pwc-archive/papers-with-abstracts huggingface dataset:
Fine-Grained Age Estimation in the wild with Attention LSTM Networks
Authors: Ke Zhang, Na Liu, Xingfang Yuan, Xinyao Guo, Ce Gao, Zhenbing Zhao, Zhanyu Ma
Date: 2018-05-26 00:00:00
Tasks: Age And Gender Classification, Age Estimation, MORPH

Age estimation from a single face image has been an essential task in the field of human-computer interaction and computer vision, which has a wide range of practical application values. Accuracy of age estimation of face images in the wild is relatively low for existing methods, because they only take into account the global features, while neglecting the fine-grained features of age-sensitive areas. We propose a novel method based on our attention long short-term memory (AL) network for fine-grained age estimation in the wild, inspired by the fine-grained categories and the visual attention mechanism. This method combines the residual networks (ResNets) or the residual network of residual network (RoR) models with LSTM units to construct AL-ResNets or AL-RoR networks to extract local features of age-sensitive regions, which effectively improves the age estimation accuracy. First, a ResNets or a RoR model pretrained on ImageNet dataset is selected as the basic model, which is then fine-tuned on the IMDB-WIKI-101 dataset for age estimation. Then, we fine-tune the ResNets or the RoR on the target age datasets to extract the global features of face images. To extract the local features of age-sensitive regions, the LSTM unit is then presented to obtain the coordinates of the agesensitive region automatically. Finally, the age group classification is conducted directly on the Adience dataset, and age-regression experiments are performed by the Deep EXpectation algorithm (DEX) on MORPH Album 2, FG-NET and 15/16LAP datasets. By combining the global and the local features, we obtain our final prediction results. Experimental results illustrate the effectiveness and robustness of the proposed AL-ResNets or AL-RoR for age estimation in the wild, where it achieves better state-of-the-art performance than all other convolutional neural network.
Deep Reinforcement Learning for Resource Management in Network Slicing
Authors: Rongpeng Li, Zhifeng Zhao, Qi Sun, Chi-Lin I, Chenyang Yang, Xianfu Chen, MinJian Zhao, Honggang Zhang
Date: 2018-05-17 00:00:00
Tasks: Deep Reinforcement Learning, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Network slicing is born as an emerging business to operators, by allowing
them to sell the customized slices to various tenants at different prices. In
order to provide better-performing and cost-efficient services, network slicing
involves challenging technical issues and urgently looks forward to intelligent
innovations to make the resource management consistent with users' activities
per slice. In that regard, deep reinforcement learning (DRL), which focuses on
how to interact with the environment by trying alternative actions and
reinforcing the tendency actions producing more rewarding consequences, is
assumed to be a promising solution. In this paper, after briefly reviewing the
fundamental concepts of DRL, we investigate the application of DRL in solving
some typical resource management for network slicing scenarios, which include
radio resource slicing and priority-based core network slicing, and demonstrate
the advantage of DRL over several competing schemes through extensive
simulations. Finally, we also discuss the possible challenges to apply DRL in
network slicing from a general perspective.
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
Authors: Yu Xiang, Tanner Schmidt, Venkatraman Narayanan, Dieter Fox
Date: 2017-11-01 00:00:00
Tasks: 6D Pose Estimation, 6D Pose Estimation using RGB, 6D Pose Estimation using RGBD, Object, Pose Estimation

Estimating the 6D pose of known objects is important for robots to interact
with the real world. The problem is challenging due to the variety of objects
as well as the complexity of a scene caused by clutter and occlusions between
objects. In this work, we introduce PoseCNN, a new Convolutional Neural Network
for 6D object pose estimation. PoseCNN estimates the 3D translation of an
object by localizing its center in the image and predicting its distance from
the camera. The 3D rotation of the object is estimated by regressing to a
quaternion representation. We also introduce a novel loss function that enables
PoseCNN to handle symmetric objects. In addition, we contribute a large scale
video dataset for 6D object pose estimation named the YCB-Video dataset. Our
dataset provides accurate 6D poses of 21 objects from the YCB dataset observed
in 92 videos with 133,827 frames. We conduct extensive experiments on our
YCB-Video dataset and the OccludedLINEMOD dataset to show that PoseCNN is
highly robust to occlusions, can handle symmetric objects, and provide accurate
pose estimation using only color images as input. When using depth data to
further refine the poses, our approach achieves state-of-the-art results on the
challenging OccludedLINEMOD dataset. Our code and dataset are available at
https://rse-lab.cs.washington.edu/projects/posecnn/.
Multichannel Sparse Blind Deconvolution on the Sphere
Authors: Yanjun Li, Yoram Bresler
Date: 2018-05-26 00:00:00
Proceeding: NeurIPS 2018 12

Multichannel blind deconvolution is the problem of recovering an unknown
signal $f$ and multiple unknown channels $x_i$ from their circular convolution
$y_i=x_i \circledast f$ ($i=1,2,\dots,N$). We consider the case where the
$x_i$'s are sparse, and convolution with $f$ is invertible. Our nonconvex
optimization formulation solves for a filter $h$ on the unit sphere that
produces sparse output $y_i\circledast h$. Under some technical assumptions, we
show that all local minima of the objective function correspond to the inverse
filter of $f$ up to an inherent sign and shift ambiguity, and all saddle points
have strictly negative curvatures. This geometric structure allows successful
recovery of $f$ and $x_i$ using a simple manifold gradient descent (MGD)
algorithm. Our theoretical findings are complemented by numerical experiments,
which demonstrate superior performance of the proposed approach over the
previous methods.
Nonlinear variable selection with continuous outcome: a nonparametric incremental forward stagewise approach
Authors: Tianwei Yu
Date: 2016-01-20 00:00:00
Tasks: BIG-bench Machine Learning, Form, regression, Variable Selection

We present a method of variable selection for the sparse generalized additive
model. The method doesn't assume any specific functional form, and can select
from a large number of candidates. It takes the form of incremental forward
stagewise regression. Given no functional form is assumed, we devised an
approach termed roughening to adjust the residuals in the iterations. In
simulations, we show the new method is competitive against popular machine
learning approaches. We also demonstrate its performance using some real
datasets. The method is available as a part of the nlnet package on CRAN
https://cran.r-project.org/package=nlnet.
Splitting source code identifiers using Bidirectional LSTM Recurrent Neural Network
Authors: Vadim Markovtsev, Waren Long, Egor Bulychev, Romain Keramitas, Konstantin Slavnov, Gabor Markowski
Date: 2018-05-26 00:00:00

Programmers make rich use of natural language in the source code they write
through identifiers and comments. Source code identifiers are selected from a
pool of tokens which are strongly related to the meaning, naming conventions,
and context. These tokens are often combined to produce more precise and
obvious designations. Such multi-part identifiers count for 97% of all naming
tokens in the Public Git Archive - the largest dataset of Git repositories to
date. We introduce a bidirectional LSTM recurrent neural network to detect
subtokens in source code identifiers. We trained that network on 41.7 million
distinct splittable identifiers collected from 182,014 open source projects in
Public Git Archive, and show that it outperforms several other machine learning
models. The proposed network can be used to improve the upstream models which
are based on source code identifiers, as well as improving developer experience
allowing writing code without switching the keyboard case.
A Distributed Quasi-Newton Algorithm for Empirical Risk Minimization with Nonsmooth Regularization
Authors: Ching-pei Lee, Cong Han Lim, Stephen J. Wright
Date: 2018-03-04 00:00:00
Tasks: Distributed Optimization

We propose a communication- and computation-efficient distributed
optimization algorithm using second-order information for solving ERM problems
with a nonsmooth regularization term. Current second-order and quasi-Newton
methods for this problem either do not work well in the distributed setting or
work only for specific regularizers. Our algorithm uses successive quadratic
approximations, and we describe how to maintain an approximation of the Hessian
and solve subproblems efficiently in a distributed manner. The proposed method
enjoys global linear convergence for a broad range of non-strongly convex
problems that includes the most commonly used ERMs, thus requiring lower
communication complexity. It also converges on non-convex problems, so has the
potential to be used on applications such as deep learning. Initial
computational results on convex problems demonstrate that our method
significantly improves on communication cost and running time over the current
state-of-the-art methods.
A novel hybrid score level and decision level fusion scheme for cancelable multi-biometric verification
Authors: Rudresh Dwivedi, Somnath Dey
Date: 2018-05-26 00:00:00
Tasks: fingerprint verification

In spite of the benefits of biometric-based authentication systems, there are
few concerns raised because of the sensitivity of biometric data to outliers,
low performance caused due to intra-class variations and privacy invasion
caused by information leakage. To address these issues, we propose a hybrid
fusion framework where only the protected modalities are combined to fulfill
the requirement of secrecy and performance improvement. This paper presents a
method to integrate cancelable modalities utilizing mean-closure weighting
(MCW) score level and Dempster-Shafer (DS) theory based decision level fusion
for iris and fingerprint to mitigate the limitations in the individual score or
decision fusion mechanisms. The proposed hybrid fusion scheme incorporates the
similarity scores from different matchers corresponding to each protected
modality. The individual scores obtained from different matchers for each
modality are combined using MCW score fusion method. The MCW technique achieves
the optimal weight for each matcher involved in the score computation. Further,
DS theory is applied to the induced scores to output the final decision. The
rigorous experimental evaluations on three virtual databases indicate that the
proposed hybrid fusion framework outperforms over the component level or
individual fusion methods (score level and decision level fusion). As a result,
we achieve (48%,66%), (72%,86%) and (49%,38%) of performance improvement over
unimodal cancelable iris and unimodal cancelable fingerprint verification
systems for Virtual_A, Virtual_B and Virtual_C databases, respectively. Also,
the proposed method is robust enough to the variability of scores and outliers
satisfying the requirement of secure authentication.
Model-based Pricing for Machine Learning in a Data Marketplace
Authors: Lingjiao Chen, Paraschos Koutris, Arun Kumar
Date: 2018-05-26 00:00:00
Tasks: BIG-bench Machine Learning

Data analytics using machine learning (ML) has become ubiquitous in science,
business intelligence, journalism and many other domains. While a lot of work
focuses on reducing the training cost, inference runtime and storage cost of ML
models, little work studies how to reduce the cost of data acquisition, which
potentially leads to a loss of sellers' revenue and buyers' affordability and
efficiency.
  In this paper, we propose a model-based pricing (MBP) framework, which
instead of pricing the data, directly prices ML model instances. We first
formally describe the desired properties of the MBP framework, with a focus on
avoiding arbitrage. Next, we show a concrete realization of the MBP framework
via a noise injection approach, which provably satisfies the desired formal
properties. Based on the proposed framework, we then provide algorithmic
solutions on how the seller can assign prices to models under different market
scenarios (such as to maximize revenue). Finally, we conduct extensive
experiments, which validate that the MBP framework can provide high revenue to
the seller, high affordability to the buyer, and also operate on low runtime
cost.
Robustness Analysis of Visual QA Models by Basic Questions
Authors: Jia-Hong Huang, Cuong Duc Dao, Modar Alfadly, C. Huck Yang, Bernard Ghanem
Date: 2017-09-14 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Visual Question Answering (VQA) models should have both high robustness and
accuracy. Unfortunately, most of the current VQA research only focuses on
accuracy because there is a lack of proper methods to measure the robustness of
VQA models. There are two main modules in our algorithm. Given a natural
language question about an image, the first module takes the question as input
and then outputs the ranked basic questions, with similarity scores, of the
main given question. The second module takes the main question, image and these
basic questions as input and then outputs the text-based answer of the main
question about the given image. We claim that a robust VQA model is one, whose
performance is not changed much when related basic questions as also made
available to it as input. We formulate the basic questions generation problem
as a LASSO optimization, and also propose a large scale Basic Question Dataset
(BQD) and Rscore (novel robustness measure), for analyzing the robustness of
VQA models. We hope our BQD will be used as a benchmark for to evaluate the
robustness of VQA models, so as to help the community build more robust and
accurate VQA models.
Revisiting Video Saliency: A Large-scale Benchmark and a New Model
Authors: Wenguan Wang, Jianbing Shen, Fang Guo, Ming-Ming Cheng, Ali Borji
Date: 2018-01-23 00:00:00
Proceeding: CVPR 2018 6
Tasks: Video Saliency Detection

In this work, we contribute to video saliency research in two ways. First, we
introduce a new benchmark for predicting human eye movements during dynamic
scene free-viewing, which is long-time urged in this field. Our dataset, named
DHF1K (Dynamic Human Fixation), consists of 1K high-quality, elaborately
selected video sequences spanning a large range of scenes, motions, object
types and background complexity. Existing video saliency datasets lack variety
and generality of common dynamic scenes and fall short in covering challenging
situations in unconstrained environments. In contrast, DHF1K makes a
significant leap in terms of scalability, diversity and difficulty, and is
expected to boost video saliency modeling. Second, we propose a novel video
saliency model that augments the CNN-LSTM network architecture with an
attention mechanism to enable fast, end-to-end saliency learning. The attention
mechanism explicitly encodes static saliency information, thus allowing LSTM to
focus on learning more flexible temporal saliency representation across
successive frames. Such a design fully leverages existing large-scale static
fixation datasets, avoids overfitting, and significantly improves training
efficiency and testing performance. We thoroughly examine the performance of
our model, with respect to state-of-the-art saliency models, on three
large-scale datasets (i.e., DHF1K, Hollywood2, UCF sports). Experimental
results over more than 1.2K testing videos containing 400K frames demonstrate
that our model outperforms other competitors.
Towards Explanation of DNN-based Prediction with Guided Feature Inversion
Authors: Mengnan Du, Ninghao Liu, Qingquan Song, Xia Hu
Date: 2018-03-19 00:00:00
Tasks: Decision Making

While deep neural networks (DNN) have become an effective computational tool,
the prediction results are often criticized by the lack of interpretability,
which is essential in many real-world applications such as health informatics.
Existing attempts based on local interpretations aim to identify relevant
features contributing the most to the prediction of DNN by monitoring the
neighborhood of a given input. They usually simply ignore the intermediate
layers of the DNN that might contain rich information for interpretation. To
bridge the gap, in this paper, we propose to investigate a guided feature
inversion framework for taking advantage of the deep architectures towards
effective interpretation. The proposed framework not only determines the
contribution of each feature in the input but also provides insights into the
decision-making process of DNN models. By further interacting with the neuron
of the target category at the output layer of the DNN, we enforce the
interpretation result to be class-discriminative. We apply the proposed
interpretation model to different CNN architectures to provide explanations for
image data and conduct extensive experiments on ImageNet and PASCAL VOC07
datasets. The interpretation results demonstrate the effectiveness of our
proposed framework in providing class-discriminative interpretation for
DNN-based prediction.
Enhanced-alignment Measure for Binary Foreground Map Evaluation
Authors: Deng-Ping Fan, Cheng Gong, Yang Cao, Bo Ren, Ming-Ming Cheng, Ali Borji
Date: 2018-05-26 00:00:00

The existing binary foreground map (FM) measures to address various types of
errors in either pixel-wise or structural ways. These measures consider
pixel-level match or image-level information independently, while cognitive
vision studies have shown that human vision is highly sensitive to both global
information and local details in scenes. In this paper, we take a detailed look
at current binary FM evaluation measures and propose a novel and effective
E-measure (Enhanced-alignment measure). Our measure combines local pixel values
with the image-level mean value in one term, jointly capturing image-level
statistics and local pixel matching information. We demonstrate the superiority
of our measure over the available measures on 4 popular datasets via 5
meta-measures, including ranking models for applications, demoting generic,
random Gaussian noise maps, ground-truth switch, as well as human judgments. We
find large improvements in almost all the meta-measures. For instance, in terms
of application ranking, we observe improvementrangingfrom9.08% to 19.65%
compared with other popular measures.
Extracting Traffic Primitives Directly from Naturalistically Logged Data for Self-Driving Applications
Authors: Wenshuo Wang, Ding Zhao
Date: 2017-09-11 00:00:00
Tasks: Autonomous Vehicles, Time Series Analysis

Developing an automated vehicle, that can handle complicated driving
scenarios and appropriately interact with other road users, requires the
ability to semantically learn and understand driving environment, oftentimes,
based on analyzing massive amounts of naturalistic driving data. An important
paradigm that allows automated vehicles to both learn from human drivers and
gain insights is understanding the principal compositions of the entire
traffic, termed as traffic primitives. However, the exploding data growth
presents a great challenge in extracting primitives from high-dimensional
time-series traffic data with various types of road users engaged. Therefore,
automatically extracting primitives is becoming one of the cost-efficient ways
to help autonomous vehicles understand and predict the complex traffic
scenarios. In addition, the extracted primitives from raw data should 1) be
appropriate for automated driving applications and also 2) be easily used to
generate new traffic scenarios. However, existing literature does not provide a
method to automatically learn these primitives from large-scale traffic data.
The contribution of this paper has two manifolds. The first one is that we
proposed a new framework to generate new traffic scenarios from a handful of
limited traffic data. The second one is that we introduce a nonparametric
Bayesian learning method -- a sticky hierarchical Dirichlet process hidden
Markov model -- to automatically extract primitives from multidimensional
traffic data without prior knowledge of the primitive settings. The developed
method is then validated using one day of naturalistic driving data. Experiment
results show that the nonparametric Bayesian learning method is able to extract
primitives from traffic scenarios where both the binary and continuous events
coexist.
Human Action Generation with Generative Adversarial Networks
Authors: Mohammad Ahangar Kiasari, Dennis Singh Moirangthem, Minho Lee
Date: 2018-05-26 00:00:00
Tasks: Action Generation, Generative Adversarial Network, Human action generation

Inspired by the recent advances in generative models, we introduce a human
action generation model in order to generate a consecutive sequence of human
motions to formulate novel actions. We propose a framework of an autoencoder
and a generative adversarial network (GAN) to produce multiple and consecutive
human actions conditioned on the initial state and the given class label. The
proposed model is trained in an end-to-end fashion, where the autoencoder is
jointly trained with the GAN. The model is trained on the NTU RGB+D dataset and
we show that the proposed model can generate different styles of actions.
Moreover, the model can successfully generate a sequence of novel actions given
different action labels as conditions. The conventional human action prediction
and generation models lack those features, which are essential for practical
applications.
Connecting Distant Entities with Induction through Conditional Random Fields for Named Entity Recognition: Precursor-Induced CRF
Authors: Wangjin Lee, Jinwook Choi
Date: 2018-05-26 00:00:00
Proceeding: WS 2018 7
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

This paper presents a method of designing specific high-order dependency
factor on the linear chain conditional random fields (CRFs) for named entity
recognition (NER). Named entities tend to be separated from each other by
multiple outside tokens in a text, and thus the first-order CRF, as well as the
second-order CRF, may innately lose transition information between distant
named entities. The proposed design uses outside label in NER as a transmission
medium of precedent entity information on the CRF. Then, empirical results
apparently demonstrate that it is possible to exploit long-distance label
dependency in the original first-order linear chain CRF structure upon NER
while reducing computational loss rather than in the second-order CRF.
Classification of crystallization outcomes using deep convolutional neural networks
Authors: Andrew E. Bruno, Patrick Charbonneau, Janet Newman, Edward H. Snell, David R. So, Vincent Vanhoucke, Christopher J. Watkins, Shawn Williams, Julie Wilson
Date: 2018-03-27 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

The Machine Recognition of Crystallization Outcomes (MARCO) initiative has
assembled roughly half a million annotated images of macromolecular
crystallization experiments from various sources and setups. Here,
state-of-the-art machine learning algorithms are trained and tested on
different parts of this data set. We find that more than 94% of the test images
can be correctly labeled, irrespective of their experimental origin. Because
crystal recognition is key to high-density screening and the systematic
analysis of crystallization experiments, this approach opens the door to both
industrial and fundamental research applications.