pwc-archive/papers-with-abstracts huggingface dataset:
Exploring the Deep Feature Space of a Cell Classification Neural Network
Authors: Ezra Webb, Cheng Lei, Chun-Jung Huang, Hirofumi Kobayashi, Hideharu Mikami, Keisuke Goda
Date: 2018-11-15 00:00:00
Tasks: Clustering, Dimensionality Reduction, General Classification, image-classification, Image Classification

In this paper, we present contemporary techniques for visualising the feature
space of a deep learning image classification neural network. These techniques
are viewed in the context of a feed-forward network trained to classify low
resolution fluorescence images of white blood cells captured using optofluidic
imaging. The model has two output classes corresponding to two different cell
types, which are often difficult to distinguish by eye. This paper has two
major sections. The first looks to develop the information space presented by
dimension reduction techniques, such as t-SNE, used to embed high-dimensional
pre-softmax layer activations into a two-dimensional plane. The second section
looks at feature visualisation by optimisation to generate feature images
representing the learned features of the network. Using and developing these
techniques we visualise class separation and structures within the dataset at
various depths using clustering algorithms and feature images; track the
development of feature complexity as we ascend the network; and begin to
extract the features the network has learnt by modulating single-channel
feature images with up-scaled neuron activation maps to distinguish their most
salient parts.
Multi-cell LSTM Based Neural Language Model
Authors: Thomas Cherian, Akshay Badola, Vineet Padmanabhan
Date: 2018-11-15 00:00:00
Tasks: Language Modeling, Language Modelling, model

Language models, being at the heart of many NLP problems, are always of great
interest to researchers. Neural language models come with the advantage of
distributed representations and long range contexts. With its particular
dynamics that allow the cycling of information within the network, `Recurrent
neural network' (RNN) becomes an ideal paradigm for neural language modeling.
Long Short-Term Memory (LSTM) architecture solves the inadequacies of the
standard RNN in modeling long-range contexts. In spite of a plethora of RNN
variants, possibility to add multiple memory cells in LSTM nodes was seldom
explored. Here we propose a multi-cell node architecture for LSTMs and study
its applicability for neural language modeling. The proposed multi-cell LSTM
language models outperform the state-of-the-art results on well-known Penn
Treebank (PTB) setup.
Towards Explainable Deep Learning for Credit Lending: A Case Study
Authors: Ceena Modarres, Mark Ibrahim, Melissa Louie, John Paisley
Date: 2018-11-15 00:00:00
Tasks: Deep Learning

Deep learning adoption in the financial services industry has been limited
due to a lack of model interpretability. However, several techniques have been
proposed to explain predictions made by a neural network. We provide an initial
investigation into these techniques for the assessment of credit risk with
neural networks.
Psychophysical evaluation of individual low-level feature influences on visual attention
Authors: David Berga, Xosé Ramón Fdez-Vidal, Xavier Otazu, Víctor Leborán, Xosé María Pardo
Date: 2018-11-15 00:00:00

In this study we provide the analysis of eye movement behavior elicited by
low-level feature distinctiveness with a dataset of synthetically-generated
image patterns. Design of visual stimuli was inspired by the ones used in
previous psychophysical experiments, namely in free-viewing and visual
searching tasks, to provide a total of 15 types of stimuli, divided according
to the task and feature to be analyzed. Our interest is to analyze the
influences of low-level feature contrast between a salient region and the rest
of distractors, providing fixation localization characteristics and reaction
time of landing inside the salient region. Eye-tracking data was collected from
34 participants during the viewing of a 230 images dataset. Results show that
saliency is predominantly and distinctively influenced by: 1. feature type, 2.
feature contrast, 3. temporality of fixations, 4. task difficulty and 5. center
bias. This experimentation proposes a new psychophysical basis for saliency
model evaluation using synthetic images.
Adversarial Resilience Learning - Towards Systemic Vulnerability Analysis for Large and Complex Systems
Authors: Lars Fischer, Jan-Menno Memmen, Eric MSP Veith, Martin Tröschel
Date: 2018-11-15 00:00:00

This paper introduces Adversarial Resilience Learning (ARL), a concept to
model, train, and analyze artificial neural networks as representations of
competitive agents in highly complex systems. In our examples, the agents
normally take the roles of attackers or defenders that aim at worsening or
improving-or keeping, respectively-defined performance indicators of the
system. Our concept provides adaptive, repeatable, actor-based testing with a
chance of detecting previously unknown attack vectors. We provide the
constitutive nomenclature of ARL and, based on it, the description of
experimental setups and results of a preliminary implementation of ARL in
simulated power systems.
Preliminary Studies on a Large Face Database
Authors: Benjamin Yip, Garrett Bingham, Katherine Kempfert, Jonathan Fabish, Troy Kling, Cuixian Chen, Yishi Wang
Date: 2018-11-15 00:00:00
Tasks: Age Estimation, General Classification, MORPH

We perform preliminary studies on a large longitudinal face database
MORPH-II, which is a benchmark dataset in the field of computer vision and
pattern recognition. First, we summarize the inconsistencies in the dataset and
introduce the steps and strategy taken for cleaning. The potential implications
of these inconsistencies on prior research are introduced. Next, we propose a
new automatic subsetting scheme for evaluation protocol. It is intended to
overcome the unbalanced racial and gender distributions of MORPH-II, while
ensuring independence between training and testing sets. Finally, we contribute
a novel global framework for age estimation that utilizes posterior
probabilities from the race classification step to compute a racecomposite age
estimate. Preliminary experimental results on MORPH-II are presented.
HCU400: An Annotated Dataset for Exploring Aural Phenomenology Through Causal Uncertainty
Authors: Ishwarya Ananthabhotla, David B. Ramsay, Joseph A. Paradiso
Date: 2018-11-15 00:00:00
Tasks: Word Embeddings

The way we perceive a sound depends on many aspects-- its ecological frequency, acoustic features, typicality, and most notably, its identified source. In this paper, we present the HCU400: a dataset of 402 sounds ranging from easily identifiable everyday sounds to intentionally obscured artificial ones. It aims to lower the barrier for the study of aural phenomenology as the largest available audio dataset to include an analysis of causal attribution. Each sample has been annotated with crowd-sourced descriptions, as well as familiarity, imageability, arousal, and valence ratings. We extend existing calculations of causal uncertainty, automating and generalizing them with word embeddings. Upon analysis we find that individuals will provide less polarized emotion ratings as a sound's source becomes increasingly ambiguous; individual ratings of familiarity and imageability, on the other hand, diverge as uncertainty increases despite a clear negative trend on average.
Contextual Care Protocol using Neural Networks and Decision Trees
Authors: Yash Pratyush Sinha, Pranshu Malviya, Minerva Panda, Syed Mohd Ali
Date: 2018-11-15 00:00:00

A contextual care protocol is used by a medical practitioner for patient
healthcare, given the context or situation that the specified patient is in.
This paper proposes a method to build an automated self-adapting protocol which
can help make relevant, early decisions for effective healthcare delivery. The
hybrid model leverages neural networks and decision trees. The neural network
estimates the chances of each disease and each tree in the decision trees
represents care protocol for a disease. These trees are subject to change in
case of aberrations found by the diagnosticians. These corrections or
prediction errors are clustered into similar groups for scalability and review
by the experts. The corrections as suggested by the experts are incorporated
into the model.
Learning to Bound the Multi-class Bayes Error
Authors: Salimeh Yasaei Sekeh, Brandon Oselio, Alfred O. Hero
Date: 2018-11-15 00:00:00
Tasks: Meta-Learning

In the context of supervised learning, meta learning uses features, metadata and other information to learn about the difficulty, behavior, or composition of the problem. Using this knowledge can be useful to contextualize classifier results or allow for targeted decisions about future data sampling. In this paper, we are specifically interested in learning the Bayes error rate (BER) based on a labeled data sample. Providing a tight bound on the BER that is also feasible to estimate has been a challenge. Previous work[1] has shown that a pairwise bound based on the sum of Henze-Penrose (HP) divergence over label pairs can be directly estimated using a sum of Friedman-Rafsky (FR) multivariate run test statistics. However, in situations in which the dataset and number of classes are large, this bound is computationally infeasible to calculate and may not be tight. Other multi-class bounds also suffer from computationally complex estimation procedures. In this paper, we present a generalized HP divergence measure that allows us to estimate the Bayes error rate with log-linear computation. We prove that the proposed bound is tighter than both the pairwise method and a bound proposed by Lin [2]. We also empirically show that these bounds are close to the BER. We illustrate the proposed method on the MNIST dataset, and show its utility for the evaluation of feature reduction strategies. We further demonstrate an approach for evaluation of deep learning architectures using the proposed bounds.
Adversarial Examples from Cryptographic Pseudo-Random Generators
Authors: Sébastien Bubeck, Yin Tat Lee, Eric Price, Ilya Razenshteyn
Date: 2018-11-15 00:00:00
Tasks: Binary Classification, General Classification

In our recent work (Bubeck, Price, Razenshteyn, arXiv:1805.10204) we argued
that adversarial examples in machine learning might be due to an inherent
computational hardness of the problem. More precisely, we constructed a binary
classification task for which (i) a robust classifier exists; yet no
non-trivial accuracy can be obtained with an efficient algorithm in (ii) the
statistical query model. In the present paper we significantly strengthen both
(i) and (ii): we now construct a task which admits (i') a maximally robust
classifier (that is it can tolerate perturbations of size comparable to the
size of the examples themselves); and moreover we prove computational hardness
of learning this task under (ii') a standard cryptographic assumption.
LinkNet: Relational Embedding for Scene Graph
Authors: Sanghyun Woo, Dahun Kim, Donghyeon Cho, In So Kweon
Date: 2018-11-15 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Graph Generation, Scene Graph Generation

Objects and their relationships are critical contents for image
understanding. A scene graph provides a structured description that captures
these properties of an image. However, reasoning about the relationships
between objects is very challenging and only a few recent works have attempted
to solve the problem of generating a scene graph from an image. In this paper,
we present a method that improves scene graph generation by explicitly modeling
inter-dependency among the entire object instances. We design a simple and
effective relational embedding module that enables our model to jointly
represent connections among all related objects, rather than focus on an object
in isolation. Our method significantly benefits the main part of the scene
graph generation task: relationship classification. Using it on top of a basic
Faster R-CNN, our model achieves state-of-the-art results on the Visual Genome
benchmark. We further push the performance by introducing global context
encoding module and geometrical layout encoding module. We validate our final
model, LinkNet, through extensive ablation studies, demonstrating its efficacy
in scene graph generation.
Neural Predictive Belief Representations
Authors: Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Bernardo A. Pires, Rémi Munos
Date: 2018-11-15 00:00:00
Tasks: Decision Making, Representation Learning

Unsupervised representation learning has succeeded with excellent results in many applications. It is an especially powerful tool to learn a good representation of environments with partial or noisy observations. In partially observable domains it is important for the representation to encode a belief state, a sufficient statistic of the observations seen so far. In this paper, we investigate whether it is possible to learn such a belief representation using modern neural architectures. Specifically, we focus on one-step frame prediction and two variants of contrastive predictive coding (CPC) as the objective functions to learn the representations. To evaluate these learned representations, we test how well they can predict various pieces of information about the underlying state of the environment, e.g., position of the agent in a 3D maze. We show that all three methods are able to learn belief representations of the environment, they encode not only the state information, but also its uncertainty, a crucial aspect of belief states. We also find that for CPC multi-step predictions and action-conditioning are critical for accurate belief representations in visually complex environments. The ability of neural representations to capture the belief information has the potential to spur new advances for learning and planning in partially observable domains, where leveraging uncertainty is essential for optimal decision making.
Pairwise Relational Networks using Local Appearance Features for Face Recognition
Authors: Bong-Nam Kang, Yonghyun Kim, Daijin Kim
Date: 2018-11-15 00:00:00
Tasks: Face Identification, Face Recognition, Face Verification

We propose a new face recognition method, called a pairwise relational
network (PRN), which takes local appearance features around landmark points on
the feature map, and captures unique pairwise relations with the same identity
and discriminative pairwise relations between different identities. The PRN
aims to determine facial part-relational structure from local appearance
feature pairs. Because meaningful pairwise relations should be identity
dependent, we add a face identity state feature, which obtains from the long
short-term memory (LSTM) units network with the sequential local appearance
features. To further improve accuracy, we combined the global appearance
features with the pairwise relational feature. Experimental results on the LFW
show that the PRN achieved 99.76% accuracy. On the YTF, PRN achieved the
state-of-the-art accuracy (96.3%). The PRN also achieved comparable results to
the state-of-the-art for both face verification and face identification tasks
on the IJB-A and IJB-B. This work is already published on ECCV 2018.
Asynchronous Stochastic Composition Optimization with Variance Reduction
Authors: Shuheng Shen, Linli Xu, Jingchang Liu, Junliang Guo, Qing Ling
Date: 2018-11-15 00:00:00
Tasks: Management, Reinforcement Learning

Composition optimization has drawn a lot of attention in a wide variety of
machine learning domains from risk management to reinforcement learning.
Existing methods solving the composition optimization problem often work in a
sequential and single-machine manner, which limits their applications in
large-scale problems. To address this issue, this paper proposes two
asynchronous parallel variance reduced stochastic compositional gradient
(AsyVRSC) algorithms that are suitable to handle large-scale data sets. The two
algorithms are AsyVRSC-Shared for the shared-memory architecture and
AsyVRSC-Distributed for the master-worker architecture. The embedded variance
reduction techniques enable the algorithms to achieve linear convergence rates.
Furthermore, AsyVRSC-Shared and AsyVRSC-Distributed enjoy provable linear
speedup, when the time delays are bounded by the data dimensionality or the
sparsity ratio of the partial gradients, respectively. Extensive experiments
are conducted to verify the effectiveness of the proposed algorithms.
Physical Signal Classification Via Deep Neural Networks
Authors: Benjamin Epstein, Roy H. Olsson III
Date: 2018-11-15 00:00:00
Tasks: Classification, General Classification

A Deep Neural Network is applied to classify physical signatures obtained
from physical sensor measurements of running gasoline and diesel-powered
vehicles and other devices. The classification provides information on the
target identities as to vehicle type and even vehicle model. The physical
measurements include acoustic, acceleration (vibration), geophonic, and
magnetic.
Deep Template Matching for Offline Handwritten Chinese Character Recognition
Authors: Zhiyuan Li, Min Jin, Qi Wu, Huaxiang Lu
Date: 2018-11-15 00:00:00
Tasks: Binary Classification, Offline Handwritten Chinese Character Recognition, Template Matching

Just like its remarkable achievements in many computer vision tasks, the
convolutional neural networks (CNN) provide an end-to-end solution in
handwritten Chinese character recognition (HCCR) with great success. However,
the process of learning discriminative features for image recognition is
difficult in cases where little data is available. In this paper, we propose a
novel method for learning siamese neural network which employ a special
structure to predict the similarity between handwritten Chinese characters and
template images. The optimization of siamese neural network can be treated as a
simple binary classification problem. When the training process has been
finished, the powerful discriminative features help us to generalize the
predictive power not just to new data, but to entirely new classes that never
appear in the training set. Experiments performed on the ICDAR-2013 offline
HCCR datasets have shown that the proposed method has a very promising
generalization ability to the new classes that never appear in the training
set.
Spatio-temporal Stacked LSTM for Temperature Prediction in Weather Forecasting
Authors: Zahra Karevan, Johan A. K. Suykens
Date: 2018-11-15 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis, Time Series Prediction, Weather Forecasting

Long Short-Term Memory (LSTM) is a well-known method used widely on sequence
learning and time series prediction. In this paper we deployed stacked LSTM
model in an application of weather forecasting. We propose a 2-layer
spatio-temporal stacked LSTM model which consists of independent LSTM models
per location in the first LSTM layer. Subsequently, the input of the second
LSTM layer is formed based on the combination of the hidden states of the first
layer LSTM models. The experiments show that by utilizing the spatial
information the prediction performance of the stacked LSTM model improves in
most of the cases.
Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction
Authors: Baichuan Yuan, Hao Li, Andrea L. Bertozzi, P. Jeffrey Brantingham, Mason A. Porter
Date: 2018-11-15 00:00:00

There is often latent network structure in spatial and temporal data and the
tools of network analysis can yield fascinating insights into such data. In
this paper, we develop a nonparametric method for network reconstruction from
spatiotemporal data sets using multivariate Hawkes processes. In contrast to
prior work on network reconstruction with point-process models, which has often
focused on exclusively temporal information, our approach uses both temporal
and spatial information and does not assume a specific parametric form of
network dynamics. This leads to an effective way of recovering an underlying
network. We illustrate our approach using both synthetic networks and networks
constructed from real-world data sets (a location-based social media network, a
narrative of crime events, and violent gang crimes). Our results demonstrate
that, in comparison to using only temporal data, our spatiotemporal approach
yields improved network reconstruction, providing a basis for meaningful
subsequent analysis --- such as community structure and motif analysis --- of
the reconstructed networks.
ShuffleDet: Real-Time Vehicle Detection Network in On-board Embedded UAV Imagery
Authors: Seyed Majid Azimi
Date: 2018-11-15 00:00:00
Tasks: object-detection, Object Detection, Real-Time Object Detection, vehicle detection

On-board real-time vehicle detection is of great significance for UAVs and
other embedded mobile platforms. We propose a computationally inexpensive
detection network for vehicle detection in UAV imagery which we call
ShuffleDet. In order to enhance the speed-wise performance, we construct our
method primarily using channel shuffling and grouped convolutions. We apply
inception modules and deformable modules to consider the size and geometric
shape of the vehicles. ShuffleDet is evaluated on CARPK and PUCPR+ datasets and
compared against the state-of-the-art real-time object detection networks.
ShuffleDet achieves 3.8 GFLOPs while it provides competitive performance on
test sets of both datasets. We show that our algorithm achieves real-time
performance by running at the speed of 14 frames per second on NVIDIA Jetson
TX2 showing high potential for this method for real-time processing in UAVs.
Effect of data reduction on sequence-to-sequence neural TTS
Authors: Javier Latorre, Jakub Lachowicz, Jaime Lorenzo-Trueba, Thomas Merritt, Thomas Drugman, Srikanth Ronanki, Klimkov Viacheslav
Date: 2018-11-15 00:00:00
Tasks: Speech Synthesis

Recent speech synthesis systems based on sampling from autoregressive neural
networks models can generate speech almost undistinguishable from human
recordings. However, these models require large amounts of data. This paper
shows that the lack of data from one speaker can be compensated with data from
other speakers. The naturalness of Tacotron2-like models trained on a blend of
5k utterances from 7 speakers is better than that of speaker dependent models
trained on 15k utterances, but in terms of stability multi-speaker models are
always more stable. We also demonstrate that models mixing only 1250 utterances
from a target speaker with 5k utterances from another 6 speakers can produce
significantly better quality than state-of-the-art DNN-guided unit selection
systems trained on more than 10 times the data from the target speaker.