pwc-archive/papers-with-abstracts huggingface dataset:
Joint Learning of Interactive Spoken Content Retrieval and Trainable User Simulator
Authors: Pei-Hung Chung, Kuan Tung, Ching-Lun Tai, Hung-Yi Lee
Date: 2018-04-01 00:00:00
Tasks: Information Retrieval, Q-Learning, Retrieval, speech-recognition, Speech Recognition

User-machine interaction is crucial for information retrieval, especially for
spoken content retrieval, because spoken content is difficult to browse, and
speech recognition has a high degree of uncertainty. In interactive retrieval,
the machine takes different actions to interact with the user to obtain better
retrieval results; here it is critical to select the most efficient action. In
previous work, deep Q-learning techniques were proposed to train an interactive
retrieval system but rely on a hand-crafted user simulator; building a reliable
user simulator is difficult. In this paper, we further improve the interactive
spoken content retrieval framework by proposing a learnable user simulator
which is jointly trained with interactive retrieval system, making the
hand-crafted user simulator unnecessary. The experimental results show that the
learned simulated users not only achieve larger rewards than the hand-crafted
ones but act more like real users.
Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification
Authors: Kamyar Nazeri, Azad Aminpour, Mehran Ebrahimi
Date: 2018-03-11 00:00:00
Tasks: Breast Cancer Histology Image Classification, Classification, General Classification, image-classification, Image Classification, Vocal Bursts Valence Prediction

This paper explores the problem of breast tissue classification of microscopy
images. Based on the predominant cancer type the goal is to classify images
into four categories of normal, benign, in situ carcinoma, and invasive
carcinoma. Given a suitable training dataset, we utilize deep learning
techniques to address the classification problem. Due to the large size of each
image in the training dataset, we propose a patch-based technique which
consists of two consecutive convolutional neural networks. The first
"patch-wise" network acts as an auto-encoder that extracts the most salient
features of image patches while the second "image-wise" network performs
classification of the whole image. The first network is pre-trained and aimed
at extracting local information while the second network obtains global
information of an input image. We trained the networks using the ICIAR 2018
grand challenge on BreAst Cancer Histology (BACH) dataset. The proposed method
yields 95 % accuracy on the validation set compared to previously reported 77 %
accuracy rates in the literature. Our code is publicly available at
https://github.com/ImagingLab/ICIAR2018
Completely Unsupervised Phoneme Recognition by Adversarially Learning Mapping Relationships from Audio Embeddings
Authors: Da-Rong Liu, Kuan-Yu Chen, Hung-Yi Lee, Lin-shan Lee
Date: 2018-04-01 00:00:00
Tasks: Generative Adversarial Network, Phoneme Recognition

Unsupervised discovery of acoustic tokens from audio corpora without
annotation and learning vector representations for these tokens have been
widely studied. Although these techniques have been shown successful in some
applications such as query-by-example Spoken Term Detection (STD), the lack of
mapping relationships between these discovered tokens and real phonemes have
limited the down-stream applications. This paper represents probably the first
attempt towards the goal of completely unsupervised phoneme recognition, or
mapping audio signals to phoneme sequences without phoneme-labeled audio data.
The basic idea is to cluster the embedded acoustic tokens and learn the mapping
between the cluster sequences and the unknown phoneme sequences with a
Generative Adversarial Network (GAN). An unsupervised phoneme recognition
accuracy of 36% was achieved in the preliminary experiments.
Parallelizing Stochastic Gradient Descent for Least Squares Regression: mini-batching, averaging, and model misspecification
Authors: Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford
Date: 2016-10-12 00:00:00
Tasks: regression

This work characterizes the benefits of averaging schemes widely used in
conjunction with stochastic gradient descent (SGD). In particular, this work
provides a sharp analysis of: (1) mini-batching, a method of averaging many
samples of a stochastic gradient to both reduce the variance of the stochastic
gradient estimate and for parallelizing SGD and (2) tail-averaging, a method
involving averaging the final few iterates of SGD to decrease the variance in
SGD's final iterate. This work presents non-asymptotic excess risk bounds for
these schemes for the stochastic approximation problem of least squares
regression.
  Furthermore, this work establishes a precise problem-dependent extent to
which mini-batch SGD yields provable near-linear parallelization speedups over
SGD with batch size one. This allows for understanding learning rate versus
batch size tradeoffs for the final iterate of an SGD method. These results are
then utilized in providing a highly parallelizable SGD method that obtains the
minimax risk with nearly the same number of serial updates as batch gradient
descent, improving significantly over existing SGD methods. A non-asymptotic
analysis of communication efficient parallelization schemes such as
model-averaging/parameter mixing methods is then provided.
  Finally, this work sheds light on some fundamental differences in SGD's
behavior when dealing with agnostic noise in the (non-realizable) least squares
regression problem. In particular, the work shows that the stepsizes that
ensure minimax risk for the agnostic case must be a function of the noise
properties.
  This paper builds on the operator view of analyzing SGD methods, introduced
by Defossez and Bach (2015), followed by developing a novel analysis in
bounding these operators to characterize the excess risk. These techniques are
of broader interest in analyzing computational aspects of stochastic
approximation.
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
Authors: Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, Bo Li
Date: 2018-04-01 00:00:00
Tasks: BIG-bench Machine Learning, regression

As machine learning becomes widely used for automated decisions, attackers have strong incentives to manipulate the results and models generated by machine learning algorithms. In this paper, we perform the first systematic study of poisoning attacks and their countermeasures for linear regression models. In poisoning attacks, attackers deliberately influence the training data to manipulate the results of a predictive model. We propose a theoretically-grounded optimization framework specifically designed for linear regression and demonstrate its effectiveness on a range of datasets and models. We also introduce a fast statistical attack that requires limited knowledge of the training process. Finally, we design a new principled defense method that is highly resilient against all poisoning attacks. We provide formal guarantees about its convergence and an upper bound on the effect of poisoning attacks when the defense is deployed. We evaluate extensively our attacks and defenses on three realistic datasets from health care, loan assessment, and real estate domains.
Robust Fruit Counting: Combining Deep Learning, Tracking, and Structure from Motion
Authors: Xu Liu, Steven W. Chen, Shreyas Aditya, Nivedha Sivakumar, Sandeep Dcunha, Chao Qu, Camillo J. Taylor, Jnaneshwar Das, Vijay Kumar
Date: 2018-04-01 00:00:00

We present a novel fruit counting pipeline that combines deep segmentation,
frame to frame tracking, and 3D localization to accurately count visible fruits
across a sequence of images. Our pipeline works on image streams from a
monocular camera, both in natural light, as well as with controlled
illumination at night. We first train a Fully Convolutional Network (FCN) and
segment video frame images into fruit and non-fruit pixels. We then track
fruits across frames using the Hungarian Algorithm where the objective cost is
determined from a Kalman Filter corrected Kanade-Lucas-Tomasi (KLT) Tracker. In
order to correct the estimated count from tracking process, we combine tracking
results with a Structure from Motion (SfM) algorithm to calculate relative 3D
locations and size estimates to reject outliers and double counted fruit
tracks. We evaluate our algorithm by comparing with ground-truth
human-annotated visual counts. Our results demonstrate that our pipeline is
able to accurately and reliably count fruits across image sequences, and the
correction step can significantly improve the counting accuracy and robustness.
Although discussed in the context of fruit counting, our work can extend to
detection, tracking, and counting of a variety of other stationary features of
interest such as leaf-spots, wilt, and blossom.
Revisiting Skip-Gram Negative Sampling Model with Rectification
Authors: Cun Mu, Guang Yang, Zheng Yan
Date: 2018-04-01 00:00:00
Tasks: model

We revisit skip-gram negative sampling (SGNS), one of the most popular
neural-network based approaches to learning distributed word representation. We
first point out the ambiguity issue undermining the SGNS model, in the sense
that the word vectors can be entirely distorted without changing the objective
value. To resolve the issue, we investigate the intrinsic structures in
solution that a good word embedding model should deliver. Motivated by this, we
rectify the SGNS model with quadratic regularization, and show that this simple
modification suffices to structure the solution in the desired manner. A
theoretical justification is presented, which provides novel insights into
quadratic regularization . Preliminary experiments are also conducted on
Google's analytical reasoning task to support the modified SGNS model.
Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using deep convolutional neural networks
Authors: Karen López-Linares, Nerea Aranjuelo, Luis Kabongo, Gregory Maclair, Nerea Lete, Mario Ceresa, Ainhoa García-Familiar, Iván Macía, Miguel A. González Ballester
Date: 2018-04-01 00:00:00
Tasks: Edge Detection, Segmentation

Computerized Tomography Angiography (CTA) based follow-up of Abdominal Aortic
Aneurysms (AAA) treated with Endovascular Aneurysm Repair (EVAR) is essential
to evaluate the progress of the patient and detect complications. In this
context, accurate quantification of post-operative thrombus volume is required.
However, a proper evaluation is hindered by the lack of automatic, robust and
reproducible thrombus segmentation algorithms. We propose a new fully automatic
approach based on Deep Convolutional Neural Networks (DCNN) for robust and
reproducible thrombus region of interest detection and subsequent fine thrombus
segmentation. The DetecNet detection network is adapted to perform region of
interest extraction from a complete CTA and a new segmentation network
architecture, based on Fully Convolutional Networks and a Holistically-Nested
Edge Detection Network, is presented. These networks are trained, validated and
tested in 13 post-operative CTA volumes of different patients using a 4-fold
cross-validation approach to provide more robustness to the results. Our
pipeline achieves a Dice score of more than 82% for post-operative thrombus
segmentation and provides a mean relative volume difference between ground
truth and automatic segmentation that lays within the experienced human
observer variance without the need of human intervention in most common cases.
A Reliability Theory of Truth
Authors: Karl Schlechta
Date: 2018-01-03 00:00:00
Tasks: Attribute

Our approach is basically a coherence approach, but we avoid the well-known
pitfalls of coherence theories of truth. Consistency is replaced by
reliability, which expresses support and attack, and, in principle, every
theory (or agent, message) counts. At the same time, we do not require a
priviledged access to "reality". A centerpiece of our approach is that we
attribute reliability also to agents, messages, etc., so an unreliable source
of information will be less important in future. Our ideas can also be extended
to value systems, and even actions, e.g., of animals.
VnCoreNLP: A Vietnamese Natural Language Processing Toolkit
Authors: Thanh Vu, Dat Quoc Nguyen, Dai Quoc Nguyen, Mark Dras, Mark Johnson
Date: 2018-01-04 00:00:00
Proceeding: NAACL 2018 6
Tasks: Dependency Parsing, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Part-Of-Speech Tagging, POS, POS Tagging, Question Answering

We present an easy-to-use and fast toolkit, namely VnCoreNLP---a Java NLP
annotation pipeline for Vietnamese. Our VnCoreNLP supports key natural language
processing (NLP) tasks including word segmentation, part-of-speech (POS)
tagging, named entity recognition (NER) and dependency parsing, and obtains
state-of-the-art (SOTA) results for these tasks. We release VnCoreNLP to
provide rich linguistic annotations to facilitate research work on Vietnamese
NLP. Our VnCoreNLP is open-source and available at:
https://github.com/vncorenlp/VnCoreNLP
CIKM AnalytiCup 2017 Lazada Product Title Quality Challenge An Ensemble of Deep and Shallow Learning to predict the Quality of Product Titles
Authors: Karamjit Singh, Vishal Sunder
Date: 2018-04-01 00:00:00

We present an approach where two different models (Deep and Shallow) are
trained separately on the data and a weighted average of the outputs is taken
as the final result. For the Deep approach, we use different combinations of
models like Convolution Neural Network, pretrained word2vec embeddings and
LSTMs to get representations which are then used to train a Deep Neural
Network. For Clarity prediction, we also use an Attentive Pooling approach for
the pooling operation so as to be aware of the Title-Category pair. For the
shallow approach, we use boosting technique LightGBM on features generated
using title and categories. We find that an ensemble of these approaches does a
better job than using them alone suggesting that the results of the deep and
shallow approach are highly complementary
EarthMapper: A Tool Box for the Semantic Segmentation of Remote Sensing Imagery
Authors: Ronald Kemker, Utsav B. Gewali, Christopher Kanan
Date: 2018-04-01 00:00:00
Tasks: Deep Learning, Segmentation, Segmentation Of Remote Sensing Imagery, Semantic Segmentation, The Semantic Segmentation Of Remote Sensing Imagery

Deep learning continues to push state-of-the-art performance for the semantic
segmentation of color (i.e., RGB) imagery; however, the lack of annotated data
for many remote sensing sensors (i.e. hyperspectral imagery (HSI)) prevents
researchers from taking advantage of this recent success. Since generating
sensor specific datasets is time intensive and cost prohibitive, remote sensing
researchers have embraced deep unsupervised feature extraction. Although these
methods have pushed state-of-the-art performance on current HSI benchmarks,
many of these tools are not readily accessible to many researchers. In this
letter, we introduce a software pipeline, which we call EarthMapper, for the
semantic segmentation of non-RGB remote sensing imagery. It includes
self-taught spatial-spectral feature extraction, various standard and deep
learning classifiers, and undirected graphical models for post-processing. We
evaluated EarthMapper on the Indian Pines and Pavia University datasets and
have released this code for public use.
I-vector Transformation Using Conditional Generative Adversarial Networks for Short Utterance Speaker Verification
Authors: Jiacen Zhang, Nakamasa Inoue, Koichi Shinoda
Date: 2018-04-01 00:00:00
Tasks: Generative Adversarial Network, Speaker Verification, Text-Independent Speaker Verification

I-vector based text-independent speaker verification (SV) systems often have
poor performance with short utterances, as the biased phonetic distribution in
a short utterance makes the extracted i-vector unreliable. This paper proposes
an i-vector compensation method using a generative adversarial network (GAN),
where its generator network is trained to generate a compensated i-vector from
a short-utterance i-vector and its discriminator network is trained to
determine whether an i-vector is generated by the generator or the one
extracted from a long utterance. Additionally, we assign two other learning
tasks to the GAN to stabilize its training and to make the generated ivector
more speaker-specific. Speaker verification experiments on the NIST SRE 2008
"10sec-10sec" condition show that our method reduced the equal error rate by
11.3% from the conventional i-vector and PLDA system.
Smooth input preparation for quantum and quantum-inspired machine learning
Authors: Zhikuan Zhao, Jack K. Fitzsimons, Patrick Rebentrost, Vedran Dunjko, Joseph F. Fitzsimons
Date: 2018-04-01 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning has recently emerged as a fruitful area for finding potential quantum computational advantage. Many of the quantum enhanced machine learning algorithms critically hinge upon the ability to efficiently produce states proportional to high-dimensional data points stored in a quantum accessible memory. Even given query access to exponentially many entries stored in a database, the construction of which is considered a one-off overhead, it has been argued that the cost of preparing such amplitude-encoded states may offset any exponential quantum advantage. Here we prove using smoothed analysis, that if the data-analysis algorithm is robust against small entry-wise input perturbation, state preparation can always be achieved with constant queries. This criterion is typically satisfied in realistic machine learning applications, where input data is subjective to moderate noise. Our results are equally applicable to the recent seminal progress in quantum-inspired algorithms, where specially constructed databases suffice for polylogarithmic classical algorithm in low-rank cases. The consequence of our finding is that for the purpose of practical machine learning, polylogarithmic processing time is possible under a general and flexible input model with quantum algorithms or quantum-inspired classical algorithms in the low-rank cases.
Syntax and Semantics of Italian Poetry in the First Half of the 20th Century
Authors: Rodolfo Delmonte
Date: 2018-02-11 00:00:00

In this paper we study, analyse and comment rhetorical figures present in
some of most interesting poetry of the first half of the twentieth century.
These figures are at first traced back to some famous poet of the past and then
compared to classical Latin prose. Linguistic theory is then called in to show
how they can be represented in syntactic structures and classified as
noncanonical structures, by positioning discontinuous or displaced linguistic
elements in Spec XP projections at various levels of constituency. Then we
introduce LFG (Lexical Functional Grammar) as the theory that allows us to
connect syntactic noncanonical structures with informational structure and
psycholinguistic theories for complexity evaluation. We end up with two
computational linguistics experiments and then evaluate the results. The first
one uses best online parsers of Italian to parse poetic structures; the second
one uses Getarun, the system created at Ca Foscari Computational Linguistics
Laboratory. As will be shown, the first approach is unable to cope with these
structures due to the use of only statistical probabilistic information. On the
contrary, the second one, being a symbolic rule based system, is by far
superior and allows also to complete both semantic an pragmatic analysis.
Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial Networks
Authors: Bo Hu, Ye Tang, Eric I-Chao Chang, Yubo Fan, Maode Lai, Yan Xu
Date: 2017-11-30 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Medical Image Analysis, Representation Learning

The visual attributes of cells, such as the nuclear morphology and chromatin
openness, are critical for histopathology image analysis. By learning
cell-level visual representation, we can obtain a rich mix of features that are
highly reusable for various tasks, such as cell-level classification, nuclei
segmentation, and cell counting. In this paper, we propose a unified generative
adversarial networks architecture with a new formulation of loss to perform
robust cell-level visual representation learning in an unsupervised setting.
Our model is not only label-free and easily trained but also capable of
cell-level unsupervised classification with interpretable visualization, which
achieves promising results in the unsupervised classification of bone marrow
cellular components. Based on the proposed cell-level visual representation
learning, we further develop a pipeline that exploits the varieties of cellular
elements to perform histopathology image classification, the advantages of
which are demonstrated on bone marrow datasets.
Real-time Progressive 3D Semantic Segmentation for Indoor Scene
Authors: Quang-Hieu Pham, Binh-Son Hua, Duc Thanh Nguyen, Sai-Kit Yeung
Date: 2018-04-01 00:00:00
Tasks: 3D Semantic Segmentation, Clustering, Scene Segmentation, Segmentation, Semantic Segmentation

The widespread adoption of autonomous systems such as drones and assistant
robots has created a need for real-time high-quality semantic scene
segmentation. In this paper, we propose an efficient yet robust technique for
on-the-fly dense reconstruction and semantic segmentation of 3D indoor scenes.
To guarantee (near) real-time performance, our method is built atop an
efficient super-voxel clustering method and a conditional random field with
higher-order constraints from structural and object cues, enabling progressive
dense semantic segmentation without any precomputation. We extensively evaluate
our method on different indoor scenes including kitchens, offices, and bedrooms
in the SceneNN and ScanNet datasets and show that our technique consistently
produces state-of-the-art segmentation results in both qualitative and
quantitative experiments.
One-Two-One Networks for Compression Artifacts Reduction in Remote Sensing
Authors: Baochang Zhang, Jiaxin Gu, Chen Chen, Jungong Han, Xiangbo Su, Xian-Bin Cao, Jianzhuang Liu
Date: 2018-04-01 00:00:00
Tasks: Blocking, Image Compression, Vocal Bursts Valence Prediction

Compression artifacts reduction (CAR) is a challenging problem in the field
of remote sensing. Most recent deep learning based methods have demonstrated
superior performance over the previous hand-crafted methods. In this paper, we
propose an end-to-end one-two-one (OTO) network, to combine different deep
models, i.e., summation and difference models, to solve the CAR problem.
Particularly, the difference model motivated by the Laplacian pyramid is
designed to obtain the high frequency information, while the summation model
aggregates the low frequency information. We provide an in-depth investigation
into our OTO architecture based on the Taylor expansion, which shows that these
two kinds of information can be fused in a nonlinear scheme to gain more
capacity of handling complicated image compression artifacts, especially the
blocking effect in compression. Extensive experiments are conducted to
demonstrate the superior performance of the OTO networks, as compared to the
state-of-the-arts on remote sensing datasets and other benchmark datasets.