pwc-archive/papers-with-abstracts huggingface dataset:
Attention-Aware Generative Adversarial Networks (ATA-GANs)
Authors: Dimitris Kastaniotis, Ioanna Ntinou, Dimitrios Tsourounis, George Economou, Spiros Fotopoulos
Date: 2018-02-25 00:00:00
Tasks: object-detection, Object Detection, Object Localization, Transfer Learning

In this work, we present a novel approach for training Generative Adversarial
Networks (GANs). Using the attention maps produced by a Teacher- Network we are
able to improve the quality of the generated images as well as perform weakly
object localization on the generated images. To this end, we generate images of
HEp-2 cells captured with Indirect Imunofluoresence (IIF) and study the ability
of our network to perform a weakly localization of the cell. Firstly, we
demonstrate that whilst GANs can learn the mapping between the input domain and
the target distribution efficiently, the discriminator network is not able to
detect the regions of interest. Secondly, we present a novel attention transfer
mechanism which allows us to enforce the discriminator to put emphasis on the
regions of interest via transfer learning. Thirdly, we show that this leads to
more realistic images, as the discriminator learns to put emphasis on the area
of interest. Fourthly, the proposed method allows one to generate both images
as well as attention maps which can be useful for data annotation e.g in object
detection.
Fair Division via Social Comparison
Authors: Rediet Abebe, Jon Kleinberg, David Parkes
Date: 2016-11-20 00:00:00

In the classical cake cutting problem, a resource must be divided among
agents with different utilities so that each agent believes they have received
a fair share of the resource relative to the other agents. We introduce a
variant of the problem in which we model an underlying social network on the
agents with a graph, and agents only evaluate their shares relative to their
neighbors' in the network. This formulation captures many situations in which
it is unrealistic to assume a global view, and also exposes interesting
phenomena in the original problem.
  Specifically, we say an allocation is locally envy-free if no agent envies a
neighbor's allocation and locally proportional if each agent values her own
allocation as much as the average value of her neighbor's allocations, with the
former implying the latter. While global envy-freeness implies local
envy-freeness, global proportionality does not imply local proportionality, or
vice versa. A general result is that for any two distinct graphs on the same
set of nodes and an allocation, there exists a set of valuation functions such
that the allocation is locally proportional on one but not the other.
  We fully characterize the set of graphs for which an oblivious single-cutter
protocol-- a protocol that uses a single agent to cut the cake into pieces
--admits a bounded protocol with $O(n^2)$ query complexity for locally
envy-free allocations in the Robertson-Webb model. We also consider the price
of envy-freeness, which compares the total utility of an optimal allocation to
the best utility of an allocation that is envy-free. We show that a lower bound
of $\Omega(\sqrt{n})$ on the price of envy-freeness for global allocations in
fact holds for local envy-freeness in any connected undirected graph. Thus,
sparse graphs surprisingly do not provide more flexibility with respect to the
quality of envy-free allocations.
One Single Deep Bidirectional LSTM Network for Word Sense Disambiguation of Text Data
Authors: Ahmad Pesaranghader, Ali Pesaranghader, Stan Matwin, Marina Sokolova
Date: 2018-02-25 00:00:00
Tasks: Articles, Word Sense Disambiguation

Due to recent technical and scientific advances, we have a wealth of
information hidden in unstructured text data such as offline/online narratives,
research articles, and clinical reports. To mine these data properly,
attributable to their innate ambiguity, a Word Sense Disambiguation (WSD)
algorithm can avoid numbers of difficulties in Natural Language Processing
(NLP) pipeline. However, considering a large number of ambiguous words in one
language or technical domain, we may encounter limiting constraints for proper
deployment of existing WSD models. This paper attempts to address the problem
of one-classifier-per-one-word WSD algorithms by proposing a single
Bidirectional Long Short-Term Memory (BLSTM) network which by considering
senses and context sequences works on all ambiguous words collectively.
Evaluated on SensEval-3 benchmark, we show the result of our model is
comparable with top-performing WSD algorithms. We also discuss how applying
additional modifications alleviates the model fault and the need for more
training data.
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
Authors: Shiyu Liang, Yixuan Li, R. Srikant
Date: 2017-06-08 00:00:00
Proceeding: ICLR 2018 1
Tasks: Out-of-Distribution Detection

We consider the problem of detecting out-of-distribution images in neural networks. We propose ODIN, a simple and effective method that does not require any change to a pre-trained neural network. Our method is based on the observation that using temperature scaling and adding small perturbations to the input can separate the softmax score distributions between in- and out-of-distribution images, allowing for more effective detection. We show in a series of experiments that ODIN is compatible with diverse network architectures and datasets. It consistently outperforms the baseline approach by a large margin, establishing a new state-of-the-art performance on this task. For example, ODIN reduces the false positive rate from the baseline 34.7% to 4.3% on the DenseNet (applied to CIFAR-10) when the true positive rate is 95%.
Seeing Small Faces from Robust Anchor's Perspective
Authors: Chenchen Zhu, Ran Tao, Khoa Luu, Marios Savvides
Date: 2018-02-25 00:00:00
Proceeding: CVPR 2018 6
Tasks: Face Detection

This paper introduces a novel anchor design to support anchor-based face
detection for superior scale-invariant performance, especially on tiny faces.
To achieve this, we explicitly address the problem that anchor-based detectors
drop performance drastically on faces with tiny sizes, e.g. less than 16x16
pixels. In this paper, we investigate why this is the case. We discover that
current anchor design cannot guarantee high overlaps between tiny faces and
anchor boxes, which increases the difficulty of training. The new Expected Max
Overlapping (EMO) score is proposed which can theoretically explain the low
overlapping issue and inspire several effective strategies of new anchor design
leading to higher face overlaps, including anchor stride reduction with new
network architectures, extra shifted anchors, and stochastic face shifting.
Comprehensive experiments show that our proposed method significantly
outperforms the baseline anchor-based detector, while consistently achieving
state-of-the-art results on challenging face detection datasets with
competitive runtime speed.
Generative Models of Visually Grounded Imagination
Authors: Ramakrishna Vedantam, Ian Fischer, Jonathan Huang, Kevin Murphy
Date: 2017-05-30 00:00:00
Proceeding: ICLR 2018 1
Tasks: Attribute

It is easy for people to imagine what a man with pink hair looks like, even
if they have never seen such a person before. We call the ability to create
images of novel semantic concepts visually grounded imagination. In this paper,
we show how we can modify variational auto-encoders to perform this task. Our
method uses a novel training objective, and a novel product-of-experts
inference network, which can handle partially specified (abstract) concepts in
a principled and efficient way. We also propose a set of easy-to-compute
evaluation metrics that capture our intuitive notions of what it means to have
good visual imagination, namely correctness, coverage, and compositionality
(the 3 C's). Finally, we perform a detailed comparison of our method with two
existing joint image-attribute VAE methods (the JMVAE method of Suzuki et.al.
and the BiVCCA method of Wang et.al.) by applying them to two datasets: the
MNIST-with-attributes dataset (which we introduce here), and the CelebA
dataset.
Wide Compression: Tensor Ring Nets
Authors: Wenqi Wang, Yifan Sun, Brian Eriksson, Wenlin Wang, Vaneet Aggarwal
Date: 2018-02-25 00:00:00
Proceeding: CVPR 2018 6
Tasks: image-classification, Image Classification

Deep neural networks have demonstrated state-of-the-art performance in a
variety of real-world applications. In order to obtain performance gains, these
networks have grown larger and deeper, containing millions or even billions of
parameters and over a thousand layers. The trade-off is that these large
architectures require an enormous amount of memory, storage, and computation,
thus limiting their usability. Inspired by the recent tensor ring
factorization, we introduce Tensor Ring Networks (TR-Nets), which significantly
compress both the fully connected layers and the convolutional layers of deep
neural networks. Our results show that our TR-Nets approach {is able to
compress LeNet-5 by $11\times$ without losing accuracy}, and can compress the
state-of-the-art Wide ResNet by $243\times$ with only 2.3\% degradation in
{Cifar10 image classification}. Overall, this compression scheme shows promise
in scientific computing and deep learning, especially for emerging
resource-constrained devices such as smartphones, wearables, and IoT devices.
Power efficient Spiking Neural Network Classifier based on memristive crossbar network for spike sorting application
Authors: Anand Kumar Mukhopadhyay, Indrajit Chakrabarti, Arindam Basu, Mrigank Sharad
Date: 2018-02-25 00:00:00
Tasks: General Classification, Spike Sorting

In this paper authors have presented a power efficient scheme for
implementing a spike sorting module. Spike sorting is an important application
in the field of neural signal acquisition for implantable biomedical systems
whose function is to map the Neural-spikes (N-spikes) correctly to the neurons
from which it originates. The accurate classification is a pre-requisite for
the succeeding systems needed in Brain-Machine-Interfaces (BMIs) to give better
performance. The primary design constraint to be satisfied for the spike sorter
module is low power with good accuracy. There lies a trade-off in terms of
power consumption between the on-chip and off-chip training of the N-spike
features. In the former case care has to be taken to make the computational
units power efficient whereas in the later the data rate of wireless
transmission should be minimized to reduce the power consumption due to the
transceivers. In this work a 2-step shared training scheme involving a K-means
sorter and a Spiking Neural Network (SNN) is elaborated for on-chip training
and classification. Also, a low power SNN classifier scheme using memristive
crossbar type architecture is compared with a fully digital implementation. The
advantage of the former classifier is that it is power efficient while
providing comparable accuracy as that of the digital implementation due to the
robustness of the SNN training algorithm which has a good tolerance for
variation in memristance.
Multiclass Common Spatial Pattern for EEG based Brain Computer Interface with Adaptive Learning Classifier
Authors: Hardik Meisheri, Nagraj Ramrao, Suman Mitra
Date: 2018-02-25 00:00:00
Tasks: Brain Computer Interface, Classification, EEG, Electroencephalogram (EEG), General Classification, Motor Imagery

In Brain Computer Interface (BCI), data generated from Electroencephalogram (EEG) is non-stationary with low signal to noise ratio and contaminated with artifacts. Common Spatial Pattern (CSP) algorithm has been proved to be effective in BCI for extracting features in motor imagery tasks, but it is prone to overfitting. Many algorithms have been devised to regularize CSP for two class problem, however they have not been effective when applied to multiclass CSP. Outliers present in data affect extracted CSP features and reduces performance of the system. In addition to this non-stationarity present in the features extracted from the CSP present a challenge in classification. We propose a method to identify and remove artifact present in the data during pre-processing stage, this helps in calculating eigenvectors which in turn generates better CSP features. To handle the non-stationarity, Self-Regulated Interval Type-2 Neuro-Fuzzy Inference System (SRIT2NFIS) was proposed in the literature for two class EEG classification problem. This paper extends the SRIT2NFIS to multiclass using Joint Approximate Diagonalization (JAD). The results on standard data set from BCI competition IV shows significant increase in the accuracies from the current state of the art methods for multiclass classification.
Functional Gradient Boosting based on Residual Network Perception
Authors: Atsushi Nitanda, Taiji Suzuki
Date: 2018-02-25 00:00:00
Proceeding: ICML 2018 7

Residual Networks (ResNets) have become state-of-the-art models in deep
learning and several theoretical studies have been devoted to understanding why
ResNet works so well. One attractive viewpoint on ResNet is that it is
optimizing the risk in a functional space by combining an ensemble of effective
features. In this paper, we adopt this viewpoint to construct a new gradient
boosting method, which is known to be very powerful in data analysis. To do so,
we formalize the gradient boosting perspective of ResNet mathematically using
the notion of functional gradients and propose a new method called ResFGB for
classification tasks by leveraging ResNet perception. Two types of
generalization guarantees are provided from the optimization perspective: one
is the margin bound and the other is the expected risk bound by the
sample-splitting technique. Experimental results show superior performance of
the proposed method over state-of-the-art methods such as LightGBM.
Cakewalk Sampling
Authors: Uri Patish, Shimon Ullman
Date: 2018-02-25 00:00:00
Tasks: Clustering, Combinatorial Optimization, valid

We study the task of finding good local optima in combinatorial optimization problems. Although combinatorial optimization is NP-hard in general, locally optimal solutions are frequently used in practice. Local search methods however typically converge to a limited set of optima that depend on their initialization. Sampling methods on the other hand can access any valid solution, and thus can be used either directly or alongside methods of the former type as a way for finding good local optima. Since the effectiveness of this strategy depends on the sampling distribution, we derive a robust learning algorithm that adapts sampling distributions towards good local optima of arbitrary objective functions. As a first use case, we empirically study the efficiency in which sampling methods can recover locally maximal cliques in undirected graphs. Not only do we show how our adaptive sampler outperforms related methods, we also show how it can even approach the performance of established clique algorithms. As a second use case, we consider how greedy algorithms can be combined with our adaptive sampler, and we demonstrate how this leads to superior performance in k-medoid clustering. Together, these findings suggest that our adaptive sampler can provide an effective strategy to combinatorial optimization problems that arise in practice.
Building Instance Classification Using Street View Images
Authors: Jian Kang, Marco Körner, Yuanyuan Wang, Hannes Taubenböck, Xiao Xiang Zhu
Date: 2018-02-25 00:00:00
Tasks: Classification, General Classification

Land-use classification based on spaceborne or aerial remote sensing images
has been extensively studied over the past decades. Such classification is
usually a patch-wise or pixel-wise labeling over the whole image. But for many
applications, such as urban population density mapping or urban utility
planning, a classification map based on individual buildings is much more
informative. However, such semantic classification still poses some fundamental
challenges, for example, how to retrieve fine boundaries of individual
buildings. In this paper, we proposed a general framework for classifying the
functionality of individual buildings. The proposed method is based on
Convolutional Neural Networks (CNNs) which classify facade structures from
street view images, such as Google StreetView, in addition to remote sensing
images which usually only show roof structures. Geographic information was
utilized to mask out individual buildings, and to associate the corresponding
street view images. We created a benchmark dataset which was used for training
and evaluating CNNs. In addition, the method was applied to generate building
classification maps on both region and city scales of several cities in Canada
and the US. Keywords: CNN, Building instance classification, Street view
images, OpenStreetMap
Online Learning of Quantum States
Authors: Scott Aaronson, Xinyi Chen, Elad Hazan, Satyen Kale, Ashwin Nayak
Date: 2018-02-25 00:00:00
Proceeding: NeurIPS 2018 12

Suppose we have many copies of an unknown $n$-qubit state $\rho$. We measure some copies of $\rho$ using a known two-outcome measurement $E_{1}$, then other copies using a measurement $E_{2}$, and so on. At each stage $t$, we generate a current hypothesis $\sigma_{t}$ about the state $\rho$, using the outcomes of the previous measurements. We show that it is possible to do this in a way that guarantees that $|\operatorname{Tr}(E_{i} \sigma_{t}) - \operatorname{Tr}(E_{i}\rho) |$, the error in our prediction for the next measurement, is at least $\varepsilon$ at most $\operatorname{O}\!\left(n / \varepsilon^2 \right) $ times. Even in the "non-realizable" setting---where there could be arbitrary noise in the measurement outcomes---we show how to output hypothesis states that do significantly worse than the best possible states at most $\operatorname{O}\!\left(\sqrt {Tn}\right) $ times on the first $T$ measurements. These results generalize a 2007 theorem by Aaronson on the PAC-learnability of quantum states, to the online and regret-minimization settings. We give three different ways to prove our results---using convex optimization, quantum postselection, and sequential fat-shattering dimension---which have different advantages in terms of parameters and portability.
Spherical CNNs
Authors: Taco S. Cohen, Mario Geiger, Jonas Koehler, Max Welling
Date: 2018-01-30 00:00:00
Proceeding: ICLR 2018 1
Tasks: Computational Efficiency, regression

Convolutional Neural Networks (CNNs) have become the method of choice for
learning problems involving 2D planar images. However, a number of problems of
recent interest have created a demand for models that can analyze spherical
images. Examples include omnidirectional vision for drones, robots, and
autonomous cars, molecular regression problems, and global weather and climate
modelling. A naive application of convolutional networks to a planar projection
of the spherical signal is destined to fail, because the space-varying
distortions introduced by such a projection will make translational weight
sharing ineffective.
  In this paper we introduce the building blocks for constructing spherical
CNNs. We propose a definition for the spherical cross-correlation that is both
expressive and rotation-equivariant. The spherical correlation satisfies a
generalized Fourier theorem, which allows us to compute it efficiently using a
generalized (non-commutative) Fast Fourier Transform (FFT) algorithm. We
demonstrate the computational efficiency, numerical accuracy, and effectiveness
of spherical CNNs applied to 3D model recognition and atomization energy
regression.
Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction with Archive
Authors: Yongsheng Liang, Zhigang Ren, Xianghua Yao, Zuren Feng, An Chen
Date: 2018-02-25 00:00:00
Tasks: global-optimization

As a typical model-based evolutionary algorithm (EA), estimation of
distribution algorithm (EDA) possesses unique characteristics and has been
widely applied to global optimization. However, the common-used Gaussian EDA
(GEDA) usually suffers from premature convergence which severely limits its
search efficiency. This study first systematically analyses the reasons for the
deficiency of the traditional GEDA, then tries to enhance its performance by
exploiting its evolution direction, and finally develops a new GEDA variant
named EDA2. Instead of only utilizing some good solutions produced in the
current generation when estimating the Gaussian model, EDA2 preserves a certain
number of high-quality solutions generated in previous generations into an
archive and takes advantage of these historical solutions to assist estimating
the covariance matrix of Gaussian model. By this means, the evolution direction
information hidden in the archive is naturally integrated into the estimated
model which in turn can guide EDA2 towards more promising solution regions.
Moreover, the new estimation method significantly reduces the population size
of EDA2 since it needs fewer individuals in the current population for model
estimation. As a result, a fast convergence can be achieved. To verify the
efficiency of EDA2, we tested it on a variety of benchmark functions and
compared it with several state-of-the-art EAs, including IPOP-CMAES, AMaLGaM,
three high-powered DE algorithms, and a new PSO algorithm. The experimental
results demonstrate that EDA2 is efficient and competitive.
Reinforcement Learning for Dynamic Bidding in Truckload Markets: an Application to Large-Scale Fleet Management with Advance Commitments
Authors: Yingfei Wang, Juliana Martins Do Nascimento, Warren Powell
Date: 2018-02-25 00:00:00
Tasks: Management, Reinforcement Learning

Truckload brokerages, a $100 billion/year industry in the U.S., plays the critical role of matching shippers with carriers, often to move loads several days into the future. Brokerages not only have to find companies that will agree to move a load, the brokerage often has to find a price that both the shipper and carrier will agree to. The price not only varies by shipper and carrier, but also by the traffic lanes and other variables such as commodity type. Brokerages have to learn about shipper and carrier response functions by offering a price and observing whether each accepts the quote. We propose a knowledge gradient policy with bootstrap aggregation for high-dimensional contextual settings to guide price experimentation by maximizing the value of information. The learning policy is tested using a carefully calibrated fleet simulator that includes a stochastic lookahead policy that simulates fleet movements, as well as the stochastic modeling of driver assignments and the carrier's load commitment policies with advance booking.