pwc-archive/papers-with-abstracts huggingface dataset:
Investigating Generative Adversarial Networks based Speech Dereverberation for Robust Speech Recognition
Authors: Ke Wang, Junbo Zhang, Sining Sun, Yujun Wang, Fei Xiang, Lei Xie
Date: 2018-03-27 00:00:00
Tasks: Robust Speech Recognition, Speech Dereverberation, Speech Enhancement, speech-recognition, Speech Recognition

We investigate the use of generative adversarial networks (GANs) in speech
dereverberation for robust speech recognition. GANs have been recently studied
for speech enhancement to remove additive noises, but there still lacks of a
work to examine their ability in speech dereverberation and the advantages of
using GANs have not been fully established. In this paper, we provide deep
investigations in the use of GAN-based dereverberation front-end in ASR. First,
we study the effectiveness of different dereverberation networks (the generator
in GAN) and find that LSTM leads a significant improvement as compared with
feed-forward DNN and CNN in our dataset. Second, further adding residual
connections in the deep LSTMs can boost the performance as well. Finally, we
find that, for the success of GAN, it is important to update the generator and
the discriminator using the same mini-batch data during training. Moreover,
using reverberant spectrogram as a condition to discriminator, as suggested in
previous studies, may degrade the performance. In summary, our GAN-based
dereverberation front-end achieves 14%-19% relative CER reduction as compared
to the baseline DNN dereverberation network when tested on a strong
multi-condition training acoustic model.
Empirical Evaluation of Speaker Adaptation on DNN based Acoustic Model
Authors: Ke Wang, Junbo Zhang, Yujun Wang, Lei Xie
Date: 2018-03-27 00:00:00

Speaker adaptation aims to estimate a speaker specific acoustic model from a
speaker independent one to minimize the mismatch between the training and
testing conditions arisen from speaker variabilities. A variety of neural
network adaptation methods have been proposed since deep learning models have
become the main stream. But there still lacks an experimental comparison
between different methods, especially when DNN-based acoustic models have been
advanced greatly. In this paper, we aim to close this gap by providing an
empirical evaluation of three typical speaker adaptation methods: LIN, LHUC and
KLD. Adaptation experiments, with different size of adaptation data, are
conducted on a strong TDNN-LSTM acoustic model. More challengingly, here, the
source and target we are concerned with are standard Mandarin speaker model and
accented Mandarin speaker model. We compare the performances of different
methods and their combinations. Speaker adaptation performance is also examined
by speaker's accent degree.
Learning Acoustic Word Embeddings with Temporal Context for Query-by-Example Speech Search
Authors: Yougen Yuan, Cheung-Chi Leung, Lei Xie, Hongjie Chen, Bin Ma, Haizhou Li
Date: 2018-06-10 00:00:00
Tasks: Dynamic Time Warping, Triplet, Word Embeddings

We propose to learn acoustic word embeddings with temporal context for
query-by-example (QbE) speech search. The temporal context includes the leading
and trailing word sequences of a word. We assume that there exist spoken word
pairs in the training database. We pad the word pairs with their original
temporal context to form fixed-length speech segment pairs. We obtain the
acoustic word embeddings through a deep convolutional neural network (CNN)
which is trained on the speech segment pairs with a triplet loss. Shifting a
fixed-length analysis window through the search content, we obtain a running
sequence of embeddings. In this way, searching for the spoken query is
equivalent to the matching of acoustic word embeddings. The experiments show
that our proposed acoustic word embeddings learned with temporal context are
effective in QbE speech search. They outperform the state-of-the-art
frame-level feature representations and reduce run-time computation since no
dynamic time warping is required in QbE speech search. We also find that it is
important to have sufficient speech segment pairs to train the deep CNN for
effective acoustic word embeddings.
Neural Feature Learning From Relational Database
Authors: Hoang Thanh Lam, Tran Ngoc Minh, Mathieu Sinn, Beat Buesser, Martin Wistuba
Date: 2018-01-16 00:00:00
Tasks: Feature Engineering

Feature engineering is one of the most important but most tedious tasks in data science. This work studies automation of feature learning from relational database. We first prove theoretically that finding the optimal features from relational data for predictive tasks is NP-hard. We propose an efficient rule-based approach based on heuristics and a deep neural network to automatically learn appropriate features from relational data. We benchmark our approaches in ensembles in past Kaggle competitions. Our new approach wins late medals and beats the state-of-the-art solutions with significant margins. To the best of our knowledge, this is the first time an automated data science system could win medals in Kaggle competitions with complex relational database.
Extending Recurrent Neural Aligner for Streaming End-to-End Speech Recognition in Mandarin
Authors: Linhao Dong, Shiyu Zhou, Wei Chen, Bo Xu
Date: 2018-06-17 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

End-to-end models have been showing superiority in Automatic Speech
Recognition (ASR). At the same time, the capacity of streaming recognition has
become a growing requirement for end-to-end models. Following these trends, an
encoder-decoder recurrent neural network called Recurrent Neural Aligner (RNA)
has been freshly proposed and shown its competitiveness on two English ASR
tasks. However, it is not clear if RNA can be further improved and applied to
other spoken language. In this work, we explore the applicability of RNA in
Mandarin Chinese and present four effective extensions: In the encoder, we
redesign the temporal down-sampling and introduce a powerful convolutional
structure. In the decoder, we utilize a regularizer to smooth the output
distribution and conduct joint training with a language model. On two Mandarin
Chinese conversational telephone speech recognition (MTS) datasets, our
Extended-RNA obtains promising performance. Particularly, it achieves 27.7%
character error rate (CER), which is superior to current state-of-the-art
result on the popular HKUST task.
Inverse Reinforcement Learning from Summary Data
Authors: Antti Kangasrääsiö, Samuel Kaski
Date: 2017-03-28 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Inverse reinforcement learning (IRL) aims to explain observed strategic
behavior by fitting reinforcement learning models to behavioral data. However,
traditional IRL methods are only applicable when the observations are in the
form of state-action paths. This assumption may not hold in many real-world
modeling settings, where only partial or summarized observations are available.
In general, we may assume that there is a summarizing function $\sigma$, which
acts as a filter between us and the true state-action paths that constitute the
demonstration. Some initial approaches to extending IRL to such situations have
been presented, but with very specific assumptions about the structure of
$\sigma$, such as that only certain state observations are missing. This paper
instead focuses on the most general case of the problem, where no assumptions
are made about the summarizing function, except that it can be evaluated. We
demonstrate that inference is still possible. The paper presents exact and
approximate inference algorithms that allow full posterior inference, which is
particularly important for assessing parameter uncertainty in this challenging
inference situation. Empirical scalability is demonstrated to reasonably sized
problems, and practical applicability is demonstrated by estimating the
posterior for a cognitive science RL model based on an observed user's task
completion time only.
GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations
Authors: Zhilin Yang, Jake Zhao, Bhuwan Dhingra, Kaiming He, William W. Cohen, Ruslan Salakhutdinov, Yann Lecun
Date: 2018-06-14 00:00:00
Tasks: image-classification, Image Classification, Natural Language Inference, Question Answering, Sentiment Analysis, Transfer Learning, Word Embeddings

Modern deep transfer learning approaches have mainly focused on learning
generic feature vectors from one task that are transferable to other tasks,
such as word embeddings in language and pretrained convolutional features in
vision. However, these approaches usually transfer unary features and largely
ignore more structured graphical representations. This work explores the
possibility of learning generic latent relational graphs that capture
dependencies between pairs of data units (e.g., words or pixels) from
large-scale unlabeled data and transferring the graphs to downstream tasks. Our
proposed transfer learning framework improves performance on various tasks
including question answering, natural language inference, sentiment analysis,
and image classification. We also show that the learned graphs are generic
enough to be transferred to different embeddings on which the graphs have not
been trained (including GloVe embeddings, ELMo embeddings, and task-specific
RNN hidden unit), or embedding-free units such as image pixels.
CompNet: Complementary Segmentation Network for Brain MRI Extraction
Authors: Raunak Dey, Yi Hong
Date: 2018-03-27 00:00:00
Tasks: Decoder, Image Segmentation, Segmentation, Semantic Segmentation, Skull Stripping

Brain extraction is a fundamental step for most brain imaging studies. In
this paper, we investigate the problem of skull stripping and propose
complementary segmentation networks (CompNets) to accurately extract the brain
from T1-weighted MRI scans, for both normal and pathological brain images. The
proposed networks are designed in the framework of encoder-decoder networks and
have two pathways to learn features from both the brain tissue and its
complementary part located outside of the brain. The complementary pathway
extracts the features in the non-brain region and leads to a robust solution to
brain extraction from MRIs with pathologies, which do not exist in our training
dataset. We demonstrate the effectiveness of our networks by evaluating them on
the OASIS dataset, resulting in the state of the art performance under the
two-fold cross-validation setting. Moreover, the robustness of our networks is
verified by testing on images with introduced pathologies and by showing its
invariance to unseen brain pathologies. In addition, our complementary network
design is general and can be extended to address other image segmentation
problems with better generalization.
Training Augmentation with Adversarial Examples for Robust Speech Recognition
Authors: Sining Sun, Ching-Feng Yeh, Mari Ostendorf, Mei-Yuh Hwang, Lei Xie
Date: 2018-06-07 00:00:00
Tasks: Data Augmentation, Robust Speech Recognition, speech-recognition, Speech Recognition

This paper explores the use of adversarial examples in training speech
recognition systems to increase robustness of deep neural network acoustic
models. During training, the fast gradient sign method is used to generate
adversarial examples augmenting the original training data. Different from
conventional data augmentation based on data transformations, the examples are
dynamically generated based on current acoustic model parameters. We assess the
impact of adversarial data augmentation in experiments on the Aurora-4 and
CHiME-4 single-channel tasks, showing improved robustness against noise and
channel variation. Further improvement is obtained when combining adversarial
examples with teacher/student training, leading to a 23% relative word error
rate reduction on Aurora-4.
Approximate Submodular Functions and Performance Guarantees
Authors: Gaurav Gupta, Sergio Pequito, Paul Bogdan
Date: 2018-06-17 00:00:00

We consider the problem of maximizing non-negative non-decreasing set
functions. Although most of the recent work focus on exploiting submodularity,
it turns out that several objectives we encounter in practice are not
submodular. Nonetheless, often we leverage the greedy algorithms used in
submodular functions to determine a solution to the non-submodular functions.
Hereafter, we propose to address the original problem by \emph{approximating}
the non-submodular function and analyze the incurred error, as well as the
performance trade-offs. To quantify the approximation error, we introduce a
novel concept of $\delta$-approximation of a function, which we used to define
the space of submodular functions that lie within an approximation error. We
provide necessary conditions on the existence of such $\delta$-approximation
functions, which might not be unique. Consequently, we characterize this
subspace which we refer to as \emph{region of submodularity}. Furthermore,
submodular functions are known to lead to different sub-optimality guarantees,
so we generalize those dependencies upon a $\delta$-approximation into the
notion of \emph{greedy curvature}. Finally, we used this latter notion to
simplify some of the existing results and efficiently (i.e., linear complexity)
determine tightened bounds on the sub-optimality guarantees using objective
functions commonly used in practical setups and validate them using real data.
Comparative survey of visual object classifiers
Authors: Hiliwi Leake Kidane
Date: 2018-06-17 00:00:00
Tasks: Classification, General Classification, Object, Survey

Classification of Visual Object Classes represents one of the most elaborated
areas of interest in Computer Vision. It is always challenging to get one
specific detector, descriptor or classifier that provides the expected object
classification result. Consequently, it critical to compare the different
detection, descriptor and classifier methods available and chose a single or
combination of two or three to get an optimal result. In this paper, we have
presented a comparative survey of different feature descriptors and
classifiers. From feature descriptors, SIFT (Sparse & Dense) and HeuSIFT
combination colour descriptors; From classification techniques, Support Vector
Classifier, K-Nearest Neighbor, ADABOOST, and fisher are covered in comparative
practical implementation survey.
Deep Neural Nets with Interpolating Function as Output Activation
Authors: Bao Wang, Xiyang Luo, Zhen Li, Wei Zhu, Zuoqiang Shi, Stanley J. Osher
Date: 2018-02-01 00:00:00
Proceeding: NeurIPS 2018 12

We replace the output layer of deep neural nets, typically the softmax
function, by a novel interpolating function. And we propose end-to-end training
and testing algorithms for this new architecture. Compared to classical neural
nets with softmax function as output activation, the surrogate with
interpolating function as output activation combines advantages of both deep
and manifold learning. The new framework demonstrates the following major
advantages: First, it is better applicable to the case with insufficient
training data. Second, it significantly improves the generalization accuracy on
a wide variety of networks. The algorithm is implemented in PyTorch, and code
will be made publicly available.
Laplacian Smoothing Gradient Descent
Authors: Stanley Osher, Bao Wang, Penghang Yin, Xiyang Luo, Farzin Barekat, Minh Pham, Alex Lin
Date: 2018-06-17 00:00:00

We propose a class of very simple modifications of gradient descent and
stochastic gradient descent. We show that when applied to a large variety of
machine learning problems, ranging from logistic regression to deep neural
nets, the proposed surrogates can dramatically reduce the variance, allow to
take a larger step size, and improve the generalization accuracy. The methods
only involve multiplying the usual (stochastic) gradient by the inverse of a
positive definitive matrix (which can be computed efficiently by FFT) with a
low condition number coming from a one-dimensional discrete Laplacian or its
high order generalizations. It also preserves the mean and increases the
smallest component and decreases the largest component. The theory of
Hamilton-Jacobi partial differential equations demonstrates that the implicit
version of the new algorithm is almost the same as doing gradient descent on a
new function which (i) has the same global minima as the original function and
(ii) is ``more convex". Moreover, we show that optimization algorithms with
these surrogates converge uniformly in the discrete Sobolev $H_\sigma^p$ sense
and reduce the optimality gap for convex optimization problems. The code is
available at:
\url{https://github.com/BaoWangMath/LaplacianSmoothing-GradientDescent}
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models
Authors: Raj Agrawal, Tamara Broderick, Caroline Uhler
Date: 2018-03-15 00:00:00
Proceeding: ICML 2018 7
Tasks: Decision Making

Learning a Bayesian network (BN) from data can be useful for decision-making
or discovering causal relationships. However, traditional methods often fail in
modern applications, which exhibit a larger number of observed variables than
data points. The resulting uncertainty about the underlying network as well as
the desire to incorporate prior information recommend a Bayesian approach to
learning the BN, but the highly combinatorial structure of BNs poses a striking
challenge for inference. The current state-of-the-art methods such as order
MCMC are faster than previous methods but prevent the use of many natural
structural priors and still have running time exponential in the maximum
indegree of the true directed acyclic graph (DAG) of the BN. We here propose an
alternative posterior approximation based on the observation that, if we
incorporate empirical conditional independence tests, we can focus on a
high-probability DAG associated with each order of the vertices. We show that
our method allows the desired flexibility in prior specification, removes
timing dependence on the maximum indegree and yields provably good posterior
approximations; in addition, we show that it achieves superior accuracy,
scalability, and sampler mixing on several datasets.
Personalized Saliency and its Prediction
Authors: Yanyu Xu, Shenghua Gao, Junru Wu, Nianyi Li, Jingyi Yu
Date: 2017-10-09 00:00:00
Tasks: Prediction, Saliency Detection

Nearly all existing visual saliency models by far have focused on predicting
a universal saliency map across all observers. Yet psychology studies suggest
that visual attention of different observers can vary significantly under
specific circumstances, especially a scene is composed of multiple salient
objects. To study such heterogenous visual attention pattern across observers,
we first construct a personalized saliency dataset and explore correlations
between visual attention, personal preferences, and image contents.
Specifically, we propose to decompose a personalized saliency map (referred to
as PSM) into a universal saliency map (referred to as USM) predictable by
existing saliency detection models and a new discrepancy map across users that
characterizes personalized saliency. We then present two solutions towards
predicting such discrepancy maps, i.e., a multi-task convolutional neural
network (CNN) framework and an extended CNN with Person-specific Information
Encoded Filters (CNN-PIEF). Extensive experimental results demonstrate the
effectiveness of our models for PSM prediction as well their generalization
capability for unseen observers.
Causal Generative Domain Adaptation Networks
Authors: Mingming Gong, Kun Zhang, Biwei Huang, Clark Glymour, DaCheng Tao, Kayhan Batmanghelich
Date: 2018-04-12 00:00:00
Tasks: Computational Efficiency, Domain Adaptation

An essential problem in domain adaptation is to understand and make use of
distribution changes across domains. For this purpose, we first propose a
flexible Generative Domain Adaptation Network (G-DAN) with specific latent
variables to capture changes in the generating process of features across
domains. By explicitly modeling the changes, one can even generate data in new
domains using the generating process with new values for the latent variables
in G-DAN. In practice, the process to generate all features together may
involve high-dimensional latent variables, requiring dealing with distributions
in high dimensions and making it difficult to learn domain changes from few
source domains. Interestingly, by further making use of the causal
representation of joint distributions, we then decompose the joint distribution
into separate modules, each of which involves different low-dimensional latent
variables and can be learned separately, leading to a Causal G-DAN (CG-DAN).
This improves both statistical and computational efficiency of the learning
procedure. Finally, by matching the feature distribution in the target domain,
we can recover the target-domain joint distribution and derive the learning
machine for the target domain. We demonstrate the efficacy of both G-DAN and
CG-DAN in domain generation and cross-domain prediction on both synthetic and
real data experiments.
Biased Embeddings from Wild Data: Measuring, Understanding and Removing
Authors: Adam Sutton, Thomas Lansdall-Welfare, Nello Cristianini
Date: 2018-06-16 00:00:00

Many modern Artificial Intelligence (AI) systems make use of data embeddings,
particularly in the domain of Natural Language Processing (NLP). These
embeddings are learnt from data that has been gathered "from the wild" and have
been found to contain unwanted biases. In this paper we make three
contributions towards measuring, understanding and removing this problem. We
present a rigorous way to measure some of these biases, based on the use of
word lists created for social psychology applications; we observe how gender
bias in occupations reflects actual gender bias in the same occupations in the
real world; and finally we demonstrate how a simple projection can
significantly reduce the effects of embedding bias. All this is part of an
ongoing effort to understand how trust can be built into AI systems.
Deformable Generator Networks: Unsupervised Disentanglement of Appearance and Geometry
Authors: Xianglei Xing, Ruiqi Gao, Tian Han, Song-Chun Zhu, Ying Nian Wu
Date: 2018-06-16 00:00:00
Tasks: Disentanglement, Transfer Learning

We present a deformable generator model to disentangle the appearance and geometric information for both image and video data in a purely unsupervised manner. The appearance generator network models the information related to appearance, including color, illumination, identity or category, while the geometric generator performs geometric warping, such as rotation and stretching, through generating deformation field which is used to warp the generated appearance to obtain the final image or video sequences. Two generators take independent latent vectors as input to disentangle the appearance and geometric information from image or video sequences. For video data, a nonlinear transition model is introduced to both the appearance and geometric generators to capture the dynamics over time. The proposed scheme is general and can be easily integrated into different generative models. An extensive set of qualitative and quantitative experiments shows that the appearance and geometric information can be well disentangled, and the learned geometric generator can be conveniently transferred to other image datasets to facilitate knowledge transfer tasks.