pwc-archive/papers-with-abstracts huggingface dataset:
PAC Battling Bandits in the Plackett-Luce Model
Authors: Aadirupa Saha, Aditya Gopalan
Date: 2018-08-12 00:00:00
Tasks: model

We introduce the probably approximately correct (PAC) \emph{Battling-Bandit}
problem with the Plackett-Luce (PL) subset choice model--an online learning
framework where at each trial the learner chooses a subset of $k$ arms from a
fixed set of $n$ arms, and subsequently observes a stochastic feedback
indicating preference information of the items in the chosen subset, e.g., the
most preferred item or ranking of the top $m$ most preferred items etc. The
objective is to identify a near-best item in the underlying PL model with high
confidence. This generalizes the well-studied PAC \emph{Dueling-Bandit} problem
over $n$ arms, which aims to recover the \emph{best-arm} from pairwise
preference information, and is known to require $O(\frac{n}{\epsilon^2} \ln
\frac{1}{\delta})$ sample complexity \citep{Busa_pl,Busa_top}. We study the
sample complexity of this problem under various feedback models: (1) Winner of
the subset (WI), and (2) Ranking of top-$m$ items (TR) for $2\le m \le k$. We
show, surprisingly, that with winner information (WI) feedback over subsets of
size $2 \leq k \leq n$, the best achievable sample complexity is still $O\left(
\frac{n}{\epsilon^2} \ln \frac{1}{\delta}\right)$, independent of $k$, and the
same as that in the Dueling Bandit setting ($k=2$). For the more general
top-$m$ ranking (TR) feedback model, we show a significantly smaller lower
bound on sample complexity of $\Omega\bigg( \frac{n}{m\epsilon^2} \ln
\frac{1}{\delta}\bigg)$, which suggests a multiplicative reduction by a factor
${m}$ owing to the additional information revealed from preferences among $m$
items instead of just $1$. We also propose two algorithms for the PAC problem
with the TR feedback model with optimal (upto logarithmic factors) sample
complexity guarantees, establishing the increase in statistical efficiency from
exploiting rank-ordered feedback.
Language Guided Fashion Image Manipulation with Feature-wise Transformations
Authors: Mehmet Günel, Erkut Erdem, Aykut Erdem
Date: 2018-08-12 00:00:00
Tasks: Image Manipulation

Developing techniques for editing an outfit image through natural sentences
and accordingly generating new outfits has promising applications for art,
fashion and design. However, it is considered as a certainly challenging task
since image manipulation should be carried out only on the relevant parts of
the image while keeping the remaining sections untouched. Moreover, this
manipulation process should generate an image that is as realistic as possible.
In this work, we propose FiLMedGAN, which leverages feature-wise linear
modulation (FiLM) to relate and transform visual features with natural language
representations without using extra spatial information. Our experiments
demonstrate that this approach, when combined with skip connections and total
variation regularization, produces more plausible results than the baseline
work, and has a better localization capability when generating new outfits
consistent with the target description.
A simulation study to distinguish prompt photon from $π^0$ and beam halo in a granular calorimeter using deep networks
Authors: Shamik Ghosh, Abhirami Harilal, A. R. Sahasransu, Ritesh Kumar Singh, Satyaki Bhattacharya
Date: 2018-08-12 00:00:00

In a hadron collider environment identification of prompt photons originating
in a hard partonic scattering process and rejection of non-prompt photons
coming from hadronic jets or from beam related sources, is the first step for
study of processes with photons in final state. Photons coming from decay of
$\pi_0$'s produced inside a hadronic jet and photons produced in catastrophic
bremsstrahlung by beam halo muons are two major sources of non-prompt photons.
In this paper the potential of deep learning methods for separating the prompt
photons from beam halo and $\pi^0$'s in the electromagnetic calorimeter of a
collider detector is investigated, using an approximate description of the CMS
detector. It is shown that, using only calorimetric information as images with
a Convolutional Neural Network, beam halo (and $\pi^{0}$) can be separated from
photon with 99.96\% (97.7\%) background rejection for 99.00\% (90.0\%) signal
efficiency which is much better than traditionally employed variables.
Multimodal Differential Network for Visual Question Generation
Authors: Badri N. Patro, Sandeep Kumar, Vinod K. Kurmi, Vinay P. Namboodiri
Date: 2018-08-12 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Natural Questions, Question Generation, Question-Generation

Generating natural questions from an image is a semantic task that requires using visual and language modality to learn multimodal representations. Images can have multiple visual and language contexts that are relevant for generating questions namely places, captions, and tags. In this paper, we propose the use of exemplars for obtaining the relevant context. We obtain this by using a Multimodal Differential Network to produce natural and engaging questions. The generated questions show a remarkable similarity to the natural questions as validated by a human study. Further, we observe that the proposed approach substantially improves over state-of-the-art benchmarks on the quantitative metrics (BLEU, METEOR, ROUGE, and CIDEr).
Text Classification using Capsules
Authors: Jaeyoung Kim, Sion Jang, Sungchul Choi, Eunjeong Park
Date: 2018-08-12 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, text-classification, Text Classification

This paper presents an empirical exploration of the use of capsule networks
for text classification. While it has been shown that capsule networks are
effective for image classification, their validity in the domain of text has
not been explored. In this paper, we show that capsule networks indeed have the
potential for text classification and that they have several advantages over
convolutional neural networks. We further suggest a simple routing method that
effectively reduces the computational complexity of dynamic routing. We
utilized seven benchmark datasets to demonstrate that capsule networks, along
with the proposed routing method provide comparable results.
Reconfigurable Inverted Index
Authors: Yusuke Matsui, Ryota Hinami, Shin'ichi Satoh
Date: 2018-08-12 00:00:00

Existing approximate nearest neighbor search systems suffer from two
fundamental problems that are of practical importance but have not received
sufficient attention from the research community. First, although existing
systems perform well for the whole database, it is difficult to run a search
over a subset of the database. Second, there has been no discussion concerning
the performance decrement after many items have been newly added to a system.
We develop a reconfigurable inverted index (Rii) to resolve these two issues.
Based on the standard IVFADC system, we design a data layout such that items
are stored linearly. This enables us to efficiently run a subset search by
switching the search method to a linear PQ scan if the size of a subset is
small. Owing to the linear layout, the data structure can be dynamically
adjusted after new items are added, maintaining the fast speed of the system.
Extensive comparisons show that Rii achieves a comparable performance with
state-of-the art systems such as Faiss.
Augmenting word2vec with latent Dirichlet allocation within a clinical application
Authors: Akshay Budhkar, Frank Rudzicz
Date: 2018-08-12 00:00:00
Proceeding: NAACL 2019 6

This paper presents three hybrid models that directly combine latent
Dirichlet allocation and word embedding for distinguishing between speakers
with and without Alzheimer's disease from transcripts of picture descriptions.
Two of our models get F-scores over the current state-of-the-art using
automatic methods on the DementiaBank dataset.
Large-Scale Learnable Graph Convolutional Networks
Authors: Hongyang Gao, Zhengyang Wang, Shuiwang Ji
Date: 2018-08-12 00:00:00
Tasks: Document Classification, Inductive Learning, Node Classification

Convolutional neural networks (CNNs) have achieved great success on grid-like
data such as images, but face tremendous challenges in learning from more
generic data such as graphs. In CNNs, the trainable local filters enable the
automatic extraction of high-level features. The computation with filters
requires a fixed number of ordered units in the receptive fields. However, the
number of neighboring units is neither fixed nor are they ordered in generic
graphs, thereby hindering the applications of convolutional operations. Here,
we address these challenges by proposing the learnable graph convolutional
layer (LGCL). LGCL automatically selects a fixed number of neighboring nodes
for each feature based on value ranking in order to transform graph data into
grid-like structures in 1-D format, thereby enabling the use of regular
convolutional operations on generic graphs. To enable model training on
large-scale graphs, we propose a sub-graph training method to reduce the
excessive memory and computational resource requirements suffered by prior
methods on graph convolutions. Our experimental results on node classification
tasks in both transductive and inductive learning settings demonstrate that our
methods can achieve consistently better performance on the Cora, Citeseer,
Pubmed citation network, and protein-protein interaction network datasets. Our
results also indicate that the proposed methods using sub-graph training
strategy are more efficient as compared to prior approaches.
Open-World Stereo Video Matching with Deep RNN
Authors: Yiran Zhong, Hongdong Li, Yuchao Dai
Date: 2018-08-12 00:00:00
Proceeding: ECCV 2018 9
Tasks: Stereo Matching, Stereo Matching Hand

Deep Learning based stereo matching methods have shown great successes and
achieved top scores across different benchmarks. However, like most data-driven
methods, existing deep stereo matching networks suffer from some well-known
drawbacks such as requiring large amount of labeled training data, and that
their performances are fundamentally limited by the generalization ability. In
this paper, we propose a novel Recurrent Neural Network (RNN) that takes a
continuous (possibly previously unseen) stereo video as input, and directly
predicts a depth-map at each frame without a pre-training process, and without
the need of ground-truth depth-maps as supervision. Thanks to the recurrent
nature (provided by two convolutional-LSTM blocks), our network is able to
memorize and learn from its past experiences, and modify its inner parameters
(network weights) to adapt to previously unseen or unfamiliar environments.
This suggests a remarkable generalization ability of the net, making it
applicable in an {\em open world} setting. Our method works robustly with
changes in scene content, image statistics, and lighting and season conditions
{\em etc}. By extensive experiments, we demonstrate that the proposed method
seamlessly adapts between different scenarios. Equally important, in terms of
the stereo matching accuracy, it outperforms state-of-the-art deep stereo
approaches on standard benchmark datasets such as KITTI and Middlebury stereo.
Neural System Identification with Spike-triggered Non-negative Matrix Factorization
Authors: Shanshan Jia, Zhaofei Yu, Arno Onken, Yonghong Tian, Tiejun Huang, Jian. K. Liu
Date: 2018-08-12 00:00:00

Neuronal circuits formed in the brain are complex with intricate connection patterns. Such complexity is also observed in the retina as a relatively simple neuronal circuit. A retinal ganglion cell receives excitatory inputs from neurons in previous layers as driving forces to fire spikes. Analytical methods are required that can decipher these components in a systematic manner. Recently a method termed spike-triggered non-negative matrix factorization (STNMF) has been proposed for this purpose. In this study, we extend the scope of the STNMF method. By using the retinal ganglion cell as a model system, we show that STNMF can detect various computational properties of upstream bipolar cells, including spatial receptive field, temporal filter, and transfer nonlinearity. In addition, we recover synaptic connection strengths from the weight matrix of STNMF. Furthermore, we show that STNMF can separate spikes of a ganglion cell into a few subsets of spikes where each subset is contributed by one presynaptic bipolar cell. Taken together, these results corroborate that STNMF is a useful method for deciphering the structure of neuronal circuits.
A Fourier View of REINFORCE
Authors: Adeel Pervez
Date: 2018-08-12 00:00:00

We show a connection between the Fourier spectrum of Boolean functions and
the REINFORCE gradient estimator for binary latent variable models. We show
that REINFORCE estimates (up to a factor) the degree-1 Fourier coefficients of
a Boolean function. Using this connection we offer a new perspective on
variance reduction in gradient estimation for latent variable models: namely,
that variance reduction involves eliminating or reducing Fourier coefficients
that do not have degree 1. We then use this connection to develop low-variance
unbiased gradient estimators for binary latent variable models such as sigmoid
belief networks. The estimator is based upon properties of the noise operator
from Boolean Fourier theory and involves a sample-dependent baseline added to
the REINFORCE estimator in a way that keeps the estimator unbiased. The
baseline can be plugged into existing gradient estimators for further variance
reduction.
Plithogeny, Plithogenic Set, Logic, Probability, and Statistics
Authors: Florentin Smarandache
Date: 2018-08-12 00:00:00
Tasks: Attribute

In this book we introduce the plithogenic set (as generalization of crisp,
fuzzy, intuitionistic fuzzy, and neutrosophic sets), plithogenic logic (as
generalization of classical, fuzzy, intuitionistic fuzzy, and neutrosophic
logics), plithogenic probability (as generalization of classical, imprecise,
and neutrosophic probabilities), and plithogenic statistics (as generalization
of classical, and neutrosophic statistics). Plithogenic Set is a set whose
elements are characterized by one or more attributes, and each attribute may
have many values. An attribute value v has a corresponding (fuzzy,
intuitionistic fuzzy, or neutrosophic) degree of appurtenance d(x,v) of the
element x, to the set P, with respect to some given criteria. In order to
obtain a better accuracy for the plithogenic aggregation operators in the
plithogenic set, logic, probability and for a more exact inclusion (partial
order), a (fuzzy, intuitionistic fuzzy, or neutrosophic) contradiction
(dissimilarity) degree is defined between each attribute value and the dominant
(most important) attribute value. The plithogenic intersection and union are
linear combinations of the fuzzy operators tnorm and tconorm, while the
plithogenic complement, inclusion, equality are influenced by the attribute
values contradiction (dissimilarity) degrees. Formal definitions of plithogenic
set, logic, probability, statistics are presented into the book, followed by
plithogenic aggregation operators, various theorems related to them, and
afterwards examples and applications of these new concepts in our everyday
life.
Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks
Authors: Chengjia Wang, Gillian Macnaught, Giorgos Papanastasiou, Tom MacGillivray, David Newby
Date: 2018-08-12 00:00:00
Tasks: Image Generation

Recently, the cycle-consistent generative adversarial networks (CycleGAN) has
been widely used for synthesis of multi-domain medical images. The
domain-specific nonlinear deformations captured by CycleGAN make the
synthesized images difficult to be used for some applications, for example,
generating pseudo-CT for PET-MR attenuation correction. This paper presents a
deformation-invariant CycleGAN (DicycleGAN) method using deformable
convolutional layers and new cycle-consistency losses. Its robustness dealing
with data that suffer from domain-specific nonlinear deformations has been
evaluated through comparison experiments performed on a multi-sequence brain MR
dataset and a multi-modality abdominal dataset. Our method has displayed its
ability to generate synthesized data that is aligned with the source while
maintaining a proper quality of signal compared to CycleGAN-generated data. The
proposed model also obtained comparable performance with CycleGAN when data
from the source and target domains are alignable through simple affine
transformations.
Denoising of 3-D Magnetic Resonance Images Using a Residual Encoder-Decoder Wasserstein Generative Adversarial Network
Authors: Maosong Ran, Jinrong Hu, Yang Chen, Hu Chen, Huaiqiang Sun, Jiliu Zhou, Yi Zhang
Date: 2018-08-12 00:00:00
Tasks: Decoder, Denoising, Generative Adversarial Network, Medical Image Analysis

Structure-preserved denoising of 3D magnetic resonance imaging (MRI) images is a critical step in medical image analysis. Over the past few years, many algorithms with impressive performances have been proposed. In this paper, inspired by the idea of deep learning, we introduce an MRI denoising method based on the residual encoder-decoder Wasserstein generative adversarial network (RED-WGAN). Specifically, to explore the structure similarity between neighboring slices, a 3D configuration is utilized as the basic processing unit. Residual autoencoders combined with deconvolution operations are introduced into the generator network. Furthermore, to alleviate the oversmoothing shortcoming of the traditional mean squared error (MSE) loss function, the perceptual similarity, which is implemented by calculating the distances in the feature space extracted by a pretrained VGG-19 network, is incorporated with the MSE and adversarial losses to form the new loss function. Extensive experiments are implemented to assess the performance of the proposed method. The experimental results show that the proposed RED-WGAN achieves performance superior to several state-of-the-art methods in both simulated and real clinical data. In particular, our method demonstrates powerful abilities in both noise suppression and structure preservation.
Fine-grained visual recognition with salient feature detection
Authors: Hui Feng, Shan-Shan Wang, Shuzhi Sam Ge
Date: 2018-08-12 00:00:00
Tasks: Classification, Fine-Grained Visual Recognition, General Classification, Object

Computer vision based fine-grained recognition has received great attention
in recent years. Existing works focus on discriminative part localization and
feature learning. In this paper, to improve the performance of fine-grained
recognition, we try to precisely locate as many salient parts of object as
possible at first. Then, we figure out the classification probability that can
be obtained by using separate parts for object classification. Finally, through
extracting efficient features from each part and combining them, then feeding
to a classifier for recognition, an improved accuracy over state-of-art
algorithms has been obtained on CUB200-2011 bird dataset.
Sequence Labeling: A Practical Approach
Authors: Adnan Akhundov, Dietrich Trautmann, Georg Groh
Date: 2018-08-12 00:00:00
Tasks: Chunking, NER, POS, POS Tagging

We take a practical approach to solving sequence labeling problem assuming
unavailability of domain expertise and scarcity of informational and
computational resources. To this end, we utilize a universal end-to-end
Bi-LSTM-based neural sequence labeling model applicable to a wide range of NLP
tasks and languages. The model combines morphological, semantic, and structural
cues extracted from data to arrive at informed predictions. The model's
performance is evaluated on eight benchmark datasets (covering three tasks:
POS-tagging, NER, and Chunking, and four languages: English, German, Dutch, and
Spanish). We observe state-of-the-art results on four of them: CoNLL-2012
(English NER), CoNLL-2002 (Dutch NER), GermEval 2014 (German NER), Tiger Corpus
(German POS-tagging), and competitive performance on the rest.
Multimodal Language Analysis with Recurrent Multistage Fusion
Authors: Paul Pu Liang, Ziyin Liu, Amir Zadeh, Louis-Philippe Morency
Date: 2018-08-12 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Emotion Recognition, Multimodal Sentiment Analysis, Sentiment Analysis

Computational modeling of human multimodal language is an emerging research
area in natural language processing spanning the language, visual and acoustic
modalities. Comprehending multimodal language requires modeling not only the
interactions within each modality (intra-modal interactions) but more
importantly the interactions between modalities (cross-modal interactions). In
this paper, we propose the Recurrent Multistage Fusion Network (RMFN) which
decomposes the fusion problem into multiple stages, each of them focused on a
subset of multimodal signals for specialized, effective fusion. Cross-modal
interactions are modeled using this multistage fusion approach which builds
upon intermediate representations of previous stages. Temporal and intra-modal
interactions are modeled by integrating our proposed fusion approach with a
system of recurrent neural networks. The RMFN displays state-of-the-art
performance in modeling human multimodal language across three public datasets
relating to multimodal sentiment analysis, emotion recognition, and speaker
traits recognition. We provide visualizations to show that each stage of fusion
focuses on a different subset of multimodal signals, learning increasingly
discriminative multimodal representations.
Addressee and Response Selection for Multilingual Conversation
Authors: Motoki Sato, Hiroki Ouch, Yuta Tsuboi
Date: 2018-08-12 00:00:00
Proceeding: COLING 2018 8
Tasks: Transfer Learning

Developing conversational systems that can converse in many languages is an
interesting challenge for natural language processing. In this paper, we
introduce multilingual addressee and response selection. In this task, a
conversational system predicts an appropriate addressee and response for an
input message in multiple languages. A key to developing such multilingual
responding systems is how to utilize high-resource language data to compensate
for low-resource language data. We present several knowledge transfer methods
for conversational systems. To evaluate our methods, we create a new
multilingual conversation dataset. Experiments on the dataset demonstrate the
effectiveness of our methods.
Iterative Global Similarity Points : A robust coarse-to-fine integration solution for pairwise 3D point cloud registration
Authors: Yue Pan, Bisheng Yang, Fuxun Liang, Zhen Dong
Date: 2018-08-12 00:00:00
Tasks: Point Cloud Registration

In this paper, we propose a coarse-to-fine integration solution inspired by
the classical ICP algorithm, to pairwise 3D point cloud registration with two
improvements of hybrid metric spaces (eg, BSC feature and Euclidean geometry
spaces) and globally optimal correspondences matching. First, we detect the
keypoints of point clouds and use the Binary Shape Context (BSC) descriptor to
encode their local features. Then, we formulate the correspondence matching
task as an energy function, which models the global similarity of keypoints on
the hybrid spaces of BSC feature and Euclidean geometry. Next, we estimate the
globally optimal correspondences through optimizing the energy function by the
Kuhn-Munkres algorithm and then calculate the transformation based on the
correspondences. Finally,we iteratively refine the transformation between two
point clouds by conducting optimal correspondences matching and transformation
calculation in a mutually reinforcing manner, to achieve the coarse-to-fine
registration under an unified framework.The proposed method is evaluated and
compared to several state-of-the-art methods on selected challenging datasets
with repetitive, symmetric and incomplete structures.Comprehensive experiments
demonstrate that the proposed IGSP algorithm obtains good performance and
outperforms the state-of-the-art methods in terms of both rotation and
translation errors.