pwc-archive/papers-with-abstracts huggingface dataset:
Dual Co-Matching Network for Multi-choice Reading Comprehension
Authors: Shuailiang Zhang, Hai Zhao, Yuwei Wu, Zhuosheng Zhang, Xi Zhou, Xiang Zhou
Date: 2019-01-27 00:00:00
Tasks: Machine Reading Comprehension, Multiple-choice, Question Answering, Reading Comprehension

Multi-choice reading comprehension is a challenging task that requires complex reasoning procedure. Given passage and question, a correct answer need to be selected from a set of candidate answers. In this paper, we propose \textbf{D}ual \textbf{C}o-\textbf{M}atching \textbf{N}etwork (\textbf{DCMN}) which model the relationship among passage, question and answer bidirectionally. Different from existing approaches which only calculate question-aware or option-aware passage representation, we calculate passage-aware question representation and passage-aware answer representation at the same time. To demonstrate the effectiveness of our model, we evaluate our model on a large-scale multiple choice machine reading comprehension dataset (i.e. RACE). Experimental result show that our proposed model achieves new state-of-the-art results.
Acceleration of the NVT-flash calculation for multicomponent mixtures using deep neural network models
Authors: Yiteng Li, Tao Zhang, Shuyu Sun
Date: 2019-01-27 00:00:00

Phase equilibrium calculation, also known as flash calculation, has been
extensively applied in petroleum engineering, not only as a standalone
application for separation process but also an integral component of
compositional reservoir simulation. It is of vital importance to accelerate
flash calculation without much compromise in accuracy and reliability, turning
it into an active research topic in the last two decades. In this study, we
establish a deep neural network model to approximate the iterative NVT-flash
calculation. A dynamic model designed for NVT flash problems is iteratively
solved to produce data for training the neural network. In order to test the
model's capacity to handle complex fluid mixtures, three real reservoir fluids
are investigated, including one Bakken oil and two Eagle Ford oils. Compared to
previous studies that follow the conventional flash framework in which
stability testing precedes phase splitting calculation, we incorporate
stability test and phase split calculation together and accomplish both two
steps by a single deep learning model. The trained model is able to identify
the single vapor, single liquid and vapor-liquid state under the subcritical
region of the investigated fluids. A number of examples are presented to show
the accuracy and efficiency of the proposed deep neural network. It is found
that the trained model makes predictions at most 244 times faster than the
iterative flash calculation under the given cases. Even though training a
multi-level network model does take a large amount of time that is comparable
to the computational time of flash calculations, the one-time offline training
process gives the deep learning model great potential to speed up compositional
reservoir simulation.
A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion
Authors: Filip Hanzely, Jakub Konečný, Nicolas Loizou, Peter Richtárik, Dmitry Grishchenko
Date: 2019-01-27 00:00:00
Tasks: Privacy Preserving

In this work we present a randomized gossip algorithm for solving the average
consensus problem while at the same time protecting the information about the
initial private values stored at the nodes. We give iteration complexity bounds
for the method and perform extensive numerical experiments.
6D Object Pose Estimation Based on 2D Bounding Box
Authors: Jin Liu, Sheng He
Date: 2019-01-27 00:00:00
Tasks: 6D Pose Estimation using RGB, Object, Pose Estimation, Translation

In this paper, we present a simple but powerful method to tackle the problem
of estimating the 6D pose of objects from a single RGB image. Our system trains
a novel convolutional neural network to regress the unit quaternion, which
represents the 3D rotation, from the partial image inside the bounding box
returned by 2D detection systems. Then we propose an algorithm we call Bounding
Box Equation to efficiently and accurately obtain the 3D translation, using 3D
rotation and 2D bounding box. Considering that the quadratic sum of the
quaternion's four elements equals to one, we add a normalization layer to keep
the network's output on the unit sphere and put forward a special loss function
for unit quaternion regression. We evaluate our method on the LineMod dataset
and experiment shows that our approach outperforms base-line and some state of
the art methods.
Resultant Based Incremental Recovery of Camera Pose from Pairwise Matches
Authors: Yoni Kasten, Meirav Galun, Ronen Basri
Date: 2019-01-27 00:00:00

Incremental (online) structure from motion pipelines seek to recover the
camera matrix associated with an image $I_n$ given $n-1$ images,
$I_1,...,I_{n-1}$, whose camera matrices have already been recovered. In this
paper, we introduce a novel solution to the six-point online algorithm to
recover the exterior parameters associated with $I_n$. Our algorithm uses just
six corresponding pairs of 2D points, extracted each from $I_n$ and from
\textit{any} of the preceding $n-1$ images, allowing the recovery of the full
six degrees of freedom of the $n$'th camera, and unlike common methods, does
not require tracking feature points in three or more images. Our novel solution
is based on constructing a Dixon resultant, yielding a solution method that is
both efficient and accurate compared to existing solutions. We further use
Bernstein's theorem to prove a tight bound on the number of complex solutions.
Our experiments demonstrate the utility of our approach.
Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study
Authors: Robert Robinson, Vanya V. Valindria, Wenjia Bai, Ozan Oktay, Bernhard Kainz, Hideaki Suzuki, Mihir M. Sanghvi, Nay Aung, Jos$é$ Miguel Paiva, Filip Zemrak, Kenneth Fung, Elena Lukaschuk, Aaron M. Lee, Valentina Carapella, Young Jin Kim, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Chris Page, Paul M. Matthews, Daniel Rueckert, Ben Glocker
Date: 2019-01-27 00:00:00
Tasks: General Classification, Image Segmentation, Segmentation, Semantic Segmentation

Background: The trend towards large-scale studies including population
imaging poses new challenges in terms of quality control (QC). This is a
particular issue when automatic processing tools, e.g. image segmentation
methods, are employed to derive quantitative measures or biomarkers for later
analyses. Manual inspection and visual QC of each segmentation isn't feasible
at large scale. However, it's important to be able to automatically detect when
a segmentation method fails so as to avoid inclusion of wrong measurements into
subsequent analyses which could lead to incorrect conclusions. Methods: To
overcome this challenge, we explore an approach for predicting segmentation
quality based on Reverse Classification Accuracy, which enables us to
discriminate between successful and failed segmentations on a per-cases basis.
We validate this approach on a new, large-scale manually-annotated set of 4,800
cardiac magnetic resonance scans. We then apply our method to a large cohort of
7,250 cardiac MRI on which we have performed manual QC. Results: We report
results used for predicting segmentation quality metrics including Dice
Similarity Coefficient (DSC) and surface-distance measures. As initial
validation, we present data for 400 scans demonstrating 99% accuracy for
classifying low and high quality segmentations using predicted DSC scores. As
further validation we show high correlation between real and predicted scores
and 95% classification accuracy on 4,800 scans for which manual segmentations
were available. We mimic real-world application of the method on 7,250 cardiac
MRI where we show good agreement between predicted quality metrics and manual
visual QC scores. Conclusions: We show that RCA has the potential for accurate
and fully automatic segmentation QC on a per-case basis in the context of
large-scale population imaging as in the UK Biobank Imaging Study.
Concrete Autoencoders for Differentiable Feature Selection and Reconstruction
Authors: Abubakar Abid, Muhammad Fatih Balin, James Zou
Date: 2019-01-27 00:00:00
Tasks: Decoder, feature selection, General Classification, General Classification Selection

We introduce the concrete autoencoder, an end-to-end differentiable method
for global feature selection, which efficiently identifies a subset of the most
informative features and simultaneously learns a neural network to reconstruct
the input data from the selected features. Our method is unsupervised, and is
based on using a concrete selector layer as the encoder and using a standard
neural network as the decoder. During the training phase, the temperature of
the concrete selector layer is gradually decreased, which encourages a
user-specified number of discrete features to be learned. During test time, the
selected features can be used with the decoder network to reconstruct the
remaining input features. We evaluate concrete autoencoders on a variety of
datasets, where they significantly outperform state-of-the-art methods for
feature selection and data reconstruction. In particular, on a large-scale gene
expression dataset, the concrete autoencoder selects a small subset of genes
whose expression levels can be use to impute the expression levels of the
remaining genes. In doing so, it improves on the current widely-used
expert-curated L1000 landmark genes, potentially reducing measurement costs by
20%. The concrete autoencoder can be implemented by adding just a few lines of
code to a standard autoencoder.
Stochastic Approximation of Smooth and Strongly Convex Functions: Beyond the $O(1/T)$ Convergence Rate
Authors: Lijun Zhang, Zhi-Hua Zhou
Date: 2019-01-27 00:00:00

Stochastic approximation (SA) is a classical approach for stochastic convex
optimization. Previous studies have demonstrated that the convergence rate of
SA can be improved by introducing either smoothness or strong convexity
condition. In this paper, we make use of smoothness and strong convexity
simultaneously to boost the convergence rate. Let $\lambda$ be the modulus of
strong convexity, $\kappa$ be the condition number, $F_*$ be the minimal risk,
and $\alpha>1$ be some small constant. First, we demonstrate that, in
expectation, an $O(1/[\lambda T^\alpha] + \kappa F_*/T)$ risk bound is
attainable when $T = \Omega(\kappa^\alpha)$. Thus, when $F_*$ is small, the
convergence rate could be faster than $O(1/[\lambda T])$ and approaches
$O(1/[\lambda T^\alpha])$ in the ideal case. Second, to further benefit from
small risk, we show that, in expectation, an $O(1/2^{T/\kappa}+F_*)$ risk bound
is achievable. Thus, the excess risk reduces exponentially until reaching
$O(F_*)$, and if $F_*=0$, we obtain a global linear convergence. Finally, we
emphasize that our proof is constructive and each risk bound is equipped with
an efficient stochastic algorithm attaining that bound.
On the Universality of Invariant Networks
Authors: Haggai Maron, Ethan Fetaya, Nimrod Segol, Yaron Lipman
Date: 2019-01-27 00:00:00

Constraining linear layers in neural networks to respect symmetry transformations from a group $G$ is a common design principle for invariant networks that has found many applications in machine learning. In this paper, we consider a fundamental question that has received little attention to date: Can these networks approximate any (continuous) invariant function? We tackle the rather general case where $G\leq S_n$ (an arbitrary subgroup of the symmetric group) that acts on $\mathbb{R}^n$ by permuting coordinates. This setting includes several recent popular invariant networks. We present two main results: First, $G$-invariant networks are universal if high-order tensors are allowed. Second, there are groups $G$ for which higher-order tensors are unavoidable for obtaining universality. $G$-invariant networks consisting of only first-order tensors are of special interest due to their practical value. We conclude the paper by proving a necessary condition for the universality of $G$-invariant networks that incorporate only first-order tensors.
Augment your batch: better training with larger batches
Authors: Elad Hoffer, Tal Ben-Nun, Itay Hubara, Niv Giladi, Torsten Hoefler, Daniel Soudry
Date: 2019-01-27 00:00:00

Large-batch SGD is important for scaling training of deep neural networks.
However, without fine-tuning hyperparameter schedules, the generalization of
the model may be hampered. We propose to use batch augmentation: replicating
instances of samples within the same batch with different data augmentations.
Batch augmentation acts as a regularizer and an accelerator, increasing both
generalization and performance scaling. We analyze the effect of batch
augmentation on gradient variance and show that it empirically improves
convergence for a wide variety of deep neural networks and datasets. Our
results show that batch augmentation reduces the number of necessary SGD
updates to achieve the same accuracy as the state-of-the-art. Overall, this
simple yet effective method enables faster training and better generalization
by allowing more computational resources to be used concurrently.
Predicting Tomorrow's Headline using Today's Twitter Deliberations
Authors: Roshni Chakraborty, Abhijeet Kharat, Apalak Khatua, Sourav Kumar Dandapat, Joydeep Chandra
Date: 2019-01-27 00:00:00
Tasks: Articles

Predicting the popularity of news article is a challenging task. Existing
literature mostly focused on article contents and polarity to predict
popularity. However, existing research has not considered the users' preference
towards a particular article. Understanding users' preference is an important
aspect for predicting the popularity of news articles. Hence, we consider the
social media data, from the Twitter platform, to address this research gap. In
our proposed model, we have considered the users' involvement as well as the
users' reaction towards an article to predict the popularity of the article. In
short, we are predicting tomorrow's headline by probing today's Twitter
discussion. We have considered 300 political news article from the New York
Post, and our proposed approach has outperformed other baseline models.
Reward Shaping via Meta-Learning
Authors: Haosheng Zou, Tongzheng Ren, Dong Yan, Hang Su, Jun Zhu
Date: 2019-01-27 00:00:00
Tasks: Meta-Learning, Reinforcement Learning, Reinforcement Learning (RL)

Reward shaping is one of the most effective methods to tackle the crucial yet
challenging problem of credit assignment in Reinforcement Learning (RL).
However, designing shaping functions usually requires much expert knowledge and
hand-engineering, and the difficulties are further exacerbated given multiple
similar tasks to solve. In this paper, we consider reward shaping on a
distribution of tasks, and propose a general meta-learning framework to
automatically learn the efficient reward shaping on newly sampled tasks,
assuming only shared state space but not necessarily action space. We first
derive the theoretically optimal reward shaping in terms of credit assignment
in model-free RL. We then propose a value-based meta-learning algorithm to
extract an effective prior over the optimal reward shaping. The prior can be
applied directly to new tasks, or provably adapted to the task-posterior while
solving the task within few gradient updates. We demonstrate the effectiveness
of our shaping through significantly improved learning efficiency and
interpretable visualizations across various settings, including notably a
successful transfer from DQN to DDPG.
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning
Authors: Chao Qu, Shie Mannor, Huan Xu, Yuan Qi, Le Song, Junwu Xiong
Date: 2019-01-27 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider the networked multi-agent reinforcement learning (MARL) problem in a fully decentralized setting, where agents learn to coordinate to achieve the joint success. This problem is widely encountered in many areas including traffic control, distributed control, and smart grids. We assume that the reward function for each agent can be different and observed only locally by the agent itself. Furthermore, each agent is located at a node of a communication network and can exchanges information only with its neighbors. Using softmax temporal consistency and a decentralized optimization method, we obtain a principled and data-efficient iterative algorithm. In the first step of each iteration, an agent computes its local policy and value gradients and then updates only policy parameters. In the second step, the agent propagates to its neighbors the messages based on its value function and then updates its own value function. Hence we name the algorithm value propagation. We prove a non-asymptotic convergence rate 1/T with the nonlinear function approximation. To the best of our knowledge, it is the first MARL algorithm with convergence guarantee in the control, off-policy and non-linear function approximation setting. We empirically demonstrate the effectiveness of our approach in experiments.
Fixup Initialization: Residual Learning Without Normalization
Authors: Hongyi Zhang, Yann N. Dauphin, Tengyu Ma
Date: 2019-01-27 00:00:00
Proceeding: ICLR 2019 5
Tasks: General Classification, image-classification, Image Classification, Machine Translation, Translation

Normalization layers are a staple in state-of-the-art deep neural network
architectures. They are widely believed to stabilize training, enable higher
learning rate, accelerate convergence and improve generalization, though the
reason for their effectiveness is still an active research topic. In this work,
we challenge the commonly-held beliefs by showing that none of the perceived
benefits is unique to normalization. Specifically, we propose fixed-update
initialization (Fixup), an initialization motivated by solving the exploding
and vanishing gradient problem at the beginning of training via properly
rescaling a standard initialization. We find training residual networks with
Fixup to be as stable as training with normalization -- even for networks with
10,000 layers. Furthermore, with proper regularization, Fixup enables residual
networks without normalization to achieve state-of-the-art performance in image
classification and machine translation.
On Symmetric Losses for Learning from Corrupted Labels
Authors: Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama
Date: 2019-01-27 00:00:00
Tasks: General Classification

This paper aims to provide a better understanding of a symmetric loss. First, we emphasize that using a symmetric loss is advantageous in the balanced error rate (BER) minimization and area under the receiver operating characteristic curve (AUC) maximization from corrupted labels. Second, we prove general theoretical properties of symmetric losses, including a classification-calibration condition, excess risk bound, conditional risk minimizer, and AUC-consistency condition. Third, since all nonnegative symmetric losses are non-convex, we propose a convex barrier hinge loss that benefits significantly from the symmetric condition, although it is not symmetric everywhere. Finally, we conduct experiments to validate the relevance of the symmetric condition.
Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP
Authors: Kefan Dong, Yuanhao Wang, Xiaoyu Chen, Li-Wei Wang
Date: 2019-01-27 00:00:00
Proceeding: ICLR 2020 1
Tasks: Q-Learning, Reinforcement Learning

A fundamental question in reinforcement learning is whether model-free algorithms are sample efficient. Recently, Jin et al. \cite{jin2018q} proposed a Q-learning algorithm with UCB exploration policy, and proved it has nearly optimal regret bound for finite-horizon episodic MDP. In this paper, we adapt Q-learning with UCB-exploration bonus to infinite-horizon MDP with discounted rewards \emph{without} accessing a generative model. We show that the \textit{sample complexity of exploration} of our algorithm is bounded by $\tilde{O}({\frac{SA}{\epsilon^2(1-\gamma)^7}})$. This improves the previously best known result of $\tilde{O}({\frac{SA}{\epsilon^4(1-\gamma)^8}})$ in this setting achieved by delayed Q-learning \cite{strehl2006pac}, and matches the lower bound in terms of $\epsilon$ as well as $S$ and $A$ except for logarithmic factors.
Variational Smoothing in Recurrent Neural Network Language Models
Authors: Lingpeng Kong, Gabor Melis, Wang Ling, Lei Yu, Dani Yogatama
Date: 2019-01-27 00:00:00
Proceeding: ICLR 2019 5
Tasks: Language Modeling, Language Modelling

We present a new theoretical perspective of data noising in recurrent neural
network language models (Xie et al., 2017). We show that each variant of data
noising is an instance of Bayesian recurrent neural networks with a particular
variational distribution (i.e., a mixture of Gaussians whose weights depend on
statistics derived from the corpus such as the unigram distribution). We use
this insight to propose a more principled method to apply at prediction time
and propose natural extensions to data noising under the variational framework.
In particular, we propose variational smoothing with tied input and output
embedding matrices and an element-wise variational smoothing method. We
empirically verify our analysis on two benchmark language modeling datasets and
demonstrate performance improvements over existing data noising methods.
Anomaly detecting and ranking of the cloud computing platform by multi-view learning
Authors: Jing Zhang
Date: 2019-01-27 00:00:00
Tasks: BIG-bench Machine Learning, Cloud Computing, General Classification, Learning Theory, MULTI-VIEW LEARNING

Anomaly detecting as an important technical in cloud computing is applied to
support smooth running of the cloud platform. Traditional detecting methods
based on statistic, analysis, etc. lead to the high false-alarm rate due to
non-adaptive and sensitive parameters setting. We presented an online model for
anomaly detecting using machine learning theory. However, most existing methods
based on machine learning linked all features from difference sub-systems into
a long feature vector directly, which is difficult to both exploit the
complement information between sub-systems and ignore multi-view features
enhancing the classification performance. Aiming to this problem, the proposed
method automatic fuses multi-view features and optimize the discriminative
model to enhance the accuracy. This model takes advantage of extreme learning
machine (ELM) to improve detection efficiency. ELM is the single hidden layer
neural network, which is transforming iterative solution the output weights to
solution of linear equations and avoiding the local optimal solution. Moreover,
we rank anomies according to the relationship between samples and the
classification boundary, and then assigning weights for ranked anomalies,
retraining the classification model finally. Our method exploits the complement
information between sub-systems sufficiently, and avoids the influence from
imbalance dataset, therefore, deal with various challenges from the cloud
computing platform. We deploy the privately cloud platform by Openstack,
verifying the proposed model and comparing results to the state-of-the-art
methods with better efficiency and simplicity.