pwc-archive/papers-with-abstracts huggingface dataset:
Enhanced 3DTV Regularization and Its Applications on Hyper-spectral Image Denoising and Compressed Sensing
Authors: Jiangjun Peng, Qi Xie, Qian Zhao, Yao Wang, Deyu Meng, Yee Leung
Date: 2018-09-18 00:00:00
Tasks: compressed sensing, Denoising, Image Denoising

The 3-D total variation (3DTV) is a powerful regularization term, which
encodes the local smoothness prior structure underlying a hyper-spectral image
(HSI), for general HSI processing tasks. This term is calculated by assuming
identical and independent sparsity structures on all bands of gradient maps
calculated along spatial and spectral HSI modes. This, however, is always
largely deviated from the real cases, where the gradient maps are generally
with different while correlated sparsity structures across all their bands.
Such deviation tends to hamper the performance of the related method by
adopting such prior term. To this is- sue, this paper proposes an enhanced 3DTV
(E-3DTV) regularization term beyond conventional 3DTV. Instead of imposing
sparsity on gradient maps themselves, the new term calculated sparsity on the
subspace bases on the gradient maps along their bands, which naturally encode
the correlation and difference across these bands, and more faithfully reflect
the insightful configurations of an HSI. The E-3DTV term can easily replace the
previous 3DTV term and be em- bedded into an HSI processing model to ameliorate
its performance. The superiority of the proposed methods is substantiated by
extensive experiments on two typical related tasks: HSI denoising and
compressed sensing, as compared with state-of-the-arts designed for both tasks.
Learning Universal Sentence Representations with Mean-Max Attention Autoencoder
Authors: Minghua Zhang, Yunfang Wu, Weikang Li, Wei Li
Date: 2018-09-18 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decoder, Sentence

In order to learn universal sentence representations, previous methods focus
on complex recurrent neural networks or supervised learning. In this paper, we
propose a mean-max attention autoencoder (mean-max AAE) within the
encoder-decoder framework. Our autoencoder rely entirely on the MultiHead
self-attention mechanism to reconstruct the input sequence. In the encoding we
propose a mean-max strategy that applies both mean and max pooling operations
over the hidden vectors to capture diverse information of the input. To enable
the information to steer the reconstruction process dynamically, the decoder
performs attention over the mean-max representation. By training our model on a
large collection of unlabelled data, we obtain high-quality representations of
sentences. Experimental results on a broad range of 10 transfer tasks
demonstrate that our model outperforms the state-of-the-art unsupervised single
methods, including the classical skip-thoughts and the advanced
skip-thoughts+LN model. Furthermore, compared with the traditional recurrent
neural network, our mean-max AAE greatly reduce the training time.
Symbolic Tensor Neural Networks for Digital Media - from Tensor Processing via BNF Graph Rules to CREAMS Applications
Authors: Wladyslaw Skarbek
Date: 2018-09-18 00:00:00

This tutorial material on Convolutional Neural Networks (CNN) and its
applications in digital media research is based on the concept of Symbolic
Tensor Neural Networks. The set of STNN expressions is specified in Backus-Naur
Form (BNF) which is annotated by constraints typical for labeled acyclic
directed graphs (DAG). The BNF induction begins from a collection of neural
unit symbols with extra (up to five) decoration fields (including tensor depth
and sharing fields). The inductive rules provide not only the general graph
structure but also the specific shortcuts for residual blocks of units. A
syntactic mechanism for network fragments modularization is introduced via user
defined units and their instances. Moreover, the dual BNF rules are specified
in order to generate the Dual Symbolic Tensor Neural Network (DSTNN). The
joined interpretation of STNN and DSTNN provides the correct flow of gradient
tensors, back propagated at the training stage. The proposed symbolic
representation of CNNs is illustrated for six generic digital media
applications (CREAMS): Compression, Recognition, Embedding, Annotation, 3D
Modeling for human-computer interfacing, and data Security based on digital
media objects. In order to make the CNN description and its gradient flow
complete, for all presented applications, the symbolic representations of
mathematically defined loss/gain functions and gradient flow equations for all
used core units, are given. The tutorial is to convince the reader that STNN is
not only a convenient symbolic notation for public presentations of CNN based
solutions for CREAMS problems but also that it is a design blueprint with a
potential for automatic generation of application source code.
U-Net for MAV-based Penstock Inspection: an Investigation of Focal Loss in Multi-class Segmentation for Corrosion Identification
Authors: Ty Nguyen, Tolga Ozaslan, Ian D. Miller, James Keller, Giuseppe Loianno, Camillo J. Taylor, Daniel D. Lee, Vijay Kumar, Joseph H. Harwood, Jennifer Wozencraft
Date: 2018-09-18 00:00:00
Tasks: GPU

Periodical inspection and maintenance of critical infrastructure such as
dams, penstocks, and locks are of significant importance to prevent
catastrophic failures. Conventional manual inspection methods require
inspectors to climb along a penstock to spot corrosion, rust and crack
formation which is unsafe, labor-intensive, and requires intensive training.
This work presents an alternative approach using a Micro Aerial Vehicle (MAV)
that autonomously flies to collect imagery which is then fed into a pretrained
deep-learning model to identify corrosion. Our simplified U-Net trained with
less than 40 image samples can do inference at 12 fps on a single GPU. We
analyze different loss functions to solve the class imbalance problem, followed
by a discussion on choosing proper metrics and weights for object classes.
Results obtained with the dataset collected from Center Hill Dam, TN show that
focal loss function, combined with a proper set of class weights yield better
segmentation results than the base loss, Softmax cross entropy. Our method can
be used in combination with planning algorithm to offer a complete, safe and
cost-efficient solution to autonomous infrastructure inspection.
Runtime Monitoring Neuron Activation Patterns
Authors: Chih-Hong Cheng, Georg Nührenberg, Hirotoshi Yasuoka
Date: 2018-09-18 00:00:00

For using neural networks in safety critical domains, it is important to know
if a decision made by a neural network is supported by prior similarities in
training. We propose runtime neuron activation pattern monitoring - after the
standard training process, one creates a monitor by feeding the training data
to the network again in order to store the neuron activation patterns in
abstract form. In operation, a classification decision over an input is further
supplemented by examining if a pattern similar (measured by Hamming distance)
to the generated pattern is contained in the monitor. If the monitor does not
contain any pattern similar to the generated pattern, it raises a warning that
the decision is not based on the training data. Our experiments show that, by
adjusting the similarity-threshold for activation patterns, the monitors can
report a significant portion of misclassfications to be not supported by
training with a small false-positive rate, when evaluated on a test set.
Switching Isotropic and Directional Exploration with Parameter Space Noise in Deep Reinforcement Learning
Authors: Izumi Karino, Kazutoshi Tanaka, Ryuma Niiyama, Yasuo Kuniyoshi
Date: 2018-09-18 00:00:00
Tasks: Deep Reinforcement Learning, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper proposes an exploration method for deep reinforcement learning
based on parameter space noise. Recent studies have experimentally shown that
parameter space noise results in better exploration than the commonly used
action space noise. Previous methods devised a way to update the diagonal
covariance matrix of a noise distribution and did not consider the direction of
the noise vector and its correlation. In addition, fast updates of the noise
distribution are required to facilitate policy learning. We propose a method
that deforms the noise distribution according to the accumulated returns and
the noises that have led to the returns. Moreover, this method switches
isotropic exploration and directional exploration in parameter space with
regard to obtained rewards. We validate our exploration strategy in the OpenAI
Gym continuous environments and modified environments with sparse rewards. The
proposed method achieves results that are competitive with a previous method at
baseline tasks. Moreover, our approach exhibits better performance in sparse
reward environments by exploration with the switching strategy.
MBS: Macroblock Scaling for CNN Model Reduction
Authors: Yu-Hsun Lin, Chun-Nan Chou, Edward Y. Chang
Date: 2018-09-18 00:00:00
Proceeding: CVPR 2019 6
Tasks: model

In this paper we propose the macroblock scaling (MBS) algorithm, which can be
applied to various CNN architectures to reduce their model size. MBS adaptively
reduces each CNN macroblock depending on its information redundancy measured by
our proposed effective flops. Empirical studies conducted with ImageNet and
CIFAR-10 attest that MBS can reduce the model size of some already compact CNN
models, e.g., MobileNetV2 (25.03% further reduction) and ShuffleNet (20.74%),
and even ultra-deep ones such as ResNet-101 (51.67%) and ResNet-1202 (72.71%)
with negligible accuracy degradation. MBS also performs better reduction at a
much lower cost than the state-of-the-art optimization-based methods do. MBS's
simplicity and efficiency, its flexibility to work with any CNN model, and its
scalability to work with models of any depth make it an attractive choice for
CNN model size reduction.
User Information Augmented Semantic Frame Parsing using Coarse-to-Fine Neural Networks
Authors: Yilin Shen, Xiangyu Zeng, Yu Wang, Hongxia Jin
Date: 2018-09-18 00:00:00
Tasks: Intent Detection, Semantic Frame Parsing, slot-filling, Slot Filling, Spoken Language Understanding

Semantic frame parsing is a crucial component in spoken language
understanding (SLU) to build spoken dialog systems. It has two main tasks:
intent detection and slot filling. Although state-of-the-art approaches showed
good results, they require large annotated training data and long training
time. In this paper, we aim to alleviate these drawbacks for semantic frame
parsing by utilizing the ubiquitous user information. We design a novel
coarse-to-fine deep neural network model to incorporate prior knowledge of user
information intermediately to better and quickly train a semantic frame parser.
Due to the lack of benchmark dataset with real user information, we synthesize
the simplest type of user information (location and time) on ATIS benchmark
data. The results show that our approach leverages such simple user information
to outperform state-of-the-art approaches by 0.25% for intent detection and
0.31% for slot filling using standard training data. When using smaller
training data, the performance improvement on intent detection and slot filling
reaches up to 1.35% and 1.20% respectively. We also show that our approach can
achieve similar performance as state-of-the-art approaches by using less than
80% annotated training data. Moreover, the training time to achieve the similar
performance is also reduced by over 60%.
Image Super-Resolution via Deterministic-Stochastic Synthesis and Local Statistical Rectification
Authors: Weifeng Ge, Bingchen Gong, Yizhou Yu
Date: 2018-09-18 00:00:00
Tasks: Image Super-Resolution, regression, Super-Resolution

Single image superresolution has been a popular research topic in the last
two decades and has recently received a new wave of interest due to deep neural
networks. In this paper, we approach this problem from a different perspective.
With respect to a downsampled low resolution image, we model a high resolution
image as a combination of two components, a deterministic component and a
stochastic component. The deterministic component can be recovered from the
low-frequency signals in the downsampled image. The stochastic component, on
the other hand, contains the signals that have little correlation with the low
resolution image. We adopt two complementary methods for generating these two
components. While generative adversarial networks are used for the stochastic
component, deterministic component reconstruction is formulated as a regression
problem solved using deep neural networks. Since the deterministic component
exhibits clearer local orientations, we design novel loss functions tailored
for such properties for training the deep regression network. These two methods
are first applied to the entire input image to produce two distinct
high-resolution images. Afterwards, these two images are fused together using
another deep neural network that also performs local statistical rectification,
which tries to make the local statistics of the fused image match the same
local statistics of the groundtruth image. Quantitative results and a user
study indicate that the proposed method outperforms existing state-of-the-art
algorithms with a clear margin.
Deep Textured 3D Reconstruction of Human Bodies
Authors: Abbhinav Venkat, Sai Sagar Jinka, Avinash Sharma
Date: 2018-09-18 00:00:00
Tasks: 3D Reconstruction

Recovering textured 3D models of non-rigid human body shapes is challenging
due to self-occlusions caused by complex body poses and shapes, clothing
obstructions, lack of surface texture, background clutter, sparse set of
cameras with non-overlapping fields of view, etc. Further, a calibration-free
environment adds additional complexity to both - reconstruction and texture
recovery. In this paper, we propose a deep learning based solution for textured
3D reconstruction of human body shapes from a single view RGB image. This is
achieved by first recovering the volumetric grid of the non-rigid human body
given a single view RGB image followed by orthographic texture view synthesis
using the respective depth projection of the reconstructed (volumetric) shape
and input RGB image. We propose to co-learn the depth information readily
available with affordable RGBD sensors (e.g., Kinect) while showing multiple
views of the same object during the training phase. We show superior
reconstruction performance in terms of quantitative and qualitative results, on
both, publicly available datasets (by simulating the depth channel with virtual
Kinect) as well as real RGBD data collected with our calibrated multi Kinect
setup.
Model-Protected Multi-Task Learning
Authors: Jian Liang, Ziqi Liu, Jiayu Zhou, Xiaoqian Jiang, Chang-Shui Zhang, Fei Wang
Date: 2018-09-18 00:00:00
Tasks: model, Multi-Task Learning, Privacy Preserving

Multi-task learning (MTL) refers to the paradigm of learning multiple related tasks together. In contrast, in single-task learning (STL) each individual task is learned independently. MTL often leads to better trained models because they can leverage the commonalities among related tasks. However, because MTL algorithms can ``leak" information from different models across different tasks, MTL poses a potential security risk. Specifically, an adversary may participate in the MTL process through one task and thereby acquire the model information for another task. The previously proposed privacy-preserving MTL methods protect data instances rather than models, and some of them may underperform in comparison with STL methods. In this paper, we propose a privacy-preserving MTL framework to prevent information from each model leaking to other models based on a perturbation of the covariance matrix of the model matrix. We study two popular MTL approaches for instantiation, namely, learning the low-rank and group-sparse patterns of the model matrix. Our algorithms can be guaranteed not to underperform compared with STL methods. We build our methods based upon tools for differential privacy, and privacy guarantees, utility bounds are provided, and heterogeneous privacy budgets are considered. The experiments demonstrate that our algorithms outperform the baseline methods constructed by existing privacy-preserving MTL methods on the proposed model-protection problem.
Automatic Judgment Prediction via Legal Reading Comprehension
Authors: Shangbang Long, Cunchao Tu, Zhiyuan Liu, Maosong Sun
Date: 2018-09-18 00:00:00
Tasks: Articles, Prediction, Reading Comprehension, text-classification, Text Classification

Automatic judgment prediction aims to predict the judicial results based on
case materials. It has been studied for several decades mainly by lawyers and
judges, considered as a novel and prospective application of artificial
intelligence techniques in the legal field. Most existing methods follow the
text classification framework, which fails to model the complex interactions
among complementary case materials. To address this issue, we formalize the
task as Legal Reading Comprehension according to the legal scenario. Following
the working protocol of human judges, LRC predicts the final judgment results
based on three types of information, including fact description, plaintiffs'
pleas, and law articles. Moreover, we propose a novel LRC model, AutoJudge,
which captures the complex semantic interactions among facts, pleas, and laws.
In experiments, we construct a real-world civil case dataset for LRC.
Experimental results on this dataset demonstrate that our model achieves
significant improvement over state-of-the-art models. We will publish all
source codes and datasets of this work on \urlgithub.com for further research.
ManifoldNet: A Deep Network Framework for Manifold-valued Data
Authors: Rudrasis Chakraborty, Jose Bouza, Jonathan Manton, Baba C. Vemuri
Date: 2018-09-11 00:00:00
Tasks: Dimensionality Reduction

Deep neural networks have become the main work horse for many tasks involving
learning from data in a variety of applications in Science and Engineering.
Traditionally, the input to these networks lie in a vector space and the
operations employed within the network are well defined on vector-spaces. In
the recent past, due to technological advances in sensing, it has become
possible to acquire manifold-valued data sets either directly or indirectly.
Examples include but are not limited to data from omnidirectional cameras on
automobiles, drones etc., synthetic aperture radar imaging, diffusion magnetic
resonance imaging, elastography and conductance imaging in the Medical Imaging
domain and others. Thus, there is need to generalize the deep neural networks
to cope with input data that reside on curved manifolds where vector space
operations are not naturally admissible. In this paper, we present a novel
theoretical framework to generalize the widely popular convolutional neural
networks (CNNs) to high dimensional manifold-valued data inputs. We call these
networks, ManifoldNets.
  In ManifoldNets, convolution operation on data residing on Riemannian
manifolds is achieved via a provably convergent recursive computation of the
weighted Fr\'{e}chet Mean (wFM) of the given data, where the weights makeup the
convolution mask, to be learned. Further, we prove that the proposed wFM layer
achieves a contraction mapping and hence ManifoldNet does not need the
non-linear ReLU unit used in standard CNNs. We present experiments, using the
ManifoldNet framework, to achieve dimensionality reduction by computing the
principal linear subspaces that naturally reside on a Grassmannian. The
experimental results demonstrate the efficacy of ManifoldNets in the context of
classification and reconstruction accuracy.
Parameterless Stochastic Natural Gradient Method for Discrete Optimization and its Application to Hyper-Parameter Optimization for Neural Network
Authors: Kouhei Nishida, Hernan Aguirre, Shota Saito, Shinichi Shirakawa, Youhei Akimoto
Date: 2018-09-18 00:00:00

Black box discrete optimization (BBDO) appears in wide range of engineering
tasks. Evolutionary or other BBDO approaches have been applied, aiming at
automating necessary tuning of system parameters, such as hyper parameter
tuning of machine learning based systems when being installed for a specific
task. However, automation is often jeopardized by the need of strategy
parameter tuning for BBDO algorithms. An expert with the domain knowledge must
undergo time-consuming strategy parameter tuning. This paper proposes a
parameterless BBDO algorithm based on information geometric optimization, a
recent framework for black box optimization using stochastic natural gradient.
Inspired by some theoretical implications, we develop an adaptation mechanism
for strategy parameters of the stochastic natural gradient method for discrete
search domains. The proposed algorithm is evaluated on commonly used test
problems. It is further extended to two examples of simultaneous optimization
of the hyper parameters and the connection weights of deep learning models,
leading to a faster optimization than the existing approaches without any
effort of parameter tuning.
Actionable Recourse in Linear Classification
Authors: Berk Ustun, Alexander Spangher, Yang Liu
Date: 2018-09-18 00:00:00
Tasks: Classification, Credit score, Decision Making, General Classification

Machine learning models are increasingly used to automate decisions that affect humans - deciding who should receive a loan, a job interview, or a social service. In such applications, a person should have the ability to change the decision of a model. When a person is denied a loan by a credit score, for example, they should be able to alter its input variables in a way that guarantees approval. Otherwise, they will be denied the loan as long as the model is deployed. More importantly, they will lack the ability to influence a decision that affects their livelihood. In this paper, we frame these issues in terms of recourse, which we define as the ability of a person to change the decision of a model by altering actionable input variables (e.g., income vs. age or marital status). We present integer programming tools to ensure recourse in linear classification problems without interfering in model development. We demonstrate how our tools can inform stakeholders through experiments on credit scoring problems. Our results show that recourse can be significantly affected by standard practices in model development, and motivate the need to evaluate recourse in practice.
Scene Text Recognition from Two-Dimensional Perspective
Authors: Minghui Liao, Jian Zhang, Zhaoyi Wan, Fengming Xie, Jiajun Liang, Pengyuan Lyu, Cong Yao, Xiang Bai
Date: 2018-09-18 00:00:00
Tasks: Scene Text Recognition, Semantic Segmentation, speech-recognition, Speech Recognition, Text Detection, Vocal Bursts Valence Prediction

Inspired by speech recognition, recent state-of-the-art algorithms mostly
consider scene text recognition as a sequence prediction problem. Though
achieving excellent performance, these methods usually neglect an important
fact that text in images are actually distributed in two-dimensional space. It
is a nature quite different from that of speech, which is essentially a
one-dimensional signal. In principle, directly compressing features of text
into a one-dimensional form may lose useful information and introduce extra
noise. In this paper, we approach scene text recognition from a two-dimensional
perspective. A simple yet effective model, called Character Attention Fully
Convolutional Network (CA-FCN), is devised for recognizing the text of
arbitrary shapes. Scene text recognition is realized with a semantic
segmentation network, where an attention mechanism for characters is adopted.
Combined with a word formation module, CA-FCN can simultaneously recognize the
script and predict the position of each character. Experiments demonstrate that
the proposed algorithm outperforms previous methods on both regular and
irregular text datasets. Moreover, it is proven to be more robust to imprecise
localizations in the text detection phase, which are very common in practice.