pwc-archive/papers-with-abstracts huggingface dataset:
Generalized Hadamard-Product Fusion Operators for Visual Question Answering
Authors: Brendan Duke, Graham W. Taylor
Date: 2018-03-26 00:00:00
Tasks: Neural Architecture Search, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

We propose a generalized class of multimodal fusion operators for the task of
visual question answering (VQA). We identify generalizations of existing
multimodal fusion operators based on the Hadamard product, and show that
specific non-trivial instantiations of this generalized fusion operator exhibit
superior performance in terms of OpenEnded accuracy on the VQA task. In
particular, we introduce Nonlinearity Ensembling, Feature Gating, and
post-fusion neural network layers as fusion operator components, culminating in
an absolute percentage point improvement of $1.1\%$ on the VQA 2.0 test-dev set
over baseline fusion operators, which use the same features as input. We use
our findings as evidence that our generalized class of fusion operators could
lead to the discovery of even superior task-specific operators when used as a
search space in an architecture search over fusion operators.
Chart Parsing Multimodal Grammars
Authors: Richard Moot
Date: 2018-04-06 00:00:00
Tasks: Vocal Bursts Type Prediction

The short note describes the chart parser for multimodal type-logical
grammars which has been developed in conjunction with the type-logical treebank
for French. The chart parser presents an incomplete but fast implementation of
proof search for multimodal type-logical grammars using the "deductive parsing"
framework. Proofs found can be transformed to natural deduction proofs.
Finding beans in burgers: Deep semantic-visual embedding with localization
Authors: Martin Engilberge, Louis Chevallier, Patrick Pérez, Matthieu Cord
Date: 2018-04-05 00:00:00
Proceeding: CVPR 2018 6
Tasks: Cross-Modal Retrieval, Image Captioning, Retrieval, Visual Grounding

Several works have proposed to learn a two-path neural network that maps
images and texts, respectively, to a same shared Euclidean space where geometry
captures useful semantic relationships. Such a multi-modal embedding can be
trained and used for various tasks, notably image captioning. In the present
work, we introduce a new architecture of this type, with a visual path that
leverages recent space-aware pooling mechanisms. Combined with a textual path
which is jointly trained from scratch, our semantic-visual embedding offers a
versatile model. Once trained under the supervision of captioned images, it
yields new state-of-the-art performance on cross-modal retrieval. It also
allows the localization of new concepts from the embedding space into any input
image, delivering state-of-the-art result on the visual grounding of phrases.
Interoceptive robustness through environment-mediated morphological development
Authors: Sam Kriegman, Nick Cheney, Francesco Corucci, Josh C. Bongard
Date: 2018-04-06 00:00:00

Typically, AI researchers and roboticists try to realize intelligent behavior
in machines by tuning parameters of a predefined structure (body plan and/or
neural network architecture) using evolutionary or learning algorithms. Another
but not unrelated longstanding property of these systems is their brittleness
to slight aberrations, as highlighted by the growing deep learning literature
on adversarial examples. Here we show robustness can be achieved by evolving
the geometry of soft robots, their control systems, and how their material
properties develop in response to one particular interoceptive stimulus
(engineering stress) during their lifetimes. By doing so we realized robots
that were equally fit but more robust to extreme material defects (such as
might occur during fabrication or by damage thereafter) than robots that did
not develop during their lifetimes, or developed in response to a different
interoceptive stimulus (pressure). This suggests that the interplay between
changes in the containing systems of agents (body plan and/or neural
architecture) at different temporal scales (evolutionary and developmental)
along different modalities (geometry, material properties, synaptic weights)
and in response to different signals (interoceptive and external perception)
all dictate those agents' abilities to evolve or learn capable and robust
strategies.
On the Robustness of Semantic Segmentation Models to Adversarial Attacks
Authors: Anurag Arnab, Ondrej Miksik, Philip H. S. Torr
Date: 2017-11-27 00:00:00
Proceeding: CVPR 2018 6
Tasks: General Classification, image-classification, Image Classification, Segmentation, Semantic Segmentation, Structured Prediction

Deep Neural Networks (DNNs) have demonstrated exceptional performance on most
recognition tasks such as image classification and segmentation. However, they
have also been shown to be vulnerable to adversarial examples. This phenomenon
has recently attracted a lot of attention but it has not been extensively
studied on multiple, large-scale datasets and structured prediction tasks such
as semantic segmentation which often require more specialised networks with
additional components such as CRFs, dilated convolutions, skip-connections and
multiscale processing. In this paper, we present what to our knowledge is the
first rigorous evaluation of adversarial attacks on modern semantic
segmentation models, using two large-scale datasets. We analyse the effect of
different network architectures, model capacity and multiscale processing, and
show that many observations made on the task of classification do not always
transfer to this more complex task. Furthermore, we show how mean-field
inference in deep structured models, multiscale processing (and more generally,
input transformations) naturally implement recently proposed adversarial
defenses. Our observations will aid future efforts in understanding and
defending against adversarial examples. Moreover, in the shorter term, we show
how to effectively benchmark robustness and show which segmentation models
should currently be preferred in safety-critical applications due to their
inherent robustness.
A comparison of deep networks with ReLU activation function and linear spline-type methods
Authors: Konstantin Eckle, Johannes Schmidt-Hieber
Date: 2018-04-06 00:00:00

Deep neural networks (DNNs) generate much richer function spaces than shallow
networks. Since the function spaces induced by shallow networks have several
approximation theoretic drawbacks, this explains, however, not necessarily the
success of deep networks. In this article we take another route by comparing
the expressive power of DNNs with ReLU activation function to piecewise linear
spline methods. We show that MARS (multivariate adaptive regression splines) is
improper learnable by DNNs in the sense that for any given function that can be
expressed as a function in MARS with $M$ parameters there exists a multilayer
neural network with $O(M \log (M/\varepsilon))$ parameters that approximates
this function up to sup-norm error $\varepsilon.$ We show a similar result for
expansions with respect to the Faber-Schauder system. Based on this, we derive
risk comparison inequalities that bound the statistical risk of fitting a
neural network by the statistical risk of spline-based methods. This shows that
deep networks perform better or only slightly worse than the considered spline
methods. We provide a constructive proof for the function approximations.
Benchmarking Single Image Dehazing and Beyond
Authors: Boyi Li, Wenqi Ren, Dengpan Fu, DaCheng Tao, Dan Feng, Wen-Jun Zeng, Zhangyang Wang
Date: 2017-12-12 00:00:00
Tasks: Benchmarking, Image Dehazing, Single Image Dehazing

We present a comprehensive study and evaluation of existing single image
dehazing algorithms, using a new large-scale benchmark consisting of both
synthetic and real-world hazy images, called REalistic Single Image DEhazing
(RESIDE). RESIDE highlights diverse data sources and image contents, and is
divided into five subsets, each serving different training or evaluation
purposes. We further provide a rich variety of criteria for dehazing algorithm
evaluation, ranging from full-reference metrics, to no-reference metrics, to
subjective evaluation and the novel task-driven evaluation. Experiments on
RESIDE shed light on the comparisons and limitations of state-of-the-art
dehazing algorithms, and suggest promising future directions.
Drug response prediction by ensemble learning and drug-induced gene expression signatures
Authors: Mehmet Tan, Ozan Fırat Özgül, Batuhan Bardak, Işıksu Ekşioğlu, Suna Sabuncuoğlu
Date: 2018-02-11 00:00:00
Tasks: Drug Response Prediction, Ensemble Learning

Chemotherapeutic response of cancer cells to a given compound is one of the
most fundamental information one requires to design anti-cancer drugs. Recent
advances in producing large drug screens against cancer cell lines provided an
opportunity to apply machine learning methods for this purpose. In addition to
cytotoxicity databases, considerable amount of drug-induced gene expression
data has also become publicly available. Following this, several methods that
exploit omics data were proposed to predict drug activity on cancer cells.
However, due to the complexity of cancer drug mechanisms, none of the existing
methods are perfect. One possible direction, therefore, is to combine the
strengths of both the methods and the databases for improved performance. We
demonstrate that integrating a large number of predictions by the proposed
method improves the performance for this task. The predictors in the ensemble
differ in several aspects such as the method itself, the number of tasks method
considers (multi-task vs. single-task) and the subset of data considered
(sub-sampling). We show that all these different aspects contribute to the
success of the final ensemble. In addition, we attempt to use the drug screen
data together with two novel signatures produced from the drug-induced gene
expression profiles of cancer cell lines. Finally, we evaluate the method
predictions by in vitro experiments in addition to the tests on data sets.The
predictions of the methods, the signatures and the software are available from
\url{http://mtan.etu.edu.tr/drug-response-prediction/}.
Adaptive Cost-sensitive Online Classification
Authors: Peilin Zhao, Yifan Zhang, Min Wu, Steven C. H. Hoi, Mingkui Tan, Junzhou Huang
Date: 2018-04-06 00:00:00
Tasks: Anomaly Detection, Classification, General Classification, Specificity

Cost-Sensitive Online Classification has drawn extensive attention in recent
years, where the main approach is to directly online optimize two well-known
cost-sensitive metrics: (i) weighted sum of sensitivity and specificity; (ii)
weighted misclassification cost. However, previous existing methods only
considered first-order information of data stream. It is insufficient in
practice, since many recent studies have proved that incorporating second-order
information enhances the prediction performance of classification models. Thus,
we propose a family of cost-sensitive online classification algorithms with
adaptive regularization in this paper. We theoretically analyze the proposed
algorithms and empirically validate their effectiveness and properties in
extensive experiments. Then, for better trade off between the performance and
efficiency, we further introduce the sketching technique into our algorithms,
which significantly accelerates the computational speed with quite slight
performance loss. Finally, we apply our algorithms to tackle several online
anomaly detection tasks from real world. Promising results prove that the
proposed algorithms are effective and efficient in solving cost-sensitive
online classification problems in various real-world domains.
Formal Ways for Measuring Relations between Concepts in Conceptual Spaces
Authors: Lucas Bechberger, Kai-Uwe Kühnberger
Date: 2018-04-06 00:00:00

The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. Instances are represented by points in a
high-dimensional space and concepts are represented by regions in this space.
In this article, we extend our recent mathematical formalization of this
framework by providing quantitative mathematical definitions for measuring
relations between concepts: We develop formal ways for computing concept size,
subsethood, implication, similarity, and betweenness. This considerably
increases the representational capabilities of our formalization and makes it
the most thorough and comprehensive formalization of conceptual spaces
developed so far.
Recurrent computations for visual pattern completion
Authors: Hanlin Tang, Martin Schrimpf, Bill Lotter, Charlotte Moerman, Ana Paredes, Josue Ortega Caro, Walter Hardesty, David Cox, Gabriel Kreiman
Date: 2017-06-07 00:00:00
Tasks: Image Classification

Making inferences from partial information constitutes a critical aspect of
cognition. During visual perception, pattern completion enables recognition of
poorly visible or occluded objects. We combined psychophysics, physiology and
computational models to test the hypothesis that pattern completion is
implemented by recurrent computations and present three pieces of evidence that
are consistent with this hypothesis. First, subjects robustly recognized
objects even when rendered <15% visible, but recognition was largely impaired
when processing was interrupted by backward masking. Second, invasive
physiological responses along the human ventral cortex exhibited visually
selective responses to partially visible objects that were delayed compared to
whole objects, suggesting the need for additional computations. These
physiological delays were correlated with the effects of backward masking.
Third, state-of-the-art feed-forward computational architectures were not
robust to partial visibility. However, recognition performance was recovered
when the model was augmented with attractor-based recurrent connectivity. These
results provide a strong argument of plausibility for the role of recurrent
computations in making visual inferences from partial information.
Analysis and development of a novel algorithm for the in-vehicle hand-usage of a smartphone
Authors: Simone Gelmini, Silvia Strada, Mara Tanelli, Sergio Savaresi, Vincenzo Biase
Date: 2018-04-06 00:00:00

Smartphone usage while driving is unanimously considered to be a really
dangerous habit due to strong correlation with road accidents. In this paper,
the problem of detecting whether the driver is using the phone during a trip is
addressed. To do this, high-frequency data from the triaxial inertial
measurement unit (IMU) integrated in almost all modern phone is processed
without relying on external inputs so as to provide a self-contained approach.
By resorting to a frequency-domain analysis, it is possible to extract from the
raw signals the useful information needed to detect when the driver is using
the phone, without being affected by the effects that vehicle motion has on the
same signals. The selected features are used to train a Support Vector Machine
(SVM) algorithm. The performance of the proposed approach are analyzed and
tested on experimental data collected during mixed naturalistic driving
scenarios, proving the effectiveness of the proposed approach.
Sequence Training of DNN Acoustic Models With Natural Gradient
Authors: Adnan Haider, Philip C. Woodland
Date: 2018-04-06 00:00:00
Tasks: Computational Efficiency

Deep Neural Network (DNN) acoustic models often use discriminative sequence
training that optimises an objective function that better approximates the word
error rate (WER) than frame-based training. Sequence training is normally
implemented using Stochastic Gradient Descent (SGD) or Hessian Free (HF)
training. This paper proposes an alternative batch style optimisation framework
that employs a Natural Gradient (NG) approach to traverse through the parameter
space. By correcting the gradient according to the local curvature of the
KL-divergence, the NG optimisation process converges more quickly than HF.
Furthermore, the proposed NG approach can be applied to any sequence
discriminative training criterion. The efficacy of the NG method is shown using
experiments on a Multi-Genre Broadcast (MGB) transcription task that
demonstrates both the computational efficiency and the accuracy of the
resulting DNN models.
Ensemble Manifold Segmentation for Model Distillation and Semi-supervised Learning
Authors: Dengxin Dai, Wen Li, Till Kroeger, Luc van Gool
Date: 2018-04-06 00:00:00
Tasks: General Classification, Multi-Task Learning, Pseudo Label, Segmentation

Manifold theory has been the central concept of many learning methods.
However, learning modern CNNs with manifold structures has not raised due
attention, mainly because of the inconvenience of imposing manifold structures
onto the architecture of the CNNs. In this paper we present ManifoldNet, a
novel method to encourage learning of manifold-aware representations. Our
approach segments the input manifold into a set of fragments. By assigning the
corresponding segmentation id as a pseudo label to every sample, we convert the
problem of preserving the local manifold structure into a point-wise
classification task. Due to its unsupervised nature, the segmentation tends to
be noisy. We mitigate this by introducing ensemble manifold segmentation (EMS).
EMS accounts for the manifold structure by dividing the training data into an
ensemble of classification training sets that contain samples of local
proximity. CNNs are trained on these ensembles under a multi-task learning
framework to conform to the manifold. ManifoldNet can be trained with only the
pseudo labels or together with task-specific labels. We evaluate ManifoldNet on
two different tasks: network imitation (distillation) and semi-supervised
learning. Our experiments show that the manifold structures are effectively
utilized for both unsupervised and semi-supervised learning.
Mix and match networks: encoder-decoder alignment for zero-pair image translation
Authors: Yaxing Wang, Joost Van de Weijer, Luis Herranz
Date: 2018-04-06 00:00:00
Proceeding: CVPR 2018 6
Tasks: Colorization, Decoder, Segmentation, Semantic Segmentation, Style Transfer, Translation

We address the problem of image translation between domains or modalities for
which no direct paired data is available (i.e. zero-pair translation). We
propose mix and match networks, based on multiple encoders and decoders aligned
in such a way that other encoder-decoder pairs can be composed at test time to
perform unseen image translation tasks between domains or modalities for which
explicit paired samples were not seen during training. We study the impact of
autoencoders, side information and losses in improving the alignment and
transferability of trained pairwise translation models to unseen translations.
We show our approach is scalable and can perform colorization and style
transfer between unseen combinations of domains. We evaluate our system in a
challenging cross-modal setting where semantic segmentation is estimated from
depth images, without explicit access to any depth-semantic segmentation
training pairs. Our model outperforms baselines based on pix2pix and CycleGAN
models.
Distributed Convex Optimization with Many Convex Constraints
Authors: Joachim Giesen, Sören Laue
Date: 2016-10-07 00:00:00

We address the problem of solving convex optimization problems with many
convex constraints in a distributed setting. Our approach is based on an
extension of the alternating direction method of multipliers (ADMM) that
recently gained a lot of attention in the Big Data context. Although it has
been invented decades ago, ADMM so far can be applied only to unconstrained
problems and problems with linear equality or inequality constraints. Our
extension can handle arbitrary inequality constraints directly. It combines the
ability of ADMM to solve convex optimization problems in a distributed setting
with the ability of the Augmented Lagrangian method to solve constrained
optimization problems, and as we show, it inherits the convergence guarantees
of ADMM and the Augmented Lagrangian method.
A Benchmarking Environment for Reinforcement Learning Based Task Oriented Dialogue Management
Authors: Iñigo Casanueva, Paweł Budzianowski, Pei-Hao Su, Nikola Mrkšić, Tsung-Hsien Wen, Stefan Ultes, Lina Rojas-Barahona, Steve Young, Milica Gašić
Date: 2017-11-29 00:00:00
Tasks: Benchmarking, Deep Reinforcement Learning, Dialogue Management, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Dialogue assistants are rapidly becoming an indispensable daily aid. To avoid
the significant effort needed to hand-craft the required dialogue flow, the
Dialogue Management (DM) module can be cast as a continuous Markov Decision
Process (MDP) and trained through Reinforcement Learning (RL). Several RL
models have been investigated over recent years. However, the lack of a common
benchmarking framework makes it difficult to perform a fair comparison between
different models and their capability to generalise to different environments.
Therefore, this paper proposes a set of challenging simulated environments for
dialogue model development and evaluation. To provide some baselines, we
investigate a number of representative parametric algorithms, namely deep
reinforcement learning algorithms - DQN, A2C and Natural Actor-Critic and
compare them to a non-parametric model, GP-SARSA. Both the environments and
policy models are implemented using the publicly available PyDial toolkit and
released on-line, in order to establish a testbed framework for further
experiments and to facilitate experimental reproducibility.
Enrichment of OntoSenseNet: Adding a Sense-annotated Telugu lexicon
Authors: Sreekavitha Parupalli, Navjyoti Singh
Date: 2018-04-06 00:00:00
Tasks: Word Sense Disambiguation

The paper describes the enrichment of OntoSenseNet - a verb-centric lexical
resource for Indian Languages. This resource contains a newly developed
Telugu-Telugu dictionary. It is important because native speakers can better
annotate the senses when both the word and its meaning are in Telugu. Hence
efforts are made to develop a soft copy of Telugu dictionary. Our resource also
has manually annotated gold standard corpus consisting 8483 verbs, 253 adverbs
and 1673 adjectives. Annotations are done by native speakers according to
defined annotation guidelines. In this paper, we provide an overview of the
annotation procedure and present the validation of our resource through
inter-annotator agreement. Concepts of sense-class and sense-type are
discussed. Additionally, we discuss the potential of lexical sense-annotated
corpora in improving word sense disambiguation (WSD) tasks. Telugu WordNet is
crowd-sourced for annotation of individual words in synsets and is compared
with the developed sense-annotated lexicon (OntoSenseNet) to examine the
improvement. Also, we present a special categorization (spatio-temporal
classification) of adjectives.
Generative adversarial network-based approach to signal reconstruction from magnitude spectrograms
Authors: Keisuke Oyamada, Hirokazu Kameoka, Takuhiro Kaneko, Kou Tanaka, Nobukatsu Hojo, Hiroyasu Ando
Date: 2018-04-06 00:00:00
Tasks: Generative Adversarial Network

In this paper, we address the problem of reconstructing a time-domain signal
(or a phase spectrogram) solely from a magnitude spectrogram. Since magnitude
spectrograms do not contain phase information, we must restore or infer phase
information to reconstruct a time-domain signal. One widely used approach for
dealing with the signal reconstruction problem was proposed by Griffin and Lim.
This method usually requires many iterations for the signal reconstruction
process and depending on the inputs, it does not always produce high-quality
audio signals. To overcome these shortcomings, we apply a learning-based
approach to the signal reconstruction problem by modeling the signal
reconstruction process using a deep neural network and training it using the
idea of a generative adversarial network. Experimental evaluations revealed
that our method was able to reconstruct signals faster with higher quality than
the Griffin-Lim method.