pwc-archive/papers-with-abstracts huggingface dataset:
RLgraph: Modular Computation Graphs for Deep Reinforcement Learning
Authors: Michael Schaarschmidt, Sven Mika, Kai Fricke, Eiko Yoneki
Date: 2018-10-21 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sensitivity

Reinforcement learning (RL) tasks are challenging to implement, execute and
test due to algorithmic instability, hyper-parameter sensitivity, and
heterogeneous distributed communication patterns. We argue for the separation
of logical component composition, backend graph definition, and distributed
execution. To this end, we introduce RLgraph, a library for designing and
executing reinforcement learning tasks in both static graph and define-by-run
paradigms. The resulting implementations are robust, incrementally testable,
and yield high performance across different deep learning frameworks and
distributed backends.
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments
Authors: Sriram Srinivasan, Marc Lanctot, Vinicius Zambaldi, Julien Perolat, Karl Tuyls, Remi Munos, Michael Bowling
Date: 2018-10-21 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Optimization of parameterized policies for reinforcement learning (RL) is an important and challenging problem in artificial intelligence. Among the most common approaches are algorithms based on gradient ascent of a score function representing discounted return. In this paper, we examine the role of these policy gradient and actor-critic algorithms in partially-observable multiagent environments. We show several candidate policy update rules and relate them to a foundation of regret minimization and multiagent learning techniques for the one-shot and tabular cases, leading to previously unknown convergence guarantees. We apply our method to model-free multiagent reinforcement learning in adversarial sequential decision problems (zero-sum imperfect information games), using RL-style function approximation. We evaluate on commonly used benchmark Poker domains, showing performance against fixed policies and empirical convergence to approximate Nash equilibria in self-play with rates similar to or better than a baseline model-free algorithm for zero sum games, without any domain-specific state space reductions.
Hierarchical ResNeXt Models for Breast Cancer Histology Image Classification
Authors: Ismaël Koné, Lahsen Boulmane
Date: 2018-10-21 00:00:00
Tasks: Breast Cancer Histology Image Classification, Classification, General Classification, image-classification, Image Classification

Microscopic histology image analysis is a cornerstone in early detection of
breast cancer. However these images are very large and manual analysis is error
prone and very time consuming. Thus automating this process is in high demand.
We proposed a hierarchical system of convolutional neural networks (CNN) that
classifies automatically patches of these images into four pathologies: normal,
benign, in situ carcinoma and invasive carcinoma. We evaluated our system on
the BACH challenge dataset of image-wise classification and a small dataset
that we used to extend it. Using a train/test split of 75%/25%, we achieved an
accuracy rate of 0.99 on the test split for the BACH dataset and 0.96 on that
of the extension. On the test of the BACH challenge, we've reached an accuracy
of 0.81 which rank us to the 8th out of 51 teams.
C2A: Crowd Consensus Analytics for Virtual Colonoscopy
Authors: Ji Hwan Park, Saad Nadeem, Seyedkoosha Mirhosseini, Arie Kaufman
Date: 2018-10-21 00:00:00

We present a medical crowdsourcing visual analytics platform called C{$^2$}A
to visualize, classify and filter crowdsourced clinical data. More
specifically, C$^2$A is used to build consensus on a clinical diagnosis by
visualizing crowd responses and filtering out anomalous activity. Crowdsourcing
medical applications have recently shown promise where the non-expert users
(the crowd) were able to achieve accuracy similar to the medical experts. This
has the potential to reduce interpretation/reading time and possibly improve
accuracy by building a consensus on the findings beforehand and letting the
medical experts make the final diagnosis. In this paper, we focus on a virtual
colonoscopy (VC) application with the clinical technicians as our target users,
and the radiologists acting as consultants and classifying segments as benign
or malignant. In particular, C$^2$A is used to analyze and explore crowd
responses on video segments, created from fly-throughs in the virtual colon.
C$^2$A provides several interactive visualization components to build crowd
consensus on video segments, to detect anomalies in the crowd data and in the
VC video segments, and finally, to improve the non-expert user's work quality
and performance by A/B testing for the optimal crowdsourcing platform and
application-specific parameters. Case studies and domain experts feedback
demonstrate the effectiveness of our framework in improving workers' output
quality, the potential to reduce the radiologists' interpretation time, and
hence, the potential to improve the traditional clinical workflow by marking
the majority of the video segments as benign based on the crowd consensus.
3D shape retrieval basing on representatives of classes
Authors: M. Benjelloun, E. W. Dadi, E. M. Daoudi
Date: 2018-10-21 00:00:00
Tasks: 3D Shape Classification, 3D Shape Retrieval, Retrieval

In this paper, we present an improvement of our proposed technique for 3D
shape retrieval in classified databases [2] which is based on representatives
of classes. Instead of systematically matching the object-query with all 3D
models of the database, our idea presented in [2] consist, for a classified
database, to represent each class by one representative that is used to orient
the retrieval process to the right class (the class excepted to contain 3D
models similar to the query). In order to increase the chance to fall in the
right class, our idea in this work is to represent each class by more than one
representative. In this case, instead of using only one representative to
decide which is the right class we use a set of representatives this will
contribute certainly to improving the relevance of retrieval results. The
obtained experimental results show that the relevance is significantly
improved.
On the Non-asymptotic and Sharp Lower Tail Bounds of Random Variables
Authors: Anru R. Zhang, Yuchen Zhou
Date: 2018-10-21 00:00:00

The non-asymptotic tail bounds of random variables play crucial roles in probability, statistics, and machine learning. Despite much success in developing upper bounds on tail probability in literature, the lower bounds on tail probabilities are relatively fewer. In this paper, we introduce systematic and user-friendly schemes for developing non-asymptotic lower bounds of tail probabilities. In addition, we develop sharp lower tail bounds for the sum of independent sub-Gaussian and sub-exponential random variables, which match the classic Hoeffding-type and Bernstein-type concentration inequalities, respectively. We also provide non-asymptotic matching upper and lower tail bounds for a suite of distributions, including gamma, beta, (regular, weighted, and noncentral) chi-square, binomial, Poisson, Irwin-Hall, etc. We apply the result to establish the matching upper and lower bounds for extreme value expectation of the sum of independent sub-Gaussian and sub-exponential random variables. A statistical application of signal identification from sparse heterogeneous mixtures is finally considered.
Visualization Framework for Colonoscopy Videos
Authors: Saad Nadeem, Arie Kaufman
Date: 2018-10-21 00:00:00

We present a visualization framework for annotating and comparing colonoscopy
videos, where these annotations can then be used for semi-automatic report
generation at the end of the procedure. Currently, there are approximately 14
million colonoscopies performed every year in the US. In this work, we create a
visualization tool to deal with the deluge of colonoscopy videos in a more
effective way. We present an interactive visualization framework for the
annotation and tagging of colonoscopy videos in an easy and intuitive way.
These annotations and tags can later be used for report generation for
electronic medical records and for comparison at an individual as well as group
level. We also present important use cases and medical expert feedback for our
visualization framework.
Transition-based Parsing with Lighter Feed-Forward Networks
Authors: David Vilares, Carlos Gómez-Rodríguez
Date: 2018-10-21 00:00:00
Proceeding: WS 2018 11

We explore whether it is possible to build lighter parsers, that are
statistically equivalent to their corresponding standard version, for a wide
set of languages showing different structures and morphologies. As testbed, we
use the Universal Dependencies and transition-based dependency parsers trained
on feed-forward networks. For these, most existing research assumes de facto
standard embedded features and relies on pre-computation tricks to obtain
speed-ups. We explore how these features and their size can be reduced and
whether this translates into speed-ups with a negligible impact on accuracy.
The experiments show that grand-daughter features can be removed for the
majority of treebanks without a significant (negative or positive) LAS
difference. They also show how the size of the embeddings can be notably
reduced.
Constituent Parsing as Sequence Labeling
Authors: Carlos Gómez-Rodríguez, David Vilares
Date: 2018-10-21 00:00:00
Proceeding: EMNLP 2018 10

We introduce a method to reduce constituent parsing to sequence labeling. For each word w_t, it generates a label that encodes: (1) the number of ancestors in the tree that the words w_t and w_{t+1} have in common, and (2) the nonterminal symbol at the lowest common ancestor. We first prove that the proposed encoding function is injective for any tree without unary branches. In practice, the approach is made extensible to all constituency trees by collapsing unary branches. We then use the PTB and CTB treebanks as testbeds and propose a set of fast baselines. We achieve 90.7% F-score on the PTB test set, outperforming the Vinyals et al. (2015) sequence-to-sequence parser. In addition, sacrificing some accuracy, our approach achieves the fastest constituent parsing speeds reported to date on PTB by a wide margin.
Mechanisms for Integrated Feature Normalization and Remaining Useful Life Estimation Using LSTMs Applied to Hard-Disks
Authors: Sanchita Basak, Saptarshi Sengupta, Abhishek Dubey
Date: 2018-10-21 00:00:00
Tasks: Transfer Learning

With emerging smart communities, improving overall system availability is becoming a major concern. In order to improve the reliability of the components in a system we propose an inference model to predict Remaining Useful Life (RUL) of those components. In this paper we work with components of backend data servers such as hard disks, that are subject to degradation. A Deep Long-Short Term Memory (LSTM) Network is used as the backbone of this fast, data-driven decision framework and dynamically captures the pattern of the incoming data. In the article, we discuss the architecture of the neural network and describe the mechanisms to choose the various hyper-parameters. Further, we describe the challenges faced in extracting effective training sets from highly unorganized and class-imbalanced big data and establish methods for online predictions with extensive data pre-processing, feature extraction and validation through online simulation sets with unknown remaining useful lives of the hard disks. Our algorithm performs especially well in predicting RUL near the critical zone of a device approaching failure. With the proposed approach we are able to predict whether a disk is going to fail in next ten days with an average precision of 0.8435. We also show that the architecture trained on a particular model can be used to predict RUL for devices in different models from same manufacturer through transfer learning.
Runtime Concurrency Control and Operation Scheduling for High Performance Neural Network Training
Authors: Jiawen Liu, Dong Li, Gokcen Kestor, Jeffrey Vetter
Date: 2018-10-21 00:00:00
Tasks: BIG-bench Machine Learning, Scheduling

Training neural network often uses a machine learning framework such as
TensorFlow and Caffe2. These frameworks employ a dataflow model where the NN
training is modeled as a directed graph composed of a set of nodes. Operations
in neural network training are typically implemented by the frameworks as
primitives and represented as nodes in the dataflow graph. Training NN models
in a dataflow-based machine learning framework involves a large number of
fine-grained operations. Those operations have diverse memory access patterns
and computation intensity. How to manage and schedule those operations is
challenging, because we have to decide the number of threads to run each
operation (concurrency control) and schedule those operations for good hardware
utilization and system throughput.
  In this paper, we extend an existing runtime system (the TensorFlow runtime)
to enable automatic concurrency control and scheduling of operations. We
explore performance modeling to predict the performance of operations with
various thread-level parallelism. Our performance model is highly accurate and
lightweight. Leveraging the performance model, our runtime system employs a set
of scheduling strategies that co-run operations to improve hardware utilization
and system throughput. Our runtime system demonstrates a big performance
benefit. Comparing with using the recommended configurations for concurrency
control and operation scheduling in TensorFlow, our approach achieves 33%
performance (execution time) improvement on average (up to 49%) for three
neural network models, and achieves high performance closing to the optimal one
manually obtained by the user.
Learning Spectral Transform Network on 3D Surface for Non-rigid Shape Analysis
Authors: Ruixuan Yu, Jian Sun, Huibin Li
Date: 2018-10-21 00:00:00
Tasks: General Classification, Metric Learning, Retrieval

Designing a network on 3D surface for non-rigid shape analysis is a
challenging task. In this work, we propose a novel spectral transform network
on 3D surface to learn shape descriptors. The proposed network architecture
consists of four stages: raw descriptor extraction, surface second-order
pooling, mixture of power function-based spectral transform, and metric
learning. The proposed network is simple and shallow. Quantitative experiments
on challenging benchmarks show its effectiveness for non-rigid shape retrieval
and classification, e.g., it achieved the highest accuracies on SHREC14, 15
datasets as well as the Range subset of SHREC17 dataset.
MS-BACO: A new Model Selection algorithm using Binary Ant Colony Optimization for neural complexity and error reduction
Authors: Saman Sadeghyan, Shahrokh Asadi
Date: 2018-10-21 00:00:00
Tasks: Combinatorial Optimization, Computational Efficiency, Model Selection, Sensitivity

Stabilizing the complexity of Feedforward Neural Networks (FNNs) for the
given approximation task can be managed by defining an appropriate model
magnitude which is also greatly correlated with the generalization quality and
computational efficiency. However, deciding on the right level of model
complexity can be highly challenging in FNN applications. In this paper, a new
Model Selection algorithm using Binary Ant Colony Optimization (MS-BACO) is
proposed in order to achieve the optimal FNN model in terms of neural
complexity and cross-entropy error. MS-BACO is a meta-heuristic algorithm that
treats the problem as a combinatorial optimization problem. By quantifying both
the amount of correlation exists among hidden neurons and the sensitivity of
the FNN output to the hidden neurons using a sample-based sensitivity analysis
method called, extended Fourier amplitude sensitivity test, the algorithm
mostly tends to select the FNN model containing hidden neurons with most
distinct hyperplanes and high contribution percentage. Performance of the
proposed algorithm with three different designs of heuristic information is
investigated. Comparison of the findings verifies that the newly introduced
algorithm is able to provide more compact and accurate FNN model.
Training Dynamic Exponential Family Models with Causal and Lateral Dependencies for Generalized Neuromorphic Computing
Authors: Hyeryung Jang, Osvaldo Simeone
Date: 2018-10-21 00:00:00
Tasks: Time Series, Time Series Analysis

Neuromorphic hardware platforms, such as Intel's Loihi chip, support the implementation of Spiking Neural Networks (SNNs) as an energy-efficient alternative to Artificial Neural Networks (ANNs). SNNs are networks of neurons with internal analogue dynamics that communicate by means of binary time series. In this work, a probabilistic model is introduced for a generalized set-up in which the synaptic time series can take values in an arbitrary alphabet and are characterized by both causal and instantaneous statistical dependencies. The model, which can be considered as an extension of exponential family harmoniums to time series, is introduced by means of a hybrid directed-undirected graphical representation. Furthermore, distributed learning rules are derived for Maximum Likelihood and Bayesian criteria under the assumption of fully observed time series in the training set.
Teaching Inverse Reinforcement Learners via Features and Demonstrations
Authors: Luis Haug, Sebastian Tschiatschek, Adish Singla
Date: 2018-10-21 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Reinforcement Learning

Learning near-optimal behaviour from an expert's demonstrations typically
relies on the assumption that the learner knows the features that the true
reward function depends on. In this paper, we study the problem of learning
from demonstrations in the setting where this is not the case, i.e., where
there is a mismatch between the worldviews of the learner and the expert. We
introduce a natural quantity, the teaching risk, which measures the potential
suboptimality of policies that look optimal to the learner in this setting. We
show that bounds on the teaching risk guarantee that the learner is able to
find a near-optimal policy using standard algorithms based on inverse
reinforcement learning. Based on these findings, we suggest a teaching scheme
in which the expert can decrease the teaching risk by updating the learner's
worldview, and thus ultimately enable her to find a near-optimal policy.
CNNPred: CNN-based stock market prediction using several data sources
Authors: Ehsan Hoseinzade, Saman Haratizadeh
Date: 2018-10-21 00:00:00
Tasks: feature selection, Stock Market Prediction

Feature extraction from financial data is one of the most important problems
in market prediction domain for which many approaches have been suggested.
Among other modern tools, convolutional neural networks (CNN) have recently
been applied for automatic feature selection and market prediction. However, in
experiments reported so far, less attention has been paid to the correlation
among different markets as a possible source of information for extracting
features. In this paper, we suggest a CNN-based framework with specially
designed CNNs, that can be applied on a collection of data from a variety of
sources, including different markets, in order to extract features for
predicting the future of those markets. The suggested framework has been
applied for predicting the next day's direction of movement for the indices of
S&P 500, NASDAQ, DJI, NYSE, and RUSSELL markets based on various sets of
initial features. The evaluations show a significant improvement in
prediction's performance compared to the state of the art baseline algorithms.
Decoding of Non-Binary LDPC Codes Using the Information Bottleneck Method
Authors: Maximilian Stark, Jan Lewandowsky, Souradip Saha, Gerhard Bauch
Date: 2018-10-21 00:00:00

Recently, a novel lookup table based decoding method for binary low-density parity-check codes has attracted considerable attention. In this approach, mutual-information maximizing lookup tables replace the conventional operations of the variable nodes and the check nodes in message passing decoding. Moreover, the exchanged messages are represented by integers with very small bit width. A machine learning framework termed the information bottleneck method is used to design the corresponding lookup tables. In this paper, we extend this decoding principle from binary to non-binary codes. This is not a straightforward extension, but requires a more sophisticated lookup table design to cope with the arithmetic in higher order Galois fields. Provided bit error rate simulations show that our proposed scheme outperforms the log-max decoding algorithm and operates close to sum-product decoding.
Label Noise Filtering Techniques to Improve Monotonic Classification
Authors: José-Ramón Cano, Julián Luengo, Salvador García
Date: 2018-10-21 00:00:00
Tasks: Classification, General Classification, Ordinal Classification

The monotonic ordinal classification has increased the interest of
researchers and practitioners within machine learning community in the last
years. In real applications, the problems with monotonicity constraints are
very frequent. To construct predictive monotone models from those problems,
many classifiers require as input a data set satisfying the monotonicity
relationships among all samples. Changing the class labels of the data set
(relabelling) is useful for this. Relabelling is assumed to be an important
building block for the construction of monotone classifiers and it is proved
that it can improve the predictive performance.
  In this paper, we will address the construction of monotone datasets
considering as noise the cases that do not meet the monotonicity restrictions.
For the first time in the specialized literature, we propose the use of noise
filtering algorithms in a preprocessing stage with a double goal: to increase
both the monotonicity index of the models and the accuracy of the predictions
for different monotonic classifiers. The experiments are performed over 12
datasets coming from classification and regression problems and show that our
scheme improves the prediction capabilities of the monotonic classifiers
instead of being applied to original and relabeled datasets. In addition, we
have included the analysis of noise filtering process in the particular case of
wine quality classification to understand its effect in the predictive models
generated.
Understanding the Acceleration Phenomenon via High-Resolution Differential Equations
Authors: Bin Shi, Simon S. Du, Michael. I. Jordan, Weijie J. Su
Date: 2018-10-21 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Gradient-based optimization algorithms can be studied from the perspective of
limiting ordinary differential equations (ODEs). Motivated by the fact that
existing ODEs do not distinguish between two fundamentally different
algorithms---Nesterov's accelerated gradient method for strongly convex
functions (NAG-SC) and Polyak's heavy-ball method---we study an alternative
limiting process that yields high-resolution ODEs. We show that these ODEs
permit a general Lyapunov function framework for the analysis of convergence in
both continuous and discrete time. We also show that these ODEs are more
accurate surrogates for the underlying algorithms; in particular, they not only
distinguish between NAG-SC and Polyak's heavy-ball method, but they allow the
identification of a term that we refer to as "gradient correction" that is
present in NAG-SC but not in the heavy-ball method and is responsible for the
qualitative difference in convergence of the two methods. We also use the
high-resolution ODE framework to study Nesterov's accelerated gradient method
for (non-strongly) convex functions, uncovering a hitherto unknown
result---that NAG-C minimizes the squared gradient norm at an inverse cubic
rate. Finally, by modifying the high-resolution ODE of NAG-C, we obtain a
family of new optimization methods that are shown to maintain the accelerated
convergence rates of NAG-C for smooth convex functions.