pwc-archive/papers-with-abstracts huggingface dataset:
Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information
Authors: Eric Larsen, Sébastien Lachapelle, Yoshua Bengio, Emma Frejinger, Simon Lacoste-Julien, Andrea Lodi
Date: 2018-07-31 00:00:00
Tasks: BIG-bench Machine Learning, Stochastic Optimization

This paper offers a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a methodology to quickly predict expected tactical descriptions of operational solutions (TDOSs). The problem we address occurs in the context of two-stage stochastic programming where the second stage is demanding computationally. We aim to predict at a high speed the expected TDOS associated with the second stage problem, conditionally on the first stage variables. This may be used in support of the solution to the overall two-stage problem by avoiding the online generation of multiple second stage scenarios and solutions. We formulate the tactical prediction problem as a stochastic optimal prediction program, whose solution we approximate with supervised machine learning. The training dataset consists of a large number of deterministic operational problems generated by controlled probabilistic sampling. The labels are computed based on solutions to these problems (solved independently and offline), employing appropriate aggregation and subselection methods to address uncertainty. Results on our motivating application on load planning for rail transportation show that deep learning models produce accurate predictions in very short computing time (milliseconds or less). The predictive accuracy is close to the lower bounds calculated based on sample average approximation of the stochastic prediction programs.
Joint Learning of Intrinsic Images and Semantic Segmentation
Authors: Anil S. Baslamisli, Thomas T. Groenestege, Partha Das, Hoang-An Le, Sezer Karaoglu, Theo Gevers
Date: 2018-07-31 00:00:00
Proceeding: ECCV 2018 9
Tasks: Intrinsic Image Decomposition, Segmentation, Semantic Segmentation

Semantic segmentation of outdoor scenes is problematic when there are
variations in imaging conditions. It is known that albedo (reflectance) is
invariant to all kinds of illumination effects. Thus, using reflectance images
for semantic segmentation task can be favorable. Additionally, not only
segmentation may benefit from reflectance, but also segmentation may be useful
for reflectance computation. Therefore, in this paper, the tasks of semantic
segmentation and intrinsic image decomposition are considered as a combined
process by exploring their mutual relationship in a joint fashion. To that end,
we propose a supervised end-to-end CNN architecture to jointly learn intrinsic
image decomposition and semantic segmentation. We analyze the gains of
addressing those two problems jointly. Moreover, new cascade CNN architectures
for intrinsic-for-segmentation and segmentation-for-intrinsic are proposed as
single tasks. Furthermore, a dataset of 35K synthetic images of natural
environments is created with corresponding albedo and shading (intrinsics), as
well as semantic labels (segmentation) assigned to each object/scene. The
experiments show that joint learning of intrinsic image decomposition and
semantic segmentation is beneficial for both tasks for natural scenes. Dataset
and models are available at: https://ivi.fnwi.uva.nl/cv/intrinseg
Extensible Grounding of Speech for Robot Instruction
Authors: Jonathan Connell
Date: 2018-07-31 00:00:00

Spoken language is a convenient interface for commanding a mobile robot. Yet
for this to work a number of base terms must be grounded in perceptual and
motor skills. We detail the language processing used on our robot ELI and
explain how this grounding is performed, how it interacts with user gestures,
and how it handles phenomena such as anaphora. More importantly, however, there
are certain concepts which the robot cannot be preprogrammed with, such as the
names of various objects in a household or the nature of specific tasks it may
be requested to perform. In these cases it is vital that there exist a method
for extending the grounding, essentially "learning by being told". We describe
how this was successfully implemented for learning new nouns and verbs in a
tabletop setting. Creating this language learning kernel may be the last
explicit programming the robot ever needs - the core mechanism could eventually
be used for imparting a vast amount of knowledge, much as a child learns from
its parents and teachers.
Inferring the ground truth through crowdsourcing
Authors: Jean Pierre Char
Date: 2018-07-31 00:00:00
Tasks: Mitosis Detection, valid

Universally valid ground truth is almost impossible to obtain or would come
at a very high cost. For supervised learning without universally valid ground
truth, a recommended approach is applying crowdsourcing: Gathering a large data
set annotated by multiple individuals of varying possibly expertise levels and
inferring the ground truth data to be used as labels to train the classifier.
Nevertheless, due to the sensitivity of the problem at hand (e.g. mitosis
detection in breast cancer histology images), the obtained data needs
verification and proper assessment before being used for classifier training.
Even in the context of organic computing systems, an indisputable ground truth
might not always exist. Therefore, it should be inferred through the
aggregation and verification of the local knowledge of each autonomous agent.
t-SNE-CUDA: GPU-Accelerated t-SNE and its Applications to Modern Data
Authors: David M. Chan, Roshan Rao, Forrest Huang, John F. Canny
Date: 2018-07-31 00:00:00
Tasks: Dimensionality Reduction, GPU

Modern datasets and models are notoriously difficult to explore and analyze
due to their inherent high dimensionality and massive numbers of samples.
Existing visualization methods which employ dimensionality reduction to two or
three dimensions are often inefficient and/or ineffective for these datasets.
This paper introduces t-SNE-CUDA, a GPU-accelerated implementation of
t-distributed Symmetric Neighbor Embedding (t-SNE) for visualizing datasets and
models. t-SNE-CUDA significantly outperforms current implementations with
50-700x speedups on the CIFAR-10 and MNIST datasets. These speedups enable, for
the first time, visualization of the neural network activations on the entire
ImageNet dataset - a feat that was previously computationally intractable. We
also demonstrate visualization performance in the NLP domain by visualizing the
GloVe embedding vectors. From these visualizations, we can draw interesting
conclusions about using the L2 metric in these embedding spaces. t-SNE-CUDA is
publicly available athttps://github.com/CannyLab/tsne-cuda
Deep learning in agriculture: A survey
Authors: Andreas Kamilaris, Francesc X. Prenafeta-Boldu
Date: 2018-07-31 00:00:00
Tasks: Deep Learning, Survey

Deep learning constitutes a recent, modern technique for image processing and
data analysis, with promising results and large potential. As deep learning has
been successfully applied in various domains, it has recently entered also the
domain of agriculture. In this paper, we perform a survey of 40 research
efforts that employ deep learning techniques, applied to various agricultural
and food production challenges. We examine the particular agricultural problems
under study, the specific models and frameworks employed, the sources, nature
and pre-processing of data used, and the overall performance achieved according
to the metrics used at each work under study. Moreover, we study comparisons of
deep learning with other existing popular techniques, in respect to differences
in classification or regression performance. Our findings indicate that deep
learning provides high accuracy, outperforming existing commonly used image
processing techniques.
Disaster Monitoring using Unmanned Aerial Vehicles and Deep Learning
Authors: Andreas Kamilaris, Francesc X. Prenafeta-Boldú
Date: 2018-07-31 00:00:00
Tasks: Deep Learning

Monitoring of disasters is crucial for mitigating their effects on the
environment and human population, and can be facilitated by the use of unmanned
aerial vehicles (UAV), equipped with camera sensors that produce aerial photos
of the areas of interest. A modern technique for recognition of events based on
aerial photos is deep learning. In this paper, we present the state of the art
work related to the use of deep learning techniques for disaster
identification. We demonstrate the potential of this technique in identifying
disasters with high accuracy, by means of a relatively simple deep learning
model. Based on a dataset of 544 images (containing disaster images such as
fires, earthquakes, collapsed buildings, tsunami and flooding, as well as
non-disaster scenes), our results show an accuracy of 91% achieved, indicating
that deep learning, combined with UAV equipped with camera sensors, have the
potential to predict disasters with high accuracy.
Attention is All We Need: Nailing Down Object-centric Attention for Egocentric Activity Recognition
Authors: Swathikiran Sudhakaran, Oswald Lanz
Date: 2018-07-31 00:00:00
Tasks: Activity Recognition, All, Egocentric Activity Recognition, Hand Segmentation

In this paper we propose an end-to-end trainable deep neural network model
for egocentric activity recognition. Our model is built on the observation that
egocentric activities are highly characterized by the objects and their
locations in the video. Based on this, we develop a spatial attention mechanism
that enables the network to attend to regions containing objects that are
correlated with the activity under consideration. We learn highly specialized
attention maps for each frame using class-specific activations from a CNN
pre-trained for generic image recognition, and use them for spatio-temporal
encoding of the video with a convolutional LSTM. Our model is trained in a
weakly supervised setting using raw video-level activity-class labels.
Nonetheless, on standard egocentric activity benchmarks our model surpasses by
up to +6% points recognition accuracy the currently best performing method that
leverages hand segmentation and object location strong supervision for
training. We visually analyze attention maps generated by the network,
revealing that the network successfully identifies the relevant objects present
in the video frames which may explain the strong recognition performance. We
also discuss an extensive ablation analysis regarding the design choices.
Scale equivariance in CNNs with vector fields
Authors: Diego Marcos, Benjamin Kellenberger, Sylvain Lobry, Devis Tuia
Date: 2018-07-31 00:00:00
Tasks: General Classification

We study the effect of injecting local scale equivariance into Convolutional
Neural Networks. This is done by applying each convolutional filter at multiple
scales. The output is a vector field encoding for the maximally activating
scale and the scale itself, which is further processed by the following
convolutional layers. This allows all the intermediate representations to be
locally scale equivariant. We show that this improves the performance of the
model by over $20\%$ in the scale equivariant task of regressing the scaling
factor applied to randomly scaled MNIST digits. Furthermore, we find it also
useful for scale invariant tasks, such as the actual classification of randomly
scaled digits. This highlights the usefulness of allowing for a compact
representation that can also learn relationships between different local scales
by keeping internal scale equivariance.
Remote sensing image regression for heterogeneous change detection
Authors: Luigi T. Luppino, Filippo M. Bianchi, Gabriele Moser, Stian N. Anfinsen
Date: 2018-07-31 00:00:00
Tasks: Change Detection, Gaussian Processes, regression

Change detection in heterogeneous multitemporal satellite images is an
emerging topic in remote sensing. In this paper we propose a framework, based
on image regression, to perform change detection in heterogeneous multitemporal
satellite images, which has become a main topic in remote sensing. Our method
learns a transformation to map the first image to the domain of the other
image, and vice versa. Four regression methods are selected to carry out the
transformation: Gaussian processes, support vector machines, random forests,
and a recently proposed kernel regression method called homogeneous pixel
transformation. To evaluate not only potentials and limitations of our
framework, but also the pros and cons of each regression method, we perform
experiments on two data sets. The results indicates that random forests achieve
good performance, are fast and robust to hyperparameters, whereas the
homogeneous pixel transformation method can achieve better accuracy at the cost
of a higher complexity.
A First Experiment on Including Text Literals in KGloVe
Authors: Michael Cochez, Martina Garofalo, Jérôme Lenßen, Maria Angela Pellegrino
Date: 2018-07-31 00:00:00
Tasks: BIG-bench Machine Learning, Graph Embedding, Knowledge Graphs

Graph embedding models produce embedding vectors for entities and relations
in Knowledge Graphs, often without taking literal properties into account. We
show an initial idea based on the combination of global graph structure with
additional information provided by textual information in properties. Our
initial experiment shows that this approach might be useful, but does not
clearly outperform earlier approaches when evaluated on machine learning tasks.
Deep Visual Odometry Methods for Mobile Robots
Authors: Jahanzaib Shabbir, Thomas Kruezer
Date: 2018-07-31 00:00:00
Tasks: Simultaneous Localization and Mapping, Visual Odometry

Technology has made navigation in 3D real time possible and this has made
possible what seemed impossible. This paper explores the aspect of deep visual
odometry methods for mobile robots. Visual odometry has been instrumental in
making this navigation successful. Noticeable challenges in mobile robots
including the inability to attain Simultaneous Localization and Mapping have
been solved by visual odometry through its cameras which are suitable for human
environments. More intuitive, precise and accurate detection have been made
possible by visual odometry in mobile robots. Another challenge in the mobile
robot world is the 3D map reconstruction for exploration. A dense map in mobile
robots can facilitate for localization and more accurate findings.
A Zero-Shot Framework for Sketch-based Image Retrieval
Authors: Sasi Kiran Yelamarthi, Shiva Krishna Reddy, Ashish Mishra, Anurag Mittal
Date: 2018-07-31 00:00:00
Tasks: Image Retrieval, Retrieval, Sketch-Based Image Retrieval

Sketch-based image retrieval (SBIR) is the task of retrieving images from a
natural image database that correspond to a given hand-drawn sketch. Ideally,
an SBIR model should learn to associate components in the sketch (say, feet,
tail, etc.) with the corresponding components in the image having similar shape
characteristics. However, current evaluation methods simply focus only on
coarse-grained evaluation where the focus is on retrieving images which belong
to the same class as the sketch but not necessarily having the same shape
characteristics as in the sketch. As a result, existing methods simply learn to
associate sketches with classes seen during training and hence fail to
generalize to unseen classes. In this paper, we propose a new benchmark for
zero-shot SBIR where the model is evaluated in novel classes that are not seen
during training. We show through extensive experiments that existing models for
SBIR that are trained in a discriminative setting learn only class specific
mappings and fail to generalize to the proposed zero-shot setting. To
circumvent this, we propose a generative approach for the SBIR task by
proposing deep conditional generative models that take the sketch as an input
and fill the missing information stochastically. Experiments on this new
benchmark created from the "Sketchy" dataset, which is a large-scale database
of sketch-photo pairs demonstrate that the performance of these generative
models is significantly better than several state-of-the-art approaches in the
proposed zero-shot framework of the coarse-grained SBIR task.
Multi-Speaker DOA Estimation Using Deep Convolutional Networks Trained with Noise Signals
Authors: Soumitro Chakrabarty, Emanuël. A. P. Habets
Date: 2018-07-31 00:00:00
Tasks: Binary Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Supervised learning based methods for source localization, being data driven,
can be adapted to different acoustic conditions via training and have been
shown to be robust to adverse acoustic environments. In this paper, a
convolutional neural network (CNN) based supervised learning method for
estimating the direction-of-arrival (DOA) of multiple speakers is proposed.
Multi-speaker DOA estimation is formulated as a multi-class multi-label
classification problem, where the assignment of each DOA label to the input
feature is treated as a separate binary classification problem. The phase
component of the short-time Fourier transform (STFT) coefficients of the
received microphone signals are directly fed into the CNN, and the features for
DOA estimation are learnt during training. Utilizing the assumption of disjoint
speaker activity in the STFT domain, a novel method is proposed to train the
CNN with synthesized noise signals. Through experimental evaluation with both
simulated and measured acoustic impulse responses, the ability of the proposed
DOA estimation approach to adapt to unseen acoustic conditions and its
robustness to unseen noise type is demonstrated. Through additional empirical
investigation, it is also shown that with an array of M microphones our
proposed framework yields the best localization performance with M-1
convolution layers. The ability of the proposed method to accurately localize
speakers in a dynamic acoustic scenario with varying number of sources is also
shown.
Regional Multi-scale Approach for Visually Pleasing Explanations of Deep Neural Networks
Authors: Dasom Seo, Kanghan Oh, Il-Seok Oh
Date: 2018-07-31 00:00:00
Tasks: Feature Importance

Recently, many methods to interpret and visualize deep neural network
predictions have been proposed and significant progress has been made. However,
a more class-discriminative and visually pleasing explanation is required.
Thus, this paper proposes a region-based approach that estimates feature
importance in terms of appropriately segmented regions. By fusing the saliency
maps generated from multi-scale segmentations, a more class-discriminative and
visually pleasing map is obtained. We incorporate this regional multi-scale
concept into a prediction difference method that is model-agnostic. An input
image is segmented in several scales using the super-pixel method, and
exclusion of a region is simulated by sampling a normal distribution
constructed using the boundary prior. The experimental results demonstrate that
the regional multi-scale method produces much more class-discriminative and
visually pleasing saliency maps.
A Two-Stream Mutual Attention Network for Semi-supervised Biomedical Segmentation with Noisy Labels
Authors: Shaobo Min, Xuejin Chen, Zheng-Jun Zha, Feng Wu, Yongdong Zhang
Date: 2018-07-31 00:00:00

\begin{abstract} Learning-based methods suffer from a deficiency of clean
annotations, especially in biomedical segmentation. Although many
semi-supervised methods have been proposed to provide extra training data,
automatically generated labels are usually too noisy to retrain models
effectively. In this paper, we propose a Two-Stream Mutual Attention Network
(TSMAN) that weakens the influence of back-propagated gradients caused by
incorrect labels, thereby rendering the network robust to unclean data. The
proposed TSMAN consists of two sub-networks that are connected by three types
of attention models in different layers. The target of each attention model is
to indicate potentially incorrect gradients in a certain layer for both
sub-networks by analyzing their inferred features using the same input. In
order to achieve this purpose, the attention models are designed based on the
propagation analysis of noisy gradients at different layers. This allows the
attention models to effectively discover incorrect labels and weaken their
influence during the parameter updating process. By exchanging multi-level
features within the two-stream architecture, the effects of noisy labels in
each sub-network are reduced by decreasing the updating gradients. Furthermore,
a hierarchical distillation is developed to provide more reliable pseudo labels
for unlabelded data, which further boosts the performance of our retrained
TSMAN. The experiments using both the HVSMR 2016 and BRATS 2015 benchmarks
demonstrate that our semi-supervised learning framework surpasses the
state-of-the-art fully-supervised results.
Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data
Authors: Sergul Aydore, Bertrand Thirion, Gael Varoquaux
Date: 2018-07-31 00:00:00
Tasks: Clustering, Denoising, Image Classification, Vocal Bursts Intensity Prediction

In many applications where collecting data is expensive, for example
neuroscience or medical imaging, the sample size is typically small compared to
the feature dimension. It is challenging in this setting to train expressive,
non-linear models without overfitting. These datasets call for intelligent
regularization that exploits known structure, such as correlations between the
features arising from the measurement device. However, existing structured
regularizers need specially crafted solvers, which are difficult to apply to
complex models. We propose a new regularizer specifically designed to leverage
structure in the data in a way that can be applied efficiently to complex
models. Our approach relies on feature grouping, using a fast clustering
algorithm inside a stochastic gradient descent loop: given a family of feature
groupings that capture feature covariations, we randomly select these groups at
each iteration. We show that this approach amounts to enforcing a denoising
regularizer on the solution. The method is easy to implement in many model
architectures, such as fully connected neural networks, and has a linear
computational cost. We apply this regularizer to a real-world fMRI dataset and
the Olivetti Faces datasets. Experiments on both datasets demonstrate that the
proposed approach produces models that generalize better than those trained
with conventional regularizers, and also improves convergence speed.
Gender Bias in Neural Natural Language Processing
Authors: Kaiji Lu, Piotr Mardziel, Fangjing Wu, Preetam Amancharla, Anupam Datta
Date: 2018-07-31 00:00:00
Tasks: coreference-resolution, Coreference Resolution, Word Embeddings

We examine whether neural natural language processing (NLP) systems reflect historical biases in training data. We define a general benchmark to quantify gender bias in a variety of neural NLP tasks. Our empirical evaluation with state-of-the-art neural coreference resolution and textbook RNN-based language models trained on benchmark datasets finds significant gender bias in how models view occupations. We then mitigate bias with CDA: a generic methodology for corpus augmentation via causal interventions that breaks associations between gendered and gender-neutral words. We empirically show that CDA effectively decreases gender bias while preserving accuracy. We also explore the space of mitigation strategies with CDA, a prior approach to word embedding debiasing (WED), and their compositions. We show that CDA outperforms WED, drastically so when word embeddings are trained. For pre-trained embeddings, the two methods can be effectively composed. We also find that as training proceeds on the original data set with gradient descent the gender bias grows as the loss reduces, indicating that the optimization encourages bias; CDA mitigates this behavior.