pwc-archive/papers-with-abstracts huggingface dataset:
Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect
Authors: Tong Wang, Cynthia Rudin
Date: 2017-10-16 00:00:00
Tasks: Causal Inference, Subgroup Discovery

A key question in causal inference analyses is how to find subgroups with elevated treatment effects. This paper takes a machine learning approach and introduces a generative model, Causal Rule Sets (CRS), for interpretable subgroup discovery. A CRS model uses a small set of short decision rules to capture a subgroup where the average treatment effect is elevated. We present a Bayesian framework for learning a causal rule set. The Bayesian model consists of a prior that favors simple models for better interpretability as well as avoiding overfitting, and a Bayesian logistic regression that captures the likelihood of data, characterizing the relation between outcomes, attributes, and subgroup membership. The Bayesian model has tunable parameters that can characterize subgroups with various sizes, providing users with more flexible choices of models from the \emph{treatment efficient frontier}. We find maximum a posteriori models using iterative discrete Monte Carlo steps in the joint solution space of rules sets and parameters. To improve search efficiency, we provide theoretically grounded heuristics and bounding strategies to prune and confine the search space. Experiments show that the search algorithm can efficiently recover true underlying subgroups. We apply CRS on public and real-world datasets from domains where interpretability is indispensable. We compare CRS with state-of-the-art rule-based subgroup discovery models. Results show that CRS achieved consistently competitive performance on datasets from various domains, represented by high treatment efficient frontiers.
A General Framework for Robust Interactive Learning
Authors: Ehsan Emamjomeh-Zadeh, David Kempe
Date: 2017-10-16 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Clustering

We propose a general framework for interactively learning models, such as
(binary or non-binary) classifiers, orderings/rankings of items, or clusterings
of data points. Our framework is based on a generalization of Angluin's
equivalence query model and Littlestone's online learning model: in each
iteration, the algorithm proposes a model, and the user either accepts it or
reveals a specific mistake in the proposal. The feedback is correct only with
probability $p > 1/2$ (and adversarially incorrect with probability $1 - p$),
i.e., the algorithm must be able to learn in the presence of arbitrary noise.
The algorithm's goal is to learn the ground truth model using few iterations.
  Our general framework is based on a graph representation of the models and
user feedback. To be able to learn efficiently, it is sufficient that there be
a graph $G$ whose nodes are the models and (weighted) edges capture the user
feedback, with the property that if $s, s^*$ are the proposed and target
models, respectively, then any (correct) user feedback $s'$ must lie on a
shortest $s$-$s^*$ path in $G$. Under this one assumption, there is a natural
algorithm reminiscent of the Multiplicative Weights Update algorithm, which
will efficiently learn $s^*$ even in the presence of noise in the user's
feedback.
  From this general result, we rederive with barely any extra effort classic
results on learning of classifiers and a recent result on interactive
clustering; in addition, we easily obtain new interactive learning algorithms
for ordering/ranking.
NeuralPower: Predict and Deploy Energy-Efficient Convolutional Neural Networks
Authors: Ermao Cai, Da-Cheng Juan, Dimitrios Stamoulis, Diana Marculescu
Date: 2017-10-15 00:00:00
Tasks: GPU

"How much energy is consumed for an inference made by a convolutional neural
network (CNN)?" With the increased popularity of CNNs deployed on the
wide-spectrum of platforms (from mobile devices to workstations), the answer to
this question has drawn significant attention. From lengthening battery life of
mobile devices to reducing the energy bill of a datacenter, it is important to
understand the energy efficiency of CNNs during serving for making an
inference, before actually training the model. In this work, we propose
NeuralPower: a layer-wise predictive framework based on sparse polynomial
regression, for predicting the serving energy consumption of a CNN deployed on
any GPU platform. Given the architecture of a CNN, NeuralPower provides an
accurate prediction and breakdown for power and runtime across all layers in
the whole network, helping machine learners quickly identify the power,
runtime, or energy bottlenecks. We also propose the "energy-precision ratio"
(EPR) metric to guide machine learners in selecting an energy-efficient CNN
architecture that better trades off the energy consumption and prediction
accuracy. The experimental results show that the prediction accuracy of the
proposed NeuralPower outperforms the best published model to date, yielding an
improvement in accuracy of up to 68.5%. We also assess the accuracy of
predictions at the network level, by predicting the runtime, power, and energy
of state-of-the-art CNN architectures, achieving an average accuracy of 88.24%
in runtime, 88.34% in power, and 97.21% in energy. We comprehensively
corroborate the effectiveness of NeuralPower as a powerful framework for
machine learners by testing it on different GPU platforms and Deep Learning
software tools.
Manifold Regularization for Kernelized LSTD
Authors: Xinyan Yan, Krzysztof Choromanski, Byron Boots, Vikas Sindhwani
Date: 2017-10-15 00:00:00
Tasks: Policy Gradient Methods, Reinforcement Learning, Reinforcement Learning (RL)

Policy evaluation or value function or Q-function approximation is a key
procedure in reinforcement learning (RL). It is a necessary component of policy
iteration and can be used for variance reduction in policy gradient methods.
Therefore its quality has a significant impact on most RL algorithms. Motivated
by manifold regularized learning, we propose a novel kernelized policy
evaluation method that takes advantage of the intrinsic geometry of the state
space learned from data, in order to achieve better sample efficiency and
higher accuracy in Q-function approximation. Applying the proposed method in
the Least-Squares Policy Iteration (LSPI) framework, we observe superior
performance compared to widely used parametric basis functions on two standard
benchmarks in terms of policy quality.
Deep multi-frame face super-resolution
Authors: E. Ustinova, V. Lempitsky
Date: 2017-09-10 00:00:00
Tasks: Face Recognition, Face Verification, Image Super-Resolution, Super-Resolution

Face verification and recognition problems have seen rapid progress in recent
years, however recognition from small size images remains a challenging task
that is inherently intertwined with the task of face super-resolution. Tackling
this problem using multiple frames is an attractive idea, yet requires solving
the alignment problem that is also challenging for low-resolution faces. Here
we present a holistic system for multi-frame recognition, alignment, and
superresolution of faces. Our neural network architecture restores the central
frame of each input sequence additionally taking into account a number of
adjacent frames and making use of sub-pixel movements. We present our results
using the popular dataset for video face recognition (YouTube Faces). We show a
notable improvement of identification score compared to several baselines
including the one based on single-image super-resolution.
A systematic study of the class imbalance problem in convolutional neural networks
Authors: Mateusz Buda, Atsuto Maki, Maciej A. Mazurowski
Date: 2017-10-15 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

In this study, we systematically investigate the impact of class imbalance on
classification performance of convolutional neural networks (CNNs) and compare
frequently used methods to address the issue. Class imbalance is a common
problem that has been comprehensively studied in classical machine learning,
yet very limited systematic research is available in the context of deep
learning. In our study, we use three benchmark datasets of increasing
complexity, MNIST, CIFAR-10 and ImageNet, to investigate the effects of
imbalance on classification and perform an extensive comparison of several
methods to address the issue: oversampling, undersampling, two-phase training,
and thresholding that compensates for prior class probabilities. Our main
evaluation metric is area under the receiver operating characteristic curve
(ROC AUC) adjusted to multi-class tasks since overall accuracy metric is
associated with notable difficulties in the context of imbalanced data. Based
on results from our experiments we conclude that (i) the effect of class
imbalance on classification performance is detrimental; (ii) the method of
addressing class imbalance that emerged as dominant in almost all analyzed
scenarios was oversampling; (iii) oversampling should be applied to the level
that completely eliminates the imbalance, whereas the optimal undersampling
ratio depends on the extent of imbalance; (iv) as opposed to some classical
machine learning models, oversampling does not cause overfitting of CNNs; (v)
thresholding should be applied to compensate for prior class probabilities when
overall number of properly classified cases is of interest.
Towards Automatic Abdominal Multi-Organ Segmentation in Dual Energy CT using Cascaded 3D Fully Convolutional Network
Authors: Shuqing Chen, Holger Roth, Sabrina Dorn, Matthias May, Alexander Cavallaro, Michael M. Lell, Marc Kachelrieß, Hirohisa ODA, Kensaku MORI, Andreas Maier
Date: 2017-10-15 00:00:00
Tasks: Organ Segmentation

Automatic multi-organ segmentation of the dual energy computed tomography
(DECT) data can be beneficial for biomedical research and clinical
applications. However, it is a challenging task. Recent advances in deep
learning showed the feasibility to use 3-D fully convolutional networks (FCN)
for voxel-wise dense predictions in single energy computed tomography (SECT).
In this paper, we proposed a 3D FCN based method for automatic multi-organ
segmentation in DECT. The work was based on a cascaded FCN and a general model
for the major organs trained on a large set of SECT data. We preprocessed the
DECT data by using linear weighting and fine-tuned the model for the DECT data.
The method was evaluated using 42 torso DECT data acquired with a clinical
dual-source CT system. Four abdominal organs (liver, spleen, left and right
kidneys) were evaluated. Cross-validation was tested. Effect of the weight on
the accuracy was researched. In all the tests, we achieved an average Dice
coefficient of 93% for the liver, 90% for the spleen, 91% for the right kidney
and 89% for the left kidney, respectively. The results show our method is
feasible and promising.
Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training
Authors: Mathieu Sinn, Ambrish Rawat
Date: 2017-05-25 00:00:00
Tasks: Generative Adversarial Network

Generative Adversarial Networks (GANs) have become a widely popular framework
for generative modelling of high-dimensional datasets. However their training
is well-known to be difficult. This work presents a rigorous statistical
analysis of GANs providing straight-forward explanations for common training
pathologies such as vanishing gradients. Furthermore, it proposes a new
training objective, Kernel GANs, and demonstrates its practical effectiveness
on large-scale real-world data sets. A key element in the analysis is the
distinction between training with respect to the (unknown) data distribution,
and its empirical counterpart. To overcome issues in GAN training, we pursue
the idea of smoothing the Jensen-Shannon Divergence (JSD) by incorporating
noise in the input distributions of the discriminator. As we show, this
effectively leads to an empirical version of the JSD in which the true and the
generator densities are replaced by kernel density estimates, which leads to
Kernel GANs.
Analysis of $p$-Laplacian Regularization in Semi-Supervised Learning
Authors: Dejan Slepčev, Matthew Thorpe
Date: 2017-07-19 00:00:00

We investigate a family of regression problems in a semi-supervised setting.
The task is to assign real-valued labels to a set of $n$ sample points,
provided a small training subset of $N$ labeled points. A goal of
semi-supervised learning is to take advantage of the (geometric) structure
provided by the large number of unlabeled data when assigning labels. We
consider random geometric graphs, with connection radius $\epsilon(n)$, to
represent the geometry of the data set. Functionals which model the task reward
the regularity of the estimator function and impose or reward the agreement
with the training data. Here we consider the discrete $p$-Laplacian
regularization.
  We investigate asymptotic behavior when the number of unlabeled points
increases, while the number of training points remains fixed. We uncover a
delicate interplay between the regularizing nature of the functionals
considered and the nonlocality inherent to the graph constructions. We
rigorously obtain almost optimal ranges on the scaling of $\epsilon(n)$ for the
asymptotic consistency to hold. We prove that the minimizers of the discrete
functionals in random setting converge uniformly to the desired continuum
limit. Furthermore we discover that for the standard model used there is a
restrictive upper bound on how quickly $\epsilon(n)$ must converge to zero as
$n \to \infty$. We introduce a new model which is as simple as the original
model, but overcomes this restriction.
NoReC: The Norwegian Review Corpus
Authors: Erik Velldal, Lilja Øvrelid, Eivind Alexander Bergem, Cathrine Stadsnes, Samia Touileb, Fredrik Jørgensen
Date: 2017-10-15 00:00:00
Proceeding: LREC 2018 5
Tasks: Opinion Mining, Sentiment Analysis

This paper presents the Norwegian Review Corpus (NoReC), created for training
and evaluating models for document-level sentiment analysis. The full-text
reviews have been collected from major Norwegian news sources and cover a range
of different domains, including literature, movies, video games, restaurants,
music and theater, in addition to product reviews across a range of categories.
Each review is labeled with a manually assigned score of 1-6, as provided by
the rating of the original author. This first release of the corpus comprises
more than 35,000 reviews. It is distributed using the CoNLL-U format,
pre-processed using UDPipe, along with a rich set of metadata. The work
reported in this paper forms part of the SANT initiative (Sentiment Analysis
for Norwegian Text), a project seeking to provide resources and tools for
sentiment analysis and opinion mining for Norwegian. As resources for sentiment
analysis have so far been unavailable for Norwegian, NoReC represents a highly
valuable and sought-after addition to Norwegian language technology.
ChineseFoodNet: A large-scale Image Dataset for Chinese Food Recognition
Authors: Xin Chen, Yu Zhu, Hua Zhou, Liang Diao, Dongyan Wang
Date: 2017-05-08 00:00:00
Tasks: Food Recognition

In this paper, we introduce a new and challenging large-scale food image
dataset called "ChineseFoodNet", which aims to automatically recognizing
pictured Chinese dishes. Most of the existing food image datasets collected
food images either from recipe pictures or selfie. In our dataset, images of
each food category of our dataset consists of not only web recipe and menu
pictures but photos taken from real dishes, recipe and menu as well.
ChineseFoodNet contains over 180,000 food photos of 208 categories, with each
category covering a large variations in presentations of same Chinese food. We
present our efforts to build this large-scale image dataset, including food
category selection, data collection, and data clean and label, in particular
how to use machine learning methods to reduce manual labeling work that is an
expensive process. We share a detailed benchmark of several state-of-the-art
deep convolutional neural networks (CNNs) on ChineseFoodNet. We further propose
a novel two-step data fusion approach referred as "TastyNet", which combines
prediction results from different CNNs with voting method. Our proposed
approach achieves top-1 accuracies of 81.43% on the validation set and 81.55%
on the test set, respectively. The latest dataset is public available for
research and can be achieved at https://sites.google.com/view/chinesefoodnet.
Clickbait Detection in Tweets Using Self-attentive Network
Authors: Yiwei Zhou
Date: 2017-10-15 00:00:00
Tasks: Clickbait Detection, Feature Engineering, General Classification

Clickbait detection in tweets remains an elusive challenge. In this paper, we
describe the solution for the Zingel Clickbait Detector at the Clickbait
Challenge 2017, which is capable of evaluating each tweet's level of click
baiting. We first reformat the regression problem as a multi-classification
problem, based on the annotation scheme. To perform multi-classification, we
apply a token-level, self-attentive mechanism on the hidden states of
bi-directional Gated Recurrent Units (biGRU), which enables the model to
generate tweets' task-specific vector representations by attending to important
tokens. The self-attentive neural network can be trained end-to-end, without
involving any manual feature engineering. Our detector ranked first in the
final evaluation of Clickbait Challenge 2017.
Sushi Dish - Object detection and classification from real images
Authors: Yeongjin Oh, Seunghyun Son, Gyumin Sim
Date: 2017-09-03 00:00:00
Tasks: Classification, General Classification, Object, object-detection, Object Detection

In conveyor belt sushi restaurants, billing is a burdened job because one has
to manually count the number of dishes and identify the color of them to
calculate the price. In a busy situation, there can be a mistake that customers
are overcharged or under-charged. To deal with this problem, we developed a
method that automatically identifies the color of dishes and calculate the
total price using real images. Our method consists of ellipse fitting and
convol-utional neural network. It achieves ellipse detection precision 85% and
recall 96% and classification accuracy 92%.
Learning to Estimate 3D Hand Pose from Single RGB Images
Authors: Christian Zimmermann, Thomas Brox
Date: 2017-05-03 00:00:00
Proceeding: ICCV 2017 10
Tasks: 3D Hand Pose Estimation, Hand Pose Estimation, Pose Estimation, Sign Language Recognition

Low-cost consumer depth cameras and deep learning have enabled reasonable 3D
hand pose estimation from single depth images. In this paper, we present an
approach that estimates 3D hand pose from regular RGB images. This task has far
more ambiguities due to the missing depth information. To this end, we propose
a deep network that learns a network-implicit 3D articulation prior. Together
with detected keypoints in the images, this network yields good estimates of
the 3D pose. We introduce a large scale 3D hand pose dataset based on synthetic
hand models for training the involved networks. Experiments on a variety of
test sets, including one on sign language recognition, demonstrate the
feasibility of 3D hand pose estimation on single color images.
Data-Driven Background Subtraction Algorithm for in-Camera Acceleration in Thermal Imagery
Authors: Konstantinos Makantasis, Antonis Nikitakis, Anastasios Doulamis, Nikolaos Doulamis, Yannis Papaefstathiou
Date: 2016-07-31 00:00:00
Tasks: High-Level Synthesis

Detection of moving objects in videos is a crucial step towards successful
surveillance and monitoring applications. A key component for such tasks is
called background subtraction and tries to extract regions of interest from the
image background for further processing or action. For this reason, its
accuracy and real-time performance is of great significance. Although,
effective background subtraction methods have been proposed, only a few of them
take into consideration the special characteristics of thermal imagery. In this
work, we propose a background subtraction scheme, which models the thermal
responses of each pixel as a mixture of Gaussians with unknown number of
components. Following a Bayesian approach, our method automatically estimates
the mixture structure, while simultaneously it avoids over/under fitting. The
pixel density estimate is followed by an efficient and highly accurate updating
mechanism, which permits our system to be automatically adapted to dynamically
changing operation conditions. We propose a reference implementation of our
method in reconfigurable hardware achieving both adequate performance and low
power consumption. Adopting a High Level Synthesis design, demanding floating
point arithmetic operations are mapped in reconfigurable hardware;
demonstrating fast-prototyping and on-field customization at the same time.
Vector Quantization using the Improved Differential Evolution Algorithm for Image Compression
Authors: Sayan Nag
Date: 2017-10-15 00:00:00
Tasks: Efficient Exploration, Image Compression, Quantization

Vector Quantization, VQ is a popular image compression technique with a
simple decoding architecture and high compression ratio. Codebook designing is
the most essential part in Vector Quantization. LindeBuzoGray, LBG is a
traditional method of generation of VQ Codebook which results in lower PSNR
value. A Codebook affects the quality of image compression, so the choice of an
appropriate codebook is a must. Several optimization techniques have been
proposed for global codebook generation to enhance the quality of image
compression. In this paper, a novel algorithm called IDE-LBG is proposed which
uses Improved Differential Evolution Algorithm coupled with LBG for generating
optimum VQ Codebooks. The proposed IDE works better than the traditional DE
with modifications in the scaling factor and the boundary control mechanism.
The IDE generates better solutions by efficient exploration and exploitation of
the search space. Then the best optimal solution obtained by the IDE is
provided as the initial Codebook for the LBG. This approach produces an
efficient Codebook with less computational time and the consequences include
excellent PSNR values and superior quality reconstructed images. It is observed
that the proposed IDE-LBG find better VQ Codebooks as compared to IPSO-LBG,
BA-LBG and FA-LBG.
Beyond similarity assessment: Selecting the optimal model for sequence alignment via the Factorized Asymptotic Bayesian algorithm
Authors: Taikai Takeda, Michiaki Hamada
Date: 2017-05-19 00:00:00
Tasks: Model Selection

Pair Hidden Markov Models (PHMMs) are probabilistic models used for pairwise
sequence alignment, a quintessential problem in bioinformatics. PHMMs include
three types of hidden states: match, insertion and deletion. Most previous
studies have used one or two hidden states for each PHMM state type. However,
few studies have examined the number of states suitable for representing
sequence data or improving alignment accuracy.We developed a novel method to
select superior models (including the number of hidden states) for PHMM. Our
method selects models with the highest posterior probability using Factorized
Information Criteria (FIC), which is widely utilised in model selection for
probabilistic models with hidden variables. Our simulations indicated this
method has excellent model selection capabilities with slightly improved
alignment accuracy. We applied our method to DNA datasets from 5 and 28
species, ultimately selecting more complex models than those used in previous
studies.