pwc-archive/papers-with-abstracts huggingface dataset:
Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data
Authors: Wei Zhao, Liang Wang, Kewei Shen, Ruoyu Jia, Jingming Liu
Date: 2019-03-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Denoising, Grammatical Error Correction, Machine Translation, Multi-Task Learning, Sentence, Translation

Neural machine translation systems have become state-of-the-art approaches for Grammatical Error Correction (GEC) task. In this paper, we propose a copy-augmented architecture for the GEC task by copying the unchanged words from the source sentence to the target sentence. Since the GEC suffers from not having enough labeled training data to achieve high accuracy. We pre-train the copy-augmented architecture with a denoising auto-encoder using the unlabeled One Billion Benchmark and make comparisons between the fully pre-trained model and a partially pre-trained model. It is the first time copying words from the source context and fully pre-training a sequence to sequence model are experimented on the GEC task. Moreover, We add token-level and sentence-level multi-task learning for the GEC task. The evaluation results on the CoNLL-2014 test set show that our approach outperforms all recently published state-of-the-art results by a large margin. The code and pre-trained models are released at https://github.com/zhawe01/fairseq-gec.
Video Extrapolation with an Invertible Linear Embedding
Authors: Robert Pottorff, Jared Nielsen, David Wingate
Date: 2019-03-01 00:00:00
Tasks: Predict Future Video Frames

We predict future video frames from complex dynamic scenes, using an
invertible neural network as the encoder of a nonlinear dynamic system with
latent linear state evolution. Our invertible linear embedding (ILE)
demonstrates successful learning, prediction and latent state inference. In
contrast to other approaches, ILE does not use any explicit reconstruction loss
or simplistic pixel-space assumptions. Instead, it leverages invertibility to
optimize the likelihood of image sequences exactly, albeit indirectly.
Comparison with a state-of-the-art method demonstrates the viability of our
approach.
Improving Grounded Natural Language Understanding through Human-Robot Dialog
Authors: Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov, Nick Walker, Yuqian Jiang, Harel Yedidsion, Justin Hart, Peter Stone, Raymond J. Mooney
Date: 2019-03-01 00:00:00
Tasks: Natural Language Understanding

Natural language understanding for robotics can require substantial domain-
and platform-specific engineering. For example, for mobile robots to
pick-and-place objects in an environment to satisfy human commands, we can
specify the language humans use to issue such commands, and connect concept
words like red can to physical object properties. One way to alleviate this
engineering for a new domain is to enable robots in human environments to adapt
dynamically---continually learning new language constructions and perceptual
concepts. In this work, we present an end-to-end pipeline for translating
natural language commands to discrete robot actions, and use clarification
dialogs to jointly improve language parsing and concept grounding. We train and
evaluate this agent in a virtual setting on Amazon Mechanical Turk, and we
transfer the learned agent to a physical robot platform to demonstrate it in
the real world.
Local Geometric Indexing of High Resolution Data for Facial Reconstruction from Sparse Markers
Authors: Matthew Cong, Lana Lan, Ronald Fedkiw
Date: 2019-03-01 00:00:00
Tasks: Physical Simulations

When considering sparse motion capture marker data, one typically struggles to balance its overfitting via a high dimensional blendshape system versus underfitting caused by smoothness constraints. With the current trend towards using more and more data, our aim is not to fit the motion capture markers with a parameterized (blendshape) model or to smoothly interpolate a surface through the marker positions, but rather to find an instance in the high resolution dataset that contains local geometry to fit each marker. Just as is true for typical machine learning applications, this approach benefits from a plethora of data, and thus we also consider augmenting the dataset via specially designed physical simulations that target the high resolution dataset such that the simulation output lies on the same so-called manifold as the data targeted.
A Sketch Based 3D Shape Retrieval Approach Based on Efficient Deep Point-to-Subspace Metric Learning
Authors: Yinjie Lei, Ziqin Zhou, Pingping Zhang, Yulan Guo, Zijun Ma, Lingqiao Liu
Date: 2019-03-01 00:00:00
Tasks: 3D Shape Classification, 3D Shape Retrieval, Metric Learning, Retrieval

A sketch based 3D shape retrieval
A Review of Stochastic Block Models and Extensions for Graph Clustering
Authors: Clement Lee, Darren J Wilkinson
Date: 2019-03-01 00:00:00
Tasks: Clustering, Graph Clustering

There have been rapid developments in model-based clustering of graphs, also known as block modelling, over the last ten years or so. We review different approaches and extensions proposed for different aspects in this area, such as the type of the graph, the clustering approach, the inference approach, and whether the number of groups is selected or estimated. We also review models that combine block modelling with topic modelling and/or longitudinal modelling, regarding how these models deal with multiple types of data. How different approaches cope with various issues will be summarised and compared, to facilitate the demand of practitioners for a concise overview of the current status of these areas of literature.
Self-supervised Learning for Single View Depth and Surface Normal Estimation
Authors: Huangying Zhan, Chamara Saroj Weerasekera, Ravi Garg, Ian Reid
Date: 2019-03-01 00:00:00
Tasks: Depth Estimation, Depth Prediction, Monocular Depth Estimation, Self-Supervised Learning, Surface Normal Estimation

In this work we present a self-supervised learning framework to
simultaneously train two Convolutional Neural Networks (CNNs) to predict depth
and surface normals from a single image. In contrast to most existing
frameworks which represent outdoor scenes as fronto-parallel planes at
piece-wise smooth depth, we propose to predict depth with surface orientation
while assuming that natural scenes have piece-wise smooth normals. We show that
a simple depth-normal consistency as a soft-constraint on the predictions is
sufficient and effective for training both these networks simultaneously. The
trained normal network provides state-of-the-art predictions while the depth
network, relying on much realistic smooth normal assumption, outperforms the
traditional self-supervised depth prediction network by a large margin on the
KITTI benchmark. Demo video: https://youtu.be/ZD-ZRsw7hdM
Inference of Human's Observation Strategy for Monitoring Robot's Behavior based on a Game-Theoretic Model of Trust
Authors: Zahra Zahedi, Sailik Sengupta, Subbarao Kambhampati
Date: 2019-03-01 00:00:00

We consider scenarios where a worker robot, who may be unaware of the human's exact expectations, may have the incentive to deviate from a preferred plan (e.g. safe but costly) when a human supervisor is not monitoring it. On the other hand, continuous monitoring of the robot's behavior is often difficult for humans because it costs them valuable resources (e.g., time, cognitive overload, etc.). Thus, to optimize the cost of monitoring while ensuring the robots follow the {\em safe} behavior and to assist the human to deal with the possible unsafe robots, we model this problem in a game-theoretic framework of trust. In settings where the human does not initially trust the robot, pure-strategy Nash Equilibrium provides a useful policy for the human. Unfortunately, we show the formulated game often lacks a pure strategy Nash equilibrium. Thus, we define the concept of a trust boundary over the mixed strategy space of the human and show that it helps to discover optimal monitoring strategies. We conduct humans subject studies that demonstrate (1) the need for coming up with optimal monitoring strategies, and (2) the benefits of using strategies suggested by our approach.
Video Summarization via Actionness Ranking
Authors: Mohamed Elfeki, Ali Borji
Date: 2019-03-01 00:00:00
Tasks: Video Summarization

To automatically produce a brief yet expressive summary of a long video, an
automatic algorithm should start by resembling the human process of summary
generation. Prior work proposed supervised and unsupervised algorithms to train
models for learning the underlying behavior of humans by increasing modeling
complexity or craft-designing better heuristics to simulate human summary
generation process. In this work, we take a different approach by analyzing a
major cue that humans exploit for the summary generation; the nature and
intensity of actions.
  We empirically observed that a frame is more likely to be included in
human-generated summaries if it contains a substantial amount of deliberate
motion performed by an agent, which is referred to as actionness. Therefore, we
hypothesize that learning to automatically generate summaries involves an
implicit knowledge of actionness estimation and ranking. We validate our
hypothesis by running a user study that explores the correlation between
human-generated summaries and actionness ranks. We also run a consensus and
behavioral analysis between human subjects to ensure reliable and consistent
results. The analysis exhibits a considerable degree of agreement among
subjects within obtained data and verifying our initial hypothesis.
  Based on the study findings, we develop a method to incorporate actionness
data to explicitly regulate a learning algorithm that is trained for summary
generation. We assess the performance of our approach to four summarization
benchmark datasets and demonstrate an evident advantage compared to
state-of-the-art summarization methods.
GAN Based Image Deblurring Using Dark Channel Prior
Authors: Shuang Zhang, Ada Zhen, Robert L. Stevenson
Date: 2019-02-28 00:00:00
Tasks: Deblurring, Image Deblurring

A conditional general adversarial network (GAN) is proposed for image
deblurring problem. It is tailored for image deblurring instead of just
applying GAN on the deblurring problem. Motivated by that, dark channel prior
is carefully picked to be incorporated into the loss function for network
training. To make it more compatible with neuron networks, its original
indifferentiable form is discarded and L2 norm is adopted instead. On both
synthetic datasets and noisy natural images, the proposed network shows
improved deblurring performance and robustness to image noise qualitatively and
quantitatively. Additionally, compared to the existing end-to-end deblurring
networks, our network structure is light-weight, which ensures less training
and testing time.
Deep Learning How to Fit an Intravoxel Incoherent Motion Model to Diffusion-Weighted MRI
Authors: Sebastiano Barbieri, Oliver J. Gurney-Champion, Remy Klaassen, Harriet C. Thoeny
Date: 2019-02-28 00:00:00
Tasks: parameter estimation

Purpose: This prospective clinical study assesses the feasibility of training a deep neural network (DNN) for intravoxel incoherent motion (IVIM) model fitting to diffusion-weighted magnetic resonance imaging (DW-MRI) data and evaluates its performance. Methods: In May 2011, ten male volunteers (age range: 29 to 53 years, mean: 37 years) underwent DW-MRI of the upper abdomen on 1.5T and 3.0T magnetic resonance scanners. Regions of interest in the left and right liver lobe, pancreas, spleen, renal cortex, and renal medulla were delineated independently by two readers. DNNs were trained for IVIM model fitting using these data; results were compared to least-squares and Bayesian approaches to IVIM fitting. Intraclass Correlation Coefficients (ICC) were used to assess consistency of measurements between readers. Intersubject variability was evaluated using Coefficients of Variation (CV). The fitting error was calculated based on simulated data and the average fitting time of each method was recorded. Results: DNNs were trained successfully for IVIM parameter estimation. This approach was associated with high consistency between the two readers (ICCs between 50 and 97%), low intersubject variability of estimated parameter values (CVs between 9.2 and 28.4), and the lowest error when compared with least-squares and Bayesian approaches. Fitting by DNNs was several orders of magnitude quicker than the other methods but the networks may need to be re-trained for different acquisition protocols or imaged anatomical regions. Conclusion: DNNs are recommended for accurate and robust IVIM model fitting to DW-MRI data. Suitable software is available at (1).
Optimal Algorithms for Ski Rental with Soft Machine-Learned Predictions
Authors: Rohan Kodialam
Date: 2019-02-28 00:00:00
Tasks: BIG-bench Machine Learning

We consider a variant of the classic Ski Rental online algorithm with
applications to machine learning. In our variant, we allow the skier access to
a black-box machine-learning algorithm that provides an estimate of the
probability that there will be at most a threshold number of ski-days. We
derive a class of optimal randomized algorithms to determine the strategy that
minimizes the worst-case expected competitive ratio for the skier given a
prediction from the machine learning algorithm,and analyze the performance and
robustness of these algorithms.
A block-random algorithm for learning on distributed, heterogeneous data
Authors: Prakash Mohan, Marc T. Henry de Frahan, Ryan King, Ray W. Grout
Date: 2019-02-28 00:00:00

Most deep learning models are based on deep neural networks with multiple
layers between input and output. The parameters defining these layers are
initialized using random values and are "learned" from data, typically using
stochastic gradient descent based algorithms. These algorithms rely on data
being randomly shuffled before optimization. The randomization of the data
prior to processing in batches that is formally required for stochastic
gradient descent algorithm to effectively derive a useful deep learning model
is expected to be prohibitively expensive for in situ model training because of
the resulting data communications across the processor nodes. We show that the
stochastic gradient descent (SGD) algorithm can still make useful progress if
the batches are defined on a per-processor basis and processed in random order
even though (i) the batches are constructed from data samples from a single
class or specific flow region, and (ii) the overall data samples are
heterogeneous. We present block-random gradient descent, a new algorithm that
works on distributed, heterogeneous data without having to pre-shuffle. This
algorithm enables in situ learning for exascale simulations. The performance of
this algorithm is demonstrated on a set of benchmark classification models and
the construction of a subgrid scale large eddy simulations (LES) model for
turbulent channel flow using a data model similar to that which will be
encountered in exascale simulation.
Massively Multilingual Neural Machine Translation
Authors: Roee Aharoni, Melvin Johnson, Orhan Firat
Date: 2019-02-28 00:00:00
Proceeding: NAACL 2019 6
Tasks: Machine Translation, NMT, Translation

Multilingual neural machine translation (NMT) enables training a single model that supports translation from multiple source languages into multiple target languages. In this paper, we push the limits of multilingual NMT in terms of number of languages being used. We perform extensive experiments in training massively multilingual NMT models, translating up to 102 languages to and from English within a single model. We explore different setups for training such models and analyze the trade-offs between translation quality and various modeling decisions. We report results on the publicly available TED talks multilingual corpus where we show that massively multilingual many-to-many models are effective in low resource settings, outperforming the previous state-of-the-art while supporting up to 59 languages. Our experiments on a large-scale dataset with 102 languages to and from English and up to one million examples per direction also show promising results, surpassing strong bilingual baselines and encouraging future work on massively multilingual NMT.
Broad Neural Network for Change Detection in Aerial Images
Authors: Shailesh Shrivastava, Alakh Aggarwal, Pratik Chattopadhyay
Date: 2019-02-28 00:00:00
Tasks: Change Detection

A change detection system takes as input two images of a region captured at two different times, and predicts which pixels in the region have undergone change over the time period. Since pixel-based analysis can be erroneous due to noise, illumination difference and other factors, contextual information is usually used to determine the class of a pixel (changed or not). This contextual information is taken into account by considering a pixel of the difference image along with its neighborhood. With the help of ground truth information, the labeled patterns are generated. Finally, Broad Learning classifier is used to get prediction about the class of each pixel. Results show that Broad Learning can classify the data set with a significantly higher F-Score than that of Multilayer Perceptron. Performance comparison has also been made with other popular classifiers, namely Multilayer Perceptron and Random Forest.
On the Effectiveness of Low Frequency Perturbations
Authors: Yash Sharma, Gavin Weiguang Ding, Marcus Brubaker
Date: 2019-02-28 00:00:00
Tasks: Adversarial Attack, Adversarial Robustness

Carefully crafted, often imperceptible, adversarial perturbations have been shown to cause state-of-the-art models to yield extremely inaccurate outputs, rendering them unsuitable for safety-critical application domains. In addition, recent work has shown that constraining the attack space to a low frequency regime is particularly effective. Yet, it remains unclear whether this is due to generally constraining the attack search space or specifically removing high frequency components from consideration. By systematically controlling the frequency components of the perturbation, evaluating against the top-placing defense submissions in the NeurIPS 2017 competition, we empirically show that performance improvements in both the white-box and black-box transfer settings are yielded only when low frequency components are preserved. In fact, the defended models based on adversarial training are roughly as vulnerable to low frequency perturbations as undefended models, suggesting that the purported robustness of state-of-the-art ImageNet defenses is reliant upon adversarial perturbations being high frequency in nature. We do find that under $\ell_\infty$ $\epsilon=16/255$, the competition distortion bound, low frequency perturbations are indeed perceptible. This questions the use of the $\ell_\infty$-norm, in particular, as a distortion metric, and, in turn, suggests that explicitly considering the frequency space is promising for learning robust models which better align with human perception.
Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees
Authors: Binghong Chen, Bo Dai, Qinjie Lin, Guo Ye, Han Liu, Le Song
Date: 2019-02-28 00:00:00
Proceeding: ICLR 2020 1
Tasks: Vocal Bursts Intensity Prediction

We propose a meta path planning algorithm named \emph{Neural Exploration-Exploitation Trees~(NEXT)} for learning from prior experience for solving new path planning problems in high dimensional continuous state and action spaces. Compared to more classical sampling-based methods like RRT, our approach achieves much better sample efficiency in high-dimensions and can benefit from prior experience of planning in similar environments. More specifically, NEXT exploits a novel neural architecture which can learn promising search directions from problem structures. The learned prior is then integrated into a UCB-type algorithm to achieve an online balance between \emph{exploration} and \emph{exploitation} when solving a new problem. We conduct thorough experiments to show that NEXT accomplishes new planning problems with more compact search trees and significantly outperforms state-of-the-art methods on several benchmarks.
Non-Parametric Adaptation for Neural Machine Translation
Authors: Ankur Bapna, Orhan Firat
Date: 2019-02-28 00:00:00
Proceeding: NAACL 2019 6
Tasks: Domain Adaptation, Machine Translation, NMT, Retrieval, Sentence, Sentence Similarity, Translation

Neural Networks trained with gradient descent are known to be susceptible to catastrophic forgetting caused by parameter shift during the training process. In the context of Neural Machine Translation (NMT) this results in poor performance on heterogeneous datasets and on sub-tasks like rare phrase translation. On the other hand, non-parametric approaches are immune to forgetting, perfectly complementing the generalization ability of NMT. However, attempts to combine non-parametric or retrieval based approaches with NMT have only been successful on narrow domains, possibly due to over-reliance on sentence level retrieval. We propose a novel n-gram level retrieval approach that relies on local phrase level similarities, allowing us to retrieve neighbors that are useful for translation even when overall sentence similarity is low. We complement this with an expressive neural network, allowing our model to extract information from the noisy retrieved context. We evaluate our semi-parametric NMT approach on a heterogeneous dataset composed of WMT, IWSLT, JRC-Acquis and OpenSubtitles, and demonstrate gains on all 4 evaluation sets. The semi-parametric nature of our approach opens the door for non-parametric domain adaptation, demonstrating strong inference-time adaptation performance on new domains without the need for any parameter updates.
Bounds on Bayes Factors for Binomial A/B Testing
Authors: Maciej Skorski
Date: 2019-02-28 00:00:00

Bayes factors, in many cases, have been proven to bridge the classic -value
based significance testing and bayesian analysis of posterior odds. This paper
discusses this phenomena within the binomial A/B testing setup (applicable for
example to conversion testing). It is shown that the bayes factor is controlled
by the \emph{Jensen-Shannon divergence} of success ratios in two tested groups,
which can be further bounded by the Welch statistic. As a result, bayesian
sample bounds almost match frequentionist's sample bounds. The link between
Jensen-Shannon divergence and Welch's test as well as the derivation are an
elegant application of tools from information geometry.
Speeding up Deep Learning with Transient Servers
Authors: Shijian Li, Robert J. Walls, Lijie Xu, Tian Guo
Date: 2019-02-28 00:00:00
Tasks: Deep Learning, GPU

Distributed training frameworks, like TensorFlow, have been proposed as a means to reduce the training time of deep learning models by using a cluster of GPU servers. While such speedups are often desirable---e.g., for rapidly evaluating new model designs---they often come with significantly higher monetary costs due to sublinear scalability. In this paper, we investigate the feasibility of using training clusters composed of cheaper transient GPU servers to get the benefits of distributed training without the high costs. We conduct the first large-scale empirical analysis, launching more than a thousand GPU servers of various capacities, aimed at understanding the characteristics of transient GPU servers and their impact on distributed training performance. Our study demonstrates the potential of transient servers with a speedup of 7.7X with more than 62.9% monetary savings for some cluster configurations. We also identify a number of important challenges and opportunities for redesigning distributed training frameworks to be transient-aware. For example, the dynamic cost and availability characteristics of transient servers suggest the need for frameworks to dynamically change cluster configurations to best take advantage of current conditions.