAB algorithms. Most existing work assume that
sensing informatio... | computer science |
7,215 | FADO: A Deterministic Detection/Learning Algorithm | cs.LG | This paper proposes and studies a detection technique for adversarial
scenarios (dubbed deterministic detection). This technique provides an
alternative detection methodology in case the usual stochastic methods are not
applicable: this can be because the studied phenomenon does not follow a
stochastic sampling scheme,... | computer science |
7,216 | A Tutorial on Canonical Correlation Methods | cs.LG | Canonical correlation analysis is a family of multivariate statistical
methods for the analysis of paired sets of variables. Since its proposition,
canonical correlation analysis has for instance been extended to extract
relations between two sets of variables when the sample size is insufficient in
relation to the dat... | computer science |
7,217 | Gaussian Lower Bound for the Information Bottleneck Limit | cs.LG | The Information Bottleneck (IB) is a conceptual method for extracting the
most compact, yet informative, representation of a set of variables, with
respect to the target. It generalizes the notion of minimal sufficient
statistics from classical parametric statistics to a broader
information-theoretic sense. The IB curv... | computer science |
7,218 | Grafting for Combinatorial Boolean Model using Frequent Itemset Mining | stat.ML | This paper introduces the combinatorial Boolean model (CBM), which is defined
as the class of linear combinations of conjunctions of Boolean attributes. This
paper addresses the issue of learning CBM from labeled data. CBM is of high
knowledge interpretability but na\"{i}ve learning of it requires exponentially
large c... | computer science |
7,219 | Online Learning for Changing Environments using Coin Betting | stat.ML | A key challenge in online learning is that classical algorithms can be slow
to adapt to changing environments. Recent studies have proposed "meta"
algorithms that convert any online learning algorithm to one that is adaptive
to changing environments, where the adaptivity is analyzed in a quantity called
the strongly-ad... | computer science |
7,220 | Theoretical limitations of Encoder-Decoder GAN architectures | cs.LG | Encoder-decoder GANs architectures (e.g., BiGAN and ALI) seek to add an
inference mechanism to the GANs setup, consisting of a small encoder deep net
that maps data-points to their succinct encodings. The intuition is that being
forced to train an encoder alongside the usual generator forces the system to
learn meaning... | computer science |
7,221 | Neural Variational Inference and Learning in Undirected Graphical Models | cs.LG | Many problems in machine learning are naturally expressed in the language of
undirected graphical models. Here, we propose black-box learning and inference
algorithms for undirected models that optimize a variational approximation to
the log-likelihood of the model. Central to our approach is an upper bound on
the log-... | computer science |
7,222 | On the Discrimination-Generalization Tradeoff in GANs | cs.LG | Generative adversarial training can be generally understood as minimizing
certain moment matching loss defined by a set of discriminator functions,
typically neural networks. The discriminator set should be large enough to be
able to uniquely identify the true distribution (discriminative), and also be
small enough to ... | computer science |
7,223 | Approximate message passing for nonconvex sparse regularization with
stability and asymptotic analysis | stat.ML | We analyse a linear regression problem with nonconvex regularization called
smoothly clipped absolute deviation (SCAD) under an overcomplete Gaussian basis
for Gaussian random data. We propose an approximate message passing (AMP)
algorithm considering nonconvex regularization, namely SCAD-AMP, and
analytically show tha... | computer science |
7,224 | Intriguing Properties of Adversarial Examples | stat.ML | It is becoming increasingly clear that many machine learning classifiers are
vulnerable to adversarial examples. In attempting to explain the origin of
adversarial examples, previous studies have typically focused on the fact that
neural networks operate on high dimensional data, they overfit, or they are too
linear. H... | computer science |
7,225 | Recency-weighted Markovian inference | cs.LG | We describe a Markov latent state space (MLSS) model, where the latent state
distribution is a decaying mixture over multiple past states. We present a
simple sampling algorithm that allows to approximate such high-order MLSS with
fixed time and memory costs. | computer science |
7,226 | Learning Credible Models | cs.LG | In many settings, it is important that a model be capable of providing
reasons for its predictions (i.e., the model must be interpretable). However,
the model's reasoning may not conform with well-established knowledge. In such
cases, while interpretable, the model lacks \textit{credibility}. In this work,
we formally ... | computer science |
7,227 | Long-Term Sequential Prediction Using Expert Advice | cs.LG | For the prediction with experts' advice setting, we consider some methods to
construct forecasting algorithms that suffer loss not much more than any expert
in the pool. In contrast to the standard approach, we investigate the case of
long-term forecasting of time series. This approach implies that each expert
issues a... | computer science |
7,228 | A Separation Principle for Control in the Age of Deep Learning | stat.ML | We review the problem of defining and inferring a "state" for a control
system based on complex, high-dimensional, highly uncertain measurement streams
such as videos. Such a state, or representation, should contain all and only
the information needed for control, and discount nuisance variability in the
data. It shoul... | computer science |
7,229 | Analysis of Dropout in Online Learning | cs.LG | Deep learning is the state-of-the-art in fields such as visual object
recognition and speech recognition. This learning uses a large number of layers
and a huge number of units and connections. Therefore, overfitting is a serious
problem with it, and the dropout which is a kind of regularization tool is
used. However, ... | computer science |
7,230 | Multi-Relevance Transfer Learning | cs.LG | Transfer learning aims to faciliate learning tasks in a label-scarce target
domain by leveraging knowledge from a related source domain with plenty of
labeled data. Often times we may have multiple domains with little or no
labeled data as targets waiting to be solved. Most existing efforts tackle
target domains separa... | computer science |
7,231 | A random matrix analysis and improvement of semi-supervised learning for
large dimensional data | cs.LG | This article provides an original understanding of the behavior of a class of
graph-oriented semi-supervised learning algorithms in the limit of large and
numerous data. It is demonstrated that the intuition at the root of these
methods collapses in this limit and that, as a result, most of them become
inconsistent. Co... | computer science |
7,232 | Alternating minimization for dictionary learning with random
initialization | stat.ML | We present theoretical guarantees for an alternating minimization algorithm
for the dictionary learning/sparse coding problem. The dictionary learning
problem is to factorize vector samples $y^{1},y^{2},\ldots, y^{n}$ into an
appropriate basis (dictionary) $A^*$ and sparse vectors $x^{1*},\ldots,x^{n*}$.
Our algorithm ... | computer science |
7,233 | Provably Accurate Double-Sparse Coding | stat.ML | Sparse coding is a crucial subroutine in algorithms for various signal
processing, deep learning, and other machine learning applications. The central
goal is to learn an overcomplete dictionary that can sparsely represent a given
input dataset. However, a key challenge is that storage, transmission, and
processing of ... | computer science |
7,234 | Quantized Memory-Augmented Neural Networks | cs.LG | Memory-augmented neural networks (MANNs) refer to a class of neural network
models equipped with external memory (such as neural Turing machines and memory
networks). These neural networks outperform conventional recurrent neural
networks (RNNs) in terms of learning long-term dependency, allowing them to
solve intrigui... | computer science |
7,235 | LSTM Networks for Data-Aware Remaining Time Prediction of Business
Process Instances | cs.LG | Predicting the completion time of business process instances would be a very
helpful aid when managing processes under service level agreement constraints.
The ability to know in advance the trend of running process instances would
allow business managers to react in time, in order to prevent delays or
undesirable situ... | computer science |
7,236 | Attend and Diagnose: Clinical Time Series Analysis using Attention
Models | stat.ML | With widespread adoption of electronic health records, there is an increased
emphasis for predictive models that can effectively deal with clinical
time-series data. Powered by Recurrent Neural Network (RNN) architectures with
Long Short-Term Memory (LSTM) units, deep neural networks have achieved
state-of-the-art resu... | computer science |
7,237 | Applications of Deep Learning and Reinforcement Learning to Biological
Data | cs.LG | Rapid advances of hardware-based technologies during the past decades have
opened up new possibilities for Life scientists to gather multimodal data in
various application domains (e.g., Omics, Bioimaging, Medical Imaging, and
[Brain/Body]-Machine Interfaces), thus generating novel opportunities for
development of dedi... | computer science |
7,238 | Few-Shot Learning with Graph Neural Networks | stat.ML | We propose to study the problem of few-shot learning with the prism of
inference on a partially observed graphical model, constructed from a
collection of input images whose label can be either observed or not. By
assimilating generic message-passing inference algorithms with their
neural-network counterparts, we defin... | computer science |
7,239 | Disease Prediction from Electronic Health Records Using Generative
Adversarial Networks | cs.LG | Electronic health records (EHRs) have contributed to the computerization of
patient records so that they can be used not only for efficient and systematic
medical services, but also for research on data science. In this paper, we
compared the disease prediction performance of generative adversarial networks
(GANs) and ... | computer science |
7,240 | Scale out for large minibatch SGD: Residual network training on
ImageNet-1K with improved accuracy and reduced time to train | stat.ML | For the past 5 years, the ILSVRC competition and the ImageNet dataset have
attracted a lot of interest from the Computer Vision community, allowing for
state-of-the-art accuracy to grow tremendously. This should be credited to the
use of deep artificial neural network designs. As these became more complex,
the storage,... | computer science |
7,241 | Semi-Supervised Learning via New Deep Network Inversion | stat.ML | We exploit a recently derived inversion scheme for arbitrary deep neural
networks to develop a new semi-supervised learning framework that applies to a
wide range of systems and problems. The approach outperforms current
state-of-the-art methods on MNIST reaching $99.14\%$ of test set accuracy while
using $5$ labeled e... | computer science |
7,242 | Alpha-Divergences in Variational Dropout | stat.ML | We investigate the use of alternative divergences to Kullback-Leibler (KL) in
variational inference(VI), based on the Variational Dropout \cite{kingma2015}.
Stochastic gradient variational Bayes (SGVB) \cite{aevb} is a general framework
for estimating the evidence lower bound (ELBO) in Variational Bayes. In this
work, ... | computer science |
7,243 | Parameter Estimation in Finite Mixture Models by Regularized Optimal
Transport: A Unified Framework for Hard and Soft Clustering | cs.LG | In this short paper, we formulate parameter estimation for finite mixture
models in the context of discrete optimal transportation with convex
regularization. The proposed framework unifies hard and soft clustering methods
for general mixture models. It also generalizes the celebrated
$k$\nobreakdash-means and expectat... | computer science |
7,244 | Machine vs Machine: Minimax-Optimal Defense Against Adversarial Examples | cs.LG | Recently, researchers have discovered that the state-of-the-art object
classifiers can be fooled easily by small perturbations in the input
unnoticeable to human eyes. It is known that an attacker can generate strong
adversarial examples if she knows the classifier parameters. Conversely, a
defender can robustify the c... | computer science |
7,245 | Attention-based Information Fusion using Multi-Encoder-Decoder Recurrent
Neural Networks | cs.LG | With the rising number of interconnected devices and sensors, modeling
distributed sensor networks is of increasing interest. Recurrent neural
networks (RNN) are considered particularly well suited for modeling sensory and
streaming data. When predicting future behavior, incorporating information from
neighboring senso... | computer science |
7,246 | Weightless: Lossy Weight Encoding For Deep Neural Network Compression | cs.LG | The large memory requirements of deep neural networks limit their deployment
and adoption on many devices. Model compression methods effectively reduce the
memory requirements of these models, usually through applying transformations
such as weight pruning or quantization. In this paper, we present a novel
scheme for l... | computer science |
7,247 | Resurrecting the sigmoid in deep learning through dynamical isometry:
theory and practice | cs.LG | It is well known that the initialization of weights in deep neural networks
can have a dramatic impact on learning speed. For example, ensuring the mean
squared singular value of a network's input-output Jacobian is $O(1)$ is
essential for avoiding the exponential vanishing or explosion of gradients. The
stronger condi... | computer science |
7,248 | ACtuAL: Actor-Critic Under Adversarial Learning | stat.ML | Generative Adversarial Networks (GANs) are a powerful framework for deep
generative modeling. Posed as a two-player minimax problem, GANs are typically
trained end-to-end on real-valued data and can be used to train a generator of
high-dimensional and realistic images. However, a major limitation of GANs is
that traini... | computer science |
7,249 | "Found in Translation": Predicting Outcomes of Complex Organic Chemistry
Reactions using Neural Sequence-to-Sequence Models | cs.LG | There is an intuitive analogy of an organic chemist's understanding of a
compound and a language speaker's understanding of a word. Consequently, it is
possible to introduce the basic concepts and analyze potential impacts of
linguistic analysis to the world of organic chemistry. In this work, we cast
the reaction pred... | computer science |
7,250 | STARK: Structured Dictionary Learning Through Rank-one Tensor Recovery | stat.ML | In recent years, a class of dictionaries have been proposed for
multidimensional (tensor) data representation that exploit the structure of
tensor data by imposing a Kronecker structure on the dictionary underlying the
data. In this work, a novel algorithm called "STARK" is provided to learn
Kronecker structured dictio... | computer science |
7,251 | Sobolev GAN | cs.LG | We propose a new Integral Probability Metric (IPM) between distributions: the
Sobolev IPM. The Sobolev IPM compares the mean discrepancy of two distributions
for functions (critic) restricted to a Sobolev ball defined with respect to a
dominant measure $\mu$. We show that the Sobolev IPM compares two distributions
in h... | computer science |
7,252 | pyLEMMINGS: Large Margin Multiple Instance Classification and Ranking
for Bioinformatics Applications | cs.LG | Motivation: A major challenge in the development of machine learning based
methods in computational biology is that data may not be accurately labeled due
to the time and resources required for experimentally annotating properties of
proteins and DNA sequences. Standard supervised learning algorithms assume
accurate in... | computer science |
7,253 | Scalable Peaceman-Rachford Splitting Method with Proximal Terms | stat.ML | Along with developing of Peaceman-Rachford Splittling Method (PRSM), many
batch algorithms based on it have been studied very deeply. But almost no
algorithm focused on the performance of stochastic version of PRSM. In this
paper, we propose a new stochastic algorithm based on PRSM, prove its
convergence rate in ergodi... | computer science |
7,254 | Feature importance scores and lossless feature pruning using Banzhaf
power indices | stat.ML | Understanding the influence of features in machine learning is crucial to
interpreting models and selecting the best features for classification. In this
work we propose the use of principles from coalitional game theory to reason
about importance of features. In particular, we propose the use of the Banzhaf
power inde... | computer science |
7,255 | Robust Matrix Elastic Net based Canonical Correlation Analysis: An
Effective Algorithm for Multi-View Unsupervised Learning | cs.LG | This paper presents a robust matrix elastic net based canonical correlation
analysis (RMEN-CCA) for multiple view unsupervised learning problems, which
emphasizes the combination of CCA and the robust matrix elastic net (RMEN) used
as coupled feature selection. The RMEN-CCA leverages the strength of the RMEN
to distill... | computer science |
7,256 | TripletGAN: Training Generative Model with Triplet Loss | cs.LG | As an effective way of metric learning, triplet loss has been widely used in
many deep learning tasks, including face recognition and person-ReID, leading
to many states of the arts. The main innovation of triplet loss is using
feature map to replace softmax in the classification task. Inspired by this
concept, we prop... | computer science |
7,257 | Joint Gaussian Processes for Biophysical Parameter Retrieval | stat.ML | Solving inverse problems is central to geosciences and remote sensing.
Radiative transfer models (RTMs) represent mathematically the physical laws
which govern the phenomena in remote sensing applications (forward models). The
numerical inversion of the RTM equations is a challenging and computationally
demanding probl... | computer science |
7,258 | On Optimal Generalizability in Parametric Learning | stat.ML | We consider the parametric learning problem, where the objective of the
learner is determined by a parametric loss function. Employing empirical risk
minimization with possibly regularization, the inferred parameter vector will
be biased toward the training samples. Such bias is measured by the cross
validation procedu... | computer science |
7,259 | LIUBoost : Locality Informed Underboosting for Imbalanced Data
Classification | cs.LG | The problem of class imbalance along with class-overlapping has become a
major issue in the domain of supervised learning. Most supervised learning
algorithms assume equal cardinality of the classes under consideration while
optimizing the cost function and this assumption does not hold true for
imbalanced datasets whi... | computer science |
7,260 | Optimizing Kernel Machines using Deep Learning | stat.ML | Building highly non-linear and non-parametric models is central to several
state-of-the-art machine learning systems. Kernel methods form an important
class of techniques that induce a reproducing kernel Hilbert space (RKHS) for
inferring non-linear models through the construction of similarity functions
from data. The... | computer science |
7,261 | Semiblind subgraph reconstruction in Gaussian graphical models | cs.LG | Consider a social network where only a few nodes (agents) have meaningful
interactions in the sense that the conditional dependency graph over node
attribute variables (behaviors) is sparse. A company that can only observe the
interactions between its own customers will generally not be able to accurately
estimate its ... | computer science |
7,262 | Z-Forcing: Training Stochastic Recurrent Networks | stat.ML | Many efforts have been devoted to training generative latent variable models
with autoregressive decoders, such as recurrent neural networks (RNN).
Stochastic recurrent models have been successful in capturing the variability
observed in natural sequential data such as speech. We unify successful ideas
from recently pr... | computer science |
7,263 | A Convex Parametrization of a New Class of Universal Kernel Functions
for use in Kernel Learning | stat.ML | We propose a new class of universal kernel functions which admit a linear
parametrization using positive semidefinite matrices. These kernels are
generalizations of the Sobolev kernel and are defined by piecewise-polynomial
functions. The class of kernels is termed "tessellated" as the resulting
discriminant is defined... | computer science |
7,264 | Efficient Estimation of Generalization Error and Bias-Variance
Components of Ensembles | cs.LG | For many applications, an ensemble of base classifiers is an effective
solution. The tuning of its parameters(number of classes, amount of data on
which each classifier is to be trained on, etc.) requires G, the generalization
error of a given ensemble. The efficient estimation of G is the focus of this
paper. The key ... | computer science |
7,265 | Variational Adaptive-Newton Method for Explorative Learning | stat.ML | We present the Variational Adaptive Newton (VAN) method which is a black-box
optimization method especially suitable for explorative-learning tasks such as
active learning and reinforcement learning. Similar to Bayesian methods, VAN
estimates a distribution that can be used for exploration, but requires
computations th... | computer science |
7,266 | Advances in Variational Inference | cs.LG | Many modern unsupervised or semi-supervised machine learning algorithms rely
on Bayesian probabilistic models. These models are usually intractable and thus
require approximate inference. Variational inference (VI) lets us approximate a
high-dimensional Bayesian posterior with a simpler variational distribution by
solv... | computer science |
7,267 | Variational Bi-LSTMs | stat.ML | Recurrent neural networks like long short-term memory (LSTM) are important
architectures for sequential prediction tasks. LSTMs (and RNNs in general)
model sequences along the forward time direction. Bidirectional LSTMs
(Bi-LSTMs) on the other hand model sequences along both forward and backward
directions and are gene... | computer science |
7,268 | Hierarchical Modeling of Seed Variety Yields and Decision Making for
Future Planting Plans | cs.LG | Eradicating hunger and malnutrition is a key development goal of the 21st
century. We address the problem of optimally identifying seed varieties to
reliably increase crop yield within a risk-sensitive decision-making framework.
Specifically, we introduce a novel hierarchical machine learning mechanism for
predicting c... | computer science |
7,269 | Towards better understanding of gradient-based attribution methods for
Deep Neural Networks | cs.LG | Understanding the flow of information in Deep Neural Networks (DNNs) is a
challenging problem that has gain increasing attention over the last few years.
While several methods have been proposed to explain network predictions, there
have been only a few attempts to compare them from a theoretical perspective.
What is m... | computer science |
7,270 | Robust Unsupervised Domain Adaptation for Neural Networks via Moment
Alignment | stat.ML | A novel approach for unsupervised domain adaptation for neural networks is
proposed that relies on a metric-based regularization of the learning process.
The metric-based regularization aims at domain-invariant latent feature
representations by means of maximizing the similarity between domain-specific
activation distr... | computer science |
7,271 | Beyond Sparsity: Tree Regularization of Deep Models for Interpretability | stat.ML | The lack of interpretability remains a key barrier to the adoption of deep
models in many applications. In this work, we explicitly regularize deep models
so human users might step through the process behind their predictions in
little time. Specifically, we train deep time-series models so their
class-probability pred... | computer science |
7,272 | Neurology-as-a-Service for the Developing World | stat.ML | Electroencephalography (EEG) is an extensively-used and well-studied
technique in the field of medical diagnostics and treatment for brain
disorders, including epilepsy, migraines, and tumors. The analysis and
interpretation of EEGs require physicians to have specialized training, which
is not common even among most do... | computer science |
7,273 | A Resizable Mini-batch Gradient Descent based on a Multi-Armed Bandit | stat.ML | Determining the appropriate batch size for mini-batch gradient descent is
always time consuming as it often relies on grid search. This paper considers a
resizable mini-batch gradient descent (RMGD) algorithm based on a multi-armed
bandit for achieving best performance in grid search by selecting an
appropriate batch s... | computer science |
7,274 | A unified deep artificial neural network approach to partial
differential equations in complex geometries | stat.ML | We use deep feedforward artificial neural networks to approximate solutions
of partial differential equations of advection and diffusion type in complex
geometries. We derive analytical expressions of the gradients of the cost
function with respect to the network parameters, as well as the gradient of the
network itsel... | computer science |
7,275 | Introduction to intelligent computing unit 1 | cs.LG | This brief note highlights some basic concepts required toward understanding
the evolution of machine learning and deep learning models. The note starts
with an overview of artificial intelligence and its relationship to biological
neuron that ultimately led to the evolution of todays intelligent models. | computer science |
7,276 | Predict Responsibly: Increasing Fairness by Learning To Defer | stat.ML | In many high-stakes ML applications, there are multiple decision-makers
involved, both automated and human. The interaction between these agents often
goes unaddressed in algorithmic development. In this work, we explore a simple
version of this interaction with a two-stage framework containing an automated
model and a... | computer science |
7,277 | MinimalRNN: Toward More Interpretable and Trainable Recurrent Neural
Networks | stat.ML | We introduce MinimalRNN, a new recurrent neural network architecture that
achieves comparable performance as the popular gated RNNs with a simplified
structure. It employs minimal updates within RNN, which not only leads to
efficient learning and testing but more importantly better interpretability and
trainability. We... | computer science |
7,278 | Tree-Structured Boosting: Connections Between Gradient Boosted Stumps
and Full Decision Trees | stat.ML | Additive models, such as produced by gradient boosting, and full interaction
models, such as classification and regression trees (CART), are widely used
algorithms that have been investigated largely in isolation. We show that these
models exist along a spectrum, revealing never-before-known connections between
these t... | computer science |
7,279 | Prediction Scores as a Window into Classifier Behavior | stat.ML | Most multi-class classifiers make their prediction for a test sample by
scoring the classes and selecting the one with the highest score. Analyzing
these prediction scores is useful to understand the classifier behavior and to
assess its reliability. We present an interactive visualization that
facilitates per-class an... | computer science |
7,280 | MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep
Networks | cs.LG | We present MorphNet, an approach to automate the design of neural network
structures. MorphNet iteratively shrinks and expands a network, shrinking via a
resource-weighted sparsifying regularizer on activations and expanding via a
uniform multiplicative factor on all layers. In contrast to previous
approaches, our meth... | computer science |
7,281 | Deep Gaussian Mixture Models | stat.ML | Deep learning is a hierarchical inference method formed by subsequent
multiple layers of learning able to more efficiently describe complex
relationships. In this work, Deep Gaussian Mixture Models are introduced and
discussed. A Deep Gaussian Mixture model (DGMM) is a network of multiple layers
of latent variables, wh... | computer science |
7,282 | Sequential Randomized Matrix Factorization for Gaussian Processes:
Efficient Predictions and Hyper-parameter Optimization | cs.LG | This paper presents a sequential randomized lowrank matrix factorization
approach for incrementally predicting values of an unknown function at test
points using the Gaussian Processes framework. It is well-known that in the
Gaussian processes framework, the computational bottlenecks are the inversion
of the (regulariz... | computer science |
7,283 | Convergence Analysis of the Dynamics of a Special Kind of Two-Layered
Neural Networks with $\ell_1$ and $\ell_2$ Regularization | stat.ML | In this paper, we made an extension to the convergence analysis of the
dynamics of two-layered bias-free networks with one $ReLU$ output. We took into
consideration two popular regularization terms: the $\ell_1$ and $\ell_2$ norm
of the parameter vector $w$, and added it to the square loss function with
coefficient $\l... | computer science |
7,284 | An Improved Oscillating-Error Classifier with Branching | cs.LG | This paper extends the earlier work on an oscillating error correction
technique. Specifically, it extends the design to include further corrections,
by adding new layers to the classifier through a branching method. This
technique is still consistent with earlier work and also neural networks in
general. With this ext... | computer science |
7,285 | A Classifying Variational Autoencoder with Application to Polyphonic
Music Generation | stat.ML | The variational autoencoder (VAE) is a popular probabilistic generative
model. However, one shortcoming of VAEs is that the latent variables cannot be
discrete, which makes it difficult to generate data from different modes of a
distribution. Here, we propose an extension of the VAE framework that
incorporates a classi... | computer science |
7,286 | Does mitigating ML's impact disparity require treatment disparity? | stat.ML | Following related work in law and policy, two notions of disparity have come
to shape the study of fairness in algorithmic decision-making. Algorithms
exhibit treatment disparity if they formally treat members of protected
subgroups differently; algorithms exhibit impact disparity when outcomes differ
across subgroups,... | computer science |
7,287 | Compression-Based Regularization with an Application to Multi-Task
Learning | stat.ML | This paper investigates, from information theoretic grounds, a learning
problem based on the principle that any regularity in a given dataset can be
exploited to extract compact features from data, i.e., using fewer bits than
needed to fully describe the data itself, in order to build meaningful
representations of a re... | computer science |
7,288 | Stein Variational Message Passing for Continuous Graphical Models | stat.ML | We propose a novel distributed inference algorithm for continuous graphical
models, by extending Stein variational gradient descent (SVGD) to leverage the
Markov dependency structure of the distribution of interest. Our approach
combines SVGD with a set of structured local kernel functions defined on the
Markov blanket... | computer science |
7,289 | Positive semi-definite embedding for dimensionality reduction and
out-of-sample extensions | cs.LG | In machine learning or statistics, it is often desirable to reduce the
dimensionality of high dimensional data. We propose to obtain the low
dimensional embedding coordinates as the eigenvectors of a positive
semi-definite kernel matrix. This kernel matrix is the solution of a
semi-definite program promoting a low rank... | computer science |
7,290 | Relaxed Oracles for Semi-Supervised Clustering | stat.ML | Pairwise "same-cluster" queries are one of the most widely used forms of
supervision in semi-supervised clustering. However, it is impractical to ask
human oracles to answer every query correctly. In this paper, we study the
influence of allowing "not-sure" answers from a weak oracle and propose an
effective algorithm ... | computer science |
7,291 | Bidirectional Conditional Generative Adversarial Networks | cs.LG | Conditional Generative Adversarial Networks (cGANs) are generative models
that can produce data samples ($x$) conditioned on both latent variables ($z$)
and known auxiliary information ($c$). We propose the Bidirectional cGAN
(BiCoGAN), which effectively disentangles $z$ and $c$ in the generation process
and provides a... | computer science |
7,292 | Residual Gated Graph ConvNets | cs.LG | Graph-structured data such as functional brain networks, social networks,
gene regulatory networks, communications networks have brought the interest in
generalizing neural networks to graph domains. In this paper, we are interested
to de- sign efficient neural network architectures for graphs with variable
length. Sev... | computer science |
7,293 | Finding Differentially Covarying Needles in a Temporally Evolving
Haystack: A Scan Statistics Perspective | stat.ML | Recent results in coupled or temporal graphical models offer schemes for
estimating the relationship structure between features when the data come from
related (but distinct) longitudinal sources. A novel application of these ideas
is for analyzing group-level differences, i.e., in identifying if trends of
estimated ob... | computer science |
7,294 | Regret Analysis for Continuous Dueling Bandit | stat.ML | The dueling bandit is a learning framework wherein the feedback information
in the learning process is restricted to a noisy comparison between a pair of
actions. In this research, we address a dueling bandit problem based on a cost
function over a continuous space. We propose a stochastic mirror descent
algorithm and ... | computer science |
7,295 | On Breast Cancer Detection: An Application of Machine Learning
Algorithms on the Wisconsin Diagnostic Dataset | cs.LG | This paper presents a comparison of six machine learning (ML) algorithms:
GRU-SVM (Agarap, 2017), Linear Regression, Multilayer Perceptron (MLP), Nearest
Neighbor (NN) search, Softmax Regression, and Support Vector Machine (SVM) on
the Wisconsin Diagnostic Breast Cancer (WDBC) dataset (Wolberg, Street, &
Mangasarian, 1... | computer science |
7,296 | Application of generative autoencoder in de novo molecular design | cs.LG | A major challenge in computational chemistry is the generation of novel
molecular structures with desirable pharmacological and physiochemical
properties. In this work, we investigate the potential use of autoencoder, a
deep learning methodology, for de novo molecular design. Various generative
autoencoders were used t... | computer science |
7,297 | Recover Missing Sensor Data with Iterative Imputing Network | cs.LG | Sensor data has been playing an important role in machine learning tasks,
complementary to the human-annotated data that is usually rather costly.
However, due to systematic or accidental mis-operations, sensor data comes very
often with a variety of missing values, resulting in considerable difficulties
in the follow-... | computer science |
7,298 | Disagreement-based combinatorial pure exploration: Efficient algorithms
and an analysis with localization | stat.ML | We design new algorithms for the combinatorial pure exploration problem in
the multi-arm bandit framework. In this problem, we are given K distributions
and a collection of subsets $\mathcal{V} \subset 2^K$ of these distributions,
and we would like to find the subset $v \in \mathcal{V}$ that has largest
cumulative mean... | computer science |
7,299 | A generative adversarial framework for positive-unlabeled classification | cs.LG | In this work, we consider the task of classifying the binary
positive-unlabeled (PU) data. The existing discriminative learning based PU
models attempt to seek an optimal re-weighting strategy for U data, so that a
decent decision boundary can be found. In contrast, we provide a totally new
paradigm to attack the binar... | computer science |
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