pwc-archive/papers-with-abstracts huggingface dataset:
Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat
Authors: Ravi Shekhar, Aashish Venkatesh, Tim Baumgärtner, Elia Bruni, Barbara Plank, Raffaella Bernardi, Raquel Fernández
Date: 2018-09-10 00:00:00
Proceeding: NAACL 2019 6
Tasks: Multi-Task Learning, Reinforcement Learning, Visual Grounding

We propose a grounded dialogue state encoder which addresses a foundational
issue on how to integrate visual grounding with dialogue system components. As
a test-bed, we focus on the GuessWhat?! game, a two-player game where the goal
is to identify an object in a complex visual scene by asking a sequence of
yes/no questions. Our visually-grounded encoder leverages synergies between
guessing and asking questions, as it is trained jointly using multi-task
learning. We further enrich our model via a cooperative learning regime. We
show that the introduction of both the joint architecture and cooperative
learning lead to accuracy improvements over the baseline system. We compare our
approach to an alternative system which extends the baseline with reinforcement
learning. Our in-depth analysis shows that the linguistic skills of the two
models differ dramatically, despite approaching comparable performance levels.
This points at the importance of analyzing the linguistic output of competing
systems beyond numeric comparison solely based on task success.
Combining imagination and heuristics to learn strategies that generalize
Authors: Erik J Peterson, Necati Alp Müyesser, Timothy Verstynen, Kyle Dunovan
Date: 2018-09-10 00:00:00
Tasks: Deep Reinforcement Learning, Hierarchical Reinforcement Learning, Position, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning can match or exceed human performance in stable contexts, but with minor changes to the environment artificial networks, unlike humans, often cannot adapt. Humans rely on a combination of heuristics to simplify computational load and imagination to extend experiential learning to new and more challenging environments. Motivated by theories of the hierarchical organization of the human prefrontal networks, we have developed a model of hierarchical reinforcement learning that combines both heuristics and imagination into a stumbler-strategist network. We test performance of this network using Wythoff's game, a gridworld environment with a known optimal strategy. We show that a heuristic labeling of each position as hot or cold, combined with imagined play, both accelerates learning and promotes transfer to novel games, while also improving model interpretability.
Does Your Phone Know Your Touch?
Authors: John Peruzzi, Phillip Andrew Wingard, David Zucker
Date: 2018-09-10 00:00:00
Tasks: Anomaly Detection, Position, regression

This paper explores supervised techniques for continuous anomaly detection
from biometric touch screen data. A capacitive sensor array used to mimic a
touch screen as used to collect touch and swipe gestures from participants. The
gestures are recorded over fixed segments of time, with position and force
measured for each gesture. Support Vector Machine, Logistic Regression, and
Gaussian mixture models were tested to learn individual touch patterns. Test
results showed true negative and true positive scores of over 95% accuracy for
all gesture types, with logistic regression models far outperforming the other
methods. A more expansive and varied data collection over longer periods of
time is needed to determine pragmatic usage of these results.
Neural Latent Relational Analysis to Capture Lexical Semantic Relations in a Vector Space
Authors: Koki Washio, Tsuneaki Kato
Date: 2018-09-10 00:00:00
Proceeding: EMNLP 2018 10

Capturing the semantic relations of words in a vector space contributes to
many natural language processing tasks. One promising approach exploits
lexico-syntactic patterns as features of word pairs. In this paper, we propose
a novel model of this pattern-based approach, neural latent relational analysis
(NLRA). NLRA can generalize co-occurrences of word pairs and lexico-syntactic
patterns, and obtain embeddings of the word pairs that do not co-occur. This
overcomes the critical data sparseness problem encountered in previous
pattern-based models. Our experimental results on measuring relational
similarity demonstrate that NLRA outperforms the previous pattern-based models.
In addition, when combined with a vector offset model, NLRA achieves a
performance comparable to that of the state-of-the-art model that exploits
additional semantic relational data.
A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunity
Authors: Hoda Heidari, Michele Loi, Krishna P. Gummadi, Andreas Krause
Date: 2018-09-10 00:00:00
Tasks: Fairness, Philosophy

We map the recently proposed notions of algorithmic fairness to economic
models of Equality of opportunity (EOP)---an extensively studied ideal of
fairness in political philosophy. We formally show that through our conceptual
mapping, many existing definition of algorithmic fairness, such as predictive
value parity and equality of odds, can be interpreted as special cases of EOP.
In this respect, our work serves as a unifying moral framework for
understanding existing notions of algorithmic fairness. Most importantly, this
framework allows us to explicitly spell out the moral assumptions underlying
each notion of fairness, and interpret recent fairness impossibility results in
a new light. Last but not least and inspired by luck egalitarian models of EOP,
we propose a new family of measures for algorithmic fairness. We illustrate our
proposal empirically and show that employing a measure of algorithmic
(un)fairness when its underlying moral assumptions are not satisfied, can have
devastating consequences for the disadvantaged group's welfare.
Toward a Standardized and More Accurate Indonesian Part-of-Speech Tagging
Authors: Kemal Kurniawan, Alham Fikri Aji
Date: 2018-09-10 00:00:00
Tasks: Part-Of-Speech Tagging, POS, POS Tagging

Previous work in Indonesian part-of-speech (POS) tagging are hard to compare
as they are not evaluated on a common dataset. Furthermore, in spite of the
success of neural network models for English POS tagging, they are rarely
explored for Indonesian. In this paper, we explored various techniques for
Indonesian POS tagging, including rule-based, CRF, and neural network-based
models. We evaluated our models on the IDN Tagged Corpus. A new
state-of-the-art of 97.47 F1 score is achieved with a recurrent neural network.
To provide a standard for future work, we release the dataset split that we
used publicly.
SPASS: Scientific Prominence Active Search System with Deep Image Captioning Network
Authors: Dicong Qiu
Date: 2018-09-10 00:00:00
Tasks: Image Captioning, Task Planning

Planetary exploration missions with Mars rovers are complicated, which
generally require elaborated task planning by human experts, from the path to
take to the images to capture. NASA has been using this process to acquire over
22 million images from the planet Mars. In order to improve the degree of
automation and thus efficiency in this process, we propose a system for
planetary rovers to actively search for prominence of prespecified scientific
features in captured images. Scientists can prespecify such search tasks in
natural language and upload them to a rover, on which the deployed system
constantly captions captured images with a deep image captioning network and
compare the auto-generated captions to the prespecified search tasks by certain
metrics so as to prioritize those images for transmission. As a beneficial side
effect, the proposed system can also be deployed to ground-based planetary data
systems as a content-based search engine.
Probabilistic Binary Neural Networks
Authors: Jorn W. T. Peters, Max Welling
Date: 2018-09-10 00:00:00
Proceeding: ICLR 2019 5

Low bit-width weights and activations are an effective way of combating the
increasing need for both memory and compute power of Deep Neural Networks. In
this work, we present a probabilistic training method for Neural Network with
both binary weights and activations, called BLRNet. By embracing stochasticity
during training, we circumvent the need to approximate the gradient of
non-differentiable functions such as sign(), while still obtaining a fully
Binary Neural Network at test time. Moreover, it allows for anytime ensemble
predictions for improved performance and uncertainty estimates by sampling from
the weight distribution. Since all operations in a layer of the BLRNet operate
on random variables, we introduce stochastic versions of Batch Normalization
and max pooling, which transfer well to a deterministic network at test time.
We evaluate the BLRNet on multiple standardized benchmarks.
Torchbearer: A Model Fitting Library for PyTorch
Authors: Ethan Harris, Matthew Painter, Jonathon Hare
Date: 2018-09-10 00:00:00
Tasks: Data Visualization, Deep Learning, model

We introduce torchbearer, a model fitting library for pytorch aimed at
researchers working on deep learning or differentiable programming. The
torchbearer library provides a high level metric and callback API that can be
used for a wide range of applications. We also include a series of built in
callbacks that can be used for: model persistence, learning rate decay,
logging, data visualization and more. The extensive documentation includes an
example library for deep learning and dynamic programming problems and can be
found at http://torchbearer.readthedocs.io. The code is licensed under the MIT
License and available at https://github.com/ecs-vlc/torchbearer.
Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement Learning
Authors: Quentin Cappart, Emmanuel Goutierre, David Bergman, Louis-Martin Rousseau
Date: 2018-09-10 00:00:00
Tasks: BIG-bench Machine Learning, Combinatorial Optimization, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Finding tight bounds on the optimal solution is a critical element of
practical solution methods for discrete optimization problems. In the last
decade, decision diagrams (DDs) have brought a new perspective on obtaining
upper and lower bounds that can be significantly better than classical bounding
mechanisms, such as linear relaxations. It is well known that the quality of
the bounds achieved through this flexible bounding method is highly reliant on
the ordering of variables chosen for building the diagram, and finding an
ordering that optimizes standard metrics is an NP-hard problem. In this paper,
we propose an innovative and generic approach based on deep reinforcement
learning for obtaining an ordering for tightening the bounds obtained with
relaxed and restricted DDs. We apply the approach to both the Maximum
Independent Set Problem and the Maximum Cut Problem. Experimental results on
synthetic instances show that the deep reinforcement learning approach, by
achieving tighter objective function bounds, generally outperforms ordering
methods commonly used in the literature when the distribution of instances is
known. To the best knowledge of the authors, this is the first paper to apply
machine learning to directly improve relaxation bounds obtained by
general-purpose bounding mechanisms for combinatorial optimization problems.
Learning to Zoom: a Saliency-Based Sampling Layer for Neural Networks
Authors: Adrià Recasens, Petr Kellnhofer, Simon Stent, Wojciech Matusik, Antonio Torralba
Date: 2018-09-10 00:00:00
Proceeding: ECCV 2018 9
Tasks: Caricature, Gaze Estimation, General Classification, image-classification, Image Classification

We introduce a saliency-based distortion layer for convolutional neural
networks that helps to improve the spatial sampling of input data for a given
task. Our differentiable layer can be added as a preprocessing block to
existing task networks and trained altogether in an end-to-end fashion. The
effect of the layer is to efficiently estimate how to sample from the original
data in order to boost task performance. For example, for an image
classification task in which the original data might range in size up to
several megapixels, but where the desired input images to the task network are
much smaller, our layer learns how best to sample from the underlying high
resolution data in a manner which preserves task-relevant information better
than uniform downsampling. This has the effect of creating distorted,
caricature-like intermediate images, in which idiosyncratic elements of the
image that improve task performance are zoomed and exaggerated. Unlike
alternative approaches such as spatial transformer networks, our proposed layer
is inspired by image saliency, computed efficiently from uniformly downsampled
data, and degrades gracefully to a uniform sampling strategy under uncertainty.
We apply our layer to improve existing networks for the tasks of human gaze
estimation and fine-grained object classification. Code for our method is
available in: http://github.com/recasens/Saliency-Sampler
xSense: Learning Sense-Separated Sparse Representations and Textual Definitions for Explainable Word Sense Networks
Authors: Ting-Yun Chang, Ta-Chung Chi, Shang-Chi Tsai, Yun-Nung Chen
Date: 2018-09-10 00:00:00
Tasks: Word Embeddings, Word Sense Disambiguation

Despite the success achieved on various natural language processing tasks,
word embeddings are difficult to interpret due to the dense vector
representations. This paper focuses on interpreting the embeddings for various
aspects, including sense separation in the vector dimensions and definition
generation. Specifically, given a context together with a target word, our
algorithm first projects the target word embedding to a high-dimensional sparse
vector and picks the specific dimensions that can best explain the semantic
meaning of the target word by the encoded contextual information, where the
sense of the target word can be indirectly inferred. Finally, our algorithm
applies an RNN to generate the textual definition of the target word in the
human readable form, which enables direct interpretation of the corresponding
word embedding. This paper also introduces a large and high-quality
context-definition dataset that consists of sense definitions together with
multiple example sentences per polysemous word, which is a valuable resource
for definition modeling and word sense disambiguation. The conducted
experiments show the superior performance in BLEU score and the human
evaluation test.
A Comparison of CNN-based Face and Head Detectors for Real-Time Video Surveillance Applications
Authors: Le Thanh Nguyen-Meidine, Eric Granger, Madhu Kiran, Louis-Antoine Blais-Morin
Date: 2018-09-10 00:00:00
Tasks: Head Detection

Detecting faces and heads appearing in video feeds are challenging tasks in
real-world video surveillance applications due to variations in appearance,
occlusions and complex backgrounds. Recently, several CNN architectures have
been proposed to increase the accuracy of detectors, although their
computational complexity can be an issue, especially for real-time
applications, where faces and heads must be detected live using high-resolution
cameras. This paper compares the accuracy and complexity of state-of-the-art
CNN architectures that are suitable for face and head detection. Single pass
and region-based architectures are reviewed and compared empirically to
baseline techniques according to accuracy and to time and memory complexity on
images from several challenging datasets. The viability of these architectures
is analyzed with real-time video surveillance applications in mind. Results
suggest that, although CNN architectures can achieve a very high level of
accuracy compared to traditional detectors, their computational cost can
represent a limitation for many practical real-time applications.
Interactive Binary Image Segmentation with Edge Preservation
Authors: Jianfeng Zhang, Liezhuo Zhang, Yuankai Teng, Xiao-Ping Zhang, Song Wang, Lili Ju
Date: 2018-09-10 00:00:00
Tasks: Image Segmentation, Interactive Segmentation, Segmentation, Semantic Segmentation, Superpixels, Video Editing

Binary image segmentation plays an important role in computer vision and has
been widely used in many applications such as image and video editing, object
extraction, and photo composition. In this paper, we propose a novel
interactive binary image segmentation method based on the Markov Random Field
(MRF) framework and the fast bilateral solver (FBS) technique. Specifically, we
employ the geodesic distance component to build the unary term. To ensure both
computation efficiency and effective responsiveness for interactive
segmentation, superpixels are used in computing geodesic distances instead of
pixels. Furthermore, we take a bilateral affinity approach for the pairwise
term in order to preserve edge information and denoise. Through the alternating
direction strategy, the MRF energy minimization problem is divided into two
subproblems, which then can be easily solved by steepest gradient descent (SGD)
and FBS respectively. Experimental results on the VGG interactive image
segmentation dataset show that the proposed algorithm outperforms several
state-of-the-art ones, and in particular, it can achieve satisfactory
edge-smooth segmentation results even when the foreground and background color
appearances are quite indistinctive.
A Brief Review of Real-World Color Image Denoising
Authors: Zhaoming Kong, Xiaowei Yang
Date: 2018-09-10 00:00:00
Tasks: Color Image Denoising, Denoising, Image Denoising, POS

Filtering real-world color images is challenging due to the complexity of
noise that can not be formulated as a certain distribution. However, the rapid
development of camera lens pos- es greater demands on image denoising in terms
of both efficiency and effectiveness. Currently, the most widely accepted
framework employs the combination of transform domain techniques and nonlocal
similarity characteristics of natural images. Based on this framework, many
competitive methods model the correlation of R, G, B channels with pre-defined
or adaptively learned transforms. In this chapter, a brief review of related
methods and publicly available datasets is presented, moreover, a new dataset
that includes more natural outdoor scenes is introduced. Extensive experiments
are performed and discussion on visual effect enhancement is included.
Multilingual Extractive Reading Comprehension by Runtime Machine Translation
Authors: Akari Asai, Akiko Eriguchi, Kazuma Hashimoto, Yoshimasa Tsuruoka
Date: 2018-09-10 00:00:00
Tasks: Machine Translation, NMT, Reading Comprehension, Translation

Despite recent work in Reading Comprehension (RC), progress has been mostly
limited to English due to the lack of large-scale datasets in other languages.
In this work, we introduce the first RC system for languages without RC
training data. Given a target language without RC training data and a pivot
language with RC training data (e.g. English), our method leverages existing RC
resources in the pivot language by combining a competitive RC model in the
pivot language with an attentive Neural Machine Translation (NMT) model. We
first translate the data from the target to the pivot language, and then obtain
an answer using the RC model in the pivot language. Finally, we recover the
corresponding answer in the original language using soft-alignment attention
scores from the NMT model. We create evaluation sets of RC data in two
non-English languages, namely Japanese and French, to evaluate our method.
Experimental results on these datasets show that our method significantly
outperforms a back-translation baseline of a state-of-the-art product-level
machine translation system.
Privacy-Preserving Deep Learning via Weight Transmission
Authors: Le Trieu Phong, Tran Thi Phuong
Date: 2018-09-10 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Privacy Preserving, Privacy Preserving Deep Learning

This paper considers the scenario that multiple data owners wish to apply a
machine learning method over the combined dataset of all owners to obtain the
best possible learning output but do not want to share the local datasets owing
to privacy concerns. We design systems for the scenario that the stochastic
gradient descent (SGD) algorithm is used as the machine learning method because
SGD (or its variants) is at the heart of recent deep learning techniques over
neural networks. Our systems differ from existing systems in the following
features: {\bf (1)} any activation function can be used, meaning that no
privacy-preserving-friendly approximation is required; {\bf (2)} gradients
computed by SGD are not shared but the weight parameters are shared instead;
and {\bf (3)} robustness against colluding parties even in the extreme case
that only one honest party exists. We prove that our systems, while
privacy-preserving, achieve the same learning accuracy as SGD and hence retain
the merit of deep learning with respect to accuracy. Finally, we conduct
several experiments using benchmark datasets, and show that our systems
outperform previous system in terms of learning accuracies.
Automated Test Generation to Detect Individual Discrimination in AI Models
Authors: Aniya Agarwal, Pranay Lohia, Seema Nagar, Kuntal Dey, Diptikalyan Saha
Date: 2018-09-10 00:00:00

Dependability on AI models is of utmost importance to ensure full acceptance
of the AI systems. One of the key aspects of the dependable AI system is to
ensure that all its decisions are fair and not biased towards any individual.
In this paper, we address the problem of detecting whether a model has an
individual discrimination. Such a discrimination exists when two individuals
who differ only in the values of their protected attributes (such as,
gender/race) while the values of their non-protected ones are exactly the same,
get different decisions. Measuring individual discrimination requires an
exhaustive testing, which is infeasible for a non-trivial system. In this
paper, we present an automated technique to generate test inputs, which is
geared towards finding individual discrimination. Our technique combines the
well-known technique called symbolic execution along with the local
explainability for generation of effective test cases. Our experimental results
clearly demonstrate that our technique produces 3.72 times more successful test
cases than the existing state-of-the-art across all our chosen benchmarks.
Classification by Re-generation: Towards Classification Based on Variational Inference
Authors: Shideh Rezaeifar, Olga Taran, Slava Voloshynovskiy
Date: 2018-09-10 00:00:00
Tasks: Classification, Decoder, General Classification, Variational Inference

As Deep Neural Networks (DNNs) are considered the state-of-the-art in many
classification tasks, the question of their semantic generalizations has been
raised. To address semantic interpretability of learned features, we introduce
a novel idea of classification by re-generation based on variational
autoencoder (VAE) in which a separate encoder-decoder pair of VAE is trained
for each class. Moreover, the proposed architecture overcomes the scalability
issue in current DNN networks as there is no need to re-train the whole network
with the addition of new classes and it can be done for each class separately.
We also introduce a criterion based on Kullback-Leibler divergence to reject
doubtful examples. This rejection criterion should improve the trust in the
obtained results and can be further exploited to reject adversarial examples.