pwc-archive/papers-with-abstracts huggingface dataset:
Robust Emotion Recognition from Low Quality and Low Bit Rate Video: A Deep Learning Approach
Authors: Bowen Cheng, Zhangyang Wang, Zhaobin Zhang, Zhu Li, Ding Liu, Jianchao Yang, Shuai Huang, Thomas S. Huang
Date: 2017-09-10 00:00:00
Tasks: Decoder, Emotion Recognition, Super-Resolution

Emotion recognition from facial expressions is tremendously useful,
especially when coupled with smart devices and wireless multimedia
applications. However, the inadequate network bandwidth often limits the
spatial resolution of the transmitted video, which will heavily degrade the
recognition reliability. We develop a novel framework to achieve robust emotion
recognition from low bit rate video. While video frames are downsampled at the
encoder side, the decoder is embedded with a deep network model for joint
super-resolution (SR) and recognition. Notably, we propose a novel max-mix
training strategy, leading to a single "One-for-All" model that is remarkably
robust to a vast range of downsampling factors. That makes our framework well
adapted for the varied bandwidths in real transmission scenarios, without
hampering scalability or efficiency. The proposed framework is evaluated on the
AVEC 2016 benchmark, and demonstrates significantly improved stand-alone
recognition performance, as well as rate-distortion (R-D) performance, than
either directly recognizing from LR frames, or separating SR and recognition.
Institutionally Distributed Deep Learning Networks
Authors: Ken Chang, Niranjan Balachandar, Carson K Lam, Darvin Yi, James M. Brown, Andrew Beers, Bruce R. Rosen, Daniel L. Rubin, Jayashree Kalpathy-Cramer
Date: 2017-09-10 00:00:00
Tasks: Deep Learning, image-classification, Image Classification

Deep learning has become a promising approach for automated medical
diagnoses. When medical data samples are limited, collaboration among multiple
institutions is necessary to achieve high algorithm performance. However,
sharing patient data often has limitations due to technical, legal, or ethical
concerns. In such cases, sharing a deep learning model is a more attractive
alternative. The best method of performing such a task is unclear, however. In
this study, we simulate the dissemination of learning deep learning network
models across four institutions using various heuristics and compare the
results with a deep learning model trained on centrally hosted patient data.
The heuristics investigated include ensembling single institution models,
single weight transfer, and cyclical weight transfer. We evaluated these
approaches for image classification in three independent image collections
(retinal fundus photos, mammography, and ImageNet). We find that cyclical
weight transfer resulted in a performance (testing accuracy = 77.3%) that was
closest to that of centrally hosted patient data (testing accuracy = 78.7%). We
also found that there is an improvement in the performance of cyclical weight
transfer heuristic with high frequency of weight transfer.
Cognitive networks: brains, internet, and civilizations
Authors: Dmitrii Yu. Manin, Yuri I. Manin
Date: 2017-09-10 00:00:00

In this short essay, we discuss some basic features of cognitive activity at
several different space-time scales: from neural networks in the brain to
civilizations. One motivation for such comparative study is its heuristic
value. Attempts to better understand the functioning of "wetware" involved in
cognitive activities of central nervous system by comparing it with a computing
device have a long tradition. We suggest that comparison with Internet might be
more adequate. We briefly touch upon such subjects as encoding, compression,
and Saussurean trichotomy langue/langage/parole in various environments.
A New Semantic Theory of Natural Language
Authors: Kun Xing
Date: 2017-09-10 00:00:00
Tasks: Descriptive

Formal Semantics and Distributional Semantics are two important semantic
frameworks in Natural Language Processing (NLP). Cognitive Semantics belongs to
the movement of Cognitive Linguistics, which is based on contemporary cognitive
science. Each framework could deal with some meaning phenomena, but none of
them fulfills all requirements proposed by applications. A unified semantic
theory characterizing all important language phenomena has both theoretical and
practical significance; however, although many attempts have been made in
recent years, no existing theory has achieved this goal yet.
  This article introduces a new semantic theory that has the potential to
characterize most of the important meaning phenomena of natural language and to
fulfill most of the necessary requirements for philosophical analysis and for
NLP applications. The theory is based on a unified representation of
information, and constructs a kind of mathematical model called cognitive model
to interpret natural language expressions in a compositional manner. It accepts
the empirical assumption of Cognitive Semantics, and overcomes most
shortcomings of Formal Semantics and of Distributional Semantics. The theory,
however, is not a simple combination of existing theories, but an extensive
generalization of classic logic and Formal Semantics. It inherits nearly all
advantages of Formal Semantics, and also provides descriptive contents for
objects and events as fine-gram as possible, descriptive contents which
represent the results of human cognition.
Efficient Online Linear Optimization with Approximation Algorithms
Authors: Dan Garber
Date: 2017-09-10 00:00:00
Proceeding: NeurIPS 2017 12

We revisit the problem of \textit{online linear optimization} in case the set
of feasible actions is accessible through an approximated linear optimization
oracle with a factor $\alpha$ multiplicative approximation guarantee. This
setting is in particular interesting since it captures natural online
extensions of well-studied \textit{offline} linear optimization problems which
are NP-hard, yet admit efficient approximation algorithms. The goal here is to
minimize the $\alpha$\textit{-regret} which is the natural extension of the
standard \textit{regret} in \textit{online learning} to this setting.
  We present new algorithms with significantly improved oracle complexity for
both the full information and bandit variants of the problem. Mainly, for both
variants, we present $\alpha$-regret bounds of $O(T^{-1/3})$, were $T$ is the
number of prediction rounds, using only $O(\log{T})$ calls to the approximation
oracle per iteration, on average. These are the first results to obtain both
average oracle complexity of $O(\log{T})$ (or even poly-logarithmic in $T$) and
$\alpha$-regret bound $O(T^{-c})$ for a constant $c>0$, for both variants.
A Product Shape Congruity Measure via Entropy in Shape Scale Space
Authors: Asli Genctav, Sibel Tari
Date: 2017-09-10 00:00:00

Product shape is one of the factors that trigger preference decisions of
customers. Congruity of shape elements and deformation of shape from the
prototype are two factors that are found to influence aesthetic response, hence
preference. We propose a measure to indirectly quantify congruity of different
parts of the shape and the degree to which the parts deviate from a sphere,
i.e. our choice of the prototype, without explicitly defining parts and their
relations. The basic signals and systems concept that we use is the entropy.
Our measure attains its lowest value for a volume enclosed by a sphere. On one
hand, deformations from the prototype cause an increase in the measure. On the
other hand, as deformations create congruent parts, our measure decreases due
to the attained harmony. Our preliminary experimental results are consistent
with our expectations.
Parle: parallelizing stochastic gradient descent
Authors: Pratik Chaudhari, Carlo Baldassi, Riccardo Zecchina, Stefano Soatto, Ameet Talwalkar, Adam Oberman
Date: 2017-07-03 00:00:00
Tasks: GPU

We propose a new algorithm called Parle for parallel training of deep
networks that converges 2-4x faster than a data-parallel implementation of SGD,
while achieving significantly improved error rates that are nearly
state-of-the-art on several benchmarks including CIFAR-10 and CIFAR-100,
without introducing any additional hyper-parameters. We exploit the phenomenon
of flat minima that has been shown to lead to improved generalization error for
deep networks. Parle requires very infrequent communication with the parameter
server and instead performs more computation on each client, which makes it
well-suited to both single-machine, multi-GPU settings and distributed
implementations.
Abductive Matching in Question Answering
Authors: Kedar Dhamdhere, Kevin S. McCurley, Mukund Sundararajan, Ankur Taly
Date: 2017-09-10 00:00:00
Tasks: BIG-bench Machine Learning, Question Answering, Semantic Parsing

We study question-answering over semi-structured data. We introduce a new way
to apply the technique of semantic parsing by applying machine learning only to
provide annotations that the system infers to be missing; all the other parsing
logic is in the form of manually authored rules. In effect, the machine
learning is used to provide non-syntactic matches, a step that is ill-suited to
manual rules. The advantage of this approach is in its debuggability and in its
transparency to the end-user. We demonstrate the effectiveness of the approach
by achieving state-of-the-art performance of 40.42% accuracy on a standard
benchmark dataset over tables from Wikipedia.
Robust Sparse Coding via Self-Paced Learning
Authors: Xiaodong Feng, Zhiwei Tang, Sen Wu
Date: 2017-09-10 00:00:00

Sparse coding (SC) is attracting more and more attention due to its
comprehensive theoretical studies and its excellent performance in many signal
processing applications. However, most existing sparse coding algorithms are
nonconvex and are thus prone to becoming stuck into bad local minima,
especially when there are outliers and noisy data. To enhance the learning
robustness, in this paper, we propose a unified framework named Self-Paced
Sparse Coding (SPSC), which gradually include matrix elements into SC learning
from easy to complex. We also generalize the self-paced learning schema into
different levels of dynamic selection on samples, features and elements
respectively. Experimental results on real-world data demonstrate the efficacy
of the proposed algorithms.
Classifying Unordered Feature Sets with Convolutional Deep Averaging Networks
Authors: Andrew Gardner, Jinko Kanno, Christian A. Duncan, Rastko R. Selmic
Date: 2017-09-10 00:00:00

Unordered feature sets are a nonstandard data structure that traditional
neural networks are incapable of addressing in a principled manner. Providing a
concatenation of features in an arbitrary order may lead to the learning of
spurious patterns or biases that do not actually exist. Another complication is
introduced if the number of features varies between each set. We propose
convolutional deep averaging networks (CDANs) for classifying and learning
representations of datasets whose instances comprise variable-size, unordered
feature sets. CDANs are efficient, permutation-invariant, and capable of
accepting sets of arbitrary size. We emphasize the importance of nonlinear
feature embeddings for obtaining effective CDAN classifiers and illustrate
their advantages in experiments versus linear embeddings and alternative
permutation-invariant and -equivariant architectures.
Name Disambiguation in Anonymized Graphs using Network Embedding
Authors: Baichuan Zhang, Mohammad Al Hasan
Date: 2017-02-08 00:00:00
Tasks: Clustering, Feature Engineering, Network Embedding, Representation Learning, Retrieval

In real-world, our DNA is unique but many people share names. This phenomenon
often causes erroneous aggregation of documents of multiple persons who are
namesake of one another. Such mistakes deteriorate the performance of document
retrieval, web search, and more seriously, cause improper attribution of credit
or blame in digital forensic. To resolve this issue, the name disambiguation
task is designed which aims to partition the documents associated with a name
reference such that each partition contains documents pertaining to a unique
real-life person. Existing solutions to this task substantially rely on feature
engineering, such as biographical feature extraction, or construction of
auxiliary features from Wikipedia. However, for many scenarios, such features
may be costly to obtain or unavailable due to the risk of privacy violation. In
this work, we propose a novel name disambiguation method. Our proposed method
is non-intrusive of privacy because instead of using attributes pertaining to a
real-life person, our method leverages only relational data in the form of
anonymized graphs. In the methodological aspect, the proposed method uses a
novel representation learning model to embed each document in a low dimensional
vector space where name disambiguation can be solved by a hierarchical
agglomerative clustering algorithm. Our experimental results demonstrate that
the proposed method is significantly better than the existing name
disambiguation methods working in a similar setting.
Steering Output Style and Topic in Neural Response Generation
Authors: Di Wang, Nebojsa Jojic, Chris Brockett, Eric Nyberg
Date: 2017-09-09 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Decoder, Response Generation, Text Generation

We propose simple and flexible training and decoding methods for influencing
output style and topic in neural encoder-decoder based language generation.
This capability is desirable in a variety of applications, including
conversational systems, where successful agents need to produce language in a
specific style and generate responses steered by a human puppeteer or external
knowledge. We decompose the neural generation process into empirically easier
sub-problems: a faithfulness model and a decoding method based on
selective-sampling. We also describe training and sampling algorithms that bias
the generation process with a specific language style restriction, or a topic
restriction. Human evaluation results show that our proposed methods are able
to restrict style and topic without degrading output quality in conversational
tasks.
Admissibility of a posterior predictive decision rule
Authors: Giri Gopalan
Date: 2015-07-22 00:00:00
Tasks: Prediction

Recent decades have seen an interest in prediction problems for which
Bayesian methodology has been used ubiquitously. Sampling from or approximating
the posterior predictive distribution in a Bayesian model allows one to make
inferential statements about potentially observable random quantities given
observed data. The purpose of this note is to use statistical decision theory
as a basis to justify the use of a posterior predictive distribution for making
a point prediction.
Identifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations
Authors: Patrick Glauner, Niklas Dahringer, Oleksandr Puhachov, Jorge Augusto Meira, Petko Valtchev, Radu State, Diogo Duarte
Date: 2017-09-09 00:00:00
Tasks: Decision Making

Power grids are critical infrastructure assets that face non-technical losses
(NTL) such as electricity theft or faulty meters. NTL may range up to 40% of
the total electricity distributed in emerging countries. Industrial NTL
detection systems are still largely based on expert knowledge when deciding
whether to carry out costly on-site inspections of customers. Electricity
providers are reluctant to move to large-scale deployments of automated systems
that learn NTL profiles from data due to the latter's propensity to suggest a
large number of unnecessary inspections. In this paper, we propose a novel
system that combines automated statistical decision making with expert
knowledge. First, we propose a machine learning framework that classifies
customers into NTL or non-NTL using a variety of features derived from the
customers' consumption data. The methodology used is specifically tailored to
the level of noise in the data. Second, in order to allow human experts to feed
their knowledge in the decision loop, we propose a method for visualizing
prediction results at various granularity levels in a spatial hologram. Our
approach allows domain experts to put the classification results into the
context of the data and to incorporate their knowledge for making the final
decisions of which customers to inspect. This work has resulted in appreciable
results on a real-world data set of 3.6M customers. Our system is being
deployed in a commercial NTL detection software.
Learning the Structure of Generative Models without Labeled Data
Authors: Stephen H. Bach, Bryan He, Alexander Ratner, Christopher Ré
Date: 2017-03-02 00:00:00
Proceeding: ICML 2017 8

Curating labeled training data has become the primary bottleneck in machine
learning. Recent frameworks address this bottleneck with generative models to
synthesize labels at scale from weak supervision sources. The generative
model's dependency structure directly affects the quality of the estimated
labels, but selecting a structure automatically without any labeled data is a
distinct challenge. We propose a structure estimation method that maximizes the
$\ell_1$-regularized marginal pseudolikelihood of the observed data. Our
analysis shows that the amount of unlabeled data required to identify the true
structure scales sublinearly in the number of possible dependencies for a broad
class of models. Simulations show that our method is 100$\times$ faster than a
maximum likelihood approach and selects $1/4$ as many extraneous dependencies.
We also show that our method provides an average of 1.5 F1 points of
improvement over existing, user-developed information extraction applications
on real-world data such as PubMed journal abstracts.
On the Definiteness of Earth Mover's Distance and Its Relation to Set Intersection
Authors: Andrew Gardner, Christian A. Duncan, Jinko Kanno, Rastko R. Selmic
Date: 2015-10-09 00:00:00
Tasks: Relation

Positive definite kernels are an important tool in machine learning that
enable efficient solutions to otherwise difficult or intractable problems by
implicitly linearizing the problem geometry. In this paper we develop a
set-theoretic interpretation of the Earth Mover's Distance (EMD) and propose
Earth Mover's Intersection (EMI), a positive definite analog to EMD for sets of
different sizes. We provide conditions under which EMD or certain
approximations to EMD are negative definite. We also present a
positive-definite-preserving transformation that can be applied to any kernel
and can also be used to derive positive definite EMD-based kernels and show
that the Jaccard index is simply the result of this transformation. Finally, we
evaluate kernels based on EMI and the proposed transformation versus EMD in
various computer vision tasks and show that EMD is generally inferior even with
indefinite kernel techniques.
Setting an attention region for convolutional neural networks using region selective features, for recognition of materials within glass vessels
Authors: Sagi Eppel
Date: 2017-08-29 00:00:00

Convolutional neural networks have emerged as the leading method for the
classification and segmentation of images. In some cases, it is desirable to
focus the attention of the net on a specific region in the image; one such case
is the recognition of the contents of transparent vessels, where the vessel
region in the image is already known. This work presents a valve filter
approach for focusing the attention of the net on a region of interest (ROI).
In this approach, the ROI is inserted into the net as a binary map. The net
uses a different set of convolution filters for the ROI and background image
regions, resulting in a different set of features being extracted from each
region. More accurately, for each filter used on the image, a corresponding
valve filter exists that acts on the ROI map and determines the regions in
which the corresponding image filter will be used. This valve filter
effectively acts as a valve that inhibits specific features in different image
regions according to the ROI map. In addition, a new data set for images of
materials in glassware vessels in a chemistry laboratory setting is presented.
This data set contains a thousand images with pixel-wise annotation according
to categories ranging from filled and empty to the exact phase of the material
inside the vessel. The results of the valve filter approach and fully
convolutional neural nets (FCN) with no ROI input are compared based on this
data set.
Optimal Transport for Deep Joint Transfer Learning
Authors: Ying Lu, Liming Chen, Alexandre Saidi
Date: 2017-09-09 00:00:00
Tasks: General Classification, image-classification, Image Classification, Transfer Learning

Training a Deep Neural Network (DNN) from scratch requires a large amount of
labeled data. For a classification task where only small amount of training
data is available, a common solution is to perform fine-tuning on a DNN which
is pre-trained with related source data. This consecutive training process is
time consuming and does not consider explicitly the relatedness between
different source and target tasks.
  In this paper, we propose a novel method to jointly fine-tune a Deep Neural
Network with source data and target data. By adding an Optimal Transport loss
(OT loss) between source and target classifier predictions as a constraint on
the source classifier, the proposed Joint Transfer Learning Network (JTLN) can
effectively learn useful knowledge for target classification from source data.
Furthermore, by using different kind of metric as cost matrix for the OT loss,
JTLN can incorporate different prior knowledge about the relatedness between
target categories and source categories.
  We carried out experiments with JTLN based on Alexnet on image classification
datasets and the results verify the effectiveness of the proposed JTLN in
comparison with standard consecutive fine-tuning. This Joint Transfer Learning
with OT loss is general and can also be applied to other kind of Neural
Networks.
Can you tell a face from a HEVC bitstream?
Authors: Saeed Ranjbar Alvar, Hyomin Choi, Ivan V. Bajic
Date: 2017-09-09 00:00:00
Tasks: Decoder, Face Detection, Image Reconstruction

Image and video analytics are being increasingly used on a massive scale. Not
only is the amount of data growing, but the complexity of the data processing
pipelines is also increasing, thereby exacerbating the problem. It is becoming
increasingly important to save computational resources wherever possible. We
focus on one of the poster problems of visual analytics -- face detection --
and approach the issue of reducing the computation by asking: Is it possible to
detect a face without full image reconstruction from the High Efficiency Video
Coding (HEVC) bitstream? We demonstrate that this is indeed possible, with
accuracy comparable to conventional face detection, by training a Convolutional
Neural Network on the output of the HEVC entropy decoder.