pwc-archive/papers-with-abstracts huggingface dataset:
Deep Variational Reinforcement Learning for POMDPs
Authors: Maximilian Igl, Luisa Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson
Date: 2018-06-06 00:00:00
Proceeding: ICML 2018 7
Tasks: Decision Making, Inductive Bias, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Many real-world sequential decision making problems are partially observable
by nature, and the environment model is typically unknown. Consequently, there
is great need for reinforcement learning methods that can tackle such problems
given only a stream of incomplete and noisy observations. In this paper, we
propose deep variational reinforcement learning (DVRL), which introduces an
inductive bias that allows an agent to learn a generative model of the
environment and perform inference in that model to effectively aggregate the
available information. We develop an n-step approximation to the evidence lower
bound (ELBO), allowing the model to be trained jointly with the policy. This
ensures that the latent state representation is suitable for the control task.
In experiments on Mountain Hike and flickering Atari we show that our method
outperforms previous approaches relying on recurrent neural networks to encode
the past.
Graphical posterior predictive classifier: Bayesian model averaging with particle Gibbs
Authors: Tatjana Pavlenko, Felix Leopoldo Rios
Date: 2017-07-21 00:00:00
Tasks: Model Selection

In this study, we present a multi-class graphical Bayesian predictive
classifier that incorporates the uncertainty in the model selection into the
standard Bayesian formalism. For each class, the dependence structure
underlying the observed features is represented by a set of decomposable
Gaussian graphical models. Emphasis is then placed on the Bayesian model
averaging which takes full account of the class-specific model uncertainty by
averaging over the posterior graph model probabilities. An explicit evaluation
of the model probabilities is well known to be infeasible. To address this
issue, we consider the particle Gibbs strategy of Olsson et al. (2018b) for
posterior sampling from decomposable graphical models which utilizes the
Christmas tree algorithm of Olsson et al. (2018a) as proposal kernel. We also
derive a strong hyper Markov law which we call the hyper normal Wishart law
that allow to perform the resultant Bayesian calculations locally. The proposed
predictive graphical classifier reveals superior performance compared to the
ordinary Bayesian predictive rule that does not account for the model
uncertainty, as well as to a number of out-of-the-box classifiers.
Action4D: Real-time Action Recognition in the Crowd and Clutter
Authors: Quanzeng You, Hao Jiang
Date: 2018-06-06 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Recognizing every person's action in a crowded and cluttered environment is a
challenging task. In this paper, we propose a real-time action recognition
method, Action4D, which gives reliable and accurate results in the real-world
settings. We propose to tackle the action recognition problem using a holistic
4D "scan" of a cluttered scene to include every detail about the people and
environment. Recognizing multiple people's actions in the cluttered 4D
representation is a new problem. In this paper, we propose novel methods to
solve this problem. We propose a new method to track people in 4D, which can
reliably detect and follow each person in real time. We propose a new deep
neural network, the Action4D-Net, to recognize the action of each tracked
person. The Action4D-Net's novel structure uses both the global feature and the
focused attention to achieve state-of-the-art result. Our real-time method is
invariant to camera view angles, resistant to clutter and able to handle crowd.
The experimental results show that the proposed method is fast, reliable and
accurate. Our method paves the way to action recognition in the real-world
applications and is ready to be deployed to enable smart homes, smart factories
and smart stores.
Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Authors: Brenden M. Lake, Marco Baroni
Date: 2017-10-31 00:00:00
Proceeding: ICML 2018 7
Tasks: Machine Translation, Translation, Zero-shot Generalization

Humans can understand and produce new utterances effortlessly, thanks to
their compositional skills. Once a person learns the meaning of a new verb
"dax," he or she can immediately understand the meaning of "dax twice" or "sing
and dax." In this paper, we introduce the SCAN domain, consisting of a set of
simple compositional navigation commands paired with the corresponding action
sequences. We then test the zero-shot generalization capabilities of a variety
of recurrent neural networks (RNNs) trained on SCAN with sequence-to-sequence
methods. We find that RNNs can make successful zero-shot generalizations when
the differences between training and test commands are small, so that they can
apply "mix-and-match" strategies to solve the task. However, when
generalization requires systematic compositional skills (as in the "dax"
example above), RNNs fail spectacularly. We conclude with a proof-of-concept
experiment in neural machine translation, suggesting that lack of systematicity
might be partially responsible for neural networks' notorious training data
thirst.
On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks
Authors: Jeremias Sulam, Aviad Aberdam, Amir Beck, Michael Elad
Date: 2018-06-02 00:00:00

Parsimonious representations are ubiquitous in modeling and processing
information. Motivated by the recent Multi-Layer Convolutional Sparse Coding
(ML-CSC) model, we herein generalize the traditional Basis Pursuit problem to a
multi-layer setting, introducing similar sparse enforcing penalties at
different representation layers in a symbiotic relation between synthesis and
analysis sparse priors. We explore different iterative methods to solve this
new problem in practice, and we propose a new Multi-Layer Iterative Soft
Thresholding Algorithm (ML-ISTA), as well as a fast version (ML-FISTA). We show
that these nested first order algorithms converge, in the sense that the
function value of near-fixed points can get arbitrarily close to the solution
of the original problem.
  We further show how these algorithms effectively implement particular
recurrent convolutional neural networks (CNNs) that generalize feed-forward
ones without introducing any parameters. We present and analyze different
architectures resulting unfolding the iterations of the proposed pursuit
algorithms, including a new Learned ML-ISTA, providing a principled way to
construct deep recurrent CNNs. Unlike other similar constructions, these
architectures unfold a global pursuit holistically for the entire network. We
demonstrate the emerging constructions in a supervised learning setting,
consistently improving the performance of classical CNNs while maintaining the
number of parameters constant.
Human-aided Multi-Entity Bayesian Networks Learning from Relational Data
Authors: Cheol Young Park, Kathryn Blackmond Laskey
Date: 2018-06-06 00:00:00
Tasks: BIG-bench Machine Learning

An Artificial Intelligence (AI) system is an autonomous system which emulates
human mental and physical activities such as Observe, Orient, Decide, and Act,
called the OODA process. An AI system performing the OODA process requires a
semantically rich representation to handle a complex real world situation and
ability to reason under uncertainty about the situation. Multi-Entity Bayesian
Networks (MEBNs) combines First-Order Logic with Bayesian Networks for
representing and reasoning about uncertainty in complex, knowledge-rich
domains. MEBN goes beyond standard Bayesian networks to enable reasoning about
an unknown number of entities interacting with each other in various types of
relationships, a key requirement for the OODA process of an AI system. MEBN
models have heretofore been constructed manually by a domain expert. However,
manual MEBN modeling is labor-intensive and insufficiently agile. To address
these problems, an efficient method is needed for MEBN modeling. One of the
methods is to use machine learning to learn a MEBN model in whole or in part
from data. In the era of Big Data, data-rich environments, characterized by
uncertainty and complexity, have become ubiquitous. The larger the data sample
is, the more accurate the results of the machine learning approach can be.
Therefore, machine learning has potential to improve the quality of MEBN models
as well as the effectiveness for MEBN modeling. In this research, we study a
MEBN learning framework to develop a MEBN model from a combination of domain
expert's knowledge and data. To evaluate the MEBN learning framework, we
conduct an experiment to compare the MEBN learning framework and the existing
manual MEBN modeling in terms of development efficiency.
Finding Convincing Arguments Using Scalable Bayesian Preference Learning
Authors: Edwin Simpson, Iryna Gurevych
Date: 2018-06-06 00:00:00
Proceeding: TACL 2018 1
Tasks: Active Learning, Variational Inference, Word Embeddings

We introduce a scalable Bayesian preference learning method for identifying
convincing arguments in the absence of gold-standard rat- ings or rankings. In
contrast to previous work, we avoid the need for separate methods to perform
quality control on training data, predict rankings and perform pairwise
classification. Bayesian approaches are an effective solution when faced with
sparse or noisy training data, but have not previously been used to identify
convincing arguments. One issue is scalability, which we address by developing
a stochastic variational inference method for Gaussian process (GP) preference
learning. We show how our method can be applied to predict argument
convincingness from crowdsourced data, outperforming the previous
state-of-the-art, particularly when trained with small amounts of unreliable
data. We demonstrate how the Bayesian approach enables more effective active
learning, thereby reducing the amount of data required to identify convincing
arguments for new users and domains. While word embeddings are principally used
with neural networks, our results show that word embeddings in combination with
linguistic features also benefit GPs when predicting argument convincingness.
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
Authors: Kyle Hundman, Valentino Constantinou, Christopher Laporte, Ian Colwell, Tom Soderstrom
Date: 2018-02-13 00:00:00
Tasks: Anomaly Detection

As spacecraft send back increasing amounts of telemetry data, improved
anomaly detection systems are needed to lessen the monitoring burden placed on
operations engineers and reduce operational risk. Current spacecraft monitoring
systems only target a subset of anomaly types and often require costly expert
knowledge to develop and maintain due to challenges involving scale and
complexity. We demonstrate the effectiveness of Long Short-Term Memory (LSTMs)
networks, a type of Recurrent Neural Network (RNN), in overcoming these issues
using expert-labeled telemetry anomaly data from the Soil Moisture Active
Passive (SMAP) satellite and the Mars Science Laboratory (MSL) rover,
Curiosity. We also propose a complementary unsupervised and nonparametric
anomaly thresholding approach developed during a pilot implementation of an
anomaly detection system for SMAP, and offer false positive mitigation
strategies along with other key improvements and lessons learned during
development.
Gaussian Mixture Reduction for Time-Constrained Approximate Inference in Hybrid Bayesian Networks
Authors: Cheol Young Park, Kathryn Blackmond Laskey, Paulo C. G. Costa, Shou Matsumoto
Date: 2018-06-06 00:00:00
Tasks: Fraud Detection, Medical Diagnosis

Hybrid Bayesian Networks (HBNs), which contain both discrete and continuous
variables, arise naturally in many application areas (e.g., image
understanding, data fusion, medical diagnosis, fraud detection). This paper
concerns inference in an important subclass of HBNs, the conditional Gaussian
(CG) networks, in which all continuous random variables have Gaussian
distributions and all children of continuous random variables must be
continuous. Inference in CG networks can be NP-hard even for special-case
structures, such as poly-trees, where inference in discrete Bayesian networks
can be performed in polynomial time. Therefore, approximate inference is
required. In approximate inference, it is often necessary to trade off accuracy
against solution time. This paper presents an extension to the Hybrid Message
Passing inference algorithm for general CG networks and an algorithm for
optimizing its accuracy given a bound on computation time. The extended
algorithm uses Gaussian mixture reduction to prevent an exponential increase in
the number of Gaussian mixture components. The trade-off algorithm performs
pre-processing to find optimal run-time settings for the extended algorithm.
Experimental results for four CG networks compare performance of the extended
algorithm with existing algorithms and show the optimal settings for these CG
networks.
Human-like generalization in a machine through predicate learning
Authors: Leonidas A. A. Doumas, Guillermo Puebla, Andrea E. Martin
Date: 2018-06-05 00:00:00

Humans readily generalize, applying prior knowledge to novel situations and
stimuli. Advances in machine learning and artificial intelligence have begun to
approximate and even surpass human performance, but machine systems reliably
struggle to generalize information to untrained situations. We describe a
neural network model that is trained to play one video game (Breakout) and
demonstrates one-shot generalization to a new game (Pong). The model
generalizes by learning representations that are functionally and formally
symbolic from training data, without feedback, and without requiring that
structured representations be specified a priori. The model uses unsupervised
comparison to discover which characteristics of the input are invariant, and to
learn relational predicates; it then applies these predicates to arguments in a
symbolic fashion, using oscillatory regularities in network firing to
dynamically bind predicates to arguments. We argue that models of human
cognition must account for far-reaching and flexible generalization, and that
in order to do so, models must be able to discover symbolic representations
from unstructured data, a process we call predicate learning. Only then can
models begin to adequately explain where human-like representations come from,
why human cognition is the way it is, and why it continues to differ from
machine intelligence in crucial ways.
Evidential Deep Learning to Quantify Classification Uncertainty
Authors: Murat Sensoy, Lance Kaplan, Melih Kandemir
Date: 2018-06-05 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Deep Learning, General Classification, Multi-class Classification, Prediction, Uncertainty Quantification

Deterministic neural nets have been shown to learn effective predictors on a
wide range of machine learning problems. However, as the standard approach is
to train the network to minimize a prediction loss, the resultant model remains
ignorant to its prediction confidence. Orthogonally to Bayesian neural nets
that indirectly infer prediction uncertainty through weight uncertainties, we
propose explicit modeling of the same using the theory of subjective logic. By
placing a Dirichlet distribution on the class probabilities, we treat
predictions of a neural net as subjective opinions and learn the function that
collects the evidence leading to these opinions by a deterministic neural net
from data. The resultant predictor for a multi-class classification problem is
another Dirichlet distribution whose parameters are set by the continuous
output of a neural net. We provide a preliminary analysis on how the
peculiarities of our new loss function drive improved uncertainty estimation.
We observe that our method achieves unprecedented success on detection of
out-of-distribution queries and endurance against adversarial perturbations.
A Comparative Study on Unsupervised Domain Adaptation Approaches for Coffee Crop Mapping
Authors: Edemir Ferreira, Mário S. Alvim, Jefersson A. dos Santos
Date: 2018-06-06 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

In this work, we investigate the application of existing unsupervised domain
adaptation (UDA) approaches to the task of transferring knowledge between crop
regions having different coffee patterns. Given a geographical region with
fully mapped coffee plantations, we observe that this knowledge can be used to
train a classifier and to map a new county with no need of samples indicated in
the target region. Experimental results show that transferring knowledge via
some UDA strategies performs better than just applying a classifier trained in
a region to predict coffee crops in a new one. However, UDA methods may lead to
negative transfer, which may indicate that domains are too different that
transferring knowledge is not appropriate. We also verify that normalization
affect significantly some UDA methods; we observe a meaningful complementary
contribution between coffee crops data; and a visual behavior suggests an
existent of a cluster of samples that are more likely to be drawn from a
specific data.
Optimal approximation of continuous functions by very deep ReLU networks
Authors: Dmitry Yarotsky
Date: 2018-02-10 00:00:00

We consider approximations of general continuous functions on
finite-dimensional cubes by general deep ReLU neural networks and study the
approximation rates with respect to the modulus of continuity of the function
and the total number of weights $W$ in the network. We establish the complete
phase diagram of feasible approximation rates and show that it includes two
distinct phases. One phase corresponds to slower approximations that can be
achieved with constant-depth networks and continuous weight assignments. The
other phase provides faster approximations at the cost of depths necessarily
growing as a power law $L\sim W^{\alpha}, 0<\alpha\le 1,$ and with necessarily
discontinuous weight assignments. In particular, we prove that constant-width
fully-connected networks of depth $L\sim W$ provide the fastest possible
approximation rate $\|f-\widetilde f\|_\infty = O(\omega_f(O(W^{-2/\nu})))$
that cannot be achieved with less deep networks.
An Analysis of the t-SNE Algorithm for Data Visualization
Authors: Sanjeev Arora, Wei Hu, Pravesh K. Kothari
Date: 2018-03-05 00:00:00
Tasks: Clustering, Data Visualization, Dimensionality Reduction

A first line of attack in exploratory data analysis is data visualization,
i.e., generating a 2-dimensional representation of data that makes clusters of
similar points visually identifiable. Standard Johnson-Lindenstrauss
dimensionality reduction does not produce data visualizations. The t-SNE
heuristic of van der Maaten and Hinton, which is based on non-convex
optimization, has become the de facto standard for visualization in a wide
range of applications.
  This work gives a formal framework for the problem of data visualization -
finding a 2-dimensional embedding of clusterable data that correctly separates
individual clusters to make them visually identifiable. We then give a rigorous
analysis of the performance of t-SNE under a natural, deterministic condition
on the "ground-truth" clusters (similar to conditions assumed in earlier
analyses of clustering) in the underlying data. These are the first provable
guarantees on t-SNE for constructing good data visualizations.
  We show that our deterministic condition is satisfied by considerably general
probabilistic generative models for clusterable data such as mixtures of
well-separated log-concave distributions. Finally, we give theoretical evidence
that t-SNE provably succeeds in partially recovering cluster structure even
when the above deterministic condition is not met.
Variational Implicit Processes
Authors: Chao Ma, Yingzhen Li, José Miguel Hernández-Lobato
Date: 2018-06-06 00:00:00
Tasks: Gaussian Processes, Stochastic Optimization

We introduce the implicit processes (IPs), a stochastic process that places implicitly defined multivariate distributions over any finite collections of random variables. IPs are therefore highly flexible implicit priors over functions, with examples including data simulators, Bayesian neural networks and non-linear transformations of stochastic processes. A novel and efficient approximate inference algorithm for IPs, namely the variational implicit processes (VIPs), is derived using generalised wake-sleep updates. This method returns simple update equations and allows scalable hyper-parameter learning with stochastic optimization. Experiments show that VIPs return better uncertainty estimates and lower errors over existing inference methods for challenging models such as Bayesian neural networks, and Gaussian processes.
Not All Attributes are Created Equal: $d_{\mathcal{X}}$-Private Mechanisms for Linear Queries
Authors: Parameswaran Kamalaruban, Victor Perrier, Hassan Jameel Asghar, Mohamed Ali Kaafar
Date: 2018-06-06 00:00:00
Tasks: All

Differential privacy provides strong privacy guarantees simultaneously enabling useful insights from sensitive datasets. However, it provides the same level of protection for all elements (individuals and attributes) in the data. There are practical scenarios where some data attributes need more/less protection than others. In this paper, we consider $d_{\mathcal{X}}$-privacy, an instantiation of the privacy notion introduced in \cite{chatzikokolakis2013broadening}, which allows this flexibility by specifying a separate privacy budget for each pair of elements in the data domain. We describe a systematic procedure to tailor any existing differentially private mechanism that assumes a query set and a sensitivity vector as input into its $d_{\mathcal{X}}$-private variant, specifically focusing on linear queries. Our proposed meta procedure has broad applications as linear queries form the basis of a range of data analysis and machine learning algorithms, and the ability to define a more flexible privacy budget across the data domain results in improved privacy/utility tradeoff in these applications. We propose several $d_{\mathcal{X}}$-private mechanisms, and provide theoretical guarantees on the trade-off between utility and privacy. We also experimentally demonstrate the effectiveness of our procedure, by evaluating our proposed $d_{\mathcal{X}}$-private Laplace mechanism on both synthetic and real datasets using a set of randomly generated linear queries.
Variational Autoencoder with Arbitrary Conditioning
Authors: Oleg Ivanov, Michael Figurnov, Dmitry Vetrov
Date: 2018-06-06 00:00:00
Proceeding: ICLR 2019 5
Tasks: Diversity, Image Inpainting, Imputation

We propose a single neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and then sample the remaining features in "one shot". The features may be both real-valued and categorical. Training of the model is performed by stochastic variational Bayes. The experimental evaluation on synthetic data, as well as feature imputation and image inpainting problems, shows the effectiveness of the proposed approach and diversity of the generated samples.