pwc-archive/papers-with-abstracts huggingface dataset:
Vector Quantized Spectral Clustering applied to Soybean Whole Genome Sequences
Authors: Aditya A. Shastri, Kapil Ahuja, Milind B. Ratnaparkhe, Aditya Shah, Aishwary Gagrani, Anant Lal
Date: 2018-09-30 00:00:00
Tasks: Clustering, Quantization

We develop a Vector Quantized Spectral Clustering (VQSC) algorithm that is a
combination of Spectral Clustering (SC) and Vector Quantization (VQ) sampling
for grouping Soybean genomes. The inspiration here is to use SC for its
accuracy and VQ to make the algorithm computationally cheap (the complexity of
SC is cubic in-terms of the input size). Although the combination of SC and VQ
is not new, the novelty of our work is in developing the crucial similarity
matrix in SC as well as use of k-medoids in VQ, both adapted for the Soybean
genome data. We compare our approach with commonly used techniques like UPGMA
(Un-weighted Pair Graph Method with Arithmetic Mean) and NJ (Neighbour
Joining). Experimental results show that our approach outperforms both these
techniques significantly in terms of cluster quality (up to 25% better cluster
quality) and time complexity (order of magnitude faster).
Benchmarks of ResNet Architecture for Atrial Fibrillation Classification
Authors: Roman Khudorozhkov, Dmitry Podvyaznikov
Date: 2018-09-30 00:00:00
Tasks: Classification, General Classification

In this work we apply variations of ResNet architecture to the task of atrial
fibrillation classification. Variations differ in number of filter after first
convolution, ResNet block layout, number of filters in block convolutions and
number of ResNet blocks between downsampling operations. We have found a range
of model size in which models with quite different configurations show similar
performance. It is likely that overall number of parameters plays dominant role
in model performance. However, configuration parameters like layout have values
that constantly lead to better results, which allows to suggest that these
parameters should be defined and fixed in the first place, while others may be
varied in a reasonable range to satisfy any existing constraints.
Deep, Skinny Neural Networks are not Universal Approximators
Authors: Jesse Johnson
Date: 2018-09-30 00:00:00
Proceeding: ICLR 2019 5

In order to choose a neural network architecture that will be effective for a
particular modeling problem, one must understand the limitations imposed by
each of the potential options. These limitations are typically described in
terms of information theoretic bounds, or by comparing the relative complexity
needed to approximate example functions between different architectures. In
this paper, we examine the topological constraints that the architecture of a
neural network imposes on the level sets of all the functions that it is able
to approximate. This approach is novel for both the nature of the limitations
and the fact that they are independent of network depth for a broad family of
activation functions.
Harmonic Alignment
Authors: Jay S. Stanley III, Scott Gigante, Guy Wolf, Smita Krishnaswamy
Date: 2018-09-30 00:00:00

We propose a novel framework for combining datasets via alignment of their intrinsic geometry. This alignment can be used to fuse data originating from disparate modalities, or to correct batch effects while preserving intrinsic data structure. Importantly, we do not assume any pointwise correspondence between datasets, but instead rely on correspondence between a (possibly unknown) subset of data features. We leverage this assumption to construct an isometric alignment between the data. This alignment is obtained by relating the expansion of data features in harmonics derived from diffusion operators defined over each dataset. These expansions encode each feature as a function of the data geometry. We use this to relate the diffusion coordinates of each dataset through our assumption of partial feature correspondence. Then, a unified diffusion geometry is constructed over the aligned data, which can also be used to correct the original data measurements. We demonstrate our method on several datasets, showing in particular its effectiveness in biological applications including fusion of single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) data measured on the same population of cells, and removal of batch effect between biological samples.
Privacy-preserving Stochastic Gradual Learning
Authors: Bo Han, Ivor W. Tsang, Xiaokui Xiao, Ling Chen, Sai-fu Fung, Celina P. Yu
Date: 2018-09-30 00:00:00
Tasks: Privacy Preserving, Stochastic Optimization

It is challenging for stochastic optimizations to handle large-scale
sensitive data safely. Recently, Duchi et al. proposed private sampling
strategy to solve privacy leakage in stochastic optimizations. However, this
strategy leads to robustness degeneration, since this strategy is equal to the
noise injection on each gradient, which adversely affects updates of the primal
variable. To address this challenge, we introduce a robust stochastic
optimization under the framework of local privacy, which is called
Privacy-pREserving StochasTIc Gradual lEarning (PRESTIGE). PRESTIGE bridges
private updates of the primal variable (by private sampling) with the gradual
curriculum learning (CL). Specifically, the noise injection leads to the issue
of label noise, but the robust learning process of CL can combat with label
noise. Thus, PRESTIGE yields "private but robust" updates of the primal
variable on the private curriculum, namely an reordered label sequence provided
by CL. In theory, we reveal the convergence rate and maximum complexity of
PRESTIGE. Empirical results on six datasets show that, PRESTIGE achieves a good
tradeoff between privacy preservation and robustness over baselines.
Pseudo-Random Number Generation using Generative Adversarial Networks
Authors: Marcello De Bernardi, MHR Khouzani, Pasquale Malacaria
Date: 2018-09-30 00:00:00

Pseudo-random number generators (PRNG) are a fundamental element of many
security algorithms. We introduce a novel approach to their implementation, by
proposing the use of generative adversarial networks (GAN) to train a neural
network to behave as a PRNG. Furthermore, we showcase a number of interesting
modifications to the standard GAN architecture. The most significant is
partially concealing the output of the GAN's generator, and training the
adversary to discover a mapping from the overt part to the concealed part. The
generator therefore learns to produce values the adversary cannot predict,
rather than to approximate an explicit reference distribution. We demonstrate
that a GAN can effectively train even a small feed-forward fully connected
neural network to produce pseudo-random number sequences with good statistical
properties. At best, subjected to the NIST test suite, the trained generator
passed around 99% of test instances and 98% of overall tests, outperforming a
number of standard non-cryptographic PRNGs.
Marrying Tracking with ELM: A Metric Constraint Guided Multiple Feature Fusion Method
Authors: Jing Zhang, Yong-Gong Ren
Date: 2018-09-30 00:00:00
Tasks: Object Tracking

Object Tracking is one important problem in computer vision and surveillance
system. The existing models mainly exploit the single-view feature (i.e. color,
texture, shape) to solve the problem, failing to describe the objects
comprehensively. In this paper, we solve the problem from multi-view
perspective by leveraging multi-view complementary and latent information, so
as to be robust to the partial occlusion and background clutter especially when
the objects are similar to the target, meanwhile addressing tracking drift.
However, one big problem is that multi-view fusion strategy can inevitably
result tracking into non-efficiency. To this end, we propose to marry ELM
(Extreme learning machine) to multi-view fusion to train the global hidden
output weight, to effectively exploit the local information from each view.
Following this principle, we propose a novel method to obtain the optimal
sample as the target object, which avoids tracking drift resulting from noisy
samples. Our method is evaluated over 12 challenge image sequences challenged
with different attributes including illumination, occlusion, deformation, etc.,
which demonstrates better performance than several state-of-the-art methods in
terms of effectiveness and robustness.
An Incremental Iterated Response Model of Pragmatics
Authors: Reuben Cohn-Gordon, Noah D. Goodman, Christopher Potts
Date: 2018-09-30 00:00:00
Proceeding: WS 2019 1
Tasks: model, Referring Expression, Referring expression generation

Recent Iterated Response (IR) models of pragmatics conceptualize language use
as a recursive process in which agents reason about each other to increase
communicative efficiency. These models are generally defined over complete
utterances. However, there is substantial evidence that pragmatic reasoning
takes place incrementally during production and comprehension. We address this
with an incremental IR model. We compare the incremental and global versions
using computational simulations, and we assess the incremental model against
existing experimental data and in the TUNA corpus for referring expression
generation, showing that the model can capture phenomena out of reach of global
versions.
A Kernel Perspective for Regularizing Deep Neural Networks
Authors: Alberto Bietti, Grégoire Mialon, Dexiong Chen, Julien Mairal
Date: 2018-09-30 00:00:00

We propose a new point of view for regularizing deep neural networks by using the norm of a reproducing kernel Hilbert space (RKHS). Even though this norm cannot be computed, it admits upper and lower approximations leading to various practical strategies. Specifically, this perspective (i) provides a common umbrella for many existing regularization principles, including spectral norm and gradient penalties, or adversarial training, (ii) leads to new effective regularization penalties, and (iii) suggests hybrid strategies combining lower and upper bounds to get better approximations of the RKHS norm. We experimentally show this approach to be effective when learning on small datasets, or to obtain adversarially robust models.
Spontaneous Facial Expression Recognition using Sparse Representation
Authors: Dawood Al Chanti, Alice Caplier
Date: 2018-09-30 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), Robust classification

Facial expression is the most natural means for human beings to communicate
their emotions. Most facial expression analysis studies consider the case of
acted expressions. Spontaneous facial expression recognition is significantly
more challenging since each person has a different way to react to a given
emotion. We consider the problem of recognizing spontaneous facial expression
by learning discriminative dictionaries for sparse representation. Facial
images are represented as a sparse linear combination of prototype atoms via
Orthogonal Matching Pursuit algorithm. Sparse codes are then used to train an
SVM classifier dedicated to the recognition task. The dictionary that
sparsifies the facial images (feature points with the same class labels should
have similar sparse codes) is crucial for robust classification. Learning
sparsifying dictionaries heavily relies on the initialization process of the
dictionary. To improve the performance of dictionaries, a random face feature
descriptor based on the Random Projection concept is developed. The
effectiveness of the proposed method is evaluated through several experiments
on the spontaneous facial expressions DynEmo database. It is also estimated on
the well-known acted facial expressions JAFFE database for a purpose of
comparison with state-of-the-art methods.
Using State Predictions for Value Regularization in Curiosity Driven Deep Reinforcement Learning
Authors: Gino Brunner, Manuel Fritsche, Oliver Richter, Roger Wattenhofer
Date: 2018-09-30 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Learning in sparse reward settings remains a challenge in Reinforcement
Learning, which is often addressed by using intrinsic rewards. One promising
strategy is inspired by human curiosity, requiring the agent to learn to
predict the future. In this paper a curiosity-driven agent is extended to use
these predictions directly for training. To achieve this, the agent predicts
the value function of the next state at any point in time. Subsequently, the
consistency of this prediction with the current value function is measured,
which is then used as a regularization term in the loss function of the
algorithm. Experiments were made on grid-world environments as well as on a 3D
navigation task, both with sparse rewards. In the first case the extended agent
is able to learn significantly faster than the baselines.
Improving Bag-of-Visual-Words Towards Effective Facial Expressive Image Classification
Authors: Dawood Al Chanti, Alice Caplier
Date: 2018-09-30 00:00:00
Tasks: Clustering, feature selection, General Classification, image-classification, Image Classification

Bag-of-Visual-Words (BoVW) approach has been widely used in the recent years
for image classification purposes. However, the limitations regarding optimal
feature selection, clustering technique, the lack of spatial organization of
the data and the weighting of visual words are crucial. These factors affect
the stability of the model and reduce performance. We propose to develop an
algorithm based on BoVW for facial expression analysis which goes beyond those
limitations. Thus the visual codebook is built by using k-Means++ method to
avoid poor clustering. To exploit reliable low level features, we search for
the best feature detector that avoids locating a large number of keypoints
which do not contribute to the classification process. Then, we propose to
compute the relative conjunction matrix in order to preserve the spatial order
of the data by coding the relationships among visual words. In addition, a
weighting scheme that reflects how important a visual word is with respect to a
given image is introduced. We speed up the learning process by using histogram
intersection kernel by Support Vector Machine to learn a discriminative
classifier. The efficiency of the proposed algorithm is compared with standard
bag of visual words method and with bag of visual words method with spatial
pyramid. Extensive experiments on the CK+, the MMI and the JAFFE databases show
good average recognition rates. Likewise, the ability to recognize spontaneous
and non-basic expressive states is investigated using the DynEmo database.
Neural Entity Reasoner for Global Consistency in NER
Authors: Xiaoxiao Yin, Daqi Zheng, Zhengdong Lu, Ruifang Liu
Date: 2018-09-30 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Sentence

We propose Neural Entity Reasoner (NE-Reasoner), a framework to introduce
global consistency of recognized entities into Neural Reasoner over Named
Entity Recognition (NER) task. Given an input sentence, the NE-Reasoner layer
can infer over multiple entities to increase the global consistency of output
labels, which then be transfered into entities for the input of next layer.
NE-Reasoner inherits and develops some features from Neural Reasoner 1) a
symbolic memory, allowing it to exchange entities between layers. 2) the
specific interaction-pooling mechanism, allowing it to connect each local word
to multiple global entities, and 3) the deep architecture, allowing it to
bootstrap the recognized entity set from coarse to fine. Like human beings,
NE-Reasoner is able to accommodate ambiguous words and Name Entities that
rarely or never met before. Despite the symbolic information the model
introduced, NE-Reasoner can still be trained effectively in an end-to-end
manner via parameter sharing strategy. NE-Reasoner can outperform conventional
NER models in most cases on both English and Chinese NER datasets. For example,
it achieves state-of-art on CoNLL-2003 English NER dataset.
Pixel and Feature Level Based Domain Adaption for Object Detection in Autonomous Driving
Authors: Yuhu Shan, Wen Feng Lu, Chee Meng Chew
Date: 2018-09-30 00:00:00
Tasks: Autonomous Driving, Domain Adaptation, Generative Adversarial Network, object-detection, Object Detection, Region Proposal, Unsupervised Domain Adaptation

Annotating large scale datasets to train modern convolutional neural networks is prohibitively expensive and time-consuming for many real tasks. One alternative is to train the model on labeled synthetic datasets and apply it in the real scenes. However, this straightforward method often fails to generalize well mainly due to the domain bias between the synthetic and real datasets. Many unsupervised domain adaptation (UDA) methods are introduced to address this problem but most of them only focus on the simple classification task. In this paper, we present a novel UDA model to solve the more complex object detection problem in the context of autonomous driving. Our model integrates both pixel level and feature level based transformtions to fulfill the cross domain detection task and can be further trained end-to-end to pursue better performance. We employ objectives of the generative adversarial network and the cycle consistency loss for image translation in the pixel space. To address the potential semantic inconsistency problem, we propose region proposal based feature adversarial training to preserve the semantics of our target objects as well as further minimize the domain shifts. Extensive experiments are conducted on several different datasets, and the results demonstrate the robustness and superiority of our method.
Text Morphing
Authors: Shaohan Huang, Yu Wu, Furu Wei, Ming Zhou
Date: 2018-09-30 00:00:00
Tasks: Sentence, Text Generation

In this paper, we introduce a novel natural language generation task, termed
as text morphing, which targets at generating the intermediate sentences that
are fluency and smooth with the two input sentences. We propose the Morphing
Networks consisting of the editing vector generation networks and the sentence
editing networks which are trained jointly. Specifically, the editing vectors
are generated with a recurrent neural networks model from the lexical gap
between the source sentence and the target sentence. Then the sentence editing
networks iteratively generate new sentences with the current editing vector and
the sentence generated in the previous step. We conduct experiments with 10
million text morphing sequences which are extracted from the Yelp review
dataset. Experiment results show that the proposed method outperforms baselines
on the text morphing task. We also discuss directions and opportunities for
future research of text morphing.
Specificity measures and reference
Authors: Albert Gatt, Nicolás Marín, Gustavo Rivas-Gervilla, Daniel Sánchez
Date: 2018-09-30 00:00:00
Proceeding: WS 2018 11
Tasks: Referring Expression, Specificity

In this paper we study empirically the validity of measures of referential
success for referring expressions involving gradual properties. More
specifically, we study the ability of several measures of referential success
to predict the success of a user in choosing the right object, given a
referring expression. Experimental results indicate that certain fuzzy measures
of success are able to predict human accuracy in reference resolution. Such
measures are therefore suitable for the estimation of the success or otherwise
of a referring expression produced by a generation algorithm, especially in
case the properties in a domain cannot be assumed to have crisp denotations.
An Overview of Blockchain Integration with Robotics and Artificial Intelligence
Authors: Vasco Lopes, Luís A. Alexandre
Date: 2018-09-30 00:00:00
Tasks: Rhythm

Blockchain technology is growing everyday at a fast-passed rhythm and it's
possible to integrate it with many systems, namely Robotics with AI services.
However, this is still a recent field and there isn't yet a clear understanding
of what it could potentially become. In this paper, we conduct an overview of
many different methods and platforms that try to leverage the power of
blockchain into robotic systems, to improve AI services or to solve problems
that are present in the major blockchains, which can lead to the ability of
creating robotic systems with increased capabilities and security. We present
an overview, discuss the methods and conclude the paper with our view on the
future of the integration of these technologies.
On the Winograd Schema: Situating Language Understanding in the Data-Information-Knowledge Continuum
Authors: Walid S. Saba
Date: 2018-09-30 00:00:00
Tasks: Natural Language Understanding

The Winograd Schema (WS) challenge, proposed as an al-ternative to the Turing
Test, has become the new standard for evaluating progress in natural language
understanding (NLU). In this paper we will not however be concerned with how
this challenge might be addressed. Instead, our aim here is threefold: (i) we
will first formally 'situate' the WS challenge in the
data-information-knowledge continuum, suggesting where in that continuum a good
WS resides; (ii) we will show that a WS is just special case of a more general
phenomenon in language understanding, namely the missing text phenomenon
(henceforth, MTP) - in particular, we will argue that what we usually call
thinking in the process of language understanding involves discovering a
significant amount of 'missing text' - text that is not explicitly stated, but
is often implicitly assumed as shared background knowledge; and (iii) we
conclude by a brief discussion on why MTP is inconsistent with the data-driven
and machine learning approach to language understanding.
A Deep Learning Framework for Single-Sided Sound Speed Inversion in Medical Ultrasound
Authors: Micha Feigin, Daniel Freedman, Brian W. Anthony
Date: 2018-09-30 00:00:00
Tasks: Diagnostic

Objective: Ultrasound elastography is gaining traction as an accessible and useful diagnostic tool for such things as cancer detection and differentiation and thyroid disease diagnostics. Unfortunately, state of the art shear wave imaging techniques, essential to promote this goal, are limited to high-end ultrasound hardware due to high power requirements; are extremely sensitive to patient and sonographer motion, and generally, suffer from low frame rates. Motivated by research and theory showing that longitudinal wave sound speed carries similar diagnostic abilities to shear wave imaging, we present an alternative approach using single sided pressure-wave sound speed measurements from channel data. Methods: In this paper, we present a single-sided sound speed inversion solution using a fully convolutional deep neural network. We use simulations for training, allowing the generation of limitless ground truth data. Results: We show that it is possible to invert for longitudinal sound speed in soft tissue at high frame rates. We validate the method on simulated data. We present highly encouraging results on limited real data. Conclusion: Sound speed inversion on channel data has significant potential, made possible in real time with deep learning technologies. Significance: Specialized shear wave ultrasound systems remain inaccessible in many locations. longitudinal sound speed and deep learning technologies enable an alternative approach to diagnosis based on tissue elasticity. High frame rates are possible.