pwc-archive/papers-with-abstracts huggingface dataset:
Online Adaptive Image Reconstruction (OnAIR) Using Dictionary Models
Authors: Brian E. Moore, Saiprasad Ravishankar, Raj Rao Nadakuditi, Jeffrey A. Fessler
Date: 2018-09-06 00:00:00
Tasks: Denoising, Image Reconstruction, Video Reconstruction

Sparsity and low-rank models have been popular for reconstructing images and videos from limited or corrupted measurements. Dictionary or transform learning methods are useful in applications such as denoising, inpainting, and medical image reconstruction. This paper proposes a framework for online (or time-sequential) adaptive reconstruction of dynamic image sequences from linear (typically undersampled) measurements. We model the spatiotemporal patches of the underlying dynamic image sequence as sparse in a dictionary, and we simultaneously estimate the dictionary and the images sequentially from streaming measurements. Multiple constraints on the adapted dictionary are also considered such as a unitary matrix, or low-rank dictionary atoms that provide additional efficiency or robustness. The proposed online algorithms are memory efficient and involve simple updates of the dictionary atoms, sparse coefficients, and images. Numerical experiments demonstrate the usefulness of the proposed methods in inverse problems such as video reconstruction or inpainting from noisy, subsampled pixels, and dynamic magnetic resonance image reconstruction from very limited measurements.
Visual Coreference Resolution in Visual Dialog using Neural Module Networks
Authors: Satwik Kottur, José M. F. Moura, Devi Parikh, Dhruv Batra, Marcus Rohrbach
Date: 2018-09-06 00:00:00
Proceeding: ECCV 2018 9
Tasks: Common Sense Reasoning, coreference-resolution, Coreference Resolution, Visual Dialog, Visual Grounding, Visual Question Answering, Visual Question Answering (VQA)

Visual dialog entails answering a series of questions grounded in an image,
using dialog history as context. In addition to the challenges found in visual
question answering (VQA), which can be seen as one-round dialog, visual dialog
encompasses several more. We focus on one such problem called visual
coreference resolution that involves determining which words, typically noun
phrases and pronouns, co-refer to the same entity/object instance in an image.
This is crucial, especially for pronouns (e.g., `it'), as the dialog agent must
first link it to a previous coreference (e.g., `boat'), and only then can rely
on the visual grounding of the coreference `boat' to reason about the pronoun
`it'. Prior work (in visual dialog) models visual coreference resolution either
(a) implicitly via a memory network over history, or (b) at a coarse level for
the entire question; and not explicitly at a phrase level of granularity. In
this work, we propose a neural module network architecture for visual dialog by
introducing two novel modules - Refer and Exclude - that perform explicit,
grounded, coreference resolution at a finer word level. We demonstrate the
effectiveness of our model on MNIST Dialog, a visually simple yet
coreference-wise complex dataset, by achieving near perfect accuracy, and on
VisDial, a large and challenging visual dialog dataset on real images, where
our model outperforms other approaches, and is more interpretable, grounded,
and consistent qualitatively.
Noise Contrastive Estimation and Negative Sampling for Conditional Models: Consistency and Statistical Efficiency
Authors: Zhuang Ma, Michael Collins
Date: 2018-09-06 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Classification, General Classification, Language Modeling, Language Modelling, parameter estimation, Question Answering

Noise Contrastive Estimation (NCE) is a powerful parameter estimation method
for log-linear models, which avoids calculation of the partition function or
its derivatives at each training step, a computationally demanding step in many
cases. It is closely related to negative sampling methods, now widely used in
NLP. This paper considers NCE-based estimation of conditional models.
Conditional models are frequently encountered in practice; however there has
not been a rigorous theoretical analysis of NCE in this setting, and we will
argue there are subtle but important questions when generalizing NCE to the
conditional case. In particular, we analyze two variants of NCE for conditional
models: one based on a classification objective, the other based on a ranking
objective. We show that the ranking-based variant of NCE gives consistent
parameter estimates under weaker assumptions than the classification-based
method; we analyze the statistical efficiency of the ranking-based and
classification-based variants of NCE; finally we describe experiments on
synthetic data and language modeling showing the effectiveness and trade-offs
of both methods.
Interpretable Visual Question Answering by Reasoning on Dependency Trees
Authors: Qingxing Cao, Bailin Li, Xiaodan Liang, Liang Lin
Date: 2018-09-06 00:00:00
Tasks: Question Answering, valid, Visual Question Answering, Visual Question Answering (VQA)

Collaborative reasoning for understanding image-question pairs is a very critical but underexplored topic in interpretable visual question answering systems. Although very recent studies have attempted to use explicit compositional processes to assemble multiple subtasks embedded in questions, their models heavily rely on annotations or handcrafted rules to obtain valid reasoning processes, which leads to either heavy workloads or poor performance on compositional reasoning. In this paper, to better align image and language domains in diverse and unrestricted cases, we propose a novel neural network model that performs global reasoning on a dependency tree parsed from the question; thus, our model is called a parse-tree-guided reasoning network (PTGRN). This network consists of three collaborative modules: i) an attention module that exploits the local visual evidence of each word parsed from the question, ii) a gated residual composition module that composes the previously mined evidence, and iii) a parse-tree-guided propagation module that passes the mined evidence along the parse tree. Thus, PTGRN is capable of building an interpretable visual question answering (VQA) system that gradually derives image cues following question-driven parse-tree reasoning. Experiments on relational datasets demonstrate the superiority of PTGRN over current state-of-the-art VQA methods, and the visualization results highlight the explainable capability of our reasoning system.
Improbotics: Exploring the Imitation Game using Machine Intelligence in Improvised Theatre
Authors: Kory W. Mathewson, Piotr Mirowski
Date: 2018-09-06 00:00:00

Theatrical improvisation (impro or improv) is a demanding form of live,
collaborative performance. Improv is a humorous and playful artform built on an
open-ended narrative structure which simultaneously celebrates effort and
failure. It is thus an ideal test bed for the development and deployment of
interactive artificial intelligence (AI)-based conversational agents, or
artificial improvisors. This case study introduces an improv show experiment
featuring human actors and artificial improvisors. We have previously developed
a deep-learning-based artificial improvisor, trained on movie subtitles, that
can generate plausible, context-based, lines of dialogue suitable for theatre
(Mathewson and Mirowski 2017). In this work, we have employed it to control
what a subset of human actors say during an improv performance. We also give
human-generated lines to a different subset of performers. All lines are
provided to actors with headphones and all performers are wearing headphones.
This paper describes a Turing test, or imitation game, taking place in a
theatre, with both the audience members and the performers left to guess who is
a human and who is a machine. In order to test scientific hypotheses about the
perception of humans versus machines we collect anonymous feedback from
volunteer performers and audience members. Our results suggest that rehearsal
increases proficiency and possibility to control events in the performance.
That said, consistency with real world experience is limited by the interface
and the mechanisms used to perform the show. We also show that human-generated
lines are shorter, more positive, and have less difficult words with more
grammar and spelling mistakes than the artificial improvisor generated lines.
Discovering Influential Factors in Variational Autoencoder
Authors: Shiqi Liu, Jingxin Liu, Qian Zhao, Xiangyong Cao, Huibin Li, Hongy-ing Meng, Sheng Liu, Deyu Meng
Date: 2018-09-06 00:00:00
Tasks: General Classification

In the field of machine learning, it is still a critical issue to identify
and supervise the learned representation without manually intervening or
intuition assistance to extract useful knowledge or serve for the downstream
tasks. In this work, we focus on supervising the influential factors extracted
by the variational autoencoder(VAE). The VAE is proposed to learn independent
low dimension representation while facing the problem that sometimes pre-set
factors are ignored. We argue that the mutual information of the input and each
learned factor of the representation plays a necessary indicator of discovering
the influential factors. We find the VAE objective inclines to induce mutual
information sparsity in factor dimension over the data intrinsic dimension and
results in some non-influential factors whose function on data reconstruction
could be ignored. We show mutual information also influences the lower bound of
VAE's reconstruction error and downstream classification task. To make such
indicator applicable, we design an algorithm for calculating the mutual
information for VAE and prove its consistency. Experimental results on MNIST,
CelebA and DEAP datasets show that mutual information can help determine
influential factors, of which some are interpretable and can be used to further
generation and classification tasks, and help discover the variant that
connects with emotion on DEAP dataset.
Describing a Knowledge Base
Authors: Qingyun Wang, Xiaoman Pan, Lifu Huang, Boliang Zhang, Zhiying Jiang, Heng Ji, Kevin Knight
Date: 2018-09-06 00:00:00
Proceeding: WS 2018 11
Tasks: Data-to-Text Generation, KB-to-Language Generation, Table-to-Text Generation, Text Generation

We aim to automatically generate natural language descriptions about an input
structured knowledge base (KB). We build our generation framework based on a
pointer network which can copy facts from the input KB, and add two attention
mechanisms: (i) slot-aware attention to capture the association between a slot
type and its corresponding slot value; and (ii) a new \emph{table position
self-attention} to capture the inter-dependencies among related slots. For
evaluation, besides standard metrics including BLEU, METEOR, and ROUGE, we
propose a KB reconstruction based metric by extracting a KB from the generation
output and comparing it with the input KB. We also create a new data set which
includes 106,216 pairs of structured KBs and their corresponding natural
language descriptions for two distinct entity types. Experiments show that our
approach significantly outperforms state-of-the-art methods. The reconstructed
KB achieves 68.8% - 72.6% F-score.
Optimal Sparse Singular Value Decomposition for High-dimensional High-order Data
Authors: Anru Zhang, Rungang Han
Date: 2018-09-06 00:00:00
Tasks: Dimensionality Reduction, Vocal Bursts Intensity Prediction

In this article, we consider the sparse tensor singular value decomposition, which aims for dimension reduction on high-dimensional high-order data with certain sparsity structure. A method named Sparse Tensor Alternating Thresholding for Singular Value Decomposition (STAT-SVD) is proposed. The proposed procedure features a novel double projection \& thresholding scheme, which provides a sharp criterion for thresholding in each iteration. Compared with regular tensor SVD model, STAT-SVD permits more robust estimation under weaker assumptions. Both the upper and lower bounds for estimation accuracy are developed. The proposed procedure is shown to be minimax rate-optimal in a general class of situations. Simulation studies show that STAT-SVD performs well under a variety of configurations. We also illustrate the merits of the proposed procedure on a longitudinal tensor dataset on European country mortality rates.
MDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for Efficient Object Detection
Authors: Wenchi Ma, Yuanwei Wu, Zongbo Wang, Guanghui Wang
Date: 2018-09-06 00:00:00
Tasks: Object, object-detection, Object Detection

Object detection in challenging situations such as scale variation,
occlusion, and truncation depends not only on feature details but also on
contextual information. Most previous networks emphasize too much on detailed
feature extraction through deeper and wider networks, which may enhance the
accuracy of object detection to certain extent. However, the feature details
are easily being changed or washed out after passing through complicated
filtering structures. To better handle these challenges, the paper proposes a
novel framework, multi-scale, deep inception convolutional neural network
(MDCN), which focuses on wider and broader object regions by activating feature
maps produced in the deep part of the network. Instead of incepting inner
layers in the shallow part of the network, multi-scale inceptions are
introduced in the deep layers. The proposed framework integrates the contextual
information into the learning process through a single-shot network structure.
It is computational efficient and avoids the hard training problem of previous
macro feature extraction network designed for shallow layers. Extensive
experiments demonstrate the effectiveness and superior performance of MDCN over
the state-of-the-art models.
Deep Recurrent Electricity Theft Detection in AMI Networks with Random Tuning of Hyper-parameters
Authors: Mahmoud Nabil, Muhammad Ismail, Mohamed Mahmoud, Mostafa Shahin, Khalid Qaraqe, Erchin Serpedin
Date: 2018-09-06 00:00:00
Tasks: Time Series, Time Series Analysis

Modern smart grids rely on advanced metering infrastructure (AMI) networks
for monitoring and billing pur- poses. However, such an approach suffers from
electricity theft cyberattacks. Different from the existing research that
utilizes shallow, static, and customer-specific-based electricity theft de-
tectors, this paper proposes a generalized deep recurrent neural network
(RNN)-based electricity theft detector that can effectively thwart these
cyberattacks. The proposed model exploits the time series nature of the
customers' electricity consumption to implement a gated recurrent unit
(GRU)-RNN, hence, improving the detection performance. In addition, the
proposed RNN-based detector adopts a random search analysis in its learning
stage to appropriately fine-tune its hyper-parameters. Extensive test studies
are carried out to investigate the detector's performance using publicly
available real data of 107,200 energy consumption days from 200 customers.
Simulation results demonstrate the superior performance of the proposed
detector compared with state-of-the-art electricity theft detectors.
Multi-view Factorization AutoEncoder with Network Constraints for Multi-omic Integrative Analysis
Authors: Tianle Ma, Aidong Zhang
Date: 2018-09-06 00:00:00
Tasks: Representation Learning

Multi-omic data provides multiple views of the same patients. Integrative
analysis of multi-omic data is crucial to elucidate the molecular underpinning
of disease etiology. However, multi-omic data has the "big p, small N" problem
(the number of features is large, but the number of samples is small), it is
challenging to train a complicated machine learning model from the multi-omic
data alone and make it generalize well. Here we propose a framework termed
Multi-view Factorization AutoEncoder with network constraints to integrate
multi-omic data with domain knowledge (biological interactions networks). Our
framework employs deep representation learning to learn feature embeddings and
patient embeddings simultaneously, enabling us to integrate feature interaction
network and patient view similarity network constraints into the training
objective. The whole framework is end-to-end differentiable. We applied our
approach to the TCGA Pan-cancer dataset and achieved satisfactory results to
predict disease progression-free interval (PFI) and patient overall survival
(OS) events. Code will be made publicly available.
An Analysis of Hierarchical Text Classification Using Word Embeddings
Authors: Roger A. Stein, Patricia A. Jaques, Joao F. Valiati
Date: 2018-09-06 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Document Classification, General Classification, text-classification, Text Classification, Word Embeddings

Efficient distributed numerical word representation models (word embeddings)
combined with modern machine learning algorithms have recently yielded
considerable improvement on automatic document classification tasks. However,
the effectiveness of such techniques has not been assessed for the hierarchical
text classification (HTC) yet. This study investigates the application of those
models and algorithms on this specific problem by means of experimentation and
analysis. We trained classification models with prominent machine learning
algorithm implementations---fastText, XGBoost, SVM, and Keras' CNN---and
noticeable word embeddings generation methods---GloVe, word2vec, and
fastText---with publicly available data and evaluated them with measures
specifically appropriate for the hierarchical context. FastText achieved an
${}_{LCA}F_1$ of 0.893 on a single-labeled version of the RCV1 dataset. An
analysis indicates that using word embeddings and its flavors is a very
promising approach for HTC.
Geometry of Deep Learning for Magnetic Resonance Fingerprinting
Authors: Mohammad Golbabaee, Dong-Dong Chen, Pedro A. Gómez, Marion I. Menzel, Mike E. Davies
Date: 2018-09-05 00:00:00
Tasks: Deep Learning, Dimensionality Reduction, Image Reconstruction, Magnetic Resonance Fingerprinting, Quantitative MRI

Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are
bottlenecked by the heavy storage and computation requirements of a
dictionary-matching (DM) step due to the growing size and complexity of the
fingerprint dictionaries in multi-parametric quantitative MRI applications. In
this paper we study a deep learning approach to address these shortcomings.
Coupled with a dimensionality reduction first layer, the proposed MRF-Net is
able to reconstruct quantitative maps by saving more than 60 times in memory
and computations required for a DM baseline. Fine-grid manifold enumeration
i.e. the MRF dictionary is only used for training the network and not during
image reconstruction. We show that the MRF-Net provides a piece-wise affine
approximation to the Bloch response manifold projection and that rather than
memorizing the dictionary, the network efficiently clusters this manifold and
learns a set of hierarchical matched-filters for affine regression of the NMR
characteristics in each segment.
Predicting Smoking Events with a Time-Varying Semi-Parametric Hawkes Process Model
Authors: Matthew Engelhard, Hongteng Xu, Lawrence Carin, Jason A Oliver, Matthew Hallyburton, F Joseph McClernon
Date: 2018-09-05 00:00:00

Health risks from cigarette smoking -- the leading cause of preventable death
in the United States -- can be substantially reduced by quitting. Although most
smokers are motivated to quit, the majority of quit attempts fail. A number of
studies have explored the role of self-reported symptoms, physiologic
measurements, and environmental context on smoking risk, but less work has
focused on the temporal dynamics of smoking events, including daily patterns
and related nicotine effects. In this work, we examine these dynamics and
improve risk prediction by modeling smoking as a self-triggering process, in
which previous smoking events modify current risk. Specifically, we fit smoking
events self-reported by 42 smokers to a time-varying semi-parametric Hawkes
process (TV-SPHP) developed for this purpose. Results show that the TV-SPHP
achieves superior prediction performance compared to related and existing
models, with the incorporation of time-varying predictors having greatest
benefit over longer prediction windows. Moreover, the impact function
illustrates previously unknown temporal dynamics of smoking, with possible
connections to nicotine metabolism to be explored in future work through a
randomized study design. By more effectively predicting smoking events and
exploring a self-triggering component of smoking risk, this work supports
development of novel or improved cessation interventions that aim to reduce
death from smoking.
Recovering a Single Community with Side Information
Authors: Hussein Saad, Aria Nosratinia
Date: 2018-09-05 00:00:00

We study the effect of the quality and quantity of side information on the
recovery of a hidden community of size $K=o(n)$ in a graph of size $n$. Side
information for each node in the graph is modeled by a random vector with the
following features: either the dimension of the vector is allowed to vary with
$n$, while log-likelihood ratio (LLR) of each component with respect to the
node label is fixed, or the LLR is allowed to vary and the vector dimension is
fixed. These two models represent the variation in quality and quantity of side
information. Under maximum likelihood detection, we calculate tight necessary
and sufficient conditions for exact recovery of the labels. We demonstrate how
side information needs to evolve with $n$ in terms of either its quantity, or
quality, to improve the exact recovery threshold. A similar set of results are
obtained for weak recovery. Under belief propagation, tight necessary and
sufficient conditions for weak recovery are calculated when the LLRs are
constant, and sufficient conditions when the LLRs vary with $n$. Moreover, we
design and analyze a local voting procedure using side information that can
achieve exact recovery when applied after belief propagation. The results for
belief propagation are validated via simulations on finite synthetic data-sets,
showing that the asymptotic results of this paper can also shed light on the
performance at finite $n$.
Attention-based Audio-Visual Fusion for Robust Automatic Speech Recognition
Authors: George Sterpu, Christian Saam, Naomi Harte
Date: 2018-09-05 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Automatic speech recognition can potentially benefit from the lip motion
patterns, complementing acoustic speech to improve the overall recognition
performance, particularly in noise. In this paper we propose an audio-visual
fusion strategy that goes beyond simple feature concatenation and learns to
automatically align the two modalities, leading to enhanced representations
which increase the recognition accuracy in both clean and noisy conditions. We
test our strategy on the TCD-TIMIT and LRS2 datasets, designed for large
vocabulary continuous speech recognition, applying three types of noise at
different power ratios. We also exploit state of the art Sequence-to-Sequence
architectures, showing that our method can be easily integrated. Results show
relative improvements from 7% up to 30% on TCD-TIMIT over the acoustic modality
alone, depending on the acoustic noise level. We anticipate that the fusion
strategy can easily generalise to many other multimodal tasks which involve
correlated modalities. Code available online on GitHub:
https://github.com/georgesterpu/Sigmedia-AVSR