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7,100 | Hyperparameter Importance Across Datasets | stat.ML | With the advent of automated machine learning, automated hyperparameter
optimization methods are by now routinely used. However, this progress is not
yet matched by equal progress on automatic analyses that yield information
beyond performance-optimizing hyperparameter settings. In this work, we aim to
answer the follo... | computer science |
7,101 | Sparse Weighted Canonical Correlation Analysis | cs.LG | Given two data matrices $X$ and $Y$, sparse canonical correlation analysis
(SCCA) is to seek two sparse canonical vectors $u$ and $v$ to maximize the
correlation between $Xu$ and $Yv$. However, classical and sparse CCA models
consider the contribution of all the samples of data matrices and thus cannot
identify an unde... | computer science |
7,102 | Manifold regularization based on Nystr{ö}m type subsampling | stat.ML | In this paper, we study the Nystr{\"o}m type subsampling for large scale
kernel methods to reduce the computational complexities of big data. We discuss
the multi-penalty regularization scheme based on Nystr{\"o}m type subsampling
which is motivated from well-studied manifold regularization schemes. We
develop a theore... | computer science |
7,103 | Graph Convolutional Networks for Classification with a Structured Label
Space | cs.LG | It is a usual practice to ignore any structural information underlying
classes in multi-class classification. In this paper, we propose a graph
convolutional network (GCN) augmented neural network classifier to exploit a
known, underlying graph structure of labels. The proposed approach resembles an
(approximate) infer... | computer science |
7,104 | Burn-In Demonstrations for Multi-Modal Imitation Learning | cs.LG | Recent work on imitation learning has generated policies that reproduce
expert behavior from multi-modal data. However, past approaches have focused
only on recreating a small number of distinct, expert maneuvers, or have relied
on supervised learning techniques that produce unstable policies. This work
extends InfoGAI... | computer science |
7,105 | Dropout as a Low-Rank Regularizer for Matrix Factorization | cs.LG | Regularization for matrix factorization (MF) and approximation problems has
been carried out in many different ways. Due to its popularity in deep
learning, dropout has been applied also for this class of problems. Despite its
solid empirical performance, the theoretical properties of dropout as a
regularizer remain qu... | computer science |
7,106 | Robust Federated Learning Using ADMM in the Presence of Data Falsifying
Byzantines | cs.LG | In this paper, we consider the problem of federated (or decentralized)
learning using ADMM with multiple agents. We consider a scenario where a
certain fraction of agents (referred to as Byzantines) provide falsified data
to the system. In this context, we study the convergence behavior of the
decentralized ADMM algori... | computer science |
7,107 | Facial Keypoints Detection | stat.ML | Detect facial keypoints is a critical element in face recognition. However,
there is difficulty to catch keypoints on the face due to complex influences
from original images, and there is no guidance to suitable algorithms. In this
paper, we study different algorithms that can be applied to locate keyponits.
Specifical... | computer science |
7,108 | Information-Theoretic Representation Learning for Positive-Unlabeled
Classification | stat.ML | Recent advances in weakly supervised classification allow us to train a
classifier only from positive and unlabeled (PU) data. However, existing PU
classification methods typically require an accurate estimate of the
class-prior probability, which is a critical bottleneck particularly for
high-dimensional data. This pr... | computer science |
7,109 | Calibrated Boosting-Forest | stat.ML | Excellent ranking power along with well calibrated probability estimates are
needed in many classification tasks. In this paper, we introduce a technique,
Calibrated Boosting-Forest that captures both. This novel technique is an
ensemble of gradient boosting machines that can support both continuous and
binary labels. ... | computer science |
7,110 | A Geometric View of Optimal Transportation and Generative Model | cs.LG | In this work, we show the intrinsic relations between optimal transportation
and convex geometry, especially the variational approach to solve Alexandrov
problem: constructing a convex polytope with prescribed face normals and
volumes. This leads to a geometric interpretation to generative models, and
leads to a novel ... | computer science |
7,111 | Learning from Incomplete Ratings using Nonlinear Multi-layer
Semi-Nonnegative Matrix Factorization | cs.LG | Recommender systems problems witness a growing interest for finding better
learning algorithms for personalized information. Matrix factorization that
estimates the user liking for an item by taking an inner product on the latent
features of users and item have been widely studied owing to its better
accuracy and scala... | computer science |
7,112 | Large Scale Graph Learning from Smooth Signals | stat.ML | Graphs are a prevalent tool in data science, as they model the inherent
structure of the data. They have been used successfully in unsupervised and
semi-supervised learning. Typically they are constructed either by connecting
nearest samples, or by learning them from data, solving an optimization
problem. While graph l... | computer science |
7,113 | A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised
Learning | stat.ML | This paper takes a step towards temporal reasoning in a dynamically changing
video, not in the pixel space that constitutes its frames, but in a latent
space that describes the non-linear dynamics of the objects in its world. We
introduce the Kalman variational auto-encoder, a framework for unsupervised
learning of seq... | computer science |
7,114 | Stochastic Variance Reduction for Policy Gradient Estimation | cs.LG | Recent advances in policy gradient methods and deep learning have
demonstrated their applicability for complex reinforcement learning problems.
However, the variance of the performance gradient estimates obtained from the
simulation is often excessive, leading to poor sample efficiency. In this
paper, we apply the stoc... | computer science |
7,115 | Boosting Adversarial Attacks with Momentum | cs.LG | Deep neural networks are vulnerable to adversarial examples, which poses
security concerns on these algorithms due to the potentially severe
consequences. Adversarial attacks serve as an important surrogate to evaluate
the robustness of deep learning models before they are deployed. However, most
of existing adversaria... | computer science |
7,116 | On the challenges of learning with inference networks on sparse,
high-dimensional data | stat.ML | We study parameter estimation in Nonlinear Factor Analysis (NFA) where the
generative model is parameterized by a deep neural network. Recent work has
focused on learning such models using inference (or recognition) networks; we
identify a crucial problem when modeling large, sparse, high-dimensional
datasets -- underf... | computer science |
7,117 | Deep Gaussian Covariance Network | cs.LG | The correlation length-scale next to the noise variance are the most used
hyperparameters for the Gaussian processes. Typically, stationary covariance
functions are used, which are only dependent on the distances between input
points and thus invariant to the translations in the input space. The
optimization of the hyp... | computer science |
7,118 | Learning to Warm-Start Bayesian Hyperparameter Optimization | stat.ML | Hyperparameter optimization undergoes extensive evaluations of validation
errors in order to find its best configuration. Bayesian optimization is now
popular for hyperparameter optimization, since it reduces the number of
validation error evaluations required. Suppose that we are given a collection
of datasets on whic... | computer science |
7,119 | Combinatorial Penalties: Which structures are preserved by convex
relaxations? | cs.LG | We consider the homogeneous and the non-homogeneous convex relaxations for
combinatorial penalty functions defined on support sets. Our study identifies
key differences in the tightness of the resulting relaxations through the
notion of the lower combinatorial envelope of a set-function along with new
necessary conditi... | computer science |
7,120 | Smooth and Sparse Optimal Transport | stat.ML | Entropic regularization is quickly emerging as a new standard in optimal
transport (OT). It enables to cast the OT computation as a differentiable and
unconstrained convex optimization problem, which can be efficiently solved
using the Sinkhorn algorithm. However, entropy keeps the transportation plan
strictly positive... | computer science |
7,121 | S-Isomap++: Multi Manifold Learning from Streaming Data | stat.ML | Manifold learning based methods have been widely used for non-linear
dimensionality reduction (NLDR). However, in many practical settings, the need
to process streaming data is a challenge for such methods, owing to the high
computational complexity involved. Moreover, most methods operate under the
assumption that the... | computer science |
7,122 | On reducing sampling variance in covariate shift using control variates | cs.LG | Covariate shift classification problems can in principle be tackled by
importance-weighting training samples. However, the sampling variance of the
risk estimator is often scaled up dramatically by the weights. This means that
during cross-validation - when the importance-weighted risk is repeatedly
evaluated - subopti... | computer science |
7,123 | Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data
Analysis | cs.LG | An increasing number of sensors on mobile, Internet of things (IoT), and
wearable devices generate time-series measurements of physical activities.
Though access to the sensory data is critical to the success of many beneficial
applications such as health monitoring or activity recognition, a wide range of
potentially ... | computer science |
7,124 | Stochastic Weighted Function Norm Regularization | cs.LG | Deep neural networks (DNNs) have become increasingly important due to their
excellent empirical performance on a wide range of problems. However,
regularization is generally achieved by indirect means, largely due to the
complex set of functions defined by a network and the difficulty in measuring
function complexity. ... | computer science |
7,125 | Concept Drift Learning with Alternating Learners | cs.LG | Data-driven predictive analytics are in use today across a number of
industrial applications, but further integration is hindered by the requirement
of similarity among model training and test data distributions. This paper
addresses the need of learning from possibly nonstationary data streams, or
under concept drift,... | computer science |
7,126 | Meta-Learning via Feature-Label Memory Network | cs.LG | Deep learning typically requires training a very capable architecture using
large datasets. However, many important learning problems demand an ability to
draw valid inferences from small size datasets, and such problems pose a
particular challenge for deep learning. In this regard, various researches on
"meta-learning... | computer science |
7,127 | Binary Classification from Positive-Confidence Data | stat.ML | Reducing labeling costs in supervised learning is a critical issue in many
practical machine learning applications. In this paper, we consider
positive-confidence (Pconf) classification, the problem of training a binary
classifier only from positive data equipped with confidence. Pconf
classification can be regarded as... | computer science |
7,128 | Machine Learning as Statistical Data Assimilation | cs.LG | We identify a strong equivalence between neural network based machine
learning (ML) methods and the formulation of statistical data assimilation
(DA), known to be a problem in statistical physics. DA, as used widely in
physical and biological sciences, systematically transfers information in
observations to a model of ... | computer science |
7,129 | Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and
Risk-sensitive Learning | stat.ML | Bayesian neural networks with latent variables (BNNs+LVs) are scalable and
flexible probabilistic models: They account for uncertainty in the estimation
of the network weights and, by making use of latent variables, they can capture
complex noise patterns in the data. In this work, we show how to separate these
two for... | computer science |
7,130 | Power Plant Performance Modeling with Concept Drift | cs.LG | Power plant is a complex and nonstationary system for which the traditional
machine learning modeling approaches fall short of expectations. The
ensemble-based online learning methods provide an effective way to continuously
learn from the dynamic environment and autonomously update models to respond to
environmental c... | computer science |
7,131 | Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor
Train Decomposition | cs.LG | We propose a method (TT-GP) for approximate inference in Gaussian Process
(GP) models. We build on previous scalable GP research including stochastic
variational inference based on inducing inputs, kernel interpolation, and
structure exploiting algebra. The key idea of our method is to use Tensor Train
decomposition fo... | computer science |
7,132 | Differentially Private Empirical Risk Minimization with Input
Perturbation | stat.ML | We propose a novel framework for the differentially private ERM, input
perturbation. Existing differentially private ERM implicitly assumed that the
data contributors submit their private data to a database expecting that the
database invokes a differentially private mechanism for publication of the
learned model. In i... | computer science |
7,133 | Distributed Deep Transfer Learning by Basic Probability Assignment | cs.LG | Transfer learning is a popular practice in deep neural networks, but
fine-tuning of large number of parameters is a hard task due to the complex
wiring of neurons between splitting layers and imbalance distributions of data
in pretrained and transferred domains. The reconstruction of the original
wiring for the target ... | computer science |
7,134 | Unified Backpropagation for Multi-Objective Deep Learning | cs.LG | A common practice in most of deep convolutional neural architectures is to
employ fully-connected layers followed by Softmax activation to minimize
cross-entropy loss for the sake of classification. Recent studies show that
substitution or addition of the Softmax objective to the cost functions of
support vector machin... | computer science |
7,135 | Finite-dimensional Gaussian approximation with linear inequality
constraints | stat.ML | Introducing inequality constraints in Gaussian process (GP) models can lead
to more realistic uncertainties in learning a great variety of real-world
problems. We consider the finite-dimensional Gaussian approach from Maatouk and
Bay (2017) which can satisfy inequality conditions everywhere (either
boundedness, monoton... | computer science |
7,136 | Dynamic classifier chains for multi-label learning | cs.LG | In this paper, we deal with the task of building a dynamic ensemble of chain
classifiers for multi-label classification. To do so, we proposed two concepts
of classifier chains algorithms that are able to change label order of the
chain without rebuilding the entire model. Such modes allows anticipating the
instance-sp... | computer science |
7,137 | A Tight Excess Risk Bound via a Unified
PAC-Bayesian-Rademacher-Shtarkov-MDL Complexity | cs.LG | We present a novel notion of complexity that interpolates between and
generalizes some classic existing complexity notions in learning theory: for
estimators like empirical risk minimization (ERM) with arbitrary bounded
losses, it is upper bounded in terms of data-independent Rademacher complexity;
for generalized Baye... | computer science |
7,138 | Learning Discrete Weights Using the Local Reparameterization Trick | cs.LG | Recent breakthroughs in computer vision make use of large deep neural
networks, utilizing the substantial speedup offered by GPUs. For applications
running on limited hardware, however, high precision real-time processing can
still be a challenge. One approach to solving this problem is training networks
with binary or... | computer science |
7,139 | Towards Black-box Iterative Machine Teaching | stat.ML | In this paper, we make an important step towards the black-box machine
teaching by considering the cross-space teaching setting, where the teacher and
the learner use different feature representations and the teacher can not fully
observe the learner's model. In such scenario, we study how the teacher is
still able to ... | computer science |
7,140 | Zeroth-Order Online Alternating Direction Method of Multipliers:
Convergence Analysis and Applications | stat.ML | In this paper, we design and analyze a new zeroth-order online algorithm,
namely, the zeroth-order online alternating direction method of multipliers
(ZOO-ADMM), which enjoys dual advantages of being gradient-free operation and
employing the ADMM to accommodate complex structured regularizers. Compared to
the first-ord... | computer science |
7,141 | Online Boosting Algorithms for Multi-label Ranking | stat.ML | We consider the multi-label ranking approach to multi-label learning.
Boosting is a natural method for multi-label ranking as it aggregates weak
predictions through majority votes, which can be directly used as scores to
produce a ranking of the labels. We design online boosting algorithms with
provable loss bounds for... | computer science |
7,142 | AutoEncoder Inspired Unsupervised Feature Selection | cs.LG | High-dimensional data in many areas such as computer vision and machine
learning tasks brings in computational and analytical difficulty. Feature
selection which selects a subset from observed features is a widely used
approach for improving performance and effectiveness of machine learning models
with high-dimensional... | computer science |
7,143 | Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At
Every Step | stat.ML | Generative adversarial networks (GANs) are a family of generative models that
do not minimize a single training criterion. Unlike other generative models,
the data distribution is learned via a game between a generator (the generative
model) and a discriminator (a teacher providing training signal) that each
minimize t... | computer science |
7,144 | A Unified Framework for Long Range and Cold Start Forecasting of
Seasonal Profiles in Time Series | stat.ML | Providing long-range forecasts is a fundamental challenge in time series
modeling, which is only compounded by the challenge of having to form such
forecasts when a time series has never previously been observed. The latter
challenge is the time series version of the cold-start problem seen in
recommender systems which... | computer science |
7,145 | Convolutional Neural Knowledge Graph Learning | cs.LG | Previous models for learning entity and relationship embeddings of knowledge
graphs such as TransE, TransH, and TransR aim to explore new links based on
learned representations. However, these models interpret relationships as
simple translations on entity embeddings. In this paper, we try to learn more
complex connect... | computer science |
7,146 | Interpretable Deep Learning applied to Plant Stress Phenotyping | stat.ML | Availability of an explainable deep learning model that can be applied to
practical real world scenarios and in turn, can consistently, rapidly and
accurately identify specific and minute traits in applicable fields of
biological sciences, is scarce. Here we consider one such real world example
viz., accurate identific... | computer science |
7,147 | Improving Accuracy of Nonparametric Transfer Learning via Vector
Segmentation | cs.LG | Transfer learning using deep neural networks as feature extractors has become
increasingly popular over the past few years. It allows to obtain
state-of-the-art accuracy on datasets too small to train a deep neural network
on its own, and it provides cutting edge descriptors that, combined with
nonparametric learning m... | computer science |
7,148 | A Correction Method of a Binary Classifier Applied to Multi-label
Pairwise Models | cs.LG | In this work, we addressed the issue of applying a stochastic classifier and
a local, fuzzy confusion matrix under the framework of multi-label
classification. We proposed a novel solution to the problem of correcting label
pairwise ensembles. The main step of the correction procedure is to compute
classifier- specific... | computer science |
7,149 | Classification on Large Networks: A Quantitative Bound via Motifs and
Graphons | cs.LG | When each data point is a large graph, graph statistics such as densities of
certain subgraphs (motifs) can be used as feature vectors for machine learning.
While intuitive, motif counts are expensive to compute and difficult to work
with theoretically. Via graphon theory, we give an explicit quantitative bound
for the... | computer science |
7,150 | Conformal predictive distributions with kernels | cs.LG | This paper reviews the checkered history of predictive distributions in
statistics and discusses two developments, one from recent literature and the
other new. The first development is bringing predictive distributions into
machine learning, whose early development was so deeply influenced by two
remarkable groups at ... | computer science |
7,151 | The Heterogeneous Ensembles of Standard Classification Algorithms
(HESCA): the Whole is Greater than the Sum of its Parts | cs.LG | Building classification models is an intrinsically practical exercise that
requires many design decisions prior to deployment. We aim to provide some
guidance in this decision making process. Specifically, given a classification
problem with real valued attributes, we consider which classifier or family of
classifiers ... | computer science |
7,152 | Deep Neural Networks | stat.ML | Deep Neural Networks (DNNs) are universal function approximators providing
state-of- the-art solutions on wide range of applications. Common perceptual
tasks such as speech recognition, image classification, and object tracking are
now commonly tackled via DNNs. Some fundamental problems remain: (1) the lack
of a mathe... | computer science |
7,153 | GeoSeq2Seq: Information Geometric Sequence-to-Sequence Networks | stat.ML | The Fisher information metric is an important foundation of information
geometry, wherein it allows us to approximate the local geometry of a
probability distribution. Recurrent neural networks such as the
Sequence-to-Sequence (Seq2Seq) networks that have lately been used to yield
state-of-the-art performance on speech... | computer science |
7,154 | mixup: Beyond Empirical Risk Minimization | cs.LG | Large deep neural networks are powerful, but exhibit undesirable behaviors
such as memorization and sensitivity to adversarial examples. In this work, we
propose mixup, a simple learning principle to alleviate these issues. In
essence, mixup trains a neural network on convex combinations of pairs of
examples and their ... | computer science |
7,155 | InterpNET: Neural Introspection for Interpretable Deep Learning | stat.ML | Humans are able to explain their reasoning. On the contrary, deep neural
networks are not. This paper attempts to bridge this gap by introducing a new
way to design interpretable neural networks for classification, inspired by
physiological evidence of the human visual system's inner-workings. This paper
proposes a neu... | computer science |
7,156 | Maximum Principle Based Algorithms for Deep Learning | cs.LG | The continuous dynamical system approach to deep learning is explored in
order to devise alternative frameworks for training algorithms. Training is
recast as a control problem and this allows us to formulate necessary
optimality conditions in continuous time using the Pontryagin's maximum
principle (PMP). A modificati... | computer science |
7,157 | Rethinking generalization requires revisiting old ideas: statistical
mechanics approaches and complex learning behavior | cs.LG | We describe an approach to understand the peculiar and counterintuitive
generalization properties of deep neural networks. The approach involves going
beyond worst-case theoretical capacity control frameworks that have been
popular in machine learning in recent years to revisit old ideas in the
statistical mechanics of... | computer science |
7,158 | Big Data Classification Using Augmented Decision Trees | stat.ML | We present an algorithm for classification tasks on big data. Experiments
conducted as part of this study indicate that the algorithm can be as accurate
as ensemble methods such as random forests or gradient boosted trees. Unlike
ensemble methods, the models produced by the algorithm can be easily
interpreted. The algo... | computer science |
7,159 | Weighting Scheme for a Pairwise Multi-label Classifier Based on the
Fuzzy Confusion Matrix | cs.LG | In this work we addressed the issue of applying a stochastic classifier and a
local, fuzzy confusion matrix under the framework of multi-label
classification. We proposed a novel solution to the problem of correcting label
pairwise ensembles. The main step of the correction procedure is to compute
classifier-specific c... | computer science |
7,160 | Joint Screening Tests for LASSO | cs.LG | This paper focusses on "safe" screening techniques for the LASSO problem.
Motivated by the need for low-complexity algorithms, we propose a new approach,
dubbed "joint" screening test, allowing to screen a set of atoms by carrying
out one single test. The approach is particularized to two different sets of
atoms, respe... | computer science |
7,161 | The Error Probability of Random Fourier Features is Dimensionality
Independent | cs.LG | We show that the error probability of reconstructing kernel matrices from
Random Fourier Features for the Gaussian kernel function is at most
$\mathcal{O}(R^{2/3} \exp(-D))$, where $D$ is the number of random features and
$R$ is the diameter of the data domain. We also provide an
information-theoretic method-independen... | computer science |
7,162 | Generalization Tower Network: A Novel Deep Neural Network Architecture
for Multi-Task Learning | cs.LG | Deep learning (DL) advances state-of-the-art reinforcement learning (RL), by
incorporating deep neural networks in learning representations from the input
to RL. However, the conventional deep neural network architecture is limited in
learning representations for multi-task RL (MT-RL), as multiple tasks can refer
to di... | computer science |
7,163 | Not-So-Random Features | cs.LG | We propose a principled method for kernel learning, which relies on a
Fourier-analytic characterization of translation-invariant or
rotation-invariant kernels. Our method produces a sequence of feature maps,
iteratively refining the SVM margin. We provide rigorous guarantees for
optimality and generalization, interpret... | computer science |
7,164 | A Self-Training Method for Semi-Supervised GANs | cs.LG | Since the creation of Generative Adversarial Networks (GANs), much work has
been done to improve their training stability, their generated image quality,
their range of application but nearly none of them explored their self-training
potential. Self-training has been used before the advent of deep learning in
order to ... | computer science |
7,165 | Revisit Fuzzy Neural Network: Demystifying Batch Normalization and ReLU
with Generalized Hamming Network | cs.LG | We revisit fuzzy neural network with a cornerstone notion of generalized
hamming distance, which provides a novel and theoretically justified framework
to re-interpret many useful neural network techniques in terms of fuzzy logic.
In particular, we conjecture and empirically illustrate that, the celebrated
batch normal... | computer science |
7,166 | The Implicit Bias of Gradient Descent on Separable Data | stat.ML | We show that gradient descent on an unregularized logistic regression
problem, for linearly separable datasets, converges to the direction of the
max-margin (hard margin SVM) solution. The result generalizes also to other
monotone decreasing loss functions with an infimum at infinity, to multi-class
problems, and to tr... | computer science |
7,167 | Topology Adaptive Graph Convolutional Networks | cs.LG | Spectral graph convolutional neural networks (CNNs) require approximation to
the convolution to alleviate the computational complexity, resulting in
performance loss. This paper proposes the topology adaptive graph convolutional
network (TAGCN), a novel graph convolutional network defined in the vertex
domain. We provi... | computer science |
7,168 | Trainable back-propagated functional transfer matrices | cs.LG | Connections between nodes of fully connected neural networks are usually
represented by weight matrices. In this article, functional transfer matrices
are introduced as alternatives to the weight matrices: Instead of using real
weights, a functional transfer matrix uses real functions with trainable
parameters to repre... | computer science |
7,169 | Efficient Localized Inference for Large Graphical Models | stat.ML | We propose a new localized inference algorithm for answering marginalization
queries in large graphical models with the correlation decay property. Given a
query variable and a large graphical model, we define a much smaller model in a
local region around the query variable in the target model so that the marginal
dist... | computer science |
7,170 | Crime incidents embedding using restricted Boltzmann machines | stat.ML | We present a new approach for detecting related crime series, by unsupervised
learning of the latent feature embeddings from narratives of crime record via
the Gaussian-Bernoulli Restricted Boltzmann Machines (RBM). This is a
drastically different approach from prior work on crime analysis, which
typically considers on... | computer science |
7,171 | Interpretation of Neural Networks is Fragile | stat.ML | In order for machine learning to be deployed and trusted in many
applications, it is crucial to be able to reliably explain why the machine
learning algorithm makes certain predictions. For example, if an algorithm
classifies a given pathology image to be a malignant tumor, then the doctor may
need to know which parts ... | computer science |
7,172 | Stochastic Zeroth-order Optimization in High Dimensions | stat.ML | We consider the problem of optimizing a high-dimensional convex function
using stochastic zeroth-order queries. Under sparsity assumptions on the
gradients or function values, we present two algorithms: a successive
component/feature selection algorithm and a noisy mirror descent algorithm
using Lasso gradient estimate... | computer science |
7,173 | Stochastic Training of Graph Convolutional Networks with Variance
Reduction | stat.ML | Graph convolutional networks (GCNs) are powerful deep neural networks for
graph-structured data. However, GCN computes the representation of a node
recursively from its neighbors, making the receptive field size grow
exponentially with the number of layers. Previous attempts on reducing the
receptive field size by subs... | computer science |
7,174 | Weight Initialization of Deep Neural Networks(DNNs) using Data
Statistics | cs.LG | Deep neural networks (DNNs) form the backbone of almost every
state-of-the-art technique in the fields such as computer vision, speech
processing, and text analysis. The recent advances in computational technology
have made the use of DNNs more practical. Despite the overwhelming performances
by DNN and the advances in... | computer science |
7,175 | Certifiable Distributional Robustness with Principled Adversarial
Training | stat.ML | Neural networks are vulnerable to adversarial examples and researchers have
proposed many heuristic attack and defense mechanisms. We take the principled
view of distributionally robust optimization, which guarantees performance
under adversarial input perturbations. By considering a Lagrangian penalty
formulation of p... | computer science |
7,176 | Variational Continual Learning | stat.ML | This paper develops variational continual learning (VCL), a simple but
general framework for continual learning that fuses online variational
inference (VI) and recent advances in Monte Carlo VI for neural networks. The
framework can successfully train both deep discriminative models and deep
generative models in compl... | computer science |
7,177 | On the Consistency of Quick Shift | stat.ML | Quick Shift is a popular mode-seeking and clustering algorithm. We present
finite sample statistical consistency guarantees for Quick Shift on mode and
cluster recovery under mild distributional assumptions. We then apply our
results to construct a consistent modal regression algorithm. | computer science |
7,178 | How deep learning works --The geometry of deep learning | cs.LG | Why and how that deep learning works well on different tasks remains a
mystery from a theoretical perspective. In this paper we draw a geometric
picture of the deep learning system by finding its analogies with two existing
geometric structures, the geometry of quantum computations and the geometry of
the diffeomorphic... | computer science |
7,179 | Unifying Value Iteration, Advantage Learning, and Dynamic Policy
Programming | stat.ML | Approximate dynamic programming algorithms, such as approximate value
iteration, have been successfully applied to many complex reinforcement
learning tasks, and a better approximate dynamic programming algorithm is
expected to further extend the applicability of reinforcement learning to
various tasks. In this paper w... | computer science |
7,180 | Fast Linear Model for Knowledge Graph Embeddings | stat.ML | This paper shows that a simple baseline based on a Bag-of-Words (BoW)
representation learns surprisingly good knowledge graph embeddings. By casting
knowledge base completion and question answering as supervised classification
problems, we observe that modeling co-occurences of entities and relations
leads to state-of-... | computer science |
7,181 | Convergence Rates of Latent Topic Models Under Relaxed Identifiability
Conditions | stat.ML | In this paper we study the frequentist convergence rate for the Latent
Dirichlet Allocation (Blei et al., 2003) topic models. We show that the maximum
likelihood estimator converges to one of the finitely many equivalent
parameters in Wasserstein's distance metric at a rate of $n^{-1/4}$ without
assuming separability o... | computer science |
7,182 | Action-depedent Control Variates for Policy Optimization via Stein's
Identity | stat.ML | Policy gradient methods have achieved remarkable successes in solving
challenging reinforcement learning problems. However, it still often suffers
from the large variance issue on policy gradient estimation, which leads to
poor sample efficiency during training. In this work, we propose a control
variate method to effe... | computer science |
7,183 | Critical Points of Neural Networks: Analytical Forms and Landscape
Properties | stat.ML | Due to the success of deep learning to solving a variety of challenging
machine learning tasks, there is a rising interest in understanding loss
functions for training neural networks from a theoretical aspect. Particularly,
the properties of critical points and the landscape around them are of
importance to determine ... | computer science |
7,184 | Empirical analysis of non-linear activation functions for Deep Neural
Networks in classification tasks | cs.LG | We provide an overview of several non-linear activation functions in a neural
network architecture that have proven successful in many machine learning
applications. We conduct an empirical analysis on the effectiveness of using
these function on the MNIST classification task, with the aim of clarifying
which functions... | computer science |
7,185 | Algorithmic learning of probability distributions from random data in
the limit | cs.LG | We study the problem of identifying a probability distribution for some given
randomly sampled data in the limit, in the context of algorithmic learning
theory as proposed recently by Vinanyi and Chater. We show that there exists a
computable partial learner for the computable probability measures, while by
Bienvenu, M... | computer science |
7,186 | Tensor Regression Meets Gaussian Processes | cs.LG | Low-rank tensor regression, a new model class that learns high-order
correlation from data, has recently received considerable attention. At the
same time, Gaussian processes (GP) are well-studied machine learning models for
structure learning. In this paper, we demonstrate interesting connections
between the two, espe... | computer science |
7,187 | Semantic Interpolation in Implicit Models | cs.LG | In implicit models, one often interpolates between sampled points in latent
space. As we show in this paper, care needs to be taken to match-up the
distributional assumptions on code vectors with the geometry of the
interpolating paths. Otherwise, typical assumptions about the quality and
semantics of in-between points... | computer science |
7,188 | Flexible Prior Distributions for Deep Generative Models | cs.LG | We consider the problem of training generative models with deep neural
networks as generators, i.e. to map latent codes to data points. Whereas the
dominant paradigm combines simple priors over codes with complex deterministic
models, we argue that it might be advantageous to use more flexible code
distributions. We de... | computer science |
7,189 | Conditional Variance Penalties and Domain Shift Robustness | stat.ML | When training a deep network for image classification, one can broadly
distinguish between two types of latent features of images that will drive the
classification. Following the notation of Gong et al. (2016), we can divide
latent features into (i) "core" features $X^\text{core}$ whose distribution
$X^\text{core}\ver... | computer science |
7,190 | Compact Multi-Class Boosted Trees | stat.ML | Gradient boosted decision trees are a popular machine learning technique, in
part because of their ability to give good accuracy with small models. We
describe two extensions to the standard tree boosting algorithm designed to
increase this advantage. The first improvement extends the boosting formalism
from scalar-val... | computer science |
7,191 | TF Boosted Trees: A scalable TensorFlow based framework for gradient
boosting | stat.ML | TF Boosted Trees (TFBT) is a new open-sourced frame-work for the distributed
training of gradient boosted trees. It is based on TensorFlow, and its
distinguishing features include a novel architecture, automatic loss
differentiation, layer-by-layer boosting that results in smaller ensembles and
faster prediction, princ... | computer science |
7,192 | Sampling and Reconstruction of Graph Signals via Weak Submodularity and
Semidefinite Relaxation | stat.ML | We study the problem of sampling a bandlimited graph signal in the presence
of noise, where the objective is to select a node subset of prescribed
cardinality that minimizes the signal reconstruction mean squared error (MSE).
To that end, we formulate the task at hand as the minimization of MSE subject
to binary constr... | computer science |
7,193 | Deep Neural Networks as Gaussian Processes | stat.ML | It has long been known that a single-layer fully-connected neural network
with an i.i.d. prior over its parameters is equivalent to a Gaussian process
(GP), in the limit of infinite network width. This correspondence enables exact
Bayesian inference for infinite width neural networks on regression tasks by
means of eva... | computer science |
7,194 | Stochastic Variational Inference for Fully Bayesian Sparse Gaussian
Process Regression Models | cs.LG | This paper presents a novel variational inference framework for deriving a
family of Bayesian sparse Gaussian process regression (SGPR) models whose
approximations are variationally optimal with respect to the full-rank GPR
model enriched with various corresponding correlation structures of the
observation noises.
Ou... | computer science |
7,195 | Active Tolerant Testing | stat.ML | In this work, we give the first algorithms for tolerant testing of nontrivial
classes in the active model: estimating the distance of a target function to a
hypothesis class C with respect to some arbitrary distribution D, using only a
small number of label queries to a polynomial-sized pool of unlabeled examples
drawn... | computer science |
7,196 | Attacking Binarized Neural Networks | cs.LG | Neural networks with low-precision weights and activations offer compelling
efficiency advantages over their full-precision equivalents. The two most
frequently discussed benefits of quantization are reduced memory consumption,
and a faster forward pass when implemented with efficient bitwise operations.
We propose a t... | computer science |
7,197 | Fixing a Broken ELBO | cs.LG | Recent work in unsupervised representation learning has focused on learning
deep directed latent-variable models. Fitting these models by maximizing the
marginal likelihood or evidence is typically intractable, thus a common
approximation is to maximize the evidence lower bound (ELBO) instead. However,
maximum likeliho... | computer science |
7,198 | Candidates v.s. Noises Estimation for Large Multi-Class Classification
Problem | stat.ML | This paper proposes a method for multi-class classification problems, where
the number of classes $K$ is large. The method, referred to as {\em Candidates
v.s. Noises Estimation} (CANE), selects a small subset of candidate classes and
samples the remaining classes. We show that CANE is always consistent and
computation... | computer science |
7,199 | Concave losses for robust dictionary learning | cs.LG | Traditional dictionary learning methods are based on quadratic convex loss
function and thus are sensitive to outliers. In this paper, we propose a
generic framework for robust dictionary learning based on concave losses. We
provide results on composition of concave functions, notably regarding
super-gradient computati... | computer science |
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