pwc-archive/papers-with-abstracts huggingface dataset:
Guess who? Multilingual approach for the automated generation of author-stylized poetry
Authors: Alexey Tikhonov, Ivan P. Yamshchikov
Date: 2018-07-17 00:00:00
Tasks: Language Modeling, Language Modelling, Text Generation

This paper addresses the problem of stylized text generation in a
multilingual setup. A version of a language model based on a long short-term
memory (LSTM) artificial neural network with extended phonetic and semantic
embeddings is used for stylized poetry generation. The quality of the resulting
poems generated by the network is estimated through bilingual evaluation
understudy (BLEU), a survey and a new cross-entropy based metric that is
suggested for the problems of such type. The experiments show that the proposed
model consistently outperforms random sample and vanilla-LSTM baselines, humans
also tend to associate machine generated texts with the target author.
Analyzing Hypersensitive AI: Instability in Corporate-Scale Machine Learning
Authors: Michaela Regneri, Malte Hoffmann, Jurij Kost, Niklas Pietsch, Timo Schulz, Sabine Stamm
Date: 2018-07-17 00:00:00
Tasks: BIG-bench Machine Learning, Recommendation Systems

Predictive geometric models deliver excellent results for many Machine
Learning use cases. Despite their undoubted performance, neural predictive
algorithms can show unexpected degrees of instability and variance,
particularly when applied to large datasets. We present an approach to measure
changes in geometric models with respect to both output consistency and
topological stability. Considering the example of a recommender system using
word2vec, we analyze the influence of single data points, approximation methods
and parameter settings. Our findings can help to stabilize models where needed
and to detect differences in informational value of data points on a large
scale.
A Hand-Held Multimedia Translation and Interpretation System with Application to Diet Management
Authors: Albert Parra, Andrew W. Haddad, Mireille Boutin, Edward J. Delp
Date: 2018-07-17 00:00:00
Tasks: Machine Translation, Management, Translation

We propose a network independent, hand-held system to translate and
disambiguate foreign restaurant menu items in real-time. The system is based on
the use of a portable multimedia device, such as a smartphones or a PDA. An
accurate and fast translation is obtained using a Machine Translation engine
and a context-specific corpora to which we apply two pre-processing steps,
called translation standardization and $n$-gram consolidation. The phrase-table
generated is orders of magnitude lighter than the ones commonly used in market
applications, thus making translations computationally less expensive, and
decreasing the battery usage. Translation ambiguities are mitigated using
multimedia information including images of dishes and ingredients, along with
ingredient lists. We implemented a prototype of our system on an iPod Touch
Second Generation for English speakers traveling in Spain. Our tests indicate
that our translation method yields higher accuracy than translation engines
such as Google Translate, and does so almost instantaneously. The memory
requirements of the application, including the database of images, are also
well within the limits of the device. By combining it with a database of
nutritional information, our proposed system can be used to help individuals
who follow a medical diet maintain this diet while traveling.
ChESS - Quick and Robust Detection of Chess-board Features
Authors: Stuart Bennett, Joan Lasenby
Date: 2013-01-23 00:00:00
Tasks: 3D Reconstruction, Camera Calibration

Localization of chess-board vertices is a common task in computer vision,
underpinning many applications, but relatively little work focusses on
designing a specific feature detector that is fast, accurate and robust. In
this paper the `Chess-board Extraction by Subtraction and Summation' (ChESS)
feature detector, designed to exclusively respond to chess-board vertices, is
presented. The method proposed is robust against noise, poor lighting and poor
contrast, requires no prior knowledge of the extent of the chess-board pattern,
is computationally very efficient, and provides a strength measure of detected
features. Such a detector has significant application both in the key field of
camera calibration, as well as in Structured Light 3D reconstruction. Evidence
is presented showing its robustness, accuracy, and efficiency in comparison to
other commonly used detectors both under simulation and in experimental 3D
reconstruction of flat plate and cylindrical objects
An Introduction to Conditional Random Fields
Authors: Charles Sutton, Andrew McCallum
Date: 2010-11-17 00:00:00
Tasks: General Classification, parameter estimation, Prediction, Structured Prediction

Often we wish to predict a large number of variables that depend on each
other as well as on other observed variables. Structured prediction methods are
essentially a combination of classification and graphical modeling, combining
the ability of graphical models to compactly model multivariate data with the
ability of classification methods to perform prediction using large sets of
input features. This tutorial describes conditional random fields, a popular
probabilistic method for structured prediction. CRFs have seen wide application
in natural language processing, computer vision, and bioinformatics. We
describe methods for inference and parameter estimation for CRFs, including
practical issues for implementing large scale CRFs. We do not assume previous
knowledge of graphical modeling, so this tutorial is intended to be useful to
practitioners in a wide variety of fields.
Decentralized Task Allocation in Multi-Robot Systems via Bipartite Graph Matching Augmented with Fuzzy Clustering
Authors: Payam Ghassemi, Souma Chowdhury
Date: 2018-07-20 00:00:00
Tasks: Clustering, Disaster Response, Graph Matching, Problem Decomposition, Task Planning, Traveling Salesman Problem

Robotic systems, working together as a team, are becoming valuable players in
different real-world applications, from disaster response to warehouse
fulfillment services. Centralized solutions for coordinating multi-robot teams
often suffer from poor scalability and vulnerability to communication
disruptions. This paper develops a decentralized multi-agent task allocation
(Dec-MATA) algorithm for multi-robot applications. The task planning problem is
posed as a maximum-weighted matching of a bipartite graph, the solution of
which using the blossom algorithm allows each robot to autonomously identify
the optimal sequence of tasks it should undertake. The graph weights are
determined based on a soft clustering process, which also plays a problem
decomposition role seeking to reduce the complexity of the individual-agents'
task assignment problems. To evaluate the new Dec-MATA algorithm, a series of
case studies (of varying complexity) are performed, with tasks being
distributed randomly over an observable 2D environment. A centralized approach,
based on a state-of-the-art MILP formulation of the multi-Traveling Salesman
problem is used for comparative analysis. While getting within 7-28% of the
optimal cost obtained by the centralized algorithm, the Dec-MATA algorithm is
found to be 1-3 orders of magnitude faster and minimally sensitive to
task-to-robot ratios, unlike the centralized algorithm.
Optimize Deep Convolutional Neural Network with Ternarized Weights and High Accuracy
Authors: Zhezhi He, Boqing Gong, Deliang Fan
Date: 2018-07-20 00:00:00
Tasks: Model Compression, Vocal Bursts Intensity Prediction

Deep convolution neural network has achieved great success in many artificial
intelligence applications. However, its enormous model size and massive
computation cost have become the main obstacle for deployment of such powerful
algorithm in the low power and resource-limited embedded systems. As the
countermeasure to this problem, in this work, we propose statistical weight
scaling and residual expansion methods to reduce the bit-width of the whole
network weight parameters to ternary values (i.e. -1, 0, +1), with the
objectives to greatly reduce model size, computation cost and accuracy
degradation caused by the model compression. With about 16x model compression
rate, our ternarized ResNet-32/44/56 could outperform full-precision
counterparts by 0.12%, 0.24% and 0.18% on CIFAR- 10 dataset. We also test our
ternarization method with AlexNet and ResNet-18 on ImageNet dataset, which both
achieve the best top-1 accuracy compared to recent similar works, with the same
16x compression rate. If further incorporating our residual expansion method,
compared to the full-precision counterpart, our ternarized ResNet-18 even
improves the top-5 accuracy by 0.61% and merely degrades the top-1 accuracy
only by 0.42% for the ImageNet dataset, with 8x model compression rate. It
outperforms the recent ABC-Net by 1.03% in top-1 accuracy and 1.78% in top-5
accuracy, with around 1.25x higher compression rate and more than 6x
computation reduction due to the weight sparsity.
Future Semantic Segmentation with Convolutional LSTM
Authors: Seyed shahabeddin Nabavi, Mrigank Rochan, Yang, Wang
Date: 2018-07-20 00:00:00
Tasks: Autonomous Driving, Decision Making, Future prediction, Segmentation, Semantic Segmentation

We consider the problem of predicting semantic segmentation of future frames
in a video. Given several observed frames in a video, our goal is to predict
the semantic segmentation map of future frames that are not yet observed. A
reliable solution to this problem is useful in many applications that require
real-time decision making, such as autonomous driving. We propose a novel model
that uses convolutional LSTM (ConvLSTM) to encode the spatiotemporal
information of observed frames for future prediction. We also extend our model
to use bidirectional ConvLSTM to capture temporal information in both
directions. Our proposed approach outperforms other state-of-the-art methods on
the benchmark dataset.
Large scale evaluation of local image feature detectors on homography datasets
Authors: Karel Lenc, Andrea Vedaldi
Date: 2018-07-20 00:00:00

We present a large scale benchmark for the evaluation of local feature
detectors. Our key innovation is the introduction of a new evaluation protocol
which extends and improves the standard detection repeatability measure. The
new protocol is better for assessment on a large number of images and reduces
the dependency of the results on unwanted distractors such as the number of
detected features and the feature magnification factor. Additionally, our
protocol provides a comprehensive assessment of the expected performance of
detectors under several practical scenarios. Using images from the
recently-introduced HPatches dataset, we evaluate a range of state-of-the-art
local feature detectors on two main tasks: viewpoint and illumination invariant
detection. Contrary to previous detector evaluations, our study contains an
order of magnitude more image sequences, resulting in a quantitative evaluation
significantly more robust to over-fitting. We also show that traditional
detectors are still very competitive when compared to recent deep-learning
alternatives.
Perceptual Video Super Resolution with Enhanced Temporal Consistency
Authors: Eduardo Pérez-Pellitero, Mehdi S. M. Sajjadi, Michael Hirsch, Bernhard Schölkopf
Date: 2018-07-20 00:00:00
Tasks: Image Super-Resolution, Super-Resolution, Video Super-Resolution

With the advent of perceptual loss functions, new possibilities in super-resolution have emerged, and we currently have models that successfully generate near-photorealistic high-resolution images from their low-resolution observations. Up to now, however, such approaches have been exclusively limited to single image super-resolution. The application of perceptual loss functions on video processing still entails several challenges, mostly related to the lack of temporal consistency of the generated images, i.e., flickering artifacts. In this work, we present a novel adversarial recurrent network for video upscaling that is able to produce realistic textures in a temporally consistent way. The proposed architecture naturally leverages information from previous frames due to its recurrent architecture, i.e. the input to the generator is composed of the low-resolution image and, additionally, the warped output of the network at the previous step. Together with a video discriminator, we also propose additional loss functions to further reinforce temporal consistency in the generated sequences. The experimental validation of our algorithm shows the effectiveness of our approach which obtains images with high perceptual quality and improved temporal consistency.
Optimal Bounds on the VC-dimension
Authors: Monika Csikos, Andrey Kupavskii, Nabil H. Mustafa
Date: 2018-07-20 00:00:00
Tasks: BIG-bench Machine Learning, Open-Ended Question Answering

The VC-dimension of a set system is a way to capture its complexity and has
been a key parameter studied extensively in machine learning and geometry
communities. In this paper, we resolve two longstanding open problems on
bounding the VC-dimension of two fundamental set systems: $k$-fold
unions/intersections of half-spaces, and the simplices set system. Among other
implications, it settles an open question in machine learning that was first
studied in the 1989 foundational paper of Blumer, Ehrenfeucht, Haussler and
Warmuth as well as by Eisenstat and Angluin and Johnson.
Boosting algorithms for uplift modeling
Authors: Michał Sołtys, Szymon Jaroszewicz
Date: 2018-07-20 00:00:00
Tasks: Marketing, Medical Procedure

Uplift modeling is an area of machine learning which aims at predicting the
causal effect of some action on a given individual. The action may be a medical
procedure, marketing campaign, or any other circumstance controlled by the
experimenter. Building an uplift model requires two training sets: the
treatment group, where individuals have been subject to the action, and the
control group, where no action has been performed. An uplift model allows then
to assess the gain resulting from taking the action on a given individual, such
as the increase in probability of patient recovery or of a product being
purchased. This paper describes an adaptation of the well-known boosting
techniques to the uplift modeling case. We formulate three desirable properties
which an uplift boosting algorithm should have. Since all three properties
cannot be satisfied simultaneously, we propose three uplift boosting
algorithms, each satisfying two of them. Experiments demonstrate the usefulness
of the proposed methods, which often dramatically improve performance of the
base models and are thus new and powerful tools for uplift modeling.
Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features
Authors: Julius von Kügelgen, Alexander Mey, Marco Loog
Date: 2018-07-20 00:00:00
Tasks: Domain Adaptation

Current methods for covariate-shift adaptation use unlabelled data to compute
importance weights or domain-invariant features, while the final model is
trained on labelled data only. Here, we consider a particular case of covariate
shift which allows us also to learn from unlabelled data, that is, combining
adaptation with semi-supervised learning. Using ideas from causality, we argue
that this requires learning with both causes, $X_C$, and effects, $X_E$, of a
target variable, $Y$, and show how this setting leads to what we call a
semi-generative model, $P(Y,X_E|X_C,\theta)$. Our approach is robust to domain
shifts in the distribution of causal features and leverages unlabelled data by
learning a direct map from causes to effects. Experiments on synthetic data
demonstrate significant improvements in classification over purely-supervised
and importance-weighting baselines.
From Face Recognition to Models of Identity: A Bayesian Approach to Learning about Unknown Identities from Unsupervised Data
Authors: Daniel C. Castro, Sebastian Nowozin
Date: 2018-07-20 00:00:00
Proceeding: ECCV 2018 9
Tasks: Face Recognition, General Classification

Current face recognition systems robustly recognize identities across a wide
variety of imaging conditions. In these systems recognition is performed via
classification into known identities obtained from supervised identity
annotations. There are two problems with this current paradigm: (1) current
systems are unable to benefit from unlabelled data which may be available in
large quantities; and (2) current systems equate successful recognition with
labelling a given input image. Humans, on the other hand, regularly perform
identification of individuals completely unsupervised, recognising the identity
of someone they have seen before even without being able to name that
individual. How can we go beyond the current classification paradigm towards a
more human understanding of identities? We propose an integrated Bayesian model
that coherently reasons about the observed images, identities, partial
knowledge about names, and the situational context of each observation. While
our model achieves good recognition performance against known identities, it
can also discover new identities from unsupervised data and learns to associate
identities with different contexts depending on which identities tend to be
observed together. In addition, the proposed semi-supervised component is able
to handle not only acquaintances, whose names are known, but also unlabelled
familiar faces and complete strangers in a unified framework.
Talking Face Generation by Adversarially Disentangled Audio-Visual Representation
Authors: Hang Zhou, Yu Liu, Ziwei Liu, Ping Luo, Xiaogang Wang
Date: 2018-07-20 00:00:00
Tasks: Face Generation, Lip Reading, Retrieval, Talking Face Generation, Video Retrieval

Talking face generation aims to synthesize a sequence of face images that
correspond to a clip of speech. This is a challenging task because face
appearance variation and semantics of speech are coupled together in the subtle
movements of the talking face regions. Existing works either construct specific
face appearance model on specific subjects or model the transformation between
lip motion and speech. In this work, we integrate both aspects and enable
arbitrary-subject talking face generation by learning disentangled audio-visual
representation. We find that the talking face sequence is actually a
composition of both subject-related information and speech-related information.
These two spaces are then explicitly disentangled through a novel
associative-and-adversarial training process. This disentangled representation
has an advantage where both audio and video can serve as inputs for generation.
Extensive experiments show that the proposed approach generates realistic
talking face sequences on arbitrary subjects with much clearer lip motion
patterns than previous work. We also demonstrate the learned audio-visual
representation is extremely useful for the tasks of automatic lip reading and
audio-video retrieval.
Surgical Phase Recognition of Short Video Shots Based on Temporal Modeling of Deep Features
Authors: Constantinos Loukas
Date: 2018-07-20 00:00:00
Tasks: Retrieval, Surgical phase recognition, Transfer Learning

Recognizing the phases of a laparoscopic surgery (LS) operation form its
video constitutes a fundamental step for efficient content representation,
indexing and retrieval in surgical video databases. In the literature, most
techniques focus on phase segmentation of the entire LS video using
hand-crafted visual features, instrument usage signals, and recently
convolutional neural networks (CNNs). In this paper we address the problem of
phase recognition of short video shots (10s) of the operation, without
utilizing information about the preceding/forthcoming video frames, their phase
labels or the instruments used. We investigate four state-of-the-art CNN
architectures (Alexnet, VGG19, GoogleNet, and ResNet101), for feature
extraction via transfer learning. Visual saliency was employed for selecting
the most informative region of the image as input to the CNN. Video shot
representation was based on two temporal pooling mechanisms. Most importantly,
we investigate the role of 'elapsed time' (from the beginning of the
operation), and we show that inclusion of this feature can increase performance
dramatically (69% vs. 75% mean accuracy). Finally, a long short-term memory
(LSTM) network was trained for video shot classification based on the fusion of
CNN features with 'elapsed time', increasing the accuracy to 86%. Our results
highlight the prominent role of visual saliency, long-range temporal recursion
and 'elapsed time' (a feature so far ignored), for surgical phase recognition.
Distance-based Kernels for Surrogate Model-based Neuroevolution
Authors: Jörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein
Date: 2018-07-20 00:00:00
Tasks: model

The topology optimization of artificial neural networks can be particularly
difficult if the fitness evaluations require expensive experiments or
simulations. For that reason, the optimization methods may need to be supported
by surrogate models. We propose different distances for a suitable surrogate
model, and compare them in a simple numerical test scenario.
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time
Authors: Feargus Pendlebury, Fabio Pierazzi, Roberto Jordaney, Johannes Kinder, Lorenzo Cavallaro
Date: 2018-07-20 00:00:00
Tasks: Active Learning, General Classification, Malware Classification

Is Android malware classification a solved problem? Published F1 scores of up to 0.99 appear to leave very little room for improvement. In this paper, we argue that results are commonly inflated due to two pervasive sources of experimental bias: "spatial bias" caused by distributions of training and testing data that are not representative of a real-world deployment; and "temporal bias" caused by incorrect time splits of training and testing sets, leading to impossible configurations. We propose a set of space and time constraints for experiment design that eliminates both sources of bias. We introduce a new metric that summarizes the expected robustness of a classifier in a real-world setting, and we present an algorithm to tune its performance. Finally, we demonstrate how this allows us to evaluate mitigation strategies for time decay such as active learning. We have implemented our solutions in TESSERACT, an open source evaluation framework for comparing malware classifiers in a realistic setting. We used TESSERACT to evaluate three Android malware classifiers from the literature on a dataset of 129K applications spanning over three years. Our evaluation confirms that earlier published results are biased, while also revealing counter-intuitive performance and showing that appropriate tuning can lead to significant improvements.
Rank Minimization for Snapshot Compressive Imaging
Authors: Yang Liu, Xin Yuan, Jinli Suo, David J. Brady, Qionghai Dai
Date: 2018-07-20 00:00:00

Snapshot compressive imaging (SCI) refers to compressive imaging systems
where multiple frames are mapped into a single measurement, with video
compressive imaging and hyperspectral compressive imaging as two representative
applications. Though exciting results of high-speed videos and hyperspectral
images have been demonstrated, the poor reconstruction quality precludes SCI
from wide applications.This paper aims to boost the reconstruction quality of
SCI via exploiting the high-dimensional structure in the desired signal. We
build a joint model to integrate the nonlocal self-similarity of
video/hyperspectral frames and the rank minimization approach with the SCI
sensing process. Following this, an alternating minimization algorithm is
developed to solve this non-convex problem. We further investigate the special
structure of the sampling process in SCI to tackle the computational workload
and memory issues in SCI reconstruction. Both simulation and real data
(captured by four different SCI cameras) results demonstrate that our proposed
algorithm leads to significant improvements compared with current
state-of-the-art algorithms. We hope our results will encourage the researchers
and engineers to pursue further in compressive imaging for real applications.