pwc-archive/papers-with-abstracts huggingface dataset:
Nazr-CNN: Fine-Grained Classification of UAV Imagery for Damage Assessment
Authors: N. Attari, F. Ofli, M. Awad, J. Lucas, S. Chawla
Date: 2016-11-20 00:00:00
Tasks: General Classification, object-detection, Object Detection, Small Data Image Classification, Transfer Learning

We propose Nazr-CNN1, a deep learning pipeline for object detection and
fine-grained classification in images acquired from Unmanned Aerial Vehicles
(UAVs) for damage assessment and monitoring. Nazr-CNN consists of two
components. The function of the first component is to localize objects (e.g.
houses or infrastructure) in an image by carrying out a pixel-level
classification. In the second component, a hidden layer of a Convolutional
Neural Network (CNN) is used to encode Fisher Vectors (FV) of the segments
generated from the first component in order to help discriminate between
different levels of damage. To showcase our approach we use data from UAVs that
were deployed to assess the level of damage in the aftermath of a devastating
cyclone that hit the island of Vanuatu in 2015. The collected images were
labeled by a crowdsourcing effort and the labeling categories consisted of
fine-grained levels of damage to built structures. Since our data set is
relatively small, a pre- trained network for pixel-level classification and FV
encoding was used. Nazr-CNN attains promising results both for object detection
and damage assessment suggesting that the integrated pipeline is robust in the
face of small data sets and labeling errors by annotators. While the focus of
Nazr-CNN is on assessment of UAV images in a post-disaster scenario, our
solution is general and can be applied in many diverse settings. We show one
such case of transfer learning to assess the level of damage in aerial images
collected after a typhoon in Philippines.
Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection
Authors: Xiaojun Xu, Chang Liu, Qian Feng, Heng Yin, Le Song, Dawn Song
Date: 2017-08-22 00:00:00
Tasks: Computer Security, Graph Embedding, Graph Matching, Malware Detection

The problem of cross-platform binary code similarity detection aims at
detecting whether two binary functions coming from different platforms are
similar or not. It has many security applications, including plagiarism
detection, malware detection, vulnerability search, etc. Existing approaches
rely on approximate graph matching algorithms, which are inevitably slow and
sometimes inaccurate, and hard to adapt to a new task. To address these issues,
in this work, we propose a novel neural network-based approach to compute the
embedding, i.e., a numeric vector, based on the control flow graph of each
binary function, then the similarity detection can be done efficiently by
measuring the distance between the embeddings for two functions. We implement a
prototype called Gemini. Our extensive evaluation shows that Gemini outperforms
the state-of-the-art approaches by large margins with respect to similarity
detection accuracy. Further, Gemini can speed up prior art's embedding
generation time by 3 to 4 orders of magnitude and reduce the required training
time from more than 1 week down to 30 minutes to 10 hours. Our real world case
studies demonstrate that Gemini can identify significantly more vulnerable
firmware images than the state-of-the-art, i.e., Genius. Our research showcases
a successful application of deep learning on computer security problems.
Learning Efficient Convolutional Networks through Network Slimming
Authors: Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, Chang-Shui Zhang
Date: 2017-08-22 00:00:00
Proceeding: ICCV 2017 10
Tasks: image-classification, Image Classification, Neural Architecture Search

The deployment of deep convolutional neural networks (CNNs) in many real
world applications is largely hindered by their high computational cost. In
this paper, we propose a novel learning scheme for CNNs to simultaneously 1)
reduce the model size; 2) decrease the run-time memory footprint; and 3) lower
the number of computing operations, without compromising accuracy. This is
achieved by enforcing channel-level sparsity in the network in a simple but
effective way. Different from many existing approaches, the proposed method
directly applies to modern CNN architectures, introduces minimum overhead to
the training process, and requires no special software/hardware accelerators
for the resulting models. We call our approach network slimming, which takes
wide and large networks as input models, but during training insignificant
channels are automatically identified and pruned afterwards, yielding thin and
compact models with comparable accuracy. We empirically demonstrate the
effectiveness of our approach with several state-of-the-art CNN models,
including VGGNet, ResNet and DenseNet, on various image classification
datasets. For VGGNet, a multi-pass version of network slimming gives a 20x
reduction in model size and a 5x reduction in computing operations.
ProbFlow: Joint Optical Flow and Uncertainty Estimation
Authors: Anne S. Wannenwetsch, Margret Keuper, Stefan Roth
Date: 2017-08-22 00:00:00
Proceeding: ICCV 2017 10
Tasks: Optical Flow Estimation, Variational Inference

Optical flow estimation remains challenging due to untextured areas, motion
boundaries, occlusions, and more. Thus, the estimated flow is not equally
reliable across the image. To that end, post-hoc confidence measures have been
introduced to assess the per-pixel reliability of the flow. We overcome the
artificial separation of optical flow and confidence estimation by introducing
a method that jointly predicts optical flow and its underlying uncertainty.
Starting from common energy-based formulations, we rely on the corresponding
posterior distribution of the flow given the images. We derive a variational
inference scheme based on mean field, which incorporates best practices from
energy minimization. An uncertainty measure is obtained along the flow at every
pixel as the (marginal) entropy of the variational distribution. We demonstrate
the flexibility of our probabilistic approach by applying it to two different
energies and on two benchmarks. We not only obtain flow results that are
competitive with the underlying energy minimization approach, but also a
reliable uncertainty measure that significantly outperforms existing post-hoc
approaches.
Towards Automatic Construction of Diverse, High-quality Image Dataset
Authors: Yazhou Yao, Jian Zhang, Fumin Shen, Li Liu, Fan Zhu, Dongxiang Zhang, Heng-Tao Shen
Date: 2017-08-22 00:00:00
Tasks: Diversity, image-classification, Image Classification, object-detection, Object Detection, Vocal Bursts Intensity Prediction

The availability of labeled image datasets has been shown critical for
high-level image understanding, which continuously drives the progress of
feature designing and models developing. However, constructing labeled image
datasets is laborious and monotonous. To eliminate manual annotation, in this
work, we propose a novel image dataset construction framework by employing
multiple textual queries. We aim at collecting diverse and accurate images for
given queries from the Web. Specifically, we formulate noisy textual queries
removing and noisy images filtering as a multi-view and multi-instance learning
problem separately. Our proposed approach not only improves the accuracy but
also enhances the diversity of the selected images. To verify the effectiveness
of our proposed approach, we construct an image dataset with 100 categories.
The experiments show significant performance gains by using the generated data
of our approach on several tasks, such as image classification, cross-dataset
generalization, and object detection. The proposed method also consistently
outperforms existing weakly supervised and web-supervised approaches.
Matrix Completion from $O(n)$ Samples in Linear Time
Authors: David Gamarnik, Quan Li, Hongyi Zhang
Date: 2017-02-08 00:00:00
Tasks: Matrix Completion

We consider the problem of reconstructing a rank-$k$ $n \times n$ matrix $M$
from a sampling of its entries. Under a certain incoherence assumption on $M$
and for the case when both the rank and the condition number of $M$ are
bounded, it was shown in \cite{CandesRecht2009, CandesTao2010, keshavan2010,
Recht2011, Jain2012, Hardt2014} that $M$ can be recovered exactly or
approximately (depending on some trade-off between accuracy and computational
complexity) using $O(n \, \text{poly}(\log n))$ samples in super-linear time
$O(n^{a} \, \text{poly}(\log n))$ for some constant $a \geq 1$.
  In this paper, we propose a new matrix completion algorithm using a novel
sampling scheme based on a union of independent sparse random regular bipartite
graphs. We show that under the same conditions w.h.p. our algorithm recovers an
$\epsilon$-approximation of $M$ in terms of the Frobenius norm using $O(n
\log^2(1/\epsilon))$ samples and in linear time $O(n \log^2(1/\epsilon))$. This
provides the best known bounds both on the sample complexity and computational
complexity for reconstructing (approximately) an unknown low-rank matrix.
  The novelty of our algorithm is two new steps of thresholding singular values
and rescaling singular vectors in the application of the "vanilla" alternating
minimization algorithm. The structure of sparse random regular graphs is used
heavily for controlling the impact of these regularization steps.
Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
Authors: Zhedong Zheng, Liang Zheng, Yi Yang
Date: 2017-01-26 00:00:00
Proceeding: ICCV 2017 10
Tasks: Fine-Grained Image Classification, Generative Adversarial Network, Person Re-Identification, Representation Learning

The main contribution of this paper is a simple semi-supervised pipeline that
only uses the original training set without collecting extra data. It is
challenging in 1) how to obtain more training data only from the training set
and 2) how to use the newly generated data. In this work, the generative
adversarial network (GAN) is used to generate unlabeled samples. We propose the
label smoothing regularization for outliers (LSRO). This method assigns a
uniform label distribution to the unlabeled images, which regularizes the
supervised model and improves the baseline. We verify the proposed method on a
practical problem: person re-identification (re-ID). This task aims to retrieve
a query person from other cameras. We adopt the deep convolutional generative
adversarial network (DCGAN) for sample generation, and a baseline convolutional
neural network (CNN) for representation learning. Experiments show that adding
the GAN-generated data effectively improves the discriminative ability of
learned CNN embeddings. On three large-scale datasets, Market-1501, CUHK03 and
DukeMTMC-reID, we obtain +4.37%, +1.6% and +2.46% improvement in rank-1
precision over the baseline CNN, respectively. We additionally apply the
proposed method to fine-grained bird recognition and achieve a +0.6%
improvement over a strong baseline. The code is available at
https://github.com/layumi/Person-reID_GAN.
Scaling Active Search using Linear Similarity Functions
Authors: Sibi Venkatesan, James K. Miller, Jeff Schneider, Artur Dubrawski
Date: 2017-04-30 00:00:00
Tasks: Information Retrieval, Retrieval

Active Search has become an increasingly useful tool in information retrieval
problems where the goal is to discover as many target elements as possible
using only limited label queries. With the advent of big data, there is a
growing emphasis on the scalability of such techniques to handle very large and
very complex datasets.
  In this paper, we consider the problem of Active Search where we are given a
similarity function between data points. We look at an algorithm introduced by
Wang et al. [2013] for Active Search over graphs and propose crucial
modifications which allow it to scale significantly. Their approach selects
points by minimizing an energy function over the graph induced by the
similarity function on the data. Our modifications require the similarity
function to be a dot-product between feature vectors of data points, equivalent
to having a linear kernel for the adjacency matrix. With this, we are able to
scale tremendously: for $n$ data points, the original algorithm runs in
$O(n^2)$ time per iteration while ours runs in only $O(nr + r^2)$ given
$r$-dimensional features.
  We also describe a simple alternate approach using a weighted-neighbor
predictor which also scales well. In our experiments, we show that our method
is competitive with existing semi-supervised approaches. We also briefly
discuss conditions under which our algorithm performs well.
The Mean and Median Criterion for Automatic Kernel Bandwidth Selection for Support Vector Data Description
Authors: Arin Chaudhuri, Deovrat Kakde, Carol Sadek, Laura Gonzalez, Seunghyun Kong
Date: 2017-08-16 00:00:00

Support vector data description (SVDD) is a popular technique for detecting
anomalies. The SVDD classifier partitions the whole space into an inlier
region, which consists of the region near the training data, and an outlier
region, which consists of points away from the training data. The computation
of the SVDD classifier requires a kernel function, and the Gaussian kernel is a
common choice for the kernel function. The Gaussian kernel has a bandwidth
parameter, whose value is important for good results. A small bandwidth leads
to overfitting, and the resulting SVDD classifier overestimates the number of
anomalies. A large bandwidth leads to underfitting, and the classifier fails to
detect many anomalies. In this paper we present a new automatic, unsupervised
method for selecting the Gaussian kernel bandwidth. The selected value can be
computed quickly, and it is competitive with existing bandwidth selection
methods.
A Data Driven Approach for Compound Figure Separation Using Convolutional Neural Networks
Authors: Satoshi Tsutsui, David Crandall
Date: 2017-03-15 00:00:00
Tasks: Transfer Learning

A key problem in automatic analysis and understanding of scientific papers is
to extract semantic information from non-textual paper components like figures,
diagrams, tables, etc. Much of this work requires a very first preprocessing
step: decomposing compound multi-part figures into individual subfigures.
Previous work in compound figure separation has been based on manually designed
features and separation rules, which often fail for less common figure types
and layouts. Moreover, few implementations for compound figure decomposition
are publicly available.
  This paper proposes a data driven approach to separate compound figures using
modern deep Convolutional Neural Networks (CNNs) to train the separator in an
end-to-end manner. CNNs eliminate the need for manually designing features and
separation rules, but require a large amount of annotated training data. We
overcome this challenge using transfer learning as well as automatically
synthesizing training exemplars. We evaluate our technique on the ImageCLEF
Medical dataset, achieving 85.9% accuracy and outperforming previous
techniques. We have released our implementation as an easy-to-use Python
library, aiming to promote further research in scientific figure mining.
Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
Authors: Maziar Raissi, George Em. Karniadakis
Date: 2017-08-02 00:00:00
Tasks: BIG-bench Machine Learning, Gaussian Processes, Small Data Image Classification

While there is currently a lot of enthusiasm about "big data", useful data is
usually "small" and expensive to acquire. In this paper, we present a new
paradigm of learning partial differential equations from {\em small} data. In
particular, we introduce \emph{hidden physics models}, which are essentially
data-efficient learning machines capable of leveraging the underlying laws of
physics, expressed by time dependent and nonlinear partial differential
equations, to extract patterns from high-dimensional data generated from
experiments. The proposed methodology may be applied to the problem of
learning, system identification, or data-driven discovery of partial
differential equations. Our framework relies on Gaussian processes, a powerful
tool for probabilistic inference over functions, that enables us to strike a
balance between model complexity and data fitting. The effectiveness of the
proposed approach is demonstrated through a variety of canonical problems,
spanning a number of scientific domains, including the Navier-Stokes,
Schr\"odinger, Kuramoto-Sivashinsky, and time dependent linear fractional
equations. The methodology provides a promising new direction for harnessing
the long-standing developments of classical methods in applied mathematics and
mathematical physics to design learning machines with the ability to operate in
complex domains without requiring large quantities of data.
Sum-Product Graphical Models
Authors: Mattia Desana, Christoph Schnörr
Date: 2017-08-21 00:00:00
Tasks: Density Estimation

This paper introduces a new probabilistic architecture called Sum-Product
Graphical Model (SPGM). SPGMs combine traits from Sum-Product Networks (SPNs)
and Graphical Models (GMs): Like SPNs, SPGMs always enable tractable inference
using a class of models that incorporate context specific independence. Like
GMs, SPGMs provide a high-level model interpretation in terms of conditional
independence assumptions and corresponding factorizations. Thus, the new
architecture represents a class of probability distributions that combines, for
the first time, the semantics of graphical models with the evaluation
efficiency of SPNs. We also propose a novel algorithm for learning both the
structure and the parameters of SPGMs. A comparative empirical evaluation
demonstrates competitive performances of our approach in density estimation.
Differentially Private Ordinary Least Squares
Authors: Or Sheffet
Date: 2015-07-09 00:00:00
Proceeding: ICML 2017 8
Tasks: Attribute, regression

Linear regression is one of the most prevalent techniques in machine
learning, however, it is also common to use linear regression for its
\emph{explanatory} capabilities rather than label prediction. Ordinary Least
Squares (OLS) is often used in statistics to establish a correlation between an
attribute (e.g. gender) and a label (e.g. income) in the presence of other
(potentially correlated) features. OLS assumes a particular model that randomly
generates the data, and derives \emph{$t$-values} --- representing the
likelihood of each real value to be the true correlation. Using $t$-values, OLS
can release a \emph{confidence interval}, which is an interval on the reals
that is likely to contain the true correlation, and when this interval does not
intersect the origin, we can \emph{reject the null hypothesis} as it is likely
that the true correlation is non-zero. Our work aims at achieving similar
guarantees on data under differentially private estimators. First, we show that
for well-spread data, the Gaussian Johnson-Lindenstrauss Transform (JLT) gives
a very good approximation of $t$-values, secondly, when JLT approximates Ridge
regression (linear regression with $l_2$-regularization) we derive, under
certain conditions, confidence intervals using the projected data, lastly, we
derive, under different conditions, confidence intervals for the "Analyze
Gauss" algorithm (Dwork et al, STOC 2014).
Cold Fusion: Training Seq2Seq Models Together with Language Models
Authors: Anuroop Sriram, Heewoo Jun, Sanjeev Satheesh, Adam Coates
Date: 2017-08-21 00:00:00
Proceeding: ICLR 2018 1
Tasks: Image Captioning, Language Modeling, Language Modelling, Machine Translation, speech-recognition, Speech Recognition, Translation

Sequence-to-sequence (Seq2Seq) models with attention have excelled at tasks
which involve generating natural language sentences such as machine
translation, image captioning and speech recognition. Performance has further
been improved by leveraging unlabeled data, often in the form of a language
model. In this work, we present the Cold Fusion method, which leverages a
pre-trained language model during training, and show its effectiveness on the
speech recognition task. We show that Seq2Seq models with Cold Fusion are able
to better utilize language information enjoying i) faster convergence and
better generalization, and ii) almost complete transfer to a new domain while
using less than 10% of the labeled training data.
STNet: Selective Tuning of Convolutional Networks for Object Localization
Authors: Mahdi Biparva, John Tsotsos
Date: 2017-08-21 00:00:00
Tasks: Object, Object Localization

Visual attention modeling has recently gained momentum in developing visual
hierarchies provided by Convolutional Neural Networks. Despite recent successes
of feedforward processing on the abstraction of concepts form raw images, the
inherent nature of feedback processing has remained computationally
controversial. Inspired by the computational models of covert visual attention,
we propose the Selective Tuning of Convolutional Networks (STNet). It is
composed of both streams of Bottom-Up and Top-Down information processing to
selectively tune the visual representation of Convolutional networks. We
experimentally evaluate the performance of STNet for the weakly-supervised
localization task on the ImageNet benchmark dataset. We demonstrate that STNet
not only successfully surpasses the state-of-the-art results but also generates
attention-driven class hypothesis maps.
Estimating a common covariance matrix for network meta-analysis of gene expression datasets in diffuse large B-cell lymphoma
Authors: Anders Ellern Bilgrau, Rasmus Froberg Brøndum, Poul Svante Eriksen, Karen Dybkær, Martin Bøgsted
Date: 2015-03-27 00:00:00

The estimation of covariance matrices of gene expressions has many
applications in cancer systems biology. Many gene expression studies, however,
are hampered by low sample size and it has therefore become popular to increase
sample size by collecting gene expression data across studies. Motivated by the
traditional meta-analysis using random effects models, we present a
hierarchical random covariance model and use it for the meta-analysis of gene
correlation networks across 11 large-scale gene expression studies of diffuse
large B-cell lymphoma (DLBCL). We suggest to use a maximum likelihood estimator
for the underlying common covariance matrix and introduce an EM algorithm for
estimation. By simulation experiments comparing the estimated covariance
matrices by cophenetic correlation and Kullback-Leibler divergence the
suggested estimator showed to perform better or not worse than a simple pooled
estimator. In a posthoc analysis of the estimated common covariance matrix for
the DLBCL data we were able to identify novel biologically meaningful gene
correlation networks with eigengenes of prognostic value. In conclusion, the
method seems to provide a generally applicable framework for meta-analysis,
when multiple features are measured and believed to share a common covariance
matrix obscured by study dependent noise.