Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
values |
|---|---|---|---|---|
7,300 | Locally Smoothed Neural Networks | cs.LG | Convolutional Neural Networks (CNN) and the locally connected layer are
limited in capturing the importance and relations of different local receptive
fields, which are often crucial for tasks such as face verification, visual
question answering, and word sequence prediction. To tackle the issue, we
propose a novel loc... | computer science |
7,301 | Hypergraph $p$-Laplacian: A Differential Geometry View | stat.ML | The graph Laplacian plays key roles in information processing of relational
data, and has analogies with the Laplacian in differential geometry. In this
paper, we generalize the analogy between graph Laplacian and differential
geometry to the hypergraph setting, and propose a novel hypergraph
$p$-Laplacian. Unlike the ... | computer science |
7,302 | Adversarial Phenomenon in the Eyes of Bayesian Deep Learning | stat.ML | Deep Learning models are vulnerable to adversarial examples, i.e.\ images
obtained via deliberate imperceptible perturbations, such that the model
misclassifies them with high confidence. However, class confidence by itself is
an incomplete picture of uncertainty. We therefore use principled Bayesian
methods to capture... | computer science |
7,303 | Decomposition Strategies for Constructive Preference Elicitation | stat.ML | We tackle the problem of constructive preference elicitation, that is the
problem of learning user preferences over very large decision problems,
involving a combinatorial space of possible outcomes. In this setting, the
suggested configuration is synthesized on-the-fly by solving a constrained
optimization problem, wh... | computer science |
7,304 | GraphGAN: Graph Representation Learning with Generative Adversarial Nets | cs.LG | The goal of graph representation learning is to embed each vertex in a graph
into a low-dimensional vector space. Existing graph representation learning
methods can be classified into two categories: generative models that learn the
underlying connectivity distribution in the graph, and discriminative models
that predi... | computer science |
7,305 | Utilizing artificial neural networks to predict demand for
weather-sensitive products at retail stores | cs.LG | One key requirement for effective supply chain management is the quality of
its inventory management. Various inventory management methods are typically
employed for different types of products based on their demand patterns,
product attributes, and supply network. In this paper, our goal is to develop
robust demand pr... | computer science |
7,306 | Likelihood Almost Free Inference Networks | cs.LG | Variational inference for latent variable models is prevalent in various
machine learning problems, typically solved by maximizing the Evidence Lower
Bound (ELBO) of the true data likelihood with respect to a variational
distribution. However, freely enriching the family of variational distribution
is challenging since... | computer science |
7,307 | From Monte Carlo to Las Vegas: Improving Restricted Boltzmann Machine
Training Through Stopping Sets | cs.LG | We propose a Las Vegas transformation of Markov Chain Monte Carlo (MCMC)
estimators of Restricted Boltzmann Machines (RBMs). We denote our approach
Markov Chain Las Vegas (MCLV). MCLV gives statistical guarantees in exchange
for random running times. MCLV uses a stopping set built from the training data
and has maximum... | computer science |
7,308 | An Improved Training Procedure for Neural Autoregressive Data Completion | cs.LG | Neural autoregressive models are explicit density estimators that achieve
state-of-the-art likelihoods for generative modeling. The D-dimensional data
distribution is factorized into an autoregressive product of one-dimensional
conditional distributions according to the chain rule. Data completion is a
more involved ta... | computer science |
7,309 | IVE-GAN: Invariant Encoding Generative Adversarial Networks | cs.LG | Generative adversarial networks (GANs) are a powerful framework for
generative tasks. However, they are difficult to train and tend to miss modes
of the true data generation process. Although GANs can learn a rich
representation of the covered modes of the data in their latent space, the
framework misses an inverse map... | computer science |
7,310 | Diversity-Promoting Bayesian Learning of Latent Variable Models | cs.LG | To address three important issues involved in latent variable models (LVMs),
including capturing infrequent patterns, achieving small-sized but expressive
models and alleviating overfitting, several studies have been devoted to
"diversifying" LVMs, which aim at encouraging the components in LVMs to be
diverse. Most exi... | computer science |
7,311 | Invariance of Weight Distributions in Rectified MLPs | cs.LG | An interesting approach to analyzing and developing tools for neural networks
that has received renewed attention is to examine the equivalent kernel of the
neural network. This is based on the fact that a fully connected feedforward
network with one hidden layer, a certain weight distribution, an activation
function, ... | computer science |
7,312 | Demystifying AlphaGo Zero as AlphaGo GAN | cs.LG | The astonishing success of AlphaGo Zero\cite{Silver_AlphaGo} invokes a
worldwide discussion of the future of our human society with a mixed mood of
hope, anxiousness, excitement and fear. We try to dymystify AlphaGo Zero by a
qualitative analysis to indicate that AlphaGo Zero can be understood as a
specially structured... | computer science |
7,313 | Quantifying the Effects of Enforcing Disentanglement on Variational
Autoencoders | stat.ML | The notion of disentangled autoencoders was proposed as an extension to the
variational autoencoder by introducing a disentanglement parameter $\beta$,
controlling the learning pressure put on the possible underlying latent
representations. For certain values of $\beta$ this kind of autoencoders is
capable of encoding ... | computer science |
7,314 | JADE: Joint Autoencoders for Dis-Entanglement | cs.LG | The problem of feature disentanglement has been explored in the literature,
for the purpose of image and video processing and text analysis.
State-of-the-art methods for disentangling feature representations rely on the
presence of many labeled samples. In this work, we present a novel method for
disentangling factors ... | computer science |
7,315 | Feature Selection Facilitates Learning Mixtures of Discrete Product
Distributions | stat.ML | Feature selection can facilitate the learning of mixtures of discrete random
variables as they arise, e.g. in crowdsourcing tasks. Intuitively, not all
workers are equally reliable but, if the less reliable ones could be
eliminated, then learning should be more robust. By analogy with Gaussian
mixture models, we seek a... | computer science |
7,316 | Malaria Likelihood Prediction By Effectively Surveying Households Using
Deep Reinforcement Learning | cs.LG | We build a deep reinforcement learning (RL) agent that can predict the
likelihood of an individual testing positive for malaria by asking questions
about their household. The RL agent learns to determine which survey question
to ask next and when to stop to make a prediction about their likelihood of
malaria based on t... | computer science |
7,317 | An Adaptive Strategy for Active Learning with Smooth Decision Boundary | stat.ML | We present the first adaptive strategy for active learning in the setting of
classification with smooth decision boundary. The problem of adaptivity (to
unknown distributional parameters) has remained opened since the seminal work
of Castro and Nowak (2007), which first established (active learning) rates for
this sett... | computer science |
7,318 | Learning Less-Overlapping Representations | cs.LG | In representation learning (RL), how to make the learned representations easy
to interpret and less overfitted to training data are two important but
challenging issues. To address these problems, we study a new type of
regulariza- tion approach that encourages the supports of weight vectors in RL
models to have small ... | computer science |
7,319 | Inference of Spatio-Temporal Functions over Graphs via Multi-Kernel
Kriged Kalman Filtering | cs.LG | Inference of space-time varying signals on graphs emerges naturally in a
plethora of network science related applications. A frequently encountered
challenge pertains to reconstructing such dynamic processes, given their values
over a subset of vertices and time instants. The present paper develops a
graph-aware kernel... | computer science |
7,320 | Training Confidence-calibrated Classifiers for Detecting
Out-of-Distribution Samples | stat.ML | The problem of detecting whether a test sample is from in-distribution (i.e.,
training distribution by a classifier) or out-of-distribution sufficiently
different from it arises in many real-world machine learning applications.
However, the state-of-art deep neural networks are known to be highly
overconfident in their... | computer science |
7,321 | Highly Efficient Human Action Recognition with Quantum Genetic Algorithm
Optimized Support Vector Machine | stat.ML | In this paper we propose the use of quantum genetic algorithm to optimize the
support vector machine (SVM) for human action recognition. The Microsoft Kinect
sensor can be used for skeleton tracking, which provides the joints' position
data. However, how to extract the motion features for representing the dynamics
of a... | computer science |
7,322 | Learning with Biased Complementary Labels | stat.ML | In this paper we study the classification problem in which we have access to
easily obtainable surrogate for the true labels, namely complementary labels,
which specify classes that observations do \textbf{not} belong to. For example,
if one is familiar with monkeys but not meerkats, a meerkat is easily
identified as n... | computer science |
7,323 | One-Shot Coresets: The Case of k-Clustering | stat.ML | Scaling clustering algorithms to massive data sets is a challenging task.
Recently, several successful approaches based on data summarization methods,
such as coresets and sketches, were proposed. While these techniques provide
provably good and small summaries, they are inherently problem dependent - the
practitioner ... | computer science |
7,324 | Data Dependent Kernel Approximation using Pseudo Random Fourier Features | cs.LG | Kernel methods are powerful and flexible approach to solve many problems in
machine learning. Due to the pairwise evaluations in kernel methods, the
complexity of kernel computation grows as the data size increases; thus the
applicability of kernel methods is limited for large scale datasets. Random
Fourier Features (R... | computer science |
7,325 | Adversary Detection in Neural Networks via Persistent Homology | cs.LG | We outline a detection method for adversarial inputs to deep neural networks.
By viewing neural network computations as graphs upon which information flows
from input space to out- put distribution, we compare the differences in graphs
induced by different inputs. Specifically, by applying persistent homology to
these ... | computer science |
7,326 | Predicting Adolescent Suicide Attempts with Neural Networks | stat.ML | Though suicide is a major public health problem in the US, machine learning
methods are not commonly used to predict an individual's risk of
attempting/committing suicide. In the present work, starting with an anonymized
collection of electronic health records for 522,056 unique, California-resident
adolescents, we dev... | computer science |
7,327 | Variational Inference for Gaussian Process Models with Linear Complexity | stat.ML | Large-scale Gaussian process inference has long faced practical challenges
due to time and space complexity that is superlinear in dataset size. While
sparse variational Gaussian process models are capable of learning from
large-scale data, standard strategies for sparsifying the model can prevent the
approximation of ... | computer science |
7,328 | Snorkel: Rapid Training Data Creation with Weak Supervision | cs.LG | Labeling training data is increasingly the largest bottleneck in deploying
machine learning systems. We present Snorkel, a first-of-its-kind system that
enables users to train state-of-the-art models without hand labeling any
training data. Instead, users write labeling functions that express arbitrary
heuristics, whic... | computer science |
7,329 | Topological Recurrent Neural Network for Diffusion Prediction | cs.LG | In this paper, we study the problem of using representation learning to
assist information diffusion prediction on graphs. In particular, we aim at
estimating the probability of an inactive node to be activated next in a
cascade. Despite the success of recent deep learning methods for diffusion, we
find that they often... | computer science |
7,330 | Semi-supervised learning of hierarchical representations of molecules
using neural message passing | stat.ML | With the rapid increase of compound databases available in medicinal and
material science, there is a growing need for learning representations of
molecules in a semi-supervised manner. In this paper, we propose an
unsupervised hierarchical feature extraction algorithm for molecules (or more
generally, graph-structured... | computer science |
7,331 | Hierarchical Policy Search via Return-Weighted Density Estimation | cs.LG | Learning an optimal policy from a multi-modal reward function is a
challenging problem in reinforcement learning (RL). Hierarchical RL (HRL)
tackles this problem by learning a hierarchical policy, where multiple option
policies are in charge of different strategies corresponding to modes of a
reward function and a gati... | computer science |
7,332 | Are GANs Created Equal? A Large-Scale Study | stat.ML | Generative adversarial networks (GAN) are a powerful subclass of generative
models. Despite a very rich research activity leading to numerous interesting
GAN algorithms, it is still very hard to assess which algorithm(s) perform
better than others. We conduct a neutral, multi-faceted large-scale empirical
study on stat... | computer science |
7,333 | Kernel-based Inference of Functions over Graphs | stat.ML | The study of networks has witnessed an explosive growth over the past decades
with several ground-breaking methods introduced. A particularly interesting --
and prevalent in several fields of study -- problem is that of inferring a
function defined over the nodes of a network. This work presents a versatile
kernel-base... | computer science |
7,334 | Plan, Attend, Generate: Planning for Sequence-to-Sequence Models | cs.LG | We investigate the integration of a planning mechanism into
sequence-to-sequence models using attention. We develop a model which can plan
ahead in the future when it computes its alignments between input and output
sequences, constructing a matrix of proposed future alignments and a commitment
vector that governs whet... | computer science |
7,335 | Contextual Outlier Interpretation | cs.LG | Outlier detection plays an essential role in many data-driven applications to
identify isolated instances that are different from the majority. While many
statistical learning and data mining techniques have been used for developing
more effective outlier detection algorithms, the interpretation of detected
outliers do... | computer science |
7,336 | Introduction to Tensor Decompositions and their Applications in Machine
Learning | stat.ML | Tensors are multidimensional arrays of numerical values and therefore
generalize matrices to multiple dimensions. While tensors first emerged in the
psychometrics community in the $20^{\text{th}}$ century, they have since then
spread to numerous other disciplines, including machine learning. Tensors and
their decomposi... | computer science |
7,337 | Semi-Supervised Few-Shot Learning with Prototypical Networks | cs.LG | We consider the problem of semi-supervised few-shot classification (when the
few labeled samples are accompanied with unlabeled data) and show how to adapt
the Prototypical Networks to this problem. We first show that using larger and
better regularized prototypical networks can improve the classification
accuracy. We ... | computer science |
7,338 | Causality Refined Diagnostic Prediction | cs.LG | Applying machine learning in the health care domain has shown promising
results in recent years. Interpretable outputs from learning algorithms are
desirable for decision making by health care personnel. In this work, we
explore the possibility of utilizing causal relationships to refine diagnostic
prediction. We focus... | computer science |
7,339 | NPC: Neighbors Progressive Competition Algorithm for Classification of
Imbalanced Data Sets | cs.LG | Learning from many real-world datasets is limited by a problem called the
class imbalance problem. A dataset is imbalanced when one class (the majority
class) has significantly more samples than the other class (the minority
class). Such datasets cause typical machine learning algorithms to perform
poorly on the classi... | computer science |
7,340 | GANs for LIFE: Generative Adversarial Networks for Likelihood Free
Inference | cs.LG | We introduce a framework using Generative Adversarial Networks (GANs) for
likelihood--free inference (LFI) and Approximate Bayesian Computation (ABC).
Our approach addresses both the key problems in likelihood--free inference,
namely how to compare distributions and how to efficiently explore the
parameter space. Our f... | computer science |
7,341 | State Space LSTM Models with Particle MCMC Inference | cs.LG | Long Short-Term Memory (LSTM) is one of the most powerful sequence models.
Despite the strong performance, however, it lacks the nice interpretability as
in state space models. In this paper, we present a way to combine the best of
both worlds by introducing State Space LSTM (SSL) models that generalizes the
earlier wo... | computer science |
7,342 | Towards Accurate Binary Convolutional Neural Network | cs.LG | We introduce a novel scheme to train binary convolutional neural networks
(CNNs) -- CNNs with weights and activations constrained to {-1,+1} at run-time.
It has been known that using binary weights and activations drastically reduce
memory size and accesses, and can replace arithmetic operations with more
efficient bit... | computer science |
7,343 | Improved Linear Embeddings via Lagrange Duality | stat.ML | Near isometric orthogonal embeddings to lower dimensions are a fundamental
tool in data science and machine learning. In this paper, we present the
construction of such embeddings that minimizes the maximum distortion for a
given set of points. We formulate the problem as a non convex constrained
optimization problem. ... | computer science |
7,344 | Learning to Adapt by Minimizing Discrepancy | cs.LG | We explore whether useful temporal neural generative models can be learned
from sequential data without back-propagation through time. We investigate the
viability of a more neurocognitively-grounded approach in the context of
unsupervised generative modeling of sequences. Specifically, we build on the
concept of predi... | computer science |
7,345 | Measuring the tendency of CNNs to Learn Surface Statistical Regularities | cs.LG | Deep CNNs are known to exhibit the following peculiarity: on the one hand
they generalize extremely well to a test set, while on the other hand they are
extremely sensitive to so-called adversarial perturbations. The extreme
sensitivity of high performance CNNs to adversarial examples casts serious
doubt that these net... | computer science |
7,346 | Modeling Information Flow Through Deep Neural Networks | cs.LG | This paper proposes a principled information theoretic analysis of
classification for deep neural network structures, e.g. convolutional neural
networks (CNN). The output of convolutional filters is modeled as a random
variable Y conditioned on the object class C and network filter bank F. The
conditional entropy (CENT... | computer science |
7,347 | Highrisk Prediction from Electronic Medical Records via Deep Attention
Networks | cs.LG | Predicting highrisk vascular diseases is a significant issue in the medical
domain. Most predicting methods predict the prognosis of patients from
pathological and radiological measurements, which are expensive and require
much time to be analyzed. Here we propose deep attention models that predict
the onset of the hig... | computer science |
7,348 | Feature discovery and visualization of robot mission data using
convolutional autoencoders and Bayesian nonparametric topic models | cs.LG | The gap between our ability to collect interesting data and our ability to
analyze these data is growing at an unprecedented rate. Recent algorithmic
attempts to fill this gap have employed unsupervised tools to discover
structure in data. Some of the most successful approaches have used
probabilistic models to uncover... | computer science |
7,349 | An interpretable latent variable model for attribute applicability in
the Amazon catalogue | stat.ML | Learning attribute applicability of products in the Amazon catalog (e.g.,
predicting that a shoe should have a value for size, but not for battery-type
at scale is a challenge. The need for an interpretable model is contingent on
(1) the lack of ground truth training data, (2) the need to utilise prior
information abou... | computer science |
7,350 | Generative Adversarial Networks for Electronic Health Records: A
Framework for Exploring and Evaluating Methods for Predicting Drug-Induced
Laboratory Test Trajectories | cs.LG | Generative Adversarial Networks (GANs) represent a promising class of
generative networks that combine neural networks with game theory. From
generating realistic images and videos to assisting musical creation, GANs are
transforming many fields of arts and sciences. However, their application to
healthcare has not bee... | computer science |
7,351 | Minimally Faithful Inversion of Graphical Models | stat.ML | Inference amortization methods allow the sharing of statistical strength
across related observations when learning to perform posterior inference.
Generally this requires the inversion of the dependency structure in the
generative model, as the modeller must design and learn a distribution to
approximate the posterior.... | computer science |
7,352 | Deep Learning with Permutation-invariant Operator for Multi-instance
Histopathology Classification | cs.LG | The computer-aided analysis of medical scans is a longstanding goal in the
medical imaging field. Currently, deep learning has became a dominant
methodology for supporting pathologists and radiologist. Deep learning
algorithms have been successfully applied to digital pathology and radiology,
nevertheless, there are st... | computer science |
7,353 | Group Sparse Bayesian Learning for Active Surveillance on Epidemic
Dynamics | stat.ML | Predicting epidemic dynamics is of great value in understanding and
controlling diffusion processes, such as infectious disease spread and
information propagation. This task is intractable, especially when surveillance
resources are very limited. To address the challenge, we study the problem of
active surveillance, i.... | computer science |
7,354 | Deep Learning Scaling is Predictable, Empirically | cs.LG | Deep learning (DL) creates impactful advances following a virtuous recipe:
model architecture search, creating large training data sets, and scaling
computation. It is widely believed that growing training sets and models should
improve accuracy and result in better products. As DL application domains grow,
we would li... | computer science |
7,355 | Deep Neural Network Architectures for Modulation Classification | cs.LG | In this work, we investigate the value of employing deep learning for the
task of wireless signal modulation recognition. Recently in [1], a framework
has been introduced by generating a dataset using GNU radio that mimics the
imperfections in a real wireless channel, and uses 10 different modulation
types. Further, a ... | computer science |
7,356 | Subject Selection on a Riemannian Manifold for Unsupervised
Cross-subject Seizure Detection | cs.LG | Inter-subject variability between individuals poses a challenge in
inter-subject brain signal analysis problems. A new algorithm for
subject-selection based on clustering covariance matrices on a Riemannian
manifold is proposed. After unsupervised selection of the subsets of relevant
subjects, data in a cluster is mapp... | computer science |
7,357 | Prediction-Constrained Topic Models for Antidepressant Recommendation | cs.LG | Supervisory signals can help topic models discover low-dimensional data
representations that are more interpretable for clinical tasks. We propose a
framework for training supervised latent Dirichlet allocation that balances two
goals: faithful generative explanations of high-dimensional data and accurate
prediction of... | computer science |
7,358 | A global feature extraction model for the effective computer aided
diagnosis of mild cognitive impairment using structural MRI images | stat.ML | Multiple modalities of biomarkers have been proved to be very sensitive in
assessing the progression of Alzheimer's disease (AD), and using these
modalities and machine learning algorithms, several approaches have been
proposed to assist in the early diagnosis of AD. Among the recent investigated
state-of-the-art appro... | computer science |
7,359 | Where Classification Fails, Interpretation Rises | cs.LG | An intriguing property of deep neural networks is their inherent
vulnerability to adversarial inputs, which significantly hinders their
application in security-critical domains. Most existing detection methods
attempt to use carefully engineered patterns to distinguish adversarial inputs
from their genuine counterparts... | computer science |
7,360 | Supervised Hashing based on Energy Minimization | cs.LG | Recently, supervised hashing methods have attracted much attention since they
can optimize retrieval speed and storage cost while preserving semantic
information. Because hashing codes learning is NP-hard, many methods resort to
some form of relaxation technique. But the performance of these methods can
easily deterior... | computer science |
7,361 | Short-term Mortality Prediction for Elderly Patients Using Medicare
Claims Data | stat.ML | Risk prediction is central to both clinical medicine and public health. While
many machine learning models have been developed to predict mortality, they are
rarely applied in the clinical literature, where classification tasks typically
rely on logistic regression. One reason for this is that existing machine
learning... | computer science |
7,362 | Learning Independent Causal Mechanisms | cs.LG | Statistical learning relies upon data sampled from a distribution, and we
usually do not care what actually generated it in the first place. From the
point of view of causal modeling, the structure of each distribution is induced
by physical mechanisms that give rise to dependencies between observables.
Mechanisms, how... | computer science |
7,363 | Vprop: Variational Inference using RMSprop | stat.ML | Many computationally-efficient methods for Bayesian deep learning rely on
continuous optimization algorithms, but the implementation of these methods
requires significant changes to existing code-bases. In this paper, we propose
Vprop, a method for Gaussian variational inference that can be implemented with
two minor c... | computer science |
7,364 | Adaptive Quantization for Deep Neural Network | cs.LG | In recent years Deep Neural Networks (DNNs) have been rapidly developed in
various applications, together with increasingly complex architectures. The
performance gain of these DNNs generally comes with high computational costs
and large memory consumption, which may not be affordable for mobile platforms.
Deep model q... | computer science |
7,365 | Stochastic Maximum Likelihood Optimization via Hypernetworks | stat.ML | This work explores maximum likelihood optimization of neural networks through
hypernetworks. A hypernetwork initializes the weights of another network, which
in turn can be employed for typical functional tasks such as regression and
classification. We optimize hypernetworks to directly maximize the conditional
likelih... | computer science |
7,366 | Episodic memory for continual model learning | cs.LG | Both the human brain and artificial learning agents operating in real-world
or comparably complex environments are faced with the challenge of online model
selection. In principle this challenge can be overcome: hierarchical Bayesian
inference provides a principled method for model selection and it converges on
the sam... | computer science |
7,367 | A dual framework for trace norm regularized low-rank tensor completion | cs.LG | One of the popular approaches for low-rank tensor completion is to use the
latent trace norm as a low-rank regularizer. However, most of the existing
works learn a sparse combination of tensors. In this work, we fill this gap by
proposing a variant of the latent trace norm which helps to learn a non-sparse
combination ... | computer science |
7,368 | Learning Sparse Neural Networks through $L_0$ Regularization | stat.ML | We propose a practical method for $L_0$ norm regularization for neural
networks: pruning the network during training by encouraging weights to become
exactly zero. Such regularization is interesting since (1) it can greatly speed
up training and inference, and (2) it can improve generalization. AIC and BIC,
well-known ... | computer science |
7,369 | Gaussian Process bandits with adaptive discretization | stat.ML | In this paper, the problem of maximizing a black-box function $f:\mathcal{X}
\to \mathbb{R}$ is studied in the Bayesian framework with a Gaussian Process
(GP) prior. In particular, a new algorithm for this problem is proposed, and
high probability bounds on its simple and cumulative regret are established.
The query po... | computer science |
7,370 | Deep linear neural networks with arbitrary loss: All local minima are
global | cs.LG | We consider deep linear networks with arbitrary differentiable loss. We
provide a short and elementary proof of the following fact: all local minima
are global minima if each hidden layer is wider than either the input or output
layer. | computer science |
7,371 | Learning Pain from Action Unit Combinations: A Weakly Supervised
Approach via Multiple Instance Learning | cs.LG | Patient pain can be detected highly reliably from facial expressions using a
set of facial muscle-based action units (AUs) defined by the Facial Action
Coding System (FACS). A key characteristic of facial expression of pain is the
simultaneous occurrence of pain-related AU combinations, whose automated
detection would ... | computer science |
7,372 | Learning a Generative Model for Validity in Complex Discrete Structures | stat.ML | Deep generative models have been successfully used to learn representations
for high-dimensional discrete spaces by representing discrete objects as
sequences, for which powerful sequence-based deep models can be employed.
Unfortunately, these techniques are significantly hindered by the fact that
these generative mode... | computer science |
7,373 | Differentially Private Dropout | stat.ML | Large data collections required for the training of neural networks often
contain sensitive information such as the medical histories of patients, and
the privacy of the training data must be preserved. In this paper, we introduce
a dropout technique that provides an elegant Bayesian interpretation to
dropout, and show... | computer science |
7,374 | A trans-disciplinary review of deep learning research for water
resources scientists | stat.ML | Deep learning (DL), a new-generation artificial neural network research, has
made profound strides in recent years. This review paper is intended to provide
water resources scientists with a simple technical overview, trans-disciplinary
progress update, and potentially inspirations about DL. Effective
architectures, mo... | computer science |
7,375 | SGAN: An Alternative Training of Generative Adversarial Networks | stat.ML | The Generative Adversarial Networks (GANs) have demonstrated impressive
performance for data synthesis, and are now used in a wide range of computer
vision tasks. In spite of this success, they gained a reputation for being
difficult to train, what results in a time-consuming and human-involved
development process to u... | computer science |
7,376 | Noisy Natural Gradient as Variational Inference | cs.LG | Variational Bayesian neural nets combine the flexibility of deep learning
with Bayesian uncertainty estimation. Unfortunately, there is a tradeoff
between cheap but simple variational families (e.g.~fully factorized) or
expensive and complicated inference procedures. We show that natural gradient
ascent with adaptive w... | computer science |
7,377 | Cost-sensitive detection with variational autoencoders for environmental
acoustic sensing | stat.ML | Environmental acoustic sensing involves the retrieval and processing of audio
signals to better understand our surroundings. While large-scale acoustic data
make manual analysis infeasible, they provide a suitable playground for machine
learning approaches. Most existing machine learning techniques developed for
enviro... | computer science |
7,378 | Gini-regularized Optimal Transport with an Application to
Spatio-Temporal Forecasting | stat.ML | Rapidly growing product lines and services require a finer-granularity
forecast that considers geographic locales. However the open question remains,
how to assess the quality of a spatio-temporal forecast? In this manuscript we
introduce a metric to evaluate spatio-temporal forecasts. This metric is based
on an Opti- ... | computer science |
7,379 | Differentially Private Variational Dropout | stat.ML | Deep neural networks with their large number of parameters are highly
flexible learning systems. The high flexibility in such networks brings with
some serious problems such as overfitting, and regularization is used to
address this problem. A currently popular and effective regularization
technique for controlling the... | computer science |
7,380 | AdaComp : Adaptive Residual Gradient Compression for Data-Parallel
Distributed Training | cs.LG | Highly distributed training of Deep Neural Networks (DNNs) on future compute
platforms (offering 100 of TeraOps/s of computational capacity) is expected to
be severely communication constrained. To overcome this limitation, new
gradient compression techniques are needed that are computationally friendly,
applicable to ... | computer science |
7,381 | RelNN: A Deep Neural Model for Relational Learning | stat.ML | Statistical relational AI (StarAI) aims at reasoning and learning in noisy
domains described in terms of objects and relationships by combining
probability with first-order logic. With huge advances in deep learning in the
current years, combining deep networks with first-order logic has been the
focus of several recen... | computer science |
7,382 | On Adaptive Estimation for Dynamic Bernoulli Bandits | stat.ML | The multi-armed bandit (MAB) problem is a classic example of the
exploration-exploitation dilemma. It is concerned with maximising the total
rewards for a gambler by sequentially pulling an arm from a multi-armed slot
machine where each arm is associated with a reward distribution. In static
MABs, the reward distributi... | computer science |
7,383 | Neumann Optimizer: A Practical Optimization Algorithm for Deep Neural
Networks | cs.LG | Progress in deep learning is slowed by the days or weeks it takes to train
large models. The natural solution of using more hardware is limited by
diminishing returns, and leads to inefficient use of additional resources. In
this paper, we present a large batch, stochastic optimization algorithm that is
both faster tha... | computer science |
7,384 | Cost-Sensitive Approach to Batch Size Adaptation for Gradient Descent | cs.LG | In this paper, we propose a novel approach to automatically determine the
batch size in stochastic gradient descent methods. The choice of the batch size
induces a trade-off between the accuracy of the gradient estimate and the cost
in terms of samples of each update. We propose to determine the batch size by
optimizin... | computer science |
7,385 | Capsule Network Performance on Complex Data | stat.ML | In recent years, convolutional neural networks (CNN) have played an important
role in the field of deep learning. Variants of CNN's have proven to be very
successful in classification tasks across different domains. However, there are
two big drawbacks to CNN's: their failure to take into account of important
spatial h... | computer science |
7,386 | DGCNN: Disordered Graph Convolutional Neural Network Based on the
Gaussian Mixture Model | cs.LG | Convolutional neural networks (CNNs) can be applied to graph similarity
matching, in which case they are called graph CNNs. Graph CNNs are attracting
increasing attention due to their effectiveness and efficiency. However, the
existing convolution approaches focus only on regular data forms and require
the transfer of ... | computer science |
7,387 | Gradient Normalization & Depth Based Decay For Deep Learning | cs.LG | In this paper we introduce a novel method of gradient normalization and decay
with respect to depth. Our method leverages the simple concept of normalizing
all gradients in a deep neural network, and then decaying said gradients with
respect to their depth in the network. Our proposed normalization and decay
techniques... | computer science |
7,388 | On Quadratic Penalties in Elastic Weight Consolidation | stat.ML | Elastic weight consolidation (EWC, Kirkpatrick et al, 2017) is a novel
algorithm designed to safeguard against catastrophic forgetting in neural
networks. EWC can be seen as an approximation to Laplace propagation (Eskin et
al, 2004), and this view is consistent with the motivation given by Kirkpatrick
et al (2017). In... | computer science |
7,389 | GibbsNet: Iterative Adversarial Inference for Deep Graphical Models | stat.ML | Directed latent variable models that formulate the joint distribution as
$p(x,z) = p(z) p(x \mid z)$ have the advantage of fast and exact sampling.
However, these models have the weakness of needing to specify $p(z)$, often
with a simple fixed prior that limits the expressiveness of the model.
Undirected latent variabl... | computer science |
7,390 | Outlier Detection by Consistent Data Selection Method | cs.LG | Often the challenge associated with tasks like fraud and spam detection[1] is
the lack of all likely patterns needed to train suitable supervised learning
models. In order to overcome this limitation, such tasks are attempted as
outlier or anomaly detection tasks. We also hypothesize that out- liers have
behavioral pat... | computer science |
7,391 | Transportation analysis of denoising autoencoders: a novel method for
analyzing deep neural networks | cs.LG | The feature map obtained from the denoising autoencoder (DAE) is investigated
by determining transportation dynamics of the DAE, which is a cornerstone for
deep learning. Despite the rapid development in its application, deep neural
networks remain analytically unexplained, because the feature maps are nested
and param... | computer science |
7,392 | Temporal Stability in Predictive Process Monitoring | cs.LG | Predictive business process monitoring is concerned with the analysis of
events produced during the execution of a business process in order to predict
as early as possible the final outcome of an ongoing case. Traditionally,
predictive process monitoring methods are optimized with respect to accuracy.
However, in envi... | computer science |
7,393 | Predicting Yelp Star Reviews Based on Network Structure with Deep
Learning | cs.LG | In this paper, we tackle the real-world problem of predicting Yelp
star-review rating based on business features (such as images, descriptions),
user features (average previous ratings), and, of particular interest, network
properties (which businesses has a user rated before). We compare multiple
models on different s... | computer science |
7,394 | CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced
Classification | cs.LG | Class imbalance classification is a challenging research problem in data
mining and machine learning, as most of the real-life datasets are often
imbalanced in nature. Existing learning algorithms maximise the classification
accuracy by correctly classifying the majority class, but misclassify the
minority class. Howev... | computer science |
7,395 | Integrated Model, Batch and Domain Parallelism in Training Neural
Networks | cs.LG | We propose a new integrated method of exploiting model, batch and domain
parallelism for the training of deep neural networks (DNNs) on large
distributed-memory computers using minibatch stochastic gradient descent (SGD).
Our goal is to find an efficient parallelization strategy for a fixed batch
size using $P$ process... | computer science |
7,396 | Practical Bayesian optimization in the presence of outliers | cs.LG | Inference in the presence of outliers is an important field of research as
outliers are ubiquitous and may arise across a variety of problems and domains.
Bayesian optimization is method that heavily relies on probabilistic inference.
This allows outstanding sample efficiency because the probabilistic machinery
provide... | computer science |
7,397 | Stochastic Low-Rank Bandits | cs.LG | Many problems in computer vision and recommender systems involve low-rank
matrices. In this work, we study the problem of finding the maximum entry of a
stochastic low-rank matrix from sequential observations. At each step, a
learning agent chooses pairs of row and column arms, and receives the noisy
product of their l... | computer science |
7,398 | Stability Selection for Structured Variable Selection | stat.ML | In variable or graph selection problems, finding a right-sized model or
controlling the number of false positives is notoriously difficult. Recently, a
meta-algorithm called Stability Selection was proposed that can provide
reliable finite-sample control of the number of false positives. Its benefits
were demonstrated ... | computer science |
7,399 | Exponential convergence of testing error for stochastic gradient methods | cs.LG | We consider binary classification problems with positive definite kernels and
square loss, and study the convergence rates of stochastic gradient methods. We
show that while the excess testing loss (squared loss) converges slowly to zero
as the number of observations (and thus iterations) goes to infinity, the
testing ... | computer science |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.