pwc-archive/papers-with-abstracts huggingface dataset:
An Ontology-Based Dialogue Management System for Banking and Finance Dialogue Systems
Authors: Duygu Altinok
Date: 2018-04-13 00:00:00
Tasks: Chatbot, Dialogue Management, Management, Specificity

Keeping the dialogue state in dialogue systems is a notoriously difficult
task. We introduce an ontology-based dialogue manage(OntoDM), a dialogue
manager that keeps the state of the conversation, provides a basis for anaphora
resolution and drives the conversation via domain ontologies. The banking and
finance area promises great potential for disambiguating the context via a rich
set of products and specificity of proper nouns, named entities and verbs. We
used ontologies both as a knowledge base and a basis for the dialogue manager;
the knowledge base component and dialogue manager components coalesce in a
sense. Domain knowledge is used to track Entities of Interest, i.e. nodes
(classes) of the ontology which happen to be products and services. In this way
we also introduced conversation memory and attention in a sense. We finely
blended linguistic methods, domain-driven keyword ranking and domain ontologies
to create ways of domain-driven conversation. Proposed framework is used in our
in-house German language banking and finance chatbots. General challenges of
German language processing and finance-banking domain chatbot language models
and lexicons are also introduced. This work is still in progress, hence no
success metrics have been introduced yet.
Evaluating historical text normalization systems: How well do they generalize?
Authors: Alexander Robertson, Sharon Goldwater
Date: 2018-04-07 00:00:00
Proceeding: NAACL 2018 6
Tasks: POS, POS Tagging, Text Normalization

We highlight several issues in the evaluation of historical text
normalization systems that make it hard to tell how well these systems would
actually work in practice---i.e., for new datasets or languages; in comparison
to more na\"ive systems; or as a preprocessing step for downstream NLP tools.
We illustrate these issues and exemplify our proposed evaluation practices by
comparing two neural models against a na\"ive baseline system. We show that the
neural models generalize well to unseen words in tests on five languages;
nevertheless, they provide no clear benefit over the na\"ive baseline for
downstream POS tagging of an English historical collection. We conclude that
future work should include more rigorous evaluation, including both intrinsic
and extrinsic measures where possible.
Spline Error Weighting for Robust Visual-Inertial Fusion
Authors: Hannes Ovrén, Per-Erik Forssén
Date: 2018-04-13 00:00:00
Proceeding: CVPR 2018

In this paper we derive and test a probability-based weighting that can
balance residuals of different types in spline fitting. In contrast to previous
formulations, the proposed spline error weighting scheme also incorporates a
prediction of the approximation error of the spline fit. We demonstrate the
effectiveness of the prediction in a synthetic experiment, and apply it to
visual-inertial fusion on rolling shutter cameras. This results in a method
that can estimate 3D structure with metric scale on generic first-person
videos. We also propose a quality measure for spline fitting, that can be used
to automatically select the knot spacing. Experiments verify that the obtained
trajectory quality corresponds well with the requested quality. Finally, by
linearly scaling the weights, we show that the proposed spline error weighting
minimizes the estimation errors on real sequences, in terms of scale and
end-point errors.
Offline and Online calibration of Mobile Robot and SLAM Device for Navigation
Authors: Ryoichi Ishikawa, Takeshi Oishi, Katsushi Ikeuchi
Date: 2018-04-13 00:00:00
Tasks: Mixed Reality, Position, Robot Navigation

Robot navigation technology is required to accomplish difficult tasks in
various environments. In navigation, it is necessary to know the information of
the external environments and the state of the robot under the environment. On
the other hand, various studies have been done on SLAM technology, which is
also used for navigation, but also applied to devices for Mixed Reality and the
like.
  In this paper, we propose a robot-device calibration method for navigation
with a device using SLAM technology on a robot. The calibration is performed by
using the position and orientation information given by the robot and the
device. In the calibration, the most efficient way of movement is clarified
according to the restriction of the robot movement. Furthermore, we also show a
method to dynamically correct the position and orientation of the robot so that
the information of the external environment and the shape information of the
robot maintain consistency in order to reduce the dynamic error occurring
during navigation.
  Our method can be easily used for various kinds of robots and localization
with sufficient precision for navigation is possible with offline calibration
and online position correction. In the experiments, we confirm the parameters
obtained by two types of offline calibration according to the degree of freedom
of robot movement and validate the effectiveness of online correction method by
plotting localized position error during robot's intense movement. Finally, we
show the demonstration of navigation using SLAM device.
Mutual Suppression Network for Video Prediction using Disentangled Features
Authors: Jungbeom Lee, Jangho Lee, Sungmin Lee, Sungroh Yoon
Date: 2018-04-13 00:00:00
Tasks: Decoder, Optical Flow Estimation, Prediction, Representation Learning, Video Prediction

Video prediction has been considered a difficult problem because the video contains not only high-dimensional spatial information but also complex temporal information. Video prediction can be performed by finding features in recent frames, and using them to generate approximations to upcoming frames. We approach this problem by disentangling spatial and temporal features in videos. We introduce a mutual suppression network (MSnet) which are trained in an adversarial manner and then produces spatial features which are free of motion information, and motion features with no spatial information. MSnet then uses motion-guided connection within an encoder-decoder-based architecture to transform spatial features from a previous frame to the time of an upcoming frame. We show how MSnet can be used for video prediction using disentangled representations. We also carry out experiments to assess the effectiveness of our method to disentangle features. MSnet obtains better results than other recent video prediction methods even though it has simpler encoders.
μ-cuDNN: Accelerating Deep Learning Frameworks with Micro-Batching
Authors: Yosuke Oyama, Tal Ben-Nun, Torsten Hoefler, Satoshi Matsuoka
Date: 2018-04-13 00:00:00
Tasks: Deep Learning, GPU

NVIDIA cuDNN is a low-level library that provides GPU kernels frequently used
in deep learning. Specifically, cuDNN implements several equivalent convolution
algorithms, whose performance and memory footprint may vary considerably,
depending on the layer dimensions. When an algorithm is automatically selected
by cuDNN, the decision is performed on a per-layer basis, and thus it often
resorts to slower algorithms that fit the workspace size constraints. We
present {\mu}-cuDNN, a transparent wrapper library for cuDNN, which divides
layers' mini-batch computation into several micro-batches. Based on Dynamic
Programming and Integer Linear Programming, {\mu}-cuDNN enables faster
algorithms by decreasing the workspace requirements. At the same time,
{\mu}-cuDNN keeps the computational semantics unchanged, so that it decouples
statistical efficiency from the hardware efficiency safely. We demonstrate the
effectiveness of {\mu}-cuDNN over two frameworks, Caffe and TensorFlow,
achieving speedups of 1.63x for AlexNet and 1.21x for ResNet-18 on P100-SXM2
GPU. These results indicate that using micro-batches can seamlessly increase
the performance of deep learning, while maintaining the same memory footprint.
Learning Deep Sketch Abstraction
Authors: Umar Riaz Muhammad, Yongxin Yang, Yi-Zhe Song, Tao Xiang, Timothy M. Hospedales
Date: 2018-04-13 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Retrieval, Reinforcement Learning, Retrieval, Sketch-Based Image Retrieval, Sketch Recognition

Human free-hand sketches have been studied in various contexts including
sketch recognition, synthesis and fine-grained sketch-based image retrieval
(FG-SBIR). A fundamental challenge for sketch analysis is to deal with
drastically different human drawing styles, particularly in terms of
abstraction level. In this work, we propose the first stroke-level sketch
abstraction model based on the insight of sketch abstraction as a process of
trading off between the recognizability of a sketch and the number of strokes
used to draw it. Concretely, we train a model for abstract sketch generation
through reinforcement learning of a stroke removal policy that learns to
predict which strokes can be safely removed without affecting recognizability.
We show that our abstraction model can be used for various sketch analysis
tasks including: (1) modeling stroke saliency and understanding the decision of
sketch recognition models, (2) synthesizing sketches of variable abstraction
for a given category, or reference object instance in a photo, and (3) training
a FG-SBIR model with photos only, bypassing the expensive photo-sketch pair
collection step.
Precise Temporal Action Localization by Evolving Temporal Proposals
Authors: Haonan Qiu, Yingbin Zheng, Hao Ye, Yao Lu, Feng Wang, Liang He
Date: 2018-04-13 00:00:00
Tasks: Action Localization, Temporal Action Localization

Locating actions in long untrimmed videos has been a challenging problem in
video content analysis. The performances of existing action localization
approaches remain unsatisfactory in precisely determining the beginning and the
end of an action. Imitating the human perception procedure with observations
and refinements, we propose a novel three-phase action localization framework.
Our framework is embedded with an Actionness Network to generate initial
proposals through frame-wise similarity grouping, and then a Refinement Network
to conduct boundary adjustment on these proposals. Finally, the refined
proposals are sent to a Localization Network for further fine-grained location
regression. The whole process can be deemed as multi-stage refinement using a
novel non-local pyramid feature under various temporal granularities. We
evaluate our framework on THUMOS14 benchmark and obtain a significant
improvement over the state-of-the-arts approaches. Specifically, the
performance gain is remarkable under precise localization with high IoU
thresholds. Our proposed framework achieves mAP@IoU=0.5 of 34.2%.
Active Learning for Efficient Testing of Student Programs
Authors: Ishan Rastogi, Aditya Kanade, Shirish Shevade
Date: 2018-04-13 00:00:00
Tasks: Active Learning

In this work, we propose an automated method to identify semantic bugs in
student programs, called ATAS, which builds upon the recent advances in both
symbolic execution and active learning. Symbolic execution is a program
analysis technique which can generate test cases through symbolic constraint
solving. Our method makes use of a reference implementation of the task as its
sole input. We compare our method with a symbolic execution-based baseline on 6
programming tasks retrieved from CodeForces comprising a total of 23K student
submissions. We show an average improvement of over 2.5x over the baseline in
terms of runtime (thus making it more suitable for online evaluation), without
a significant degradation in evaluation accuracy.
Non-local Neural Networks
Authors: Xiaolong Wang, Ross Girshick, Abhinav Gupta, Kaiming He
Date: 2017-11-21 00:00:00
Proceeding: CVPR 2018 6
Tasks: Action Classification, Action Recognition, Instance Segmentation, Keypoint Detection, Object Detection, Pose Estimation, Position, Text-To-SQL, Video Classification

Both convolutional and recurrent operations are building blocks that process
one local neighborhood at a time. In this paper, we present non-local
operations as a generic family of building blocks for capturing long-range
dependencies. Inspired by the classical non-local means method in computer
vision, our non-local operation computes the response at a position as a
weighted sum of the features at all positions. This building block can be
plugged into many computer vision architectures. On the task of video
classification, even without any bells and whistles, our non-local models can
compete or outperform current competition winners on both Kinetics and Charades
datasets. In static image recognition, our non-local models improve object
detection/segmentation and pose estimation on the COCO suite of tasks. Code is
available at https://github.com/facebookresearch/video-nonlocal-net .
Deep learning for universal linear embeddings of nonlinear dynamics
Authors: Bethany Lusch, J. Nathan Kutz, Steven L. Brunton
Date: 2017-12-27 00:00:00
Tasks: Deep Learning

Identifying coordinate transformations that make strongly nonlinear dynamics
approximately linear is a central challenge in modern dynamical systems. These
transformations have the potential to enable prediction, estimation, and
control of nonlinear systems using standard linear theory. The Koopman operator
has emerged as a leading data-driven embedding, as eigenfunctions of this
operator provide intrinsic coordinates that globally linearize the dynamics.
However, identifying and representing these eigenfunctions has proven to be
mathematically and computationally challenging. This work leverages the power
of deep learning to discover representations of Koopman eigenfunctions from
trajectory data of dynamical systems. Our network is parsimonious and
interpretable by construction, embedding the dynamics on a low-dimensional
manifold that is of the intrinsic rank of the dynamics and parameterized by the
Koopman eigenfunctions. In particular, we identify nonlinear coordinates on
which the dynamics are globally linear using a modified auto-encoder. We also
generalize Koopman representations to include a ubiquitous class of systems
that exhibit continuous spectra, ranging from the simple pendulum to nonlinear
optics and broadband turbulence. Our framework parametrizes the continuous
frequency using an auxiliary network, enabling a compact and efficient
embedding at the intrinsic rank, while connecting our models to half a century
of asymptotics. In this way, we benefit from the power and generality of deep
learning, while retaining the physical interpretability of Koopman embeddings.
Fast, Parameter free Outlier Identification for Robust PCA
Authors: Vishnu Menon, Sheetal Kalyani
Date: 2018-04-13 00:00:00

Robust PCA, the problem of PCA in the presence of outliers has been
extensively investigated in the last few years. Here we focus on Robust PCA in
the column sparse outlier model. The existing methods for column sparse outlier
model assumes either the knowledge of the dimension of the lower dimensional
subspace or the fraction of outliers in the system. However in many
applications knowledge of these parameters is not available. Motivated by this
we propose a parameter free outlier identification method for robust PCA which
a) does not require the knowledge of outlier fraction, b) does not require the
knowledge of the dimension of the underlying subspace, c) is computationally
simple and fast. Further, analytical guarantees are derived for outlier
identification and the performance of the algorithm is compared with the
existing state of the art methods.
Successful Nash Equilibrium Agent for a 3-Player Imperfect-Information Game
Authors: Sam Ganzfried, Austin Nowak, Joannier Pinales
Date: 2018-04-13 00:00:00

Creating strong agents for games with more than two players is a major open
problem in AI. Common approaches are based on approximating game-theoretic
solution concepts such as Nash equilibrium, which have strong theoretical
guarantees in two-player zero-sum games, but no guarantees in non-zero-sum
games or in games with more than two players. We describe an agent that is able
to defeat a variety of realistic opponents using an exact Nash equilibrium
strategy in a 3-player imperfect-information game. This shows that, despite a
lack of theoretical guarantees, agents based on Nash equilibrium strategies can
be successful in multiplayer games after all.
DAiSEE: Towards User Engagement Recognition in the Wild
Authors: Abhay Gupta, Arjun D'Cunha, Kamal Awasthi, Vineeth Balasubramanian
Date: 2016-09-07 00:00:00
Tasks: General Classification, Video Classification

We introduce DAiSEE, the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration in the wild. The dataset has four levels of labels namely - very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. We have also established benchmark results on this dataset using state-of-the-art video classification methods that are available today. We believe that DAiSEE will provide the research community with challenges in feature extraction, context-based inference, and development of suitable machine learning methods for related tasks, thus providing a springboard for further research. The dataset is available for download at https://people.iith.ac.in/vineethnb/resources/daisee/index.html.
Deep Motion Boundary Detection
Authors: Xiaoqing Yin, Xiyang Dai, Xinchao Wang, Maojun Zhang, DaCheng Tao, Larry Davis
Date: 2018-04-13 00:00:00
Tasks: Boundary Detection, Optical Flow Estimation

Motion boundary detection is a crucial yet challenging problem. Prior methods
focus on analyzing the gradients and distributions of optical flow fields, or
use hand-crafted features for motion boundary learning. In this paper, we
propose the first dedicated end-to-end deep learning approach for motion
boundary detection, which we term as MoBoNet. We introduce a refinement network
structure which takes source input images, initial forward and backward optical
flows as well as corresponding warping errors as inputs and produces
high-resolution motion boundaries. Furthermore, we show that the obtained
motion boundaries, through a fusion sub-network we design, can in turn guide
the optical flows for removing the artifacts. The proposed MoBoNet is generic
and works with any optical flows. Our motion boundary detection and the refined
optical flow estimation achieve results superior to the state of the art.
FishEyeRecNet: A Multi-Context Collaborative Deep Network for Fisheye Image Rectification
Authors: Xiaoqing Yin, Xinchao Wang, Jun Yu, Maojun Zhang, Pascal Fua, DaCheng Tao
Date: 2018-04-13 00:00:00
Proceeding: ECCV 2018 9

Images captured by fisheye lenses violate the pinhole camera assumption and
suffer from distortions. Rectification of fisheye images is therefore a crucial
preprocessing step for many computer vision applications. In this paper, we
propose an end-to-end multi-context collaborative deep network for removing
distortions from single fisheye images. In contrast to conventional approaches,
which focus on extracting hand-crafted features from input images, our method
learns high-level semantics and low-level appearance features simultaneously to
estimate the distortion parameters. To facilitate training, we construct a
synthesized dataset that covers various scenes and distortion parameter
settings. Experiments on both synthesized and real-world datasets show that the
proposed model significantly outperforms current state of the art methods. Our
code and synthesized dataset will be made publicly available.
A Grid Based Adversarial Clustering Algorithm
Authors: Wutao Wei, Nikhil Gupta, Bowei Xi
Date: 2018-04-13 00:00:00
Tasks: Clustering

Nowadays more and more data are gathered for detecting and preventing cyber attacks. In cyber security applications, data analytics techniques have to deal with active adversaries that try to deceive the data analytics models and avoid being detected. The existence of such adversarial behavior motivates the development of robust and resilient adversarial learning techniques for various tasks. Most of the previous work focused on adversarial classification techniques, which assumed the existence of a reasonably large amount of carefully labeled data instances. However, in practice, labeling the data instances often requires costly and time-consuming human expertise and becomes a significant bottleneck. Meanwhile, a large number of unlabeled instances can also be used to understand the adversaries' behavior. To address the above mentioned challenges, in this paper, we develop a novel grid based adversarial clustering algorithm. Our adversarial clustering algorithm is able to identify the core normal regions, and to draw defensive walls around the centers of the normal objects utilizing game theoretic ideas. Our algorithm also identifies sub-clusters of attack objects, the overlapping areas within clusters, and outliers which may be potential anomalies.
A Hybrid Model for Identity Obfuscation by Face Replacement
Authors: Qianru Sun, Ayush Tewari, Weipeng Xu, Mario Fritz, Christian Theobalt, Bernt Schiele
Date: 2018-04-13 00:00:00
Proceeding: ECCV 2018 9
Tasks: Face Generation, Image Generation

As more and more personal photos are shared and tagged in social media,
avoiding privacy risks such as unintended recognition becomes increasingly
challenging. We propose a new hybrid approach to obfuscate identities in photos
by head replacement. Our approach combines state of the art parametric face
synthesis with latest advances in Generative Adversarial Networks (GAN) for
data-driven image synthesis. On the one hand, the parametric part of our method
gives us control over the facial parameters and allows for explicit
manipulation of the identity. On the other hand, the data-driven aspects allow
for adding fine details and overall realism as well as seamless blending into
the scene context. In our experiments, we show highly realistic output of our
system that improves over the previous state of the art in obfuscation rate
while preserving a higher similarity to the original image content.
Clustering via Boundary Erosion
Authors: Cheng-Hao Deng, Wan-Lei Zhao
Date: 2018-04-12 00:00:00
Tasks: Clustering

Clustering analysis identifies samples as groups based on either their mutual
closeness or homogeneity. In order to detect clusters in arbitrary shapes, a
novel and generic solution based on boundary erosion is proposed. The clusters
are assumed to be separated by relatively sparse regions. The samples are
eroded sequentially according to their dynamic boundary densities. The erosion
starts from low density regions, invading inwards, until all the samples are
eroded out. By this manner, boundaries between different clusters become more
and more apparent. It therefore offers a natural and powerful way to separate
the clusters when the boundaries between them are hard to be drawn at once.
With the sequential order of being eroded, the sequential boundary levels are
produced, following which the clusters in arbitrary shapes are automatically
reconstructed. As demonstrated across various clustering tasks, it is able to
outperform most of the state-of-the-art algorithms and its performance is
nearly perfect in some scenarios.