pwc-archive/papers-with-abstracts huggingface dataset:
A New Channel Boosted Convolutional Neural Network using Transfer Learning
Authors: Asifullah Khan, Anabia Sohail, Amna Ali
Date: 2018-04-23 00:00:00
Tasks: Transfer Learning

We present a novel architectural enhancement of Channel Boosting in a deep convolutional neural network (CNN). This idea of Channel Boosting exploits both the channel dimension of CNN (learning from multiple input channels) and Transfer learning (TL). TL is utilized at two different stages; channel generation and channel exploitation. In the proposed methodology, a deep CNN is boosted by various channels available through TL from already trained Deep Neural Networks, in addition to its original channel. The deep architecture of CNN then exploits the original and boosted channels down the stream for learning discriminative patterns. Churn prediction in telecom is a challenging task due to the high dimensionality and imbalanced nature of the data. Therefore, churn prediction data is used to evaluate the performance of the proposed Channel Boosted CNN (CB CNN). In the first phase, informative discriminative features are being extracted using a stacked autoencoder, and then in the second phase, these features are combined with the original features to form Channel Boosted images. Finally, the knowledge gained by a pretrained CNN is exploited by employing TL. The results are promising and show the ability of the Channel Boosting concept in learning complex classification problems by discerning even minute differences in churners and nonchurners. The proposed work validates the concept observed from the evolution of recent CNN architectures that the innovative restructuring of a CNN architecture may increase the networks representative capacity.
An Online RFID Localization in the Manufacturing Shopfloor
Authors: Andri Ashfahani, Mahardhika Pratama, Edwin Lughofer, Qing Cai, Huang Sheng
Date: 2018-05-20 00:00:00

{Radio Frequency Identification technology has gained popularity for cheap and easy deployment. In the realm of manufacturing shopfloor, it can be used to track the location of manufacturing objects to achieve better efficiency. The underlying challenge of localization lies in the non-stationary characteristics of manufacturing shopfloor which calls for an adaptive life-long learning strategy in order to arrive at accurate localization results. This paper presents an evolving model based on a novel evolving intelligent system, namely evolving Type-2 Quantum Fuzzy Neural Network (eT2QFNN), which features an interval type-2 quantum fuzzy set with uncertain jump positions. The quantum fuzzy set possesses a graded membership degree which enables better identification of overlaps between classes. The eT2QFNN works fully in the evolving mode where all parameters including the number of rules are automatically adjusted and generated on the fly. The parameter adjustment scenario relies on decoupled extended Kalman filter method. Our numerical study shows that eT2QFNN is able to deliver comparable accuracy compared to state-of-the-art algorithms.
Learning From Noisy Singly-labeled Data
Authors: Ashish Khetan, Zachary C. Lipton, Anima Anandkumar
Date: 2017-12-13 00:00:00
Proceeding: ICLR 2018 1

Supervised learning depends on annotated examples, which are taken to be the
\emph{ground truth}. But these labels often come from noisy crowdsourcing
platforms, like Amazon Mechanical Turk. Practitioners typically collect
multiple labels per example and aggregate the results to mitigate noise (the
classic crowdsourcing problem). Given a fixed annotation budget and unlimited
unlabeled data, redundant annotation comes at the expense of fewer labeled
examples. This raises two fundamental questions: (1) How can we best learn from
noisy workers? (2) How should we allocate our labeling budget to maximize the
performance of a classifier? We propose a new algorithm for jointly modeling
labels and worker quality from noisy crowd-sourced data. The alternating
minimization proceeds in rounds, estimating worker quality from disagreement
with the current model and then updating the model by optimizing a loss
function that accounts for the current estimate of worker quality. Unlike
previous approaches, even with only one annotation per example, our algorithm
can estimate worker quality. We establish a generalization error bound for
models learned with our algorithm and establish theoretically that it's better
to label many examples once (vs less multiply) when worker quality is above a
threshold. Experiments conducted on both ImageNet (with simulated noisy
workers) and MS-COCO (using the real crowdsourced labels) confirm our
algorithm's benefits.
Tree Memory Networks for Modelling Long-term Temporal Dependencies
Authors: Tharindu Fernando, Simon Denman, Aaron McFadyen, Sridha Sridharan, Clinton Fookes
Date: 2017-03-12 00:00:00
Tasks: Machine Translation, Part-Of-Speech Tagging, Question Answering, Trajectory Prediction, Visual Question Answering, Visual Question Answering (VQA)

In the domain of sequence modelling, Recurrent Neural Networks (RNN) have
been capable of achieving impressive results in a variety of application areas
including visual question answering, part-of-speech tagging and machine
translation. However this success in modelling short term dependencies has not
successfully transitioned to application areas such as trajectory prediction,
which require capturing both short term and long term relationships. In this
paper, we propose a Tree Memory Network (TMN) for modelling long term and short
term relationships in sequence-to-sequence mapping problems. The proposed
network architecture is composed of an input module, controller and a memory
module. In contrast to related literature, which models the memory as a
sequence of historical states, we model the memory as a recursive tree
structure. This structure more effectively captures temporal dependencies
across both short term and long term sequences using its hierarchical
structure. We demonstrate the effectiveness and flexibility of the proposed TMN
in two practical problems, aircraft trajectory modelling and pedestrian
trajectory modelling in a surveillance setting, and in both cases we outperform
the current state-of-the-art. Furthermore, we perform an in depth analysis on
the evolution of the memory module content over time and provide visual
evidence on how the proposed TMN is able to map both long term and short term
relationships efficiently via a hierarchical structure.
Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration
Authors: Xiaoshuai Zhang, Yiping Lu, Jiaying Liu, Bin Dong
Date: 2018-05-20 00:00:00
Proceeding: ICLR 2019 5
Tasks: Denoising, Image Deblocking, Image Denoising, Image Restoration

In this paper, we propose a new control framework called the moving endpoint
control to restore images corrupted by different degradation levels in one
model. The proposed control problem contains a restoration dynamics which is
modeled by an RNN. The moving endpoint, which is essentially the terminal time
of the associated dynamics, is determined by a policy network. We call the
proposed model the dynamically unfolding recurrent restorer (DURR). Numerical
experiments show that DURR is able to achieve state-of-the-art performances on
blind image denoising and JPEG image deblocking. Furthermore, DURR can well
generalize to images with higher degradation levels that are not included in
the training stage.
A Lyapunov-based Approach to Safe Reinforcement Learning
Authors: Yin-Lam Chow, Ofir Nachum, Edgar Duenez-Guzman, Mohammad Ghavamzadeh
Date: 2018-05-20 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

In many real-world reinforcement learning (RL) problems, besides optimizing
the main objective function, an agent must concurrently avoid violating a
number of constraints. In particular, besides optimizing performance it is
crucial to guarantee the safety of an agent during training as well as
deployment (e.g. a robot should avoid taking actions - exploratory or not -
which irrevocably harm its hardware). To incorporate safety in RL, we derive
algorithms under the framework of constrained Markov decision problems (CMDPs),
an extension of the standard Markov decision problems (MDPs) augmented with
constraints on expected cumulative costs. Our approach hinges on a novel
\emph{Lyapunov} method. We define and present a method for constructing
Lyapunov functions, which provide an effective way to guarantee the global
safety of a behavior policy during training via a set of local, linear
constraints. Leveraging these theoretical underpinnings, we show how to use the
Lyapunov approach to systematically transform dynamic programming (DP) and RL
algorithms into their safe counterparts. To illustrate their effectiveness, we
evaluate these algorithms in several CMDP planning and decision-making tasks on
a safety benchmark domain. Our results show that our proposed method
significantly outperforms existing baselines in balancing constraint
satisfaction and performance.
Object Localization with a Weakly Supervised CapsNet
Authors: Weitang Liu, Emad Barsoum, John D. Owens
Date: 2018-05-20 00:00:00
Tasks: Object, Object Localization, Object Recognition, Transfer Learning, Translation

Inspired by CapsNet's routing-by-agreement mechanism with its ability to learn object properties, we explore if those properties in turn can determine new properties of the objects, such as the locations. We then propose a CapsNet architecture with object coordinate atoms and a modified routing-by-agreement algorithm with unevenly distributed initial routing probabilities. The model is based on CapsNet but uses a routing algorithm to find the objects' approximate positions in the image coordinate system. We also discussed how to derive the property of translation through coordinate atoms and we show the importance of sparse representation. We train our model on the single moving MNIST dataset with class labels. Our model can learn and derive the coordinates of the digits better than its convolution counterpart that lacks a routing-by-agreement algorithm, and can also perform well when testing on the multi-digit moving MNIST and KTH datasets. The results show our method reaches the state-of-art performance on object localization without any extra localization techniques and modules as in prior work.
Predicting drug response of tumors from integrated genomic profiles by deep neural networks
Authors: Yu-Chiao Chiu, Hung-I Harry Chen, Tinghe Zhang, Songyao Zhang, Aparna Gorthi, Li-Ju Wang, Yufei Huang, Yidong Chen
Date: 2018-05-20 00:00:00

The study of high-throughput genomic profiles from a pharmacogenomics
viewpoint has provided unprecedented insights into the oncogenic features
modulating drug response. A recent screening of ~1,000 cancer cell lines to a
collection of anti-cancer drugs illuminated the link between genotypes and
vulnerability. However, due to essential differences between cell lines and
tumors, the translation into predicting drug response in tumors remains
challenging. Here we proposed a DNN model to predict drug response based on
mutation and expression profiles of a cancer cell or a tumor. The model
contains a mutation and an expression encoders pre-trained using a large
pan-cancer dataset to abstract core representations of high-dimension data,
followed by a drug response predictor network. Given a pair of mutation and
expression profiles, the model predicts IC50 values of 265 drugs. We trained
and tested the model on a dataset of 622 cancer cell lines and achieved an
overall prediction performance of mean squared error at 1.96 (log-scale IC50
values). The performance was superior in prediction error or stability than two
classical methods and four analog DNNs of our model. We then applied the model
to predict drug response of 9,059 tumors of 33 cancer types. The model
predicted both known, including EGFR inhibitors in non-small cell lung cancer
and tamoxifen in ER+ breast cancer, and novel drug targets. The comprehensive
analysis further revealed the molecular mechanisms underlying the resistance to
a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer
potential of a novel agent, CX-5461, in treating gliomas and hematopoietic
malignancies. Overall, our model and findings improve the prediction of drug
response and the identification of novel therapeutic options.
Multi-view Common Component Discriminant Analysis for Cross-view Classification
Authors: Xinge You, Jiamiao Xu, Wei Yuan, Xiao-Yuan Jing, DaCheng Tao, Taiping Zhang
Date: 2018-05-14 00:00:00
Tasks: General Classification

Cross-view classification that means to classify samples from heterogeneous
views is a significant yet challenging problem in computer vision. A promising
approach to handle this problem is the multi-view subspace learning (MvSL),
which intends to find a common subspace for multi-view data. Despite the
satisfactory results achieved by existing methods, the performance of previous
work will be dramatically degraded when multi-view data lies on nonlinear
manifolds. To circumvent this drawback, we propose Multi-view Common Component
Discriminant Analysis (MvCCDA) to handle view discrepancy, discriminability and
nonlinearity in a joint manner. Specifically, our MvCCDA incorporates
supervised information and local geometric information into the common
component extraction process to learn a discriminant common subspace and to
discover the nonlinear structure embedded in multi-view data. We develop a
kernel method of MvCCDA to further boost the performance of MvCCDA. Beyond
kernel extension, optimization and complexity analysis of MvCCDA are also
presented for completeness. Our MvCCDA is competitive with the state-of-the-art
MvSL based methods on four benchmark datasets, demonstrating its superiority.
Density-Adaptive Kernel based Efficient Reranking Approaches for Person Reidentification
Authors: Ruo-Pei Guo, Chun-Guang Li, Yonghua Li, Jia-Ru Lin, Jun Guo
Date: 2018-05-20 00:00:00
Tasks: Person Re-Identification, Reranking

Person reidentification (ReID) refers to the task of verifying the identity of a pedestrian observed from nonoverlapping views in a surveillance camera network. It has recently been validated that reranking can achieve remarkable performance improvements in person ReID systems. However, current reranking approaches either require feedback from users or suffer from burdensome computational costs. In this paper, we propose to exploit a density-adaptive smooth kernel technique to achieve efficient and effective reranking. Specifically, we adopt a smooth kernel function to formulate the neighbor relationships among data samples with a density-adaptive parameter. Based on this new formulation, we present two simple yet effective reranking methods, termed \emph{inverse} density-adaptive kernel based reranking (inv-DAKR) and \emph{bidirectional} density-adaptive kernel based reranking (bi-DAKR), in which the local density information in the vicinity of each gallery sample is elegantly exploited. Moreover, we extend the proposed inv-DAKR and bi-DAKR methods to incorporate the available extra probe samples and demonstrate that when and why these extra probe samples are able to improve the local neighborhood and thus further refine the ranking results. Extensive experiments are conducted on six benchmark datasets, including: PRID450s, VIPeR, CUHK03, GRID, Market-1501 and Mars. The experimental results demonstrate that our proposals are effective and efficient.
The UN Parallel Corpus Annotated for Translation Direction
Authors: Elad Tolochinsky, Ohad Mosafi, Ella Rabinovich, Shuly Wintner
Date: 2018-05-20 00:00:00
Tasks: Classification, General Classification, Translation

This work distinguishes between translated and original text in the UN
protocol corpus. By modeling the problem as classification problem, we can
achieve up to 95% classification accuracy. We begin by deriving a parallel
corpus for different language-pairs annotated for translation direction, and
then classify the data by using various feature extraction methods. We compare
the different methods as well as the ability to distinguish between translated
and original texts in the different languages. The annotated corpus is publicly
available.
Algorithms and Theory for Multiple-Source Adaptation
Authors: Judy Hoffman, Mehryar Mohri, Ningshan Zhang
Date: 2018-05-20 00:00:00
Proceeding: NeurIPS 2018 12

This work includes a number of novel contributions for the multiple-source
adaptation problem. We present new normalized solutions with strong theoretical
guarantees for the cross-entropy loss and other similar losses. We also provide
new guarantees that hold in the case where the conditional probabilities for
the source domains are distinct. Moreover, we give new algorithms for
determining the distribution-weighted combination solution for the
cross-entropy loss and other losses. We report the results of a series of
experiments with real-world datasets. We find that our algorithm outperforms
competing approaches by producing a single robust model that performs well on
any target mixture distribution. Altogether, our theory, algorithms, and
empirical results provide a full solution for the multiple-source adaptation
problem with very practical benefits.
Machine Learning for Predictive Analytics of Compute Cluster Jobs
Authors: Dan Andresen, William Hsu, Huichen Yang, Adedolapo Okanlawon
Date: 2018-05-20 00:00:00
Tasks: BIG-bench Machine Learning, CPU, General Classification

We address the problem of predicting whether sufficient memory and CPU
resources have been requested for jobs at submission time. For this purpose, we
examine the task of training a supervised machine learning system to predict
the outcome - whether the job will fail specifically due to insufficient
resources - as a classification task. Sufficiently high accuracy, precision,
and recall at this task facilitates more anticipatory decision support
applications in the domain of HPC resource allocation. Our preliminary results
using a new test bed show that the probability of failed jobs is associated
with information freely available at job submission time and may thus be usable
by a learning system for user modeling that gives personalized feedback to
users.
RGB-Depth SLAM Review
Authors: Redhwan Jamiruddin, Ali Osman Sari, Jahanzaib Shabbir, Tarique Anwer
Date: 2018-05-20 00:00:00
Tasks: Simultaneous Localization and Mapping

Simultaneous Localization and Mapping (SLAM) have made the real-time dense
reconstruction possible increasing the prospects of navigation, tracking, and
augmented reality problems. Some breakthroughs have been achieved in this
regard during past few decades and more remarkable works are still going on.
This paper presents an overview of SLAM approaches that have been developed
till now. Kinect Fusion algorithm, its variants, and further developed
approaches are discussed in detailed. The algorithms and approaches are
compared for their effectiveness in tracking and mapping based on Root Mean
Square error over online available datasets.
Model Agnostic Time Series Analysis via Matrix Estimation
Authors: Anish Agarwal, Muhammad Jehangir Amjad, Devavrat Shah, Dennis Shen
Date: 2018-02-25 00:00:00
Tasks: Imputation, Missing Values, regression, Time Series, Time Series Analysis

We propose an algorithm to impute and forecast a time series by transforming
the observed time series into a matrix, utilizing matrix estimation to recover
missing values and de-noise observed entries, and performing linear regression
to make predictions. At the core of our analysis is a representation result,
which states that for a large model class, the transformed time series matrix
is (approximately) low-rank. In effect, this generalizes the widely used
Singular Spectrum Analysis (SSA) in time series literature, and allows us to
establish a rigorous link between time series analysis and matrix estimation.
The key to establishing this link is constructing a Page matrix with
non-overlapping entries rather than a Hankel matrix as is commonly done in the
literature (e.g., SSA). This particular matrix structure allows us to provide
finite sample analysis for imputation and prediction, and prove the asymptotic
consistency of our method. Another salient feature of our algorithm is that it
is model agnostic with respect to both the underlying time dynamics and the
noise distribution in the observations. The noise agnostic property of our
approach allows us to recover the latent states when only given access to noisy
and partial observations a la a Hidden Markov Model; e.g., recovering the
time-varying parameter of a Poisson process without knowing that the underlying
process is Poisson. Furthermore, since our forecasting algorithm requires
regression with noisy features, our approach suggests a matrix estimation based
method - coupled with a novel, non-standard matrix estimation error metric - to
solve the error-in-variable regression problem, which could be of interest in
its own right. Through synthetic and real-world datasets, we demonstrate that
our algorithm outperforms standard software packages (including R libraries) in
the presence of missing data as well as high levels of noise.
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
Authors: Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu
Date: 2018-05-20 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Action Recognition, Action Recognition, graph construction, Skeleton Based Action Recognition, Temporal Action Localization, Vocal Bursts Valence Prediction

In skeleton-based action recognition, graph convolutional networks (GCNs), which model the human body skeletons as spatiotemporal graphs, have achieved remarkable performance. However, in existing GCN-based methods, the topology of the graph is set manually, and it is fixed over all layers and input samples. This may not be optimal for the hierarchical GCN and diverse samples in action recognition tasks. In addition, the second-order information (the lengths and directions of bones) of the skeleton data, which is naturally more informative and discriminative for action recognition, is rarely investigated in existing methods. In this work, we propose a novel two-stream adaptive graph convolutional network (2s-AGCN) for skeleton-based action recognition. The topology of the graph in our model can be either uniformly or individually learned by the BP algorithm in an end-to-end manner. This data-driven method increases the flexibility of the model for graph construction and brings more generality to adapt to various data samples. Moreover, a two-stream framework is proposed to model both the first-order and the second-order information simultaneously, which shows notable improvement for the recognition accuracy. Extensive experiments on the two large-scale datasets, NTU-RGBD and Kinetics-Skeleton, demonstrate that the performance of our model exceeds the state-of-the-art with a significant margin.
Deep Forward and Inverse Perceptual Models for Tracking and Prediction
Authors: Alexander Lambert, Amirreza Shaban, Amit Raj, Zhen Liu, Byron Boots
Date: 2017-10-31 00:00:00
Tasks: Image Generation, State Estimation

We consider the problems of learning forward models that map state to
high-dimensional images and inverse models that map high-dimensional images to
state in robotics. Specifically, we present a perceptual model for generating
video frames from state with deep networks, and provide a framework for its use
in tracking and prediction tasks. We show that our proposed model greatly
outperforms standard deconvolutional methods and GANs for image generation,
producing clear, photo-realistic images. We also develop a convolutional neural
network model for state estimation and compare the result to an Extended Kalman
Filter to estimate robot trajectories. We validate all models on a real robotic
system.