pwc-archive/papers-with-abstracts huggingface dataset:
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Authors: Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel R. Bowman
Date: 2018-04-20 00:00:00
Proceeding: WS 2018 11
Tasks: Diagnostic, Natural Language Inference, Natural Language Understanding, QQP, Transfer Learning

For natural language understanding (NLU) technology to be maximally useful,
both practically and as a scientific object of study, it must be general: it
must be able to process language in a way that is not exclusively tailored to
any one specific task or dataset. In pursuit of this objective, we introduce
the General Language Understanding Evaluation benchmark (GLUE), a tool for
evaluating and analyzing the performance of models across a diverse range of
existing NLU tasks. GLUE is model-agnostic, but it incentivizes sharing
knowledge across tasks because certain tasks have very limited training data.
We further provide a hand-crafted diagnostic test suite that enables detailed
linguistic analysis of NLU models. We evaluate baselines based on current
methods for multi-task and transfer learning and find that they do not
immediately give substantial improvements over the aggregate performance of
training a separate model per task, indicating room for improvement in
developing general and robust NLU systems.
Mapping Images to Psychological Similarity Spaces Using Neural Networks
Authors: Lucas Bechberger, Elektra Kypridemou
Date: 2018-04-20 00:00:00
Tasks: BIG-bench Machine Learning

The cognitive framework of conceptual spaces bridges the gap between symbolic and subsymbolic AI by proposing an intermediate conceptual layer where knowledge is represented geometrically. There are two main approaches for obtaining the dimensions of this conceptual similarity space: using similarity ratings from psychological experiments and using machine learning techniques. In this paper, we propose a combination of both approaches by using psychologically derived similarity ratings to constrain the machine learning process. This way, a mapping from stimuli to conceptual spaces can be learned that is both supported by psychological data and allows generalization to unseen stimuli. The results of a first feasibility study support our proposed approach.
Generating a Fusion Image: One's Identity and Another's Shape
Authors: Donggyu Joo, Do-Yeon Kim, Junmo Kim
Date: 2018-04-20 00:00:00
Proceeding: CVPR 2018 6

Generating a novel image by manipulating two input images is an interesting research problem in the study of generative adversarial networks (GANs). We propose a new GAN-based network that generates a fusion image with the identity of input image x and the shape of input image y. Our network can simultaneously train on more than two image datasets in an unsupervised manner. We define an identity loss LI to catch the identity of image x and a shape loss LS to get the shape of y. In addition, we propose a novel training method called Min-Patch training to focus the generator on crucial parts of an image, rather than its entirety. We show qualitative results on the VGG Youtube Pose dataset, Eye dataset (MPIIGaze and UnityEyes), and the Photo-Sketch-Cartoon dataset.
View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition
Authors: Pengfei Zhang, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Jianru Xue, Nanning Zheng
Date: 2018-04-20 00:00:00
Tasks: Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

Skeleton-based human action recognition has recently attracted increasing attention thanks to the accessibility and the popularity of 3D skeleton data. One of the key challenges in skeleton-based action recognition lies in the large view variations when capturing data. In order to alleviate the effects of view variations, this paper introduces a novel view adaptation scheme, which automatically determines the virtual observation viewpoints in a learning based data driven manner. We design two view adaptive neural networks, i.e., VA-RNN based on RNN, and VA-CNN based on CNN. For each network, a novel view adaptation module learns and determines the most suitable observation viewpoints, and transforms the skeletons to those viewpoints for the end-to-end recognition with a main classification network. Ablation studies find that the proposed view adaptive models are capable of transforming the skeletons of various viewpoints to much more consistent virtual viewpoints which largely eliminates the viewpoint influence. In addition, we design a two-stream scheme (referred to as VA-fusion) that fuses the scores of the two networks to provide the fused prediction. Extensive experimental evaluations on five challenging benchmarks demonstrate that the effectiveness of the proposed view-adaptive networks and superior performance over state-of-the-art approaches. The source code is available at https://github.com/microsoft/View-Adaptive-Neural-Networks-for-Skeleton-based-Human-Action-Recognition.
Learning More Robust Features with Adversarial Training
Authors: Shuangtao Li, Yuanke Chen, Yanlin Peng, Lin Bai
Date: 2018-04-20 00:00:00

In recent years, it has been found that neural networks can be easily fooled
by adversarial examples, which is a potential safety hazard in some
safety-critical applications. Many researchers have proposed various method to
make neural networks more robust to white-box adversarial attacks, but an
effective method have not been found so far. In this short paper, we focus on
the robustness of the features learned by neural networks. We show that the
features learned by neural networks are not robust, and find that the
robustness of the learned features is closely related to the resistance against
adversarial examples of neural networks. We also find that adversarial training
against fast gradients sign method (FGSM) does not make the leaned features
very robust, even if it can make the trained networks very resistant to FGSM
attack. Then we propose a method, which can be seen as an extension of
adversarial training, to train neural networks to learn more robust features.
We perform experiments on MNIST and CIFAR-10 to evaluate our method, and the
experiment results show that this method greatly improves the robustness of the
learned features and the resistance to adversarial attacks.
Sequence Modeling via Segmentations
Authors: Chong Wang, Yining Wang, Po-Sen Huang, Abdel-rahman Mohamed, Dengyong Zhou, Li Deng
Date: 2017-02-24 00:00:00
Proceeding: ICML 2017 8
Tasks: Segmentation, speech-recognition, Speech Recognition, Text Segmentation, valid

Segmental structure is a common pattern in many types of sequences such as
phrases in human languages. In this paper, we present a probabilistic model for
sequences via their segmentations. The probability of a segmented sequence is
calculated as the product of the probabilities of all its segments, where each
segment is modeled using existing tools such as recurrent neural networks.
Since the segmentation of a sequence is usually unknown in advance, we sum over
all valid segmentations to obtain the final probability for the sequence. An
efficient dynamic programming algorithm is developed for forward and backward
computations without resorting to any approximation. We demonstrate our
approach on text segmentation and speech recognition tasks. In addition to
quantitative results, we also show that our approach can discover meaningful
segments in their respective application contexts.
Notes on computational-to-statistical gaps: predictions using statistical physics
Authors: Afonso S. Bandeira, Amelia Perry, Alexander S. Wein
Date: 2018-03-29 00:00:00

In these notes we describe heuristics to predict computational-to-statistical
gaps in certain statistical problems. These are regimes in which the underlying
statistical problem is information-theoretically possible although no efficient
algorithm exists, rendering the problem essentially unsolvable for large
instances. The methods we describe here are based on mature, albeit
non-rigorous, tools from statistical physics.
  These notes are based on a lecture series given by the authors at the Courant
Institute of Mathematical Sciences in New York City, on May 16th, 2017.
A-CCNN: adaptive ccnn for density estimation and crowd counting
Authors: Saeed Amirgholipour Kasmani, Xiangjian He, Wenjing Jia, Dadong Wang, Michelle Zeibots
Date: 2018-04-19 00:00:00
Tasks: Crowd Counting, Density Estimation, Object Counting

Crowd counting, for estimating the number of people in a crowd using
vision-based computer techniques, has attracted much interest in the research
community. Although many attempts have been reported, real-world problems, such
as huge variation in subjects' sizes in images and serious occlusion among
people, make it still a challenging problem. In this paper, we propose an
Adaptive Counting Convolutional Neural Network (A-CCNN) and consider the scale
variation of objects in a frame adaptively so as to improve the accuracy of
counting. Our method takes advantages of contextual information to provide more
accurate and adaptive density maps and crowd counting in a scene. Extensively
experimental evaluation is conducted using different benchmark datasets for
object-counting and shows that the proposed approach is effective and
outperforms state-of-the-art approaches.
Sentence Simplification with Memory-Augmented Neural Networks
Authors: Tu Vu, Baotian Hu, Tsendsuren Munkhdalai, Hong Yu
Date: 2018-04-20 00:00:00
Proceeding: NAACL 2018 6
Tasks: Machine Translation, Sentence, Text Simplification, Translation

Sentence simplification aims to simplify the content and structure of complex
sentences, and thus make them easier to interpret for human readers, and easier
to process for downstream NLP applications. Recent advances in neural machine
translation have paved the way for novel approaches to the task. In this paper,
we adapt an architecture with augmented memory capacities called Neural
Semantic Encoders (Munkhdalai and Yu, 2017) for sentence simplification. Our
experiments demonstrate the effectiveness of our approach on different
simplification datasets, both in terms of automatic evaluation measures and
human judgments.
Calibration-free B0 correction of EPI data using structured low rank matrix recovery
Authors: Arvind Balachandrasekaran, Merry Mani, Mathews Jacob
Date: 2018-04-20 00:00:00
Tasks: Time Series, Time Series Analysis

We introduce a structured low rank algorithm for the calibration-free
compensation of field inhomogeneity artifacts in Echo Planar Imaging (EPI) MRI
data. We acquire the data using two EPI readouts that differ in echo-time (TE).
Using time segmentation, we reformulate the field inhomogeneity compensation
problem as the recovery of an image time series from highly undersampled
Fourier measurements. The temporal profile at each pixel is modeled as a single
exponential, which is exploited to fill in the missing entries. We show that
the exponential behavior at each pixel, along with the spatial smoothness of
the exponential parameters, can be exploited to derive a 3D annihilation
relation in the Fourier domain. This relation translates to a low rank property
on a structured multi-fold Toeplitz matrix, whose entries correspond to the
measured k-space samples. We introduce a fast two-step algorithm for the
completion of the Toeplitz matrix from the available samples. In the first
step, we estimate the null space vectors of the Toeplitz matrix using only its
fully sampled rows. The null space is then used to estimate the signal
subspace, which facilitates the efficient recovery of the time series of
images. We finally demonstrate the proposed approach on spherical MR phantom
data and human data and show that the artifacts are significantly reduced. The
proposed approach could potentially be used to compensate for time varying
field map variations in dynamic applications such as functional MRI.
High Dynamic Range SLAM with Map-Aware Exposure Time Control
Authors: Sergey V. Alexandrov, Johann Prankl, Michael Zillich, Markus Vincze
Date: 2018-04-20 00:00:00
Tasks: Vocal Bursts Intensity Prediction

The research in dense online 3D mapping is mostly focused on the geometrical
accuracy and spatial extent of the reconstructions. Their color appearance is
often neglected, leading to inconsistent colors and noticeable artifacts. We
rectify this by extending a state-of-the-art SLAM system to accumulate colors
in HDR space. We replace the simplistic pixel intensity averaging scheme with
HDR color fusion rules tailored to the incremental nature of SLAM and a noise
model suitable for off-the-shelf RGB-D cameras. Our main contribution is a
map-aware exposure time controller. It makes decisions based on the global
state of the map and predicted camera motion, attempting to maximize the
information gain of each observation. We report a set of experiments
demonstrating the improved texture quality and advantages of using the custom
controller that is tightly integrated in the mapping loop.
Rollable Latent Space for Azimuth Invariant SAR Target Recognition
Authors: Kazutoshi Sagi, Takahiro Toizumi, Yuzo Senda
Date: 2018-02-06 00:00:00
Tasks: Data Augmentation

This paper proposes rollable latent space (RLS) for an azimuth invariant
synthetic aperture radar (SAR) target recognition. Scarce labeled data and
limited viewing direction are critical issues in SAR target recognition.The RLS
is a designed space in which rolling of latent features corresponds to 3D
rotation of an object. Thus latent features of an arbitrary view can be
inferred using those of different views. This characteristic further enables us
to augment data from limited viewing in RLS. RLS-based classifiers with and
without data augmentation and a conventional classifier trained with target
front shots are evaluated over untrained target back shots. Results show that
the RLS-based classifier with augmentation improves an accuracy by 30% compared
to the conventional classifier.
On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach
Authors: Nikolai Smolyanskiy, Alexey Kamenev, Stan Birchfield
Date: 2018-03-26 00:00:00
Tasks: Autonomous Vehicles, Depth Estimation, GPU, Stereo Depth Estimation

We revisit the problem of visual depth estimation in the context of autonomous vehicles. Despite the progress on monocular depth estimation in recent years, we show that the gap between monocular and stereo depth accuracy remains large$-$a particularly relevant result due to the prevalent reliance upon monocular cameras by vehicles that are expected to be self-driving. We argue that the challenges of removing this gap are significant, owing to fundamental limitations of monocular vision. As a result, we focus our efforts on depth estimation by stereo. We propose a novel semi-supervised learning approach to training a deep stereo neural network, along with a novel architecture containing a machine-learned argmax layer and a custom runtime (that will be shared publicly) that enables a smaller version of our stereo DNN to run on an embedded GPU. Competitive results are shown on the KITTI 2015 stereo dataset. We also evaluate the recent progress of stereo algorithms by measuring the impact upon accuracy of various design criteria.
Communication Compression for Decentralized Training
Authors: Hanlin Tang, Shaoduo Gan, Ce Zhang, Tong Zhang, Ji Liu
Date: 2018-03-17 00:00:00
Proceeding: NeurIPS 2018 12

Optimizing distributed learning systems is an art of balancing between
computation and communication. There have been two lines of research that try
to deal with slower networks: {\em communication compression} for low bandwidth
networks, and {\em decentralization} for high latency networks. In this paper,
We explore a natural question: {\em can the combination of both techniques lead
to a system that is robust to both bandwidth and latency?}
  Although the system implication of such combination is trivial, the
underlying theoretical principle and algorithm design is challenging: unlike
centralized algorithms, simply compressing exchanged information, even in an
unbiased stochastic way, within the decentralized network would accumulate the
error and fail to converge. In this paper, we develop a framework of
compressed, decentralized training and propose two different strategies, which
we call {\em extrapolation compression} and {\em difference compression}. We
analyze both algorithms and prove both converge at the rate of $O(1/\sqrt{nT})$
where $n$ is the number of workers and $T$ is the number of iterations,
matching the convergence rate for full precision, centralized training. We
validate our algorithms and find that our proposed algorithm outperforms the
best of merely decentralized and merely quantized algorithm significantly for
networks with {\em both} high latency and low bandwidth.
D$^2$: Decentralized Training over Decentralized Data
Authors: Hanlin Tang, Xiangru Lian, Ming Yan, Ce Zhang, Ji Liu
Date: 2018-03-19 00:00:00
Tasks: image-classification, Image Classification

While training a machine learning model using multiple workers, each of which
collects data from their own data sources, it would be most useful when the
data collected from different workers can be {\em unique} and {\em different}.
Ironically, recent analysis of decentralized parallel stochastic gradient
descent (D-PSGD) relies on the assumption that the data hosted on different
workers are {\em not too different}. In this paper, we ask the question: {\em
Can we design a decentralized parallel stochastic gradient descent algorithm
that is less sensitive to the data variance across workers?} In this paper, we
present D$^2$, a novel decentralized parallel stochastic gradient descent
algorithm designed for large data variance \xr{among workers} (imprecisely,
"decentralized" data). The core of D$^2$ is a variance blackuction extension of
the standard D-PSGD algorithm, which improves the convergence rate from
$O\left({\sigma \over \sqrt{nT}} + {(n\zeta^2)^{\frac{1}{3}} \over
T^{2/3}}\right)$ to $O\left({\sigma \over \sqrt{nT}}\right)$ where $\zeta^{2}$
denotes the variance among data on different workers. As a result, D$^2$ is
robust to data variance among workers. We empirically evaluated D$^2$ on image
classification tasks where each worker has access to only the data of a limited
set of labels, and find that D$^2$ significantly outperforms D-PSGD.
ECG Heartbeat Classification: A Deep Transferable Representation
Authors: Mohammad Kachuee, Shayan Fazeli, Majid Sarrafzadeh
Date: 2018-04-19 00:00:00
Tasks: Arrhythmia Detection, Electrocardiography (ECG), General Classification, Heartbeat Classification, Myocardial infarction detection, Visual Question Answering (VQA)

Electrocardiogram (ECG) can be reliably used as a measure to monitor the
functionality of the cardiovascular system. Recently, there has been a great
attention towards accurate categorization of heartbeats. While there are many
commonalities between different ECG conditions, the focus of most studies has
been classifying a set of conditions on a dataset annotated for that task
rather than learning and employing a transferable knowledge between different
tasks. In this paper, we propose a method based on deep convolutional neural
networks for the classification of heartbeats which is able to accurately
classify five different arrhythmias in accordance with the AAMI EC57 standard.
Furthermore, we suggest a method for transferring the knowledge acquired on
this task to the myocardial infarction (MI) classification task. We evaluated
the proposed method on PhysionNet's MIT-BIH and PTB Diagnostics datasets.
According to the results, the suggested method is able to make predictions with
the average accuracies of 93.4% and 95.9% on arrhythmia classification and MI
classification, respectively.
No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs
Authors: Akshay Rangesh, Mohan M. Trivedi
Date: 2018-02-23 00:00:00
Tasks: Autonomous Vehicles, Multi-Object Tracking, Object, Object Tracking, Online Multi-Object Tracking, Spatial Reasoning

Online multi-object tracking (MOT) is extremely important for high-level
spatial reasoning and path planning for autonomous and highly-automated
vehicles. In this paper, we present a modular framework for tracking multiple
objects (vehicles), capable of accepting object proposals from different sensor
modalities (vision and range) and a variable number of sensors, to produce
continuous object tracks. This work is a generalization of the MDP framework
for MOT, with some key extensions - First, we track objects across multiple
cameras and across different sensor modalities. This is done by fusing object
proposals across sensors accurately and efficiently. Second, the objects of
interest (targets) are tracked directly in the real world. This is a departure
from traditional techniques where objects are simply tracked in the image
plane. Doing so allows the tracks to be readily used by an autonomous agent for
navigation and related tasks.
  To verify the effectiveness of our approach, we test it on real world highway
data collected from a heavily sensorized testbed capable of capturing
full-surround information. We demonstrate that our framework is well-suited to
track objects through entire maneuvers around the ego-vehicle, some of which
take more than a few minutes to complete. We also leverage the modularity of
our approach by comparing the effects of including/excluding different sensors,
changing the total number of sensors, and the quality of object proposals on
the final tracking result.
GritNet: Student Performance Prediction with Deep Learning
Authors: Byung-Hak Kim, Ethan Vizitei, Varun Ganapathi
Date: 2018-04-19 00:00:00
Tasks: Deep Learning, Prediction

Student performance prediction - where a machine forecasts the future
performance of students as they interact with online coursework - is a
challenging problem. Reliable early-stage predictions of a student's future
performance could be critical to facilitate timely educational interventions
during a course. However, very few prior studies have explored this problem
from a deep learning perspective. In this paper, we recast the student
performance prediction problem as a sequential event prediction problem and
propose a new deep learning based algorithm, termed GritNet, which builds upon
the bidirectional long short term memory (BLSTM). Our results, from real
Udacity students' graduation predictions, show that the GritNet not only
consistently outperforms the standard logistic-regression based method, but
that improvements are substantially pronounced in the first few weeks when
accurate predictions are most challenging.
Preference-Guided Planning: An Active Elicitation Approach
Authors: Mayukh Das, Phillip Odom, Md. Rakibul Islam, Janardhan Rao, Doppa, Dan Roth, Sriraam Natarajan
Date: 2018-04-19 00:00:00

Planning with preferences has been employed extensively to quickly generate
high-quality plans. However, it may be difficult for the human expert to supply
this information without knowledge of the reasoning employed by the planner and
the distribution of planning problems. We consider the problem of actively
eliciting preferences from a human expert during the planning process.
Specifically, we study this problem in the context of the Hierarchical Task
Network (HTN) planning framework as it allows easy interaction with the human.
Our experimental results on several diverse planning domains show that the
preferences gathered using the proposed approach improve the quality and speed
of the planner, while reducing the burden on the human expert.