pwc-archive/papers-with-abstracts huggingface dataset:
YASENN: Explaining Neural Networks via Partitioning Activation Sequences
Authors: Yaroslav Zharov, Denis Korzhenkov, Pavel Shvechikov, Alexander Tuzhilin
Date: 2018-11-07 00:00:00
Tasks: Interpretable Machine Learning, Network Interpretation

We introduce a novel approach to feed-forward neural network interpretation
based on partitioning the space of sequences of neuron activations. In line
with this approach, we propose a model-specific interpretation method, called
YASENN. Our method inherits many advantages of model-agnostic distillation,
such as an ability to focus on the particular input region and to express an
explanation in terms of features different from those observed by a neural
network. Moreover, examination of distillation error makes the method
applicable to the problems with low tolerance to interpretation mistakes.
Technically, YASENN distills the network with an ensemble of layer-wise
gradient boosting decision trees and encodes the sequences of neuron
activations with leaf indices. The finite number of unique codes induces a
partitioning of the input space. Each partition may be described in a variety
of ways, including examination of an interpretable model (e.g. a logistic
regression or a decision tree) trained to discriminate between objects of those
partitions. Our experiments provide an intuition behind the method and
demonstrate revealed artifacts in neural network decision making.
Uncertainty in Quantum Rule-Based Systems
Authors: Vicente Moret-Bonillo, Isaac Fernández-Varela, Diego Alvarez-Estevez
Date: 2018-11-07 00:00:00
Tasks: valid

This article deals with the problem of the uncertainty in rule-based systems
(RBS), but from the perspective of quantum computing (QC). In this work we
first remember the characteristics of Quantum Rule-Based Systems (QRBS), a
concept defined in a previous article by one of the authors of this paper, and
we introduce the problem of quantum uncertainty. We assume that the subjective
uncertainty that affects the facts of classical RBSs can be treated as a direct
consequence of the probabilistic nature of quantum mechanics (QM), and we also
assume that the uncertainty associated with a given hypothesis is a consequence
of the propagation of the imprecision through the inferential circuits of RBSs.
This article does not intend to contribute anything new to the QM field: it is
a work of artificial intelligence (AI) that uses QC techniques to solve the
problem of uncertainty in RBSs. Bearing the above arguments in mind a quantum
model is proposed. This model has been applied to a problem already defined by
one of the authors of this work in a previous publication and which is briefly
described in this article. Then the model is generalized, and it is thoroughly
evaluated. The results obtained show that QC is a valid, effective and
efficient method to deal with the inherent uncertainty of RBSs
Improved Audio Embeddings by Adjacency-Based Clustering with Applications in Spoken Term Detection
Authors: Sung-Feng Huang, Yi-Chen Chen, Hung-Yi Lee, Lin-shan Lee
Date: 2018-11-07 00:00:00
Tasks: Clustering

Embedding audio signal segments into vectors with fixed dimensionality is
attractive because all following processing will be easier and more efficient,
for example modeling, classifying or indexing. Audio Word2Vec previously
proposed was shown to be able to represent audio segments for spoken words as
such vectors carrying information about the phonetic structures of the signal
segments. However, each linguistic unit (word, syllable, phoneme in text form)
corresponds to unlimited number of audio segments with vector representations
inevitably spread over the embedding space, which causes some confusion. It is
therefore desired to better cluster the audio embeddings such that those
corresponding to the same linguistic unit can be more compactly distributed. In
this paper, inspired by Siamese networks, we propose some approaches to achieve
the above goal. This includes identifying positive and negative pairs from
unlabeled data for Siamese style training, disentangling acoustic factors such
as speaker characteristics from the audio embedding, handling unbalanced data
distribution, and having the embedding processes learn from the adjacency
relationships among data points. All these can be done in an unsupervised way.
Improved performance was obtained in preliminary experiments on the LibriSpeech
data set, including clustering characteristics analysis and applications of
spoken term detection.
Promising Accurate Prefix Boosting for sequence-to-sequence ASR
Authors: Murali Karthick Baskar, Lukáš Burget, Shinji Watanabe, Martin Karafiát, Takaaki Hori, Jan Honza Černocký
Date: 2018-11-07 00:00:00

In this paper, we present promising accurate prefix boosting (PAPB), a
discriminative training technique for attention based sequence-to-sequence
(seq2seq) ASR. PAPB is devised to unify the training and testing scheme in an
effective manner. The training procedure involves maximizing the score of each
partial correct sequence obtained during beam search compared to other
hypotheses. The training objective also includes minimization of token
(character) error rate. PAPB shows its efficacy by achieving 10.8\% and 3.8\%
WER with and without RNNLM respectively on Wall Street Journal dataset.
Learning to Compose Topic-Aware Mixture of Experts for Zero-Shot Video Captioning
Authors: Xin Wang, Jiawei Wu, Da Zhang, Yu Su, William Yang Wang
Date: 2018-11-07 00:00:00
Tasks: Mixture-of-Experts, Video Captioning

Although promising results have been achieved in video captioning, existing
models are limited to the fixed inventory of activities in the training corpus,
and do not generalize to open vocabulary scenarios. Here we introduce a novel
task, zero-shot video captioning, that aims at describing out-of-domain videos
of unseen activities. Videos of different activities usually require different
captioning strategies in many aspects, i.e. word selection, semantic
construction, and style expression etc, which poses a great challenge to depict
novel activities without paired training data. But meanwhile, similar
activities share some of those aspects in common. Therefore, We propose a
principled Topic-Aware Mixture of Experts (TAMoE) model for zero-shot video
captioning, which learns to compose different experts based on different topic
embeddings, implicitly transferring the knowledge learned from seen activities
to unseen ones. Besides, we leverage external topic-related text corpus to
construct the topic embedding for each activity, which embodies the most
relevant semantic vectors within the topic. Empirical results not only validate
the effectiveness of our method in utilizing semantic knowledge for video
captioning, but also show its strong generalization ability when describing
novel activities.
Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks
Authors: Yuenan Hou, Zheng Ma, Chunxiao Liu, Chen Change Loy
Date: 2018-11-07 00:00:00
Tasks: Image Segmentation, Multi-Task Learning, Optical Flow Estimation, Semantic Segmentation, Steering Control

The training of many existing end-to-end steering angle prediction models
heavily relies on steering angles as the supervisory signal. Without learning
from much richer contexts, these methods are susceptible to the presence of
sharp road curves, challenging traffic conditions, strong shadows, and severe
lighting changes. In this paper, we considerably improve the accuracy and
robustness of predictions through heterogeneous auxiliary networks feature
mimicking, a new and effective training method that provides us with much
richer contextual signals apart from steering direction. Specifically, we train
our steering angle predictive model by distilling multi-layer knowledge from
multiple heterogeneous auxiliary networks that perform related but different
tasks, e.g., image segmentation or optical flow estimation. As opposed to
multi-task learning, our method does not require expensive annotations of
related tasks on the target set. This is made possible by applying contemporary
off-the-shelf networks on the target set and mimicking their features in
different layers after transformation. The auxiliary networks are discarded
after training without affecting the runtime efficiency of our model. Our
approach achieves a new state-of-the-art on Udacity and Comma.ai, outperforming
the previous best by a large margin of 12.8% and 52.1%, respectively.
Encouraging results are also shown on Berkeley Deep Drive (BDD) dataset.
Early Prediction of Acute Kidney Injury in Critical Care Setting Using Clinical Notes
Authors: Yikuan Li, Liang Yao, Chengsheng Mao, Anand Srivastava, Xiaoqian Jiang, Yuan Luo
Date: 2018-11-07 00:00:00
Tasks: ICU Admission

Acute kidney injury (AKI) in critically ill patients is associated with
significant morbidity and mortality. Development of novel methods to identify
patients with AKI earlier will allow for testing of novel strategies to prevent
or reduce the complications of AKI. We developed data-driven prediction models
to estimate the risk of new AKI onset. We generated models from clinical notes
within the first 24 hours following intensive care unit (ICU) admission
extracted from Medical Information Mart for Intensive Care III (MIMIC-III).
From the clinical notes, we generated clinically meaningful word and concept
representations and embeddings, respectively. Five supervised learning
classifiers and knowledge-guided deep learning architecture were used to
construct prediction models. The best configuration yielded a competitive AUC
of 0.779. Our work suggests that natural language processing of clinical notes
can be applied to assist clinicians in identifying the risk of incident AKI
onset in critically ill patients upon admission to the ICU.
Bayesian State Estimation for Unobservable Distribution Systems via Deep Learning
Authors: Kursat Rasim Mestav, Jaime Luengo-Rozas, Lang Tong
Date: 2018-11-07 00:00:00
Tasks: Bayesian Inference, Deep Learning, State Estimation

The problem of state estimation for unobservable distribution systems is
considered. A deep learning approach to Bayesian state estimation is proposed
for real-time applications. The proposed technique consists of distribution
learning of stochastic power injection, a Monte Carlo technique for the
training of a deep neural network for state estimation, and a Bayesian bad-data
detection and filtering algorithm. Structural characteristics of the deep
neural networks are investigated. Simulations illustrate the accuracy of
Bayesian state estimation for unobservable systems and demonstrate the benefit
of employing a deep neural network. Numerical results show the robustness of
Bayesian state estimation against modeling and estimation errors and the
presence of bad and missing data. Comparing with pseudo-measurement techniques,
direct Bayesian state estimation via deep learning neural network outperforms
existing benchmarks.
The relationship between linguistic expression and symptoms of depression, anxiety, and suicidal thoughts: A longitudinal study of blog content
Authors: B. ODea, T. W. Boonstra, M. E. Larsen, T. Nguyen, S. Venkatesh, H. Christensen
Date: 2018-11-07 00:00:00

Due to its popularity and availability, social media data may present a new
way to identify individuals who are experiencing mental illness. By analysing
blog content, this study aimed to investigate the associations between
linguistic features and symptoms of depression, generalised anxiety, and
suicidal ideation. This study utilised a longitudinal study design. Individuals
who blogged were invited to participate in a study in which they completed
fortnightly mental health questionnaires including the PHQ9 and GAD7 for a
period of 36 weeks. Linguistic features were extracted from blog data using the
LIWC tool. Bivariate and multivariate analyses were performed to investigate
the correlations between the linguistic features and mental health scores
between subjects. We then used the multivariate regression model to predict
longitudinal changes in mood within subjects. A total of 153 participants
consented to taking part, with 38 participants completing the required number
of questionnaires and blog posts during the study period. Between-subject
analysis revealed that several linguistic features, including tentativeness and
non-fluencies, were significantly associated with depression and anxiety
symptoms, but not suicidal thoughts. Within-subject analysis showed no robust
correlations between linguistic features and changes in mental health score.
This study provides further support for the relationship between linguistic
features within social media data and symptoms of depression and anxiety. The
lack of robust within-subject correlations indicate that the relationship
observed at the group level may not generalise to individual changes over time.
Component-based Attention for Large-scale Trademark Retrieval
Authors: Osman Tursun, Simon Denman, Sabesan Sivapalan, Sridha Sridharan, Clinton Fookes, Sandra Mau
Date: 2018-11-07 00:00:00
Tasks: Retrieval

The demand for large-scale trademark retrieval (TR) systems has significantly increased to combat the rise in international trademark infringement. Unfortunately, the ranking accuracy of current approaches using either hand-crafted or pre-trained deep convolution neural network (DCNN) features is inadequate for large-scale deployments. We show in this paper that the ranking accuracy of TR systems can be significantly improved by incorporating hard and soft attention mechanisms, which direct attention to critical information such as figurative elements and reduce attention given to distracting and uninformative elements such as text and background. Our proposed approach achieves state-of-the-art results on a challenging large-scale trademark dataset.
Y^2Seq2Seq: Cross-Modal Representation Learning for 3D Shape and Text by Joint Reconstruction and Prediction of View and Word Sequences
Authors: Zhizhong Han, Mingyang Shang, Xiyang Wang, Yu-Shen Liu, Matthias Zwicker
Date: 2018-11-07 00:00:00
Tasks: 3D Shape Representation, Cross-Modal Retrieval, Representation Learning, Retrieval

A recent method employs 3D voxels to represent 3D shapes, but this limits the
approach to low resolutions due to the computational cost caused by the cubic
complexity of 3D voxels. Hence the method suffers from a lack of detailed
geometry. To resolve this issue, we propose Y^2Seq2Seq, a view-based model, to
learn cross-modal representations by joint reconstruction and prediction of
view and word sequences. Specifically, the network architecture of Y^2Seq2Seq
bridges the semantic meaning embedded in the two modalities by two coupled `Y'
like sequence-to-sequence (Seq2Seq) structures. In addition, our novel
hierarchical constraints further increase the discriminability of the
cross-modal representations by employing more detailed discriminative
information. Experimental results on cross-modal retrieval and 3D shape
captioning show that Y^2Seq2Seq outperforms the state-of-the-art methods.
View Inter-Prediction GAN: Unsupervised Representation Learning for 3D Shapes by Learning Global Shape Memories to Support Local View Predictions
Authors: Zhizhong Han, Mingyang Shang, Yu-Shen Liu, Matthias Zwicker
Date: 2018-11-07 00:00:00
Tasks: 3D Point Cloud Linear Classification, Prediction, Representation Learning

In this paper we present a novel unsupervised representation learning
approach for 3D shapes, which is an important research challenge as it avoids
the manual effort required for collecting supervised data. Our method trains an
RNN-based neural network architecture to solve multiple view inter-prediction
tasks for each shape. Given several nearby views of a shape, we define view
inter-prediction as the task of predicting the center view between the input
views, and reconstructing the input views in a low-level feature space. The key
idea of our approach is to implement the shape representation as a
shape-specific global memory that is shared between all local view
inter-predictions for each shape. Intuitively, this memory enables the system
to aggregate information that is useful to better solve the view
inter-prediction tasks for each shape, and to leverage the memory as a
view-independent shape representation. Our approach obtains the best results
using a combination of L_2 and adversarial losses for the view inter-prediction
task. We show that VIP-GAN outperforms state-of-the-art methods in unsupervised
3D feature learning on three large scale 3D shape benchmarks.
Style Separation and Synthesis via Generative Adversarial Networks
Authors: Rui Zhang, Sheng Tang, Yu Li, Junbo Guo, Yongdong Zhang, Jintao Li, Shuicheng Yan
Date: 2018-11-07 00:00:00
Tasks: Generative Adversarial Network, Object

Style synthesis attracts great interests recently, while few works focus on
its dual problem "style separation". In this paper, we propose the Style
Separation and Synthesis Generative Adversarial Network (S3-GAN) to
simultaneously implement style separation and style synthesis on object
photographs of specific categories. Based on the assumption that the object
photographs lie on a manifold, and the contents and styles are independent, we
employ S3-GAN to build mappings between the manifold and a latent vector space
for separating and synthesizing the contents and styles. The S3-GAN consists of
an encoder network, a generator network, and an adversarial network. The
encoder network performs style separation by mapping an object photograph to a
latent vector. Two halves of the latent vector represent the content and style,
respectively. The generator network performs style synthesis by taking a
concatenated vector as input. The concatenated vector contains the style half
vector of the style target image and the content half vector of the content
target image. Once obtaining the images from the generator network, an
adversarial network is imposed to generate more photo-realistic images.
Experiments on CelebA and UT Zappos 50K datasets demonstrate that the S3-GAN
has the capacity of style separation and synthesis simultaneously, and could
capture various styles in a single model.
Learning acoustic word embeddings with phonetically associated triplet network
Authors: Hyungjun Lim, Younggwan Kim, Youngmoon Jung, Myunghun Jung, Hoirin Kim
Date: 2018-11-07 00:00:00
Tasks: Triplet, Word Embeddings

Previous researches on acoustic word embeddings used in query-by-example
spoken term detection have shown remarkable performance improvements when using
a triplet network. However, the triplet network is trained using only a limited
information about acoustic similarity between words. In this paper, we propose
a novel architecture, phonetically associated triplet network (PATN), which
aims at increasing discriminative power of acoustic word embeddings by
utilizing phonetic information as well as word identity. The proposed model is
learned to minimize a combined loss function that was made by introducing a
cross entropy loss to the lower layer of LSTM-based triplet network. We
observed that the proposed method performs significantly better than the
baseline triplet network on a word discrimination task with the WSJ dataset
resulting in over 20% relative improvement in recall rate at 1.0 false alarm
per hour. Finally, we examined the generalization ability by conducting the
out-of-domain test on the RM dataset.
CNN-based MultiChannel End-to-End Speech Recognition for everyday home environments
Authors: Nelson Yalta, Shinji Watanabe, Takaaki Hori, Kazuhiro Nakadai, Tetsuya OGATA
Date: 2018-11-07 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, speech-recognition, Speech Recognition

Casual conversations involving multiple speakers and noises from surrounding devices are common in everyday environments, which degrades the performances of automatic speech recognition systems. These challenging characteristics of environments are the target of the CHiME-5 challenge. By employing a convolutional neural network (CNN)-based multichannel end-to-end speech recognition system, this study attempts to overcome the presents difficulties in everyday environments. The system comprises of an attention-based encoder-decoder neural network that directly generates a text as an output from a sound input. The multichannel CNN encoder, which uses residual connections and batch renormalization, is trained with augmented data, including white noise injection. The experimental results show that the word error rate is reduced by 8.5% and 0.6% absolute from a single channel end-to-end and the best baseline (LF-MMI TDNN) on the CHiME-5 corpus, respectively.
Distributionally Robust Graphical Models
Authors: Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian D. Ziebart
Date: 2018-11-07 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Prediction, Structured Prediction

In many structured prediction problems, complex relationships between
variables are compactly defined using graphical structures. The most prevalent
graphical prediction methods---probabilistic graphical models and large margin
methods---have their own distinct strengths but also possess significant
drawbacks. Conditional random fields (CRFs) are Fisher consistent, but they do
not permit integration of customized loss metrics into their learning process.
Large-margin models, such as structured support vector machines (SSVMs), have
the flexibility to incorporate customized loss metrics, but lack Fisher
consistency guarantees. We present adversarial graphical models (AGM), a
distributionally robust approach for constructing a predictor that performs
robustly for a class of data distributions defined using a graphical structure.
Our approach enjoys both the flexibility of incorporating customized loss
metrics into its design as well as the statistical guarantee of Fisher
consistency. We present exact learning and prediction algorithms for AGM with
time complexity similar to existing graphical models and show the practical
benefits of our approach with experiments.
Scalable Bottom-up Subspace Clustering using FP-Trees for High Dimensional Data
Authors: Minh Tuan Doan, Jianzhong Qi, Sutharshan Rajasegarar, Christopher Leckie
Date: 2018-11-07 00:00:00
Tasks: Clustering

Subspace clustering aims to find groups of similar objects (clusters) that
exist in lower dimensional subspaces from a high dimensional dataset. It has a
wide range of applications, such as analysing high dimensional sensor data or
DNA sequences. However, existing algorithms have limitations in finding
clusters in non-disjoint subspaces and scaling to large data, which impinge
their applicability in areas such as bioinformatics and the Internet of Things.
We aim to address such limitations by proposing a subspace clustering algorithm
using a bottom-up strategy. Our algorithm first searches for base clusters in
low dimensional subspaces. It then forms clusters in higher-dimensional
subspaces using these base clusters, which we formulate as a frequent pattern
mining problem. This formulation enables efficient search for clusters in
higher-dimensional subspaces, which is done using FP-trees. The proposed
algorithm is evaluated against traditional bottom-up clustering algorithms and
state-of-the-art subspace clustering algorithms. The experimental results show
that the proposed algorithm produces clusters with high accuracy, and scales
well to large volumes of data. We also demonstrate the algorithm's performance
using real-life data, including ten genomic datasets and a car parking
occupancy dataset.