pwc-archive/papers-with-abstracts huggingface dataset:
Automatic Anomaly Detection in the Cloud Via Statistical Learning
Authors: Jordan Hochenbaum, Owen S. Vallis, Arun Kejariwal
Date: 2017-04-24 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis

Performance and high availability have become increasingly important drivers,
amongst other drivers, for user retention in the context of web services such
as social networks, and web search. Exogenic and/or endogenic factors often
give rise to anomalies, making it very challenging to maintain high
availability, while also delivering high performance. Given that
service-oriented architectures (SOA) typically have a large number of services,
with each service having a large set of metrics, automatic detection of
anomalies is non-trivial.
  Although there exists a large body of prior research in anomaly detection,
existing techniques are not applicable in the context of social network data,
owing to the inherent seasonal and trend components in the time series data.
  To this end, we developed two novel statistical techniques for automatically
detecting anomalies in cloud infrastructure data. Specifically, the techniques
employ statistical learning to detect anomalies in both application, and system
metrics. Seasonal decomposition is employed to filter the trend and seasonal
components of the time series, followed by the use of robust statistical
metrics -- median and median absolute deviation (MAD) -- to accurately detect
anomalies, even in the presence of seasonal spikes.
  We demonstrate the efficacy of the proposed techniques from three different
perspectives, viz., capacity planning, user behavior, and supervised learning.
In particular, we used production data for evaluation, and we report Precision,
Recall, and F-measure in each case.
Fast and Accurate Neural Word Segmentation for Chinese
Authors: Deng Cai, Hai Zhao, Zhisong Zhang, Yuan Xin, Yongjian Wu, Feiyue Huang
Date: 2017-04-24 00:00:00
Proceeding: ACL 2017 7
Tasks: Chinese Word Segmentation, Feature Engineering, Segmentation

Neural models with minimal feature engineering have achieved competitive
performance against traditional methods for the task of Chinese word
segmentation. However, both training and working procedures of the current
neural models are computationally inefficient. This paper presents a greedy
neural word segmenter with balanced word and character embedding inputs to
alleviate the existing drawbacks. Our segmenter is truly end-to-end, capable of
performing segmentation much faster and even more accurate than
state-of-the-art neural models on Chinese benchmark datasets.
Model-based Iterative Restoration for Binary Document Image Compression with Dictionary Learning
Authors: Yandong Guo, Cheng Lu, Jan P. Allebach, Charles A. Bouman
Date: 2017-04-24 00:00:00
Proceeding: CVPR 2017 7
Tasks: Dictionary Learning, Image Compression

The inherent noise in the observed (e.g., scanned) binary document image
degrades the image quality and harms the compression ratio through breaking the
pattern repentance and adding entropy to the document images. In this paper, we
design a cost function in Bayesian framework with dictionary learning.
Minimizing our cost function produces a restored image which has better quality
than that of the observed noisy image, and a dictionary for representing and
encoding the image. After the restoration, we use this dictionary (from the
same cost function) to encode the restored image following the
symbol-dictionary framework by JBIG2 standard with the lossless mode.
Experimental results with a variety of document images demonstrate that our
method improves the image quality compared with the observed image, and
simultaneously improves the compression ratio. For the test images with
synthetic noise, our method reduces the number of flipped pixels by 48.2% and
improves the compression ratio by 36.36% as compared with the best encoding
methods. For the test images with real noise, our method visually improves the
image quality, and outperforms the cutting-edge method by 28.27% in terms of
the compression ratio.
A Light CNN for Deep Face Representation with Noisy Labels
Authors: Xiang Wu, Ran He, Zhenan Sun, Tieniu Tan
Date: 2015-11-09 00:00:00
Tasks: Face Identification, Face Recognition, Face Verification, feature selection, Image Editing Dection

The volume of convolutional neural network (CNN) models proposed for face
recognition has been continuously growing larger to better fit large amount of
training data. When training data are obtained from internet, the labels are
likely to be ambiguous and inaccurate. This paper presents a Light CNN
framework to learn a compact embedding on the large-scale face data with
massive noisy labels. First, we introduce a variation of maxout activation,
called Max-Feature-Map (MFM), into each convolutional layer of CNN. Different
from maxout activation that uses many feature maps to linearly approximate an
arbitrary convex activation function, MFM does so via a competitive
relationship. MFM can not only separate noisy and informative signals but also
play the role of feature selection between two feature maps. Second, three
networks are carefully designed to obtain better performance meanwhile reducing
the number of parameters and computational costs. Lastly, a semantic
bootstrapping method is proposed to make the prediction of the networks more
consistent with noisy labels. Experimental results show that the proposed
framework can utilize large-scale noisy data to learn a Light model that is
efficient in computational costs and storage spaces. The learned single network
with a 256-D representation achieves state-of-the-art results on various face
benchmarks without fine-tuning. The code is released on
https://github.com/AlfredXiangWu/LightCNN.
Data-adaptive statistics for multiple hypothesis testing in high-dimensional settings
Authors: Weixin Cai, Nima S. Hejazi, Alan E. Hubbard
Date: 2017-04-24 00:00:00
Tasks: Astronomy, Marketing, Two-sample testing, valid, Vocal Bursts Intensity Prediction

Current statistical inference problems in areas like astronomy, genomics, and
marketing routinely involve the simultaneous testing of thousands -- even
millions -- of null hypotheses. For high-dimensional multivariate
distributions, these hypotheses may concern a wide range of parameters, with
complex and unknown dependence structures among variables. In analyzing such
hypothesis testing procedures, gains in efficiency and power can be achieved by
performing variable reduction on the set of hypotheses prior to testing. We
present in this paper an approach using data-adaptive multiple testing that
serves exactly this purpose. This approach applies data mining techniques to
screen the full set of covariates on equally sized partitions of the whole
sample via cross-validation. This generalized screening procedure is used to
create average ranks for covariates, which are then used to generate a reduced
(sub)set of hypotheses, from which we compute test statistics that are
subsequently subjected to standard multiple testing corrections. The principal
advantage of this methodology lies in its providing valid statistical inference
without the \textit{a priori} specifying which hypotheses will be tested. Here,
we present the theoretical details of this approach, confirm its validity via a
simulation study, and exemplify its use by applying it to the analysis of data
on microRNA differential expression.
IM2CAD
Authors: Hamid Izadinia, Qi Shan, Steven M. Seitz
Date: 2016-08-18 00:00:00
Proceeding: CVPR 2017 7
Tasks: Scene Understanding

Given a single photo of a room and a large database of furniture CAD models,
our goal is to reconstruct a scene that is as similar as possible to the scene
depicted in the photograph, and composed of objects drawn from the database. We
present a completely automatic system to address this IM2CAD problem that
produces high quality results on challenging imagery from interior home design
and remodeling websites. Our approach iteratively optimizes the placement and
scale of objects in the room to best match scene renderings to the input photo,
using image comparison metrics trained via deep convolutional neural nets. By
operating jointly on the full scene at once, we account for inter-object
occlusions. We also show the applicability of our method in standard scene
understanding benchmarks where we obtain significant improvement.
Learning to Create and Reuse Words in Open-Vocabulary Neural Language Modeling
Authors: Kazuya Kawakami, Chris Dyer, Phil Blunsom
Date: 2017-04-23 00:00:00
Proceeding: ACL 2017 7
Tasks: Articles, Language Modeling, Language Modelling

Fixed-vocabulary language models fail to account for one of the most
characteristic statistical facts of natural language: the frequent creation and
reuse of new word types. Although character-level language models offer a
partial solution in that they can create word types not attested in the
training corpus, they do not capture the "bursty" distribution of such words.
In this paper, we augment a hierarchical LSTM language model that generates
sequences of word tokens character by character with a caching mechanism that
learns to reuse previously generated words. To validate our model we construct
a new open-vocabulary language modeling corpus (the Multilingual Wikipedia
Corpus, MWC) from comparable Wikipedia articles in 7 typologically diverse
languages and demonstrate the effectiveness of our model across this range of
languages.
Skeleton Key: Image Captioning by Skeleton-Attribute Decomposition
Authors: Yufei Wang, Zhe Lin, Xiaohui Shen, Scott Cohen, Garrison W. Cottrell
Date: 2017-04-23 00:00:00
Proceeding: CVPR 2017 7
Tasks: Attribute, Image Captioning, Image Description, Language Modeling, Language Modelling, Sentence

Recently, there has been a lot of interest in automatically generating
descriptions for an image. Most existing language-model based approaches for
this task learn to generate an image description word by word in its original
word order. However, for humans, it is more natural to locate the objects and
their relationships first, and then elaborate on each object, describing
notable attributes. We present a coarse-to-fine method that decomposes the
original image description into a skeleton sentence and its attributes, and
generates the skeleton sentence and attribute phrases separately. By this
decomposition, our method can generate more accurate and novel descriptions
than the previous state-of-the-art. Experimental results on the MS-COCO and a
larger scale Stock3M datasets show that our algorithm yields consistent
improvements across different evaluation metrics, especially on the SPICE
metric, which has much higher correlation with human ratings than the
conventional metrics. Furthermore, our algorithm can generate descriptions with
varied length, benefiting from the separate control of the skeleton and
attributes. This enables image description generation that better accommodates
user preferences.
Differentiable Scheduled Sampling for Credit Assignment
Authors: Kartik Goyal, Chris Dyer, Taylor Berg-Kirkpatrick
Date: 2017-04-23 00:00:00
Proceeding: ACL 2017 7
Tasks: Machine Translation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Translation

We demonstrate that a continuous relaxation of the argmax operation can be
used to create a differentiable approximation to greedy decoding for
sequence-to-sequence (seq2seq) models. By incorporating this approximation into
the scheduled sampling training procedure (Bengio et al., 2015)--a well-known
technique for correcting exposure bias--we introduce a new training objective
that is continuous and differentiable everywhere and that can provide
informative gradients near points where previous decoding decisions change
their value. In addition, by using a related approximation, we demonstrate a
similar approach to sampled-based training. Finally, we show that our approach
outperforms cross-entropy training and scheduled sampling procedures in two
sequence prediction tasks: named entity recognition and machine translation.
Proxy Templates for Inverse Compositional Photometric Bundle Adjustment
Authors: Christopher Ham, Simon Lucey, Surya Singh
Date: 2017-04-23 00:00:00
Tasks: CPU

Recent advances in 3D vision have demonstrated the strengths of photometric
bundle adjustment. By directly minimizing reprojected pixel errors, instead of
geometric reprojection errors, such methods can achieve sub-pixel alignment
accuracy in both high and low textured regions. Typically, these problems are
solved using a forwards compositional Lucas-Kanade formulation parameterized by
6-DoF rigid camera poses and a depth per point in the structure. For large
problems the most CPU-intensive component of the pipeline is the creation and
factorization of the Hessian matrix at each iteration. For many warps, the
inverse compositional formulation can offer significant speed-ups since the
Hessian need only be inverted once. In this paper, we show that an ordinary
inverse compositional formulation does not work for warps of this type of
parameterization due to ill-conditioning of its partial derivatives. However,
we show that it is possible to overcome this limitation by introducing the
concept of a proxy template image. We show an order of magnitude improvement in
speed, with little effect on quality, going from forwards to inverse
compositional in our own photometric bundle adjustment method designed for
object-centric structure from motion. This means less processing time for large
systems or denser reconstructions under the same real-time constraints. We
additionally show that this theory can be readily applied to existing methods
by integrating it with the recently released Direct Sparse Odometry SLAM
algorithm.
Naturalizing a Programming Language via Interactive Learning
Authors: Sida I. Wang, Samuel Ginn, Percy Liang, Christoper D. Manning
Date: 2017-04-23 00:00:00
Proceeding: ACL 2017 7

Our goal is to create a convenient natural language interface for performing
well-specified but complex actions such as analyzing data, manipulating text,
and querying databases. However, existing natural language interfaces for such
tasks are quite primitive compared to the power one wields with a programming
language. To bridge this gap, we start with a core programming language and
allow users to "naturalize" the core language incrementally by defining
alternative, more natural syntax and increasingly complex concepts in terms of
compositions of simpler ones. In a voxel world, we show that a community of
users can simultaneously teach a common system a diverse language and use it to
build hundreds of complex voxel structures. Over the course of three days,
these users went from using only the core language to using the naturalized
language in 85.9\% of the last 10K utterances.
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
Authors: Francesco Conti, Robert Schilling, Pasquale Davide Schiavone, Antonio Pullini, Davide Rossi, Frank Kagan Gürkaynak, Michael Muehlberghuber, Michael Gautschi, Igor Loi, Germain Haugou, Stefan Mangard, Luca Benini
Date: 2016-12-18 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Face Detection, Seizure Detection

Near-sensor data analytics is a promising direction for IoT endpoints, as it
minimizes energy spent on communication and reduces network load - but it also
poses security concerns, as valuable data is stored or sent over the network at
various stages of the analytics pipeline. Using encryption to protect sensitive
data at the boundary of the on-chip analytics engine is a way to address data
security issues. To cope with the combined workload of analytics and encryption
in a tight power envelope, we propose Fulmine, a System-on-Chip based on a
tightly-coupled multi-core cluster augmented with specialized blocks for
compute-intensive data processing and encryption functions, supporting software
programmability for regular computing tasks. The Fulmine SoC, fabricated in
65nm technology, consumes less than 20mW on average at 0.8V achieving an
efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to
25MIPS/mW in software. As a strong argument for real-life flexible application
of our platform, we show experimental results for three secure analytics use
cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN
consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with
secured remote recognition in 5.74pJ/op; and seizure detection with encrypted
data collection from EEG within 12.7pJ/op.
Learning to Parse and Translate Improves Neural Machine Translation
Authors: Akiko Eriguchi, Yoshimasa Tsuruoka, Kyunghyun Cho
Date: 2017-02-12 00:00:00
Proceeding: ACL 2017 7
Tasks: Machine Translation, NMT, Translation

There has been relatively little attention to incorporating linguistic prior
to neural machine translation. Much of the previous work was further
constrained to considering linguistic prior on the source side. In this paper,
we propose a hybrid model, called NMT+RNNG, that learns to parse and translate
by combining the recurrent neural network grammar into the attention-based
neural machine translation. Our approach encourages the neural machine
translation model to incorporate linguistic prior during training, and lets it
translate on its own afterward. Extensive experiments with four language pairs
show the effectiveness of the proposed NMT+RNNG.
Learning with Value-Ramp
Authors: Tom J. Ameloot, Jan Van den Bussche
Date: 2016-08-12 00:00:00

We study a learning principle based on the intuition of forming ramps. The
agent tries to follow an increasing sequence of values until the agent meets a
peak of reward. The resulting Value-Ramp algorithm is natural, easy to
configure, and has a robust implementation with natural numbers.
General Video Game AI: Learning from Screen Capture
Authors: Kamolwan Kunanusont, Simon M. Lucas, Diego Perez-Liebana
Date: 2017-04-23 00:00:00

General Video Game Artificial Intelligence is a general game playing
framework for Artificial General Intelligence research in the video-games
domain. In this paper, we propose for the first time a screen capture learning
agent for General Video Game AI framework. A Deep Q-Network algorithm was
applied and improved to develop an agent capable of learning to play different
games in the framework. After testing this algorithm using various games of
different categories and difficulty levels, the results suggest that our
proposed screen capture learning agent has the potential to learn many
different games using only a single learning algorithm.
Population Seeding Techniques for Rolling Horizon Evolution in General Video Game Playing
Authors: Rauca D. Gaina, Simon M. Lucas, Diego Perez-Liebana
Date: 2017-04-23 00:00:00
Tasks: Evolutionary Algorithms

While Monte Carlo Tree Search and closely related methods have dominated
General Video Game Playing, recent research has demonstrated the promise of
Rolling Horizon Evolutionary Algorithms as an interesting alternative. However,
there is little attention paid to population initialization techniques in the
setting of general real-time video games. Therefore, this paper proposes the
use of population seeding to improve the performance of Rolling Horizon
Evolution and presents the results of two methods, One Step Look Ahead and
Monte Carlo Tree Search, tested on 20 games of the General Video Game AI corpus
with multiple evolution parameter values (population size and individual
length). An in-depth analysis is carried out between the results of the seeding
methods and the vanilla Rolling Horizon Evolution. In addition, the paper
presents a comparison to a Monte Carlo Tree Search algorithm. The results are
promising, with seeding able to boost performance significantly over baseline
evolution and even match the high level of play obtained by the Monte Carlo
Tree Search.
A* CCG Parsing with a Supertag and Dependency Factored Model
Authors: Masashi Yoshikawa, Hiroshi Noji, Yuji Matsumoto
Date: 2017-04-23 00:00:00
Proceeding: ACL 2017 7
Tasks: Sentence

We propose a new A* CCG parsing model in which the probability of a tree is
decomposed into factors of CCG categories and its syntactic dependencies both
defined on bi-directional LSTMs. Our factored model allows the precomputation
of all probabilities and runs very efficiently, while modeling sentence
structures explicitly via dependencies. Our model achieves the state-of-the-art
results on English and Japanese CCG parsing.
Second-order Temporal Pooling for Action Recognition
Authors: Anoop Cherian, Stephen Gould
Date: 2017-04-23 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Deep learning models for video-based action recognition usually generate
features for short clips (consisting of a few frames); such clip-level features
are aggregated to video-level representations by computing statistics on these
features. Typically zero-th (max) or the first-order (average) statistics are
used. In this paper, we explore the benefits of using second-order statistics.
Specifically, we propose a novel end-to-end learnable feature aggregation
scheme, dubbed temporal correlation pooling that generates an action descriptor
for a video sequence by capturing the similarities between the temporal
evolution of clip-level CNN features computed across the video. Such a
descriptor, while being computationally cheap, also naturally encodes the
co-activations of multiple CNN features, thereby providing a richer
characterization of actions than their first-order counterparts. We also
propose higher-order extensions of this scheme by computing correlations after
embedding the CNN features in a reproducing kernel Hilbert space. We provide
experiments on benchmark datasets such as HMDB-51 and UCF-101, fine-grained
datasets such as MPII Cooking activities and JHMDB, as well as the recent
Kinetics-600. Our results demonstrate the advantages of higher-order pooling
schemes that when combined with hand-crafted features (as is standard practice)
achieves state-of-the-art accuracy.
Neural Machine Translation via Binary Code Prediction
Authors: Yusuke Oda, Philip Arthur, Graham Neubig, Koichiro Yoshino, Satoshi Nakamura
Date: 2017-04-23 00:00:00
Proceeding: ACL 2017 7
Tasks: Machine Translation, Prediction, Translation

In this paper, we propose a new method for calculating the output layer in
neural machine translation systems. The method is based on predicting a binary
code for each word and can reduce computation time/memory requirements of the
output layer to be logarithmic in vocabulary size in the best case. In
addition, we also introduce two advanced approaches to improve the robustness
of the proposed model: using error-correcting codes and combining softmax and
binary codes. Experiments on two English-Japanese bidirectional translation
tasks show proposed models achieve BLEU scores that approach the softmax, while
reducing memory usage to the order of less than 1/10 and improving decoding
speed on CPUs by x5 to x10.