pwc-archive/papers-with-abstracts huggingface dataset:
EML-NET:An Expandable Multi-Layer NETwork for Saliency Prediction
Authors: Sen Jia, Neil D. B. Bruce
Date: 2018-05-02 00:00:00
Tasks: Saliency Prediction, Scene Understanding

Saliency prediction can benefit from training that involves scene
understanding that may be tangential to the central task; this may include
understanding places, spatial layout, objects or involve different datasets and
their bias. One can combine models, but to do this in a sophisticated manner
can be complex, and also result in unwieldy networks or produce competing
objectives that are hard to balance. In this paper, we propose a scalable
system to leverage multiple powerful deep CNN models to better extract visual
features for saliency prediction. Our design differs from previous studies in
that the whole system is trained in an almost end-to-end piece-wise fashion.
The encoder and decoder components are separately trained to deal with
complexity tied to the computational paradigm and required space. Furthermore,
the encoder can contain more than one CNN model to extract features, and models
can have different architectures or be pre-trained on different datasets. This
parallel design yields a better computational paradigm overcoming limits to the
variety of information or inference that can be combined at the encoder stage
towards deeper networks and a more powerful encoding. Our network can be easily
expanded almost without any additional cost, and other pre-trained CNN models
can be incorporated availing a wider range of visual knowledge. We denote our
expandable multi-layer network as EML-NET and our method achieves the
state-of-the-art results on the public saliency benchmarks, SALICON, MIT300 and
CAT2000.
Covariate Shift Estimation based Adaptive Ensemble Learning for Handling Non-Stationarity in Motor Imagery related EEG-based Brain-Computer Interface
Authors: Haider Raza, Dheeraj Rathee, ShangMing Zhou, Hubert Cecotti, Girijesh Prasad
Date: 2018-05-02 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), Ensemble Learning, Motor Imagery

The non-stationary nature of electroencephalography (EEG) signals makes an
EEG-based brain-computer interface (BCI) a dynamic system, thus improving its
performance is a challenging task. In addition, it is well-known that due to
non-stationarity based covariate shifts, the input data distributions of
EEG-based BCI systems change during inter- and intra-session transitions, which
poses great difficulty for developments of online adaptive data-driven systems.
Ensemble learning approaches have been used previously to tackle this
challenge. However, passive scheme based implementation leads to poor
efficiency while increasing high computational cost. This paper presents a
novel integration of covariate shift estimation and unsupervised adaptive
ensemble learning (CSE-UAEL) to tackle non-stationarity in motor-imagery (MI)
related EEG classification. The proposed method first employs an exponentially
weighted moving average model to detect the covariate shifts in the common
spatial pattern features extracted from MI related brain responses. Then, a
classifier ensemble was created and updated over time to account for changes in
streaming input data distribution wherein new classifiers are added to the
ensemble in accordance with estimated shifts. Furthermore, using two publicly
available BCI-related EEG datasets, the proposed method was extensively
compared with the state-of-the-art single-classifier based passive scheme,
single-classifier based active scheme and ensemble based passive schemes. The
experimental results show that the proposed active scheme based ensemble
learning algorithm significantly enhances the BCI performance in MI
classifications.
Hypothesis Only Baselines in Natural Language Inference
Authors: Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, Benjamin Van Durme
Date: 2018-05-02 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Natural Language Inference

We propose a hypothesis only baseline for diagnosing Natural Language
Inference (NLI). Especially when an NLI dataset assumes inference is occurring
based purely on the relationship between a context and a hypothesis, it follows
that assessing entailment relations while ignoring the provided context is a
degenerate solution. Yet, through experiments on ten distinct NLI datasets, we
find that this approach, which we refer to as a hypothesis-only model, is able
to significantly outperform a majority class baseline across a number of NLI
datasets. Our analysis suggests that statistical irregularities may allow a
model to perform NLI in some datasets beyond what should be achievable without
access to the context.
RFCDE: Random Forests for Conditional Density Estimation
Authors: Taylor Pospisil, Ann B. Lee
Date: 2018-04-16 00:00:00
Tasks: Density Estimation, General Classification, regression

Random forests is a common non-parametric regression technique which performs
well for mixed-type data and irrelevant covariates, while being robust to
monotonic variable transformations. Existing random forest implementations
target regression or classification. We introduce the RFCDE package for fitting
random forest models optimized for nonparametric conditional density
estimation, including joint densities for multiple responses. This enables
analysis of conditional probability distributions which is useful for
propagating uncertainty and of joint distributions that describe relationships
between multiple responses and covariates. RFCDE is released under the MIT
open-source license and can be accessed at https://github.com/tpospisi/rfcde .
Both R and Python versions, which call a common C++ library, are available.
Split and Rephrase: Better Evaluation and a Stronger Baseline
Authors: Roee Aharoni, Yoav Goldberg
Date: 2018-05-02 00:00:00
Tasks: Memorization, Sentence, Split and Rephrase

Splitting and rephrasing a complex sentence into several shorter sentences
that convey the same meaning is a challenging problem in NLP. We show that
while vanilla seq2seq models can reach high scores on the proposed benchmark
(Narayan et al., 2017), they suffer from memorization of the training set which
contains more than 89% of the unique simple sentences from the validation and
test sets. To aid this, we present a new train-development-test data split and
neural models augmented with a copy-mechanism, outperforming the best reported
baseline by 8.68 BLEU and fostering further progress on the task.
Explaining Anomalies in Groups with Characterizing Subspace Rules
Authors: Meghanath Macha, Leman Akoglu
Date: 2017-08-20 00:00:00
Tasks: Anomaly Detection, Descriptive

Anomaly detection has numerous applications and has been studied vastly. We
consider a complementary problem that has a much sparser literature: anomaly
description. Interpretation of anomalies is crucial for practitioners for
sense-making, troubleshooting, and planning actions. To this end, we present a
new approach called x-PACS (for eXplaining Patterns of Anomalies with
Characterizing Subspaces), which "reverse-engineers" the known anomalies by
identifying (1) the groups (or patterns) that they form, and (2) the
characterizing subspace and feature rules that separate each anomalous pattern
from normal instances. Explaining anomalies in groups not only saves analyst
time and gives insight into various types of anomalies, but also draws
attention to potentially critical, repeating anomalies.
  In developing x-PACS, we first construct a desiderata for the anomaly
description problem. From a descriptive data mining perspective, our method
exhibits five desired properties in our desiderata. Namely, it can unearth
anomalous patterns (i) of multiple different types, (ii) hidden in arbitrary
subspaces of a high dimensional space, (iii) interpretable by the analysts,
(iv) different from normal patterns of the data, and finally (v) succinct,
providing the shortest data description. Furthermore, x-PACS is highly
parallelizable and scales linearly in terms of data size.
  No existing work on anomaly description satisfies all of these properties
simultaneously. While not our primary goal, the anomalous patterns we find
serve as interpretable "signatures" and can be used for detection. We show the
effectiveness of x-PACS in explanation as well as detection on real-world
datasets as compared to state-of-the-art.
Unsupervised Learning using Pretrained CNN and Associative Memory Bank
Authors: Qun Liu, Supratik Mukhopadhyay
Date: 2018-05-02 00:00:00
Tasks: Few-Shot Image Classification, Fine-Grained Image Classification, Image Classification, Object Recognition, Semi-Supervised Image Classification

Deep Convolutional features extracted from a comprehensive labeled dataset,
contain substantial representations which could be effectively used in a new
domain. Despite the fact that generic features achieved good results in many
visual tasks, fine-tuning is required for pretrained deep CNN models to be more
effective and provide state-of-the-art performance. Fine tuning using the
backpropagation algorithm in a supervised setting, is a time and resource
consuming process. In this paper, we present a new architecture and an approach
for unsupervised object recognition that addresses the above mentioned problem
with fine tuning associated with pretrained CNN-based supervised deep learning
approaches while allowing automated feature extraction. Unlike existing works,
our approach is applicable to general object recognition tasks. It uses a
pretrained (on a related domain) CNN model for automated feature extraction
pipelined with a Hopfield network based associative memory bank for storing
patterns for classification purposes. The use of associative memory bank in our
framework allows eliminating backpropagation while providing competitive
performance on an unseen dataset.
Fine-Grained Facial Expression Analysis Using Dimensional Emotion Model
Authors: Feng Zhou, Shu Kong, Charless Fowlkes, Tao Chen, Baiying Lei
Date: 2018-05-02 00:00:00
Tasks: General Classification, regression

Automated facial expression analysis has a variety of applications in
human-computer interaction. Traditional methods mainly analyze prototypical
facial expressions of no more than eight discrete emotions as a classification
task. However, in practice, spontaneous facial expressions in naturalistic
environment can represent not only a wide range of emotions, but also different
intensities within an emotion family. In such situation, these methods are not
reliable or adequate. In this paper, we propose to train deep convolutional
neural networks (CNNs) to analyze facial expressions explainable in a
dimensional emotion model. The proposed method accommodates not only a set of
basic emotion expressions, but also a full range of other emotions and subtle
emotion intensities that we both feel in ourselves and perceive in others in
our daily life. Specifically, we first mapped facial expressions into
dimensional measures so that we transformed facial expression analysis from a
classification problem to a regression one. We then tested our CNN-based
methods for facial expression regression and these methods demonstrated
promising performance. Moreover, we improved our method by a bilinear pooling
which encodes second-order statistics of features. We showed such bilinear-CNN
models significantly outperformed their respective baselines.
A Numerical Framework for Efficient Motion Estimation on Evolving Sphere-Like Surfaces based on Brightness and Mass Conservation Laws
Authors: Lukas F. Lang
Date: 2018-05-02 00:00:00
Tasks: Motion Estimation, Optical Flow Estimation

In this work we consider brightness and mass conservation laws for motion
estimation on evolving Riemannian 2-manifolds that allow for a radial
parametrisation from the 2-sphere. While conservation of brightness constitutes
the foundation for optical flow methods and has been generalised to said
scenario, we formulate in this article the principle of mass conservation for
time-varying surfaces which are embedded in Euclidean 3-space and derive a
generalised continuity equation. The main motivation for this work is efficient
cell motion estimation in time-lapse (4D) volumetric fluorescence microscopy
images of a living zebrafish embryo. Increasing spatial and temporal resolution
of modern microscopes require efficient analysis of such data. With this
application in mind we address this need and follow an emerging paradigm in
this field: dimensional reduction. In light of the ill-posedness of considered
conservation laws we employ Tikhonov regularisation and propose the use of
spatially varying regularisation functionals that recover motion only in
regions with cells. For the efficient numerical solution we devise a Galerkin
method based on compactly supported (tangent) vectorial basis functions.
Furthermore, for the fast and accurate estimation of the evolving sphere-like
surface from scattered data we utilise surface interpolation with
spatio-temporal regularisation. We present numerical results based on
aforementioned zebrafish microscopy data featuring fluorescently labelled
cells.
Comparison of Classical and Nonlinear Models for Short-Term Electricity Price Prediction
Authors: Elaheh Fata, Igor Kadota, Ian Schneider
Date: 2018-05-02 00:00:00
Tasks: BIG-bench Machine Learning, regression

Electricity is bought and sold in wholesale markets at prices that fluctuate
significantly. Short-term forecasting of electricity prices is an important
endeavor because it helps electric utilities control risk and because it
influences competitive strategy for generators. As the "smart grid" grows,
short-term price forecasts are becoming an important input to bidding and
control algorithms for battery operators and demand response aggregators. While
the statistics and machine learning literature offers many proposed methods for
electricity price prediction, there is no consensus supporting a single best
approach. We test two contrasting machine learning approaches for predicting
electricity prices, regression decision trees and recurrent neural networks
(RNNs), and compare them to a more traditional ARIMA implementation. We conduct
the analysis on a challenging dataset of electricity prices from ERCOT, in
Texas, where price fluctuation is especially high. We find that regression
decision trees in particular achieves high performance compared to the other
methods, suggesting that regression trees should be more carefully considered
for electricity price forecasting.
Cross-domain CNN for Hyperspectral Image Classification
Authors: Hyungtae Lee, Sungmin Eum, Heesung Kwon
Date: 2018-01-31 00:00:00
Tasks: Classification, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

In this paper, we address the dataset scarcity issue with the hyperspectral
image classification. As only a few thousands of pixels are available for
training, it is difficult to effectively learn high-capacity Convolutional
Neural Networks (CNNs). To cope with this problem, we propose a novel
cross-domain CNN containing the shared parameters which can co-learn across
multiple hyperspectral datasets. The network also contains the non-shared
portions designed to handle the dataset specific spectral characteristics and
the associated classification tasks. Our approach is the first attempt to learn
a CNN for multiple hyperspectral datasets, in an end-to-end fashion. Moreover,
we have experimentally shown that the proposed network trained on three of the
widely used datasets outperform all the baseline networks which are trained on
single dataset.
k-Nearest Neighbors by Means of Sequence to Sequence Deep Neural Networks and Memory Networks
Authors: Yiming Xu, Diego Klabjan
Date: 2018-04-27 00:00:00
Tasks: General Classification

k-Nearest Neighbors is one of the most fundamental but effective classification models. In this paper, we propose two families of models built on a sequence to sequence model and a memory network model to mimic the k-Nearest Neighbors model, which generate a sequence of labels, a sequence of out-of-sample feature vectors and a final label for classification, and thus they could also function as oversamplers. We also propose 'out-of-core' versions of our models which assume that only a small portion of data can be loaded into memory. Computational experiments show that our models on structured datasets outperform k-Nearest Neighbors, a feed-forward neural network, XGBoost, lightGBM, random forest and a memory network, due to the fact that our models must produce additional output and not just the label. On image and text datasets, the performance of our model is close to many state-of-the-art deep models. As an oversampler on imbalanced datasets, the sequence to sequence kNN model often outperforms Synthetic Minority Over-sampling Technique and Adaptive Synthetic Sampling.
Automatic Inference of Cross-modal Connection Topologies for X-CNNs
Authors: Laurynas Karazija, Petar Veličković, Pietro Liò
Date: 2018-05-02 00:00:00

This paper introduces a way to learn cross-modal convolutional neural network
(X-CNN) architectures from a base convolutional network (CNN) and the training
data to reduce the design cost and enable applying cross-modal networks in
sparse data environments. Two approaches for building X-CNNs are presented. The
base approach learns the topology in a data-driven manner, by using
measurements performed on the base CNN and supplied data. The iterative
approach performs further optimisation of the topology through a combined
learning procedure, simultaneously learning the topology and training the
network. The approaches were evaluated agains examples of hand-designed X-CNNs
and their base variants, showing superior performance and, in some cases,
gaining an additional 9% of accuracy. From further considerations, we conclude
that the presented methodology takes less time than any manual approach would,
whilst also significantly reducing the design complexity. The application of
the methods is fully automated and implemented in Xsertion library.
k-SVRG: Variance Reduction for Large Scale Optimization
Authors: Anant Raj, Sebastian U. Stich
Date: 2018-05-02 00:00:00

Variance reduced stochastic gradient (SGD) methods converge significantly
faster than the vanilla SGD counterpart. However, these methods are not very
practical on large scale problems, as they either i) require frequent passes
over the full data to recompute gradients---without making any progress during
this time (like for SVRG), or ii)~they require additional memory that can
surpass the size of the input problem (like for SAGA). In this work, we propose
$k$-SVRG that addresses these issues by making best use of the \emph{available}
memory and minimizes the stalling phases without progress. We prove linear
convergence of $k$-SVRG on strongly convex problems and convergence to
stationary points on non-convex problems. Numerical experiments show the
effectiveness of our method.
SaaS: Speed as a Supervisor for Semi-supervised Learning
Authors: Safa Cicek, Alhussein Fawzi, Stefano Soatto
Date: 2018-05-02 00:00:00
Proceeding: ECCV 2018 9

We introduce the SaaS Algorithm for semi-supervised learning, which uses
learning speed during stochastic gradient descent in a deep neural network to
measure the quality of an iterative estimate of the posterior probability of
unknown labels. Training speed in supervised learning correlates strongly with
the percentage of correct labels, so we use it as an inference criterion for
the unknown labels, without attempting to infer the model parameters at first.
Despite its simplicity, SaaS achieves state-of-the-art results in
semi-supervised learning benchmarks.
modAL: A modular active learning framework for Python
Authors: Tivadar Danka, Peter Horvath
Date: 2018-05-02 00:00:00
Tasks: Active Learning

modAL is a modular active learning framework for Python, aimed to make active
learning research and practice simpler. Its distinguishing features are (i)
clear and modular object oriented design (ii) full compatibility with
scikit-learn models and workflows. These features make fast prototyping and
easy extensibility possible, aiding the development of real-life active
learning pipelines and novel algorithms as well. modAL is fully open source,
hosted on GitHub at https://github.com/cosmic-cortex/modAL. To assure code
quality, extensive unit tests are provided and continuous integration is
applied. In addition, a detailed documentation with several tutorials are also
available for ease of use. The framework is available in PyPI and distributed
under the MIT license.
Detect-and-Track: Efficient Pose Estimation in Videos
Authors: Rohit Girdhar, Georgia Gkioxari, Lorenzo Torresani, Manohar Paluri, Du Tran
Date: 2017-12-26 00:00:00
Proceeding: CVPR 2018 6
Tasks: Human Detection, Keypoint Estimation, Multi-Object Tracking, Object Tracking, Pose Estimation, Pose Tracking, Video Understanding

This paper addresses the problem of estimating and tracking human body
keypoints in complex, multi-person video. We propose an extremely lightweight
yet highly effective approach that builds upon the latest advancements in human
detection and video understanding. Our method operates in two-stages: keypoint
estimation in frames or short clips, followed by lightweight tracking to
generate keypoint predictions linked over the entire video. For frame-level
pose estimation we experiment with Mask R-CNN, as well as our own proposed 3D
extension of this model, which leverages temporal information over small clips
to generate more robust frame predictions. We conduct extensive ablative
experiments on the newly released multi-person video pose estimation benchmark,
PoseTrack, to validate various design choices of our model. Our approach
achieves an accuracy of 55.2% on the validation and 51.8% on the test set using
the Multi-Object Tracking Accuracy (MOTA) metric, and achieves state of the art
performance on the ICCV 2017 PoseTrack keypoint tracking challenge.
Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation
Authors: Yudong Chen, Yuejie Chi
Date: 2018-02-23 00:00:00
Tasks: Collaborative Filtering, Dimensionality Reduction

Low-rank modeling plays a pivotal role in signal processing and machine
learning, with applications ranging from collaborative filtering, video
surveillance, medical imaging, to dimensionality reduction and adaptive
filtering. Many modern high-dimensional data and interactions thereof can be
modeled as lying approximately in a low-dimensional subspace or manifold,
possibly with additional structures, and its proper exploitations lead to
significant reduction of costs in sensing, computation and storage. In recent
years, there is a plethora of progress in understanding how to exploit low-rank
structures using computationally efficient procedures in a provable manner,
including both convex and nonconvex approaches. On one side, convex relaxations
such as nuclear norm minimization often lead to statistically optimal
procedures for estimating low-rank matrices, where first-order methods are
developed to address the computational challenges; on the other side, there is
emerging evidence that properly designed nonconvex procedures, such as
projected gradient descent, often provide globally optimal solutions with a
much lower computational cost in many problems. This survey article will
provide a unified overview of these recent advances on low-rank matrix
estimation from incomplete measurements. Attention is paid to rigorous
characterization of the performance of these algorithms, and to problems where
the low-rank matrix have additional structural properties that require new
algorithmic designs and theoretical analysis.