pwc-archive/papers-with-abstracts huggingface dataset:
Deep multi-survey classification of variable stars
Authors: Carlos Aguirre, Karim Pichara, Ignacio Becker
Date: 2018-10-21 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Classification Of Variable Stars, Data Augmentation, General Classification, image-classification, Image Classification, Survey, Time Series Analysis

During the last decade, a considerable amount of effort has been made to
classify variable stars using different machine learning techniques. Typically,
light curves are represented as vectors of statistical descriptors or features
that are used to train various algorithms. These features demand big
computational powers that can last from hours to days, making impossible to
create scalable and efficient ways of automatically classifying variable stars.
Also, light curves from different surveys cannot be integrated and analyzed
together when using features, because of observational differences. For
example, having variations in cadence and filters, feature distributions become
biased and require expensive data-calibration models. The vast amount of data
that will be generated soon make necessary to develop scalable machine learning
architectures without expensive integration techniques. Convolutional Neural
Networks have shown impressing results in raw image classification and
representation within the machine learning literature. In this work, we present
a novel Deep Learning model for light curve classification, mainly based on
convolutional units. Our architecture receives as input the differences between
time and magnitude of light curves. It captures the essential classification
patterns regardless of cadence and filter. In addition, we introduce a novel
data augmentation schema for unevenly sampled time series. We test our method
using three different surveys: OGLE-III; Corot; and VVV, which differ in
filters, cadence, and area of the sky. We show that besides the benefit of
scalability, our model obtains state of the art levels accuracy in light curve
classification benchmarks.
Data models for service failure prediction in supply-chain networks
Authors: Monika Sharma, Tristan Glatard, Eric Gelinas, Mariam Tagmouti, Brigitte Jaumard
Date: 2018-10-20 00:00:00
Tasks: Specificity

We aim to predict and explain service failures in supply-chain networks, more
precisely among last-mile pickup and delivery services to customers. We analyze
a dataset of 500,000 services using (1) supervised classification with Random
Forests, and (2) Association Rules. Our classifier reaches an average
sensitivity of 0.7 and an average specificity of 0.7 for the 5 studied types of
failure. Association Rules reassert the importance of confirmation calls to
prevent failures due to customers not at home, show the importance of the time
window size, slack time, and geographical location of the customer for the
other failure types, and highlight the effect of the retailer company on
several failure types. To reduce the occurrence of service failures, our data
models could be coupled to optimizers, or used to define counter-measures to be
taken by human dispatchers.
SpotNet - Learned iterations for cell detection in image-based immunoassays
Authors: Pol del Aguila Pla, Vidit Saxena, Joakim Jaldén
Date: 2018-10-15 00:00:00
Tasks: Cell Detection

Accurate cell detection and counting in the image-based ELISpot and
FluoroSpot immunoassays is a challenging task. Methodology recently proposed by
our group matches human accuracy by leveraging knowledge of the underlying
physical process of these assays and using state-of-the-art iterative
techniques to solve an inverse problem. Nonetheless, thousands of
computationally expensive iterations are often needed to reach a near-optimal
solution. In this paper, we exploit the structure of the iterations to design a
parameterized computation graph, SpotNet, that learns the characteristic
patterns embedded within several training images and their respective cell
secretion information. Further, we compare SpotNet to a customized
convolutional neural network layout for cell detection based on recent
advances. We show empirical evidence that, while both designs obtain a
detection performance far beyond that of a human expert, SpotNet is
substantially easier to train and obtains better estimates of cell secretion.
Toward an AI Physicist for Unsupervised Learning
Authors: Tailin Wu, Max Tegmark
Date: 2018-10-24 00:00:00
Tasks: Lifelong learning

We investigate opportunities and challenges for improving unsupervised machine learning using four common strategies with a long history in physics: divide-and-conquer, Occam's razor, unification and lifelong learning. Instead of using one model to learn everything, we propose a novel paradigm centered around the learning and manipulation of *theories*, which parsimoniously predict both aspects of the future (from past observations) and the domain in which these predictions are accurate. Specifically, we propose a novel generalized-mean-loss to encourage each theory to specialize in its comparatively advantageous domain, and a differentiable description length objective to downweight bad data and "snap" learned theories into simple symbolic formulas. Theories are stored in a "theory hub", which continuously unifies learned theories and can propose theories when encountering new environments. We test our implementation, the toy "AI Physicist" learning agent, on a suite of increasingly complex physics environments. From unsupervised observation of trajectories through worlds involving random combinations of gravity, electromagnetism, harmonic motion and elastic bounces, our agent typically learns faster and produces mean-squared prediction errors about a billion times smaller than a standard feedforward neural net of comparable complexity, typically recovering integer and rational theory parameters exactly. Our agent successfully identifies domains with different laws of motion also for a nonlinear chaotic double pendulum in a piecewise constant force field.
Spatiotemporal CNNs for Pornography Detection in Videos
Authors: Murilo Varges da Silva, Aparecido Nilceu Marana
Date: 2018-10-24 00:00:00
Tasks: Pornography Detection

With the increasing use of social networks and mobile devices, the number of
videos posted on the Internet is growing exponentially. Among the inappropriate
contents published on the Internet, pornography is one of the most worrying as
it can be accessed by teens and children. Two spatiotemporal CNNs, VGG-C3D CNN
and ResNet R(2+1)D CNN, were assessed for pornography detection in videos in
the present study. Experimental results using the Pornography-800 dataset
showed that these spatiotemporal CNNs performed better than some
state-of-the-art methods based on bag of visual words and are competitive with
other CNN-based approaches, reaching accuracy of 95.1%.
Neighbourhood Consensus Networks
Authors: Ignacio Rocco, Mircea Cimpoi, Relja Arandjelović, Akihiko Torii, Tomas Pajdla, Josef Sivic
Date: 2018-10-24 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Semantic correspondence, Visual Localization

We address the problem of finding reliable dense correspondences between a
pair of images. This is a challenging task due to strong appearance differences
between the corresponding scene elements and ambiguities generated by
repetitive patterns. The contributions of this work are threefold. First,
inspired by the classic idea of disambiguating feature matches using semi-local
constraints, we develop an end-to-end trainable convolutional neural network
architecture that identifies sets of spatially consistent matches by analyzing
neighbourhood consensus patterns in the 4D space of all possible
correspondences between a pair of images without the need for a global
geometric model. Second, we demonstrate that the model can be trained
effectively from weak supervision in the form of matching and non-matching
image pairs without the need for costly manual annotation of point to point
correspondences. Third, we show the proposed neighbourhood consensus network
can be applied to a range of matching tasks including both category- and
instance-level matching, obtaining the state-of-the-art results on the PF
Pascal dataset and the InLoc indoor visual localization benchmark.
Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing
Authors: Yadollah Yaghoobzadeh, Hinrich Schütze
Date: 2018-10-24 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Entity Typing, Multiview Learning, MULTI-VIEW LEARNING

Knowledge bases (KBs) are paramount in NLP. We employ multiview learning for
increasing accuracy and coverage of entity type information in KBs. We rely on
two metaviews: language and representation. For language, we consider
high-resource and low-resource languages from Wikipedia. For representation, we
consider representations based on the context distribution of the entity (i.e.,
on its embedding), on the entity's name (i.e., on its surface form) and on its
description in Wikipedia. The two metaviews language and representation can be
freely combined: each pair of language and representation (e.g., German
embedding, English description, Spanish name) is a distinct view. Our
experiments on entity typing with fine-grained classes demonstrate the
effectiveness of multiview learning. We release MVET, a large multiview - and,
in particular, multilingual - entity typing dataset we created. Mono- and
multilingual fine-grained entity typing systems can be evaluated on this
dataset.
Sleep-like slow oscillations improve visual classification through synaptic homeostasis and memory association in a thalamo-cortical model
Authors: Cristiano Capone, Elena Pastorelli, Bruno Golosio, Pier Stanislao Paolucci
Date: 2018-10-24 00:00:00
Tasks: General Classification, Image Classification, Retrieval

The occurrence of sleep passed through the evolutionary sieve and is widespread in animal species. Sleep is known to be beneficial to cognitive and mnemonic tasks, while chronic sleep deprivation is detrimental. Despite the importance of the phenomenon, a complete understanding of its functions and underlying mechanisms is still lacking. In this paper, we show interesting effects of deep-sleep-like slow oscillation activity on a simplified thalamo-cortical model which is trained to encode, retrieve and classify images of handwritten digits. During slow oscillations, spike-timing-dependent-plasticity (STDP) produces a differential homeostatic process. It is characterized by both a specific unsupervised enhancement of connections among groups of neurons associated to instances of the same class (digit) and a simultaneous down-regulation of stronger synapses created by the training. This hierarchical organization of post-sleep internal representations favours higher performances in retrieval and classification tasks. The mechanism is based on the interaction between top-down cortico-thalamic predictions and bottom-up thalamo-cortical projections during deep-sleep-like slow oscillations. Indeed, when learned patterns are replayed during sleep, cortico-thalamo-cortical connections favour the activation of other neurons coding for similar thalamic inputs, promoting their association. Such mechanism hints at possible applications to artificial learning systems.
Forecasting Individualized Disease Trajectories using Interpretable Deep Learning
Authors: Ahmed M. Alaa, Mihaela van der Schaar
Date: 2018-10-24 00:00:00
Tasks: Deep Learning, Disease Prediction, Disease Trajectory Forecasting, State Space Models

Disease progression models are instrumental in predicting individual-level
health trajectories and understanding disease dynamics. Existing models are
capable of providing either accurate predictions of patients prognoses or
clinically interpretable representations of disease pathophysiology, but not
both. In this paper, we develop the phased attentive state space (PASS) model
of disease progression, a deep probabilistic model that captures complex
representations for disease progression while maintaining clinical
interpretability. Unlike Markovian state space models which assume memoryless
dynamics, PASS uses an attention mechanism to induce "memoryful" state
transitions, whereby repeatedly updated attention weights are used to focus on
past state realizations that best predict future states. This gives rise to
complex, non-stationary state dynamics that remain interpretable through the
generated attention weights, which designate the relationships between the
realized state variables for individual patients. PASS uses phased LSTM units
(with time gates controlled by parametrized oscillations) to generate the
attention weights in continuous time, which enables handling
irregularly-sampled and potentially missing medical observations. Experiments
on data from a realworld cohort of patients show that PASS successfully
balances the tradeoff between accuracy and interpretability: it demonstrates
superior predictive accuracy and learns insightful individual-level
representations of disease progression.
Precipitation Nowcasting: Leveraging bidirectional LSTM and 1D CNN
Authors: Maitreya Patel, Anery Patel, Dr. Ranendu Ghosh
Date: 2018-10-24 00:00:00
Tasks: Deep Learning, Time Series, Time Series Analysis, Time Series Forecasting, Weather Forecasting

Short-term rainfall forecasting, also known as precipitation nowcasting has
become a potentially fundamental technology impacting significant real-world
applications ranging from flight safety, rainstorm alerts to farm irrigation
timings. Since weather forecasting involves identifying the underlying
structure in a huge amount of data, deep-learning based precipitation
nowcasting has intuitively outperformed the traditional linear extrapolation
methods. Our research work intends to utilize the recent advances in deep
learning to nowcasting, a multi-variable time series forecasting problem.
Specifically, we leverage a bidirectional LSTM (Long Short-Term Memory) neural
network architecture which remarkably captures the temporal features and
long-term dependencies from historical data. To further our studies, we compare
the bidirectional LSTM network with 1D CNN model to prove the capabilities of
sequence models over feed-forward neural architectures in forecasting related
problems.
Noisy Blackbox Optimization with Multi-Fidelity Queries: A Tree Search Approach
Authors: Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai
Date: 2018-10-24 00:00:00

We study the problem of black-box optimization of a noisy function in the
presence of low-cost approximations or fidelities, which is motivated by
problems like hyper-parameter tuning. In hyper-parameter tuning evaluating the
black-box function at a point involves training a learning algorithm on a large
data-set at a particular hyper-parameter and evaluating the validation error.
Even a single such evaluation can be prohibitively expensive. Therefore, it is
beneficial to use low-cost approximations, like training the learning algorithm
on a sub-sampled version of the whole data-set. These low-cost
approximations/fidelities can however provide a biased and noisy estimate of
the function value. In this work, we incorporate the multi-fidelity setup in
the powerful framework of noisy black-box optimization through tree-like
hierarchical partitions. We propose a multi-fidelity bandit based tree-search
algorithm for the problem and provide simple regret bounds for our algorithm.
Finally, we validate the performance of our algorithm on real and synthetic
datasets, where it outperforms several benchmarks.
Learning Negotiating Behavior Between Cars in Intersections using Deep Q-Learning
Authors: Tommy Tram, Anton Jansson, Robin Grönberg, Mohammad Ali, Jonas Sjöberg
Date: 2018-10-24 00:00:00
Tasks: Q-Learning

This paper concerns automated vehicles negotiating with other vehicles,
typically human driven, in crossings with the goal to find a decision algorithm
by learning typical behaviors of other vehicles. The vehicle observes distance
and speed of vehicles on the intersecting road and use a policy that adapts its
speed along its pre-defined trajectory to pass the crossing efficiently. Deep
Q-learning is used on simulated traffic with different predefined driver
behaviors and intentions. The results show a policy that is able to cross the
intersection avoiding collision with other vehicles 98% of the time, while at
the same time not being too passive. Moreover, inferring information over time
is important to distinguish between different intentions and is shown by
comparing the collision rate between a Deep Recurrent Q-Network at 0.85% and a
Deep Q-learning at 1.75%.
Distilling with Performance Enhanced Students
Authors: Jack Turner, Elliot J. Crowley, Valentin Radu, José Cano, Amos Storkey, Michael O'Boyle
Date: 2018-10-24 00:00:00
Tasks: Model Compression

The task of accelerating large neural networks on general purpose hardware
has, in recent years, prompted the use of channel pruning to reduce network
size. However, the efficacy of pruning based approaches has since been called
into question. In this paper, we turn to distillation for model
compression---specifically, attention transfer---and develop a simple method
for discovering performance enhanced student networks. We combine channel
saliency metrics with empirical observations of runtime performance to design
more accurate networks for a given latency budget. We apply our methodology to
residual and densely-connected networks, and show that we are able to find
resource-efficient student networks on different hardware platforms while
maintaining very high accuracy. These performance-enhanced student networks
achieve up to 10% boosts in top-1 ImageNet accuracy over their channel-pruned
counterparts for the same inference time.
Evolving Graphs with Semantic Neutral Drift
Authors: Timothy Atkinson, Detlef Plump, Susan Stepney
Date: 2018-10-24 00:00:00

We introduce the concept of Semantic Neutral Drift (SND) for genetic programming (GP), where we exploit equivalence laws to design semantics preserving mutations guaranteed to preserve individuals' fitness scores. A number of digital circuit benchmark problems have been implemented with rule-based graph programs and empirically evaluated, demonstrating quantitative improvements in evolutionary performance. Analysis reveals that the benefits of the designed SND reside in more complex processes than simple growth of individuals, and that there are circumstances where it is beneficial to choose otherwise detrimental parameters for a GP system if that facilitates the inclusion of SND.
UAVid: A Semantic Segmentation Dataset for UAV Imagery
Authors: Ye Lyu, George Vosselman, Gui-Song Xia, Alper Yilmaz, Michael Ying Yang
Date: 2018-10-24 00:00:00
Tasks: 4k, Autonomous Driving, Object Recognition, Scene Understanding, Segmentation, Semantic Segmentation, Video Semantic Segmentation

Semantic segmentation has been one of the leading research interests in computer vision recently. It serves as a perception foundation for many fields, such as robotics and autonomous driving. The fast development of semantic segmentation attributes enormously to the large scale datasets, especially for the deep learning related methods. There already exist several semantic segmentation datasets for comparison among semantic segmentation methods in complex urban scenes, such as the Cityscapes and CamVid datasets, where the side views of the objects are captured with a camera mounted on the driving car. There also exist semantic labeling datasets for the airborne images and the satellite images, where the top views of the objects are captured. However, only a few datasets capture urban scenes from an oblique Unmanned Aerial Vehicle (UAV) perspective, where both of the top view and the side view of the objects can be observed, providing more information for object recognition. In this paper, we introduce our UAVid dataset, a new high-resolution UAV semantic segmentation dataset as a complement, which brings new challenges, including large scale variation, moving object recognition and temporal consistency preservation. Our UAV dataset consists of 30 video sequences capturing 4K high-resolution images in slanted views. In total, 300 images have been densely labeled with 8 classes for the semantic labeling task. We have provided several deep learning baseline methods with pre-training, among which the proposed Multi-Scale-Dilation net performs the best via multi-scale feature extraction. Our UAVid website and the labeling tool have been published https://uavid.nl/.
Variational Semi-supervised Aspect-term Sentiment Analysis via Transformer
Authors: Xingyi Cheng, Weidi Xu, Taifeng Wang, Wei Chu
Date: 2018-10-24 00:00:00
Proceeding: CONLL 2019 11
Tasks: Aspect-Based Sentiment Analysis (ABSA), Natural Language Understanding, Sentiment Analysis, Variational Inference

Aspect-term sentiment analysis (ATSA) is a longstanding challenge in natural language understanding. It requires fine-grained semantical reasoning about a target entity appeared in the text. As manual annotation over the aspects is laborious and time-consuming, the amount of labeled data is limited for supervised learning. This paper proposes a semi-supervised method for the ATSA problem by using the Variational Autoencoder based on Transformer (VAET), which models the latent distribution via variational inference. By disentangling the latent representation into the aspect-specific sentiment and the lexical context, our method induces the underlying sentiment prediction for the unlabeled data, which then benefits the ATSA classifier. Our method is classifier agnostic, i.e., the classifier is an independent module and various advanced supervised models can be integrated. Experimental results are obtained on the SemEval 2014 task 4 and show that our method is effective with four classical classifiers. The proposed method outperforms two general semisupervised methods and achieves state-of-the-art performance.
A Map Equation with Metadata: Varying the Role of Attributes in Community Detection
Authors: Scott Emmons, Peter J. Mucha
Date: 2018-10-24 00:00:00
Tasks: Community Detection

Much of the community detection literature studies structural communities, communities defined solely by the connectivity patterns of the network. Often, networks contain additional metadata which can inform community detection such as the grade and gender of students in a high school social network. In this work, we introduce a tuning parameter to the content map equation that allows users of the Infomap community detection algorithm to control the metadata's relative importance for identifying network structure. On synthetic networks, we show that our algorithm can overcome the structural detectability limit when the metadata is well-aligned with community structure. On real-world networks, we show how our algorithm can achieve greater mutual information with the metadata at a cost in the traditional map equation. Our tuning parameter, like the focusing knob of a microscope, allows users to "zoom in" and "zoom out" on communities with varying levels of focus on the metadata.