pwc-archive/papers-with-abstracts huggingface dataset:
Bi-Directional Lattice Recurrent Neural Networks for Confidence Estimation
Authors: Qiujia Li, Preben Ness, Anton Ragni, Mark Gales
Date: 2018-10-30 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Information Retrieval, Retrieval, speech-recognition, Speech Recognition

The standard approach to mitigate errors made by an automatic speech
recognition system is to use confidence scores associated with each predicted
word. In the simplest case, these scores are word posterior probabilities
whilst more complex schemes utilise bi-directional recurrent neural network
(BiRNN) models. A number of upstream and downstream applications, however, rely
on confidence scores assigned not only to 1-best hypotheses but to all words
found in confusion networks or lattices. These include but are not limited to
speaker adaptation, semi-supervised training and information retrieval.
Although word posteriors could be used in those applications as confidence
scores, they are known to have reliability issues. To make improved confidence
scores more generally available, this paper shows how BiRNNs can be extended
from 1-best sequences to confusion network and lattice structures. Experiments
are conducted using one of the Cambridge University submissions to the IARPA
OpenKWS 2016 competition. The results show that confusion network and
lattice-based BiRNNs can provide a significant improvement in confidence
estimation.
An Online-Learning Approach to Inverse Optimization
Authors: Andreas Bärmann, Alexander Martin, Sebastian Pokutta, Oskar Schneider
Date: 2018-10-30 00:00:00

In this paper, we demonstrate how to learn the objective function of a decision-maker while only observing the problem input data and the decision-maker's corresponding decisions over multiple rounds. We present exact algorithms for this online version of inverse optimization which converge at a rate of $ \mathcal{O}(1/\sqrt{T}) $ in the number of observations~$T$ and compare their further properties. Especially, they all allow taking decisions which are essentially as good as those of the observed decision-maker already after relatively few iterations, but are suited best for different settings each. Our approach is based on online learning and works for linear objectives over arbitrary feasible sets for which we have a linear optimization oracle. As such, it generalizes previous approaches based on KKT-system decomposition and dualization. We also introduce several generalizations, such as the approximate learning of non-linear objective functions, dynamically changing as well as parameterized objectives and the case of suboptimal observed decisions. When applied to the stochastic offline case, our algorithms are able to give guarantees on the quality of the learned objectives in expectation. Finally, we show the effectiveness and possible applications of our methods in indicative computational experiments.
R$^3$SGM: Real-time Raster-Respecting Semi-Global Matching for Power-Constrained Systems
Authors: Oscar Rahnama, Tommaso Cavallari, Stuart Golodetz, Simon Walker, Philip H. S. Torr
Date: 2018-10-30 00:00:00
Tasks: Depth Estimation, Stereo Depth Estimation

Stereo depth estimation is used for many computer vision applications. Though
many popular methods strive solely for depth quality, for real-time mobile
applications (e.g. prosthetic glasses or micro-UAVs), speed and power
efficiency are equally, if not more, important. Many real-world systems rely on
Semi-Global Matching (SGM) to achieve a good accuracy vs. speed balance, but
power efficiency is hard to achieve with conventional hardware, making the use
of embedded devices such as FPGAs attractive for low-power applications.
However, the full SGM algorithm is ill-suited to deployment on FPGAs, and so
most FPGA variants of it are partial, at the expense of accuracy. In a non-FPGA
context, the accuracy of SGM has been improved by More Global Matching (MGM),
which also helps tackle the streaking artifacts that afflict SGM. In this
paper, we propose a novel, resource-efficient method that is inspired by MGM's
techniques for improving depth quality, but which can be implemented to run in
real time on a low-power FPGA. Through evaluation on multiple datasets (KITTI
and Middlebury), we show that in comparison to other real-time capable stereo
approaches, we can achieve a state-of-the-art balance between accuracy, power
efficiency and speed, making our approach highly desirable for use in real-time
systems with limited power.
SDFN: Segmentation-based Deep Fusion Network for Thoracic Disease Classification in Chest X-ray Images
Authors: Han Liu, Lei Wang, Yandong Nan, Faguang Jin, Qi. Wang, Jiantao Pu
Date: 2018-10-30 00:00:00
Tasks: General Classification, Thoracic Disease Classification

This study aims to automatically diagnose thoracic diseases depicted on the chest x-ray (CXR) images using deep convolutional neural networks. The existing methods generally used the entire CXR images for training purposes, but this strategy may suffer from two drawbacks. First, potential misalignment or the existence of irrelevant objects in the entire CXR images may cause unnecessary noise and thus limit the network performance. Second, the relatively low image resolution caused by the resizing operation, which is a common preprocessing procedure for training neural networks, may lead to the loss of image details, making it difficult to detect pathologies with small lesion regions. To address these issues, we present a novel method termed as segmentation-based deep fusion network (SDFN), which leverages the domain knowledge and the higherresolution information of local lung regions. Specifically, the local lung regions were identified and cropped by the Lung Region Generator (LRG). Two CNN-based classification models were then used as feature extractors to obtain the discriminative features of the entire CXR images and the cropped lung region images. Lastly, the obtained features were fused by the feature fusion module for disease classification. Evaluated by the NIH benchmark split on the Chest X-ray 14 Dataset, our experimental result demonstrated that the developed method achieved more accurate disease classification compared with the available approaches via the receiver operating characteristic (ROC) analyses. It was also found that the SDFN could localize the lesion regions more precisely as compared to the traditional method.
Combining Distant and Direct Supervision for Neural Relation Extraction
Authors: Iz Beltagy, Kyle Lo, Waleed Ammar
Date: 2018-10-30 00:00:00
Proceeding: NAACL 2019 6
Tasks: Relation, Relation Extraction

In relation extraction with distant supervision, noisy labels make it
difficult to train quality models. Previous neural models addressed this
problem using an attention mechanism that attends to sentences that are likely
to express the relations. We improve such models by combining the distant
supervision data with an additional directly-supervised data, which we use as
supervision for the attention weights. We find that joint training on both
types of supervision leads to a better model because it improves the model's
ability to identify noisy sentences. In addition, we find that sigmoidal
attention weights with max pooling achieves better performance over the
commonly used weighted average attention in this setup. Our proposed method
achieves a new state-of-the-art result on the widely used FB-NYT dataset.
Joint detection and matching of feature points in multimodal images
Authors: Elad Ben Baruch, Yosi Keller
Date: 2018-10-30 00:00:00

In this work, we propose a novel Convolutional Neural Network (CNN) architecture for the joint detection and matching of feature points in images acquired by different sensors using a single forward pass. The resulting feature detector is tightly coupled with the feature descriptor, in contrast to classical approaches (SIFT, etc.), where the detection phase precedes and differs from computing the descriptor. Our approach utilizes two CNN subnetworks, the first being a Siamese CNN and the second, consisting of dual non-weight-sharing CNNs. This allows simultaneous processing and fusion of the joint and disjoint cues in the multimodal image patches. The proposed approach is experimentally shown to outperform contemporary state-of-the-art schemes when applied to multiple datasets of multimodal images. It is also shown to provide repeatable feature points detections across multisensor images, outperforming state-of-the-art detectors. To the best of our knowledge, it is the first unified approach for the detection and matching of such images.
NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval
Authors: Canjia Li, Yingfei Sun, Ben He, Le Wang, Kai Hui, Andrew Yates, Le Sun, Jungang Xu
Date: 2018-10-30 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Ad-Hoc Information Retrieval, Information Retrieval, Retrieval

Pseudo-relevance feedback (PRF) is commonly used to boost the performance of
traditional information retrieval (IR) models by using top-ranked documents to
identify and weight new query terms, thereby reducing the effect of
query-document vocabulary mismatches. While neural retrieval models have
recently demonstrated strong results for ad-hoc retrieval, combining them with
PRF is not straightforward due to incompatibilities between existing PRF
approaches and neural architectures. To bridge this gap, we propose an
end-to-end neural PRF framework that can be used with existing neural IR models
by embedding different neural models as building blocks. Extensive experiments
on two standard test collections confirm the effectiveness of the proposed NPRF
framework in improving the performance of two state-of-the-art neural IR
models.
MPNA: A Massively-Parallel Neural Array Accelerator with Dataflow Optimization for Convolutional Neural Networks
Authors: Muhammad Abdullah Hanif, Rachmad Vidya Wicaksana Putra, Muhammad Tanvir, Rehan Hafiz, Semeen Rehman, Muhammad Shafique
Date: 2018-10-30 00:00:00

The state-of-the-art accelerators for Convolutional Neural Networks (CNNs)
typically focus on accelerating only the convolutional layers, but do not
prioritize the fully-connected layers much. Hence, they lack a synergistic
optimization of the hardware architecture and diverse dataflows for the
complete CNN design, which can provide a higher potential for
performance/energy efficiency. Towards this, we propose a novel
Massively-Parallel Neural Array (MPNA) accelerator that integrates two
heterogeneous systolic arrays and respective highly-optimized dataflow patterns
to jointly accelerate both the convolutional (CONV) and the fully-connected
(FC) layers. Besides fully-exploiting the available off-chip memory bandwidth,
these optimized dataflows enable high data-reuse of all the data types (i.e.,
weights, input and output activations), and thereby enable our MPNA to achieve
high energy savings. We synthesized our MPNA architecture using the ASIC design
flow for a 28nm technology, and performed functional and timing validation
using multiple real-world complex CNNs. MPNA achieves 149.7GOPS/W at 280MHz and
consumes 239mW. Experimental results show that our MPNA architecture provides
1.7x overall performance improvement compared to state-of-the-art accelerator,
and 51% energy saving compared to the baseline architecture.
JavaScript Convolutional Neural Networks for Keyword Spotting in the Browser: An Experimental Analysis
Authors: Jaejun Lee, Raphael Tang, Jimmy Lin
Date: 2018-10-30 00:00:00
Tasks: Keyword Spotting, Model Compression

Used for simple commands recognition on devices from smart routers to mobile
phones, keyword spotting systems are everywhere. Ubiquitous as well are web
applications, which have grown in popularity and complexity over the last
decade with significant improvements in usability under cross-platform
conditions. However, despite their obvious advantage in natural language
interaction, voice-enabled web applications are still far and few between. In
this work, we attempt to bridge this gap by bringing keyword spotting
capabilities directly into the browser. To our knowledge, we are the first to
demonstrate a fully-functional implementation of convolutional neural networks
in pure JavaScript that runs in any standards-compliant browser. We also apply
network slimming, a model compression technique, to explore the
accuracy-efficiency tradeoffs, reporting latency measurements on a range of
devices and software. Overall, our robust, cross-device implementation for
keyword spotting realizes a new paradigm for serving neural network
applications, and one of our slim models reduces latency by 66% with a minimal
decrease in accuracy of 4% from 94% to 90%.
Stochastic Optimal Control of Epidemic Processes in Networks
Authors: Lars Lorch, Abir De, Samir Bhatt, William Trouleau, Utkarsh Upadhyay, Manuel Gomez-Rodriguez
Date: 2018-10-30 00:00:00
Tasks: Point Processes

We approach the development of models and control strategies of
susceptible-infected-susceptible (SIS) epidemic processes from the perspective
of marked temporal point processes and stochastic optimal control of stochastic
differential equations (SDEs) with jumps. In contrast to previous work, this
novel perspective is particularly well-suited to make use of fine-grained data
about disease outbreaks and lets us overcome the shortcomings of current
control strategies. Our control strategy resorts to treatment intensities to
determine who to treat and when to do so to minimize the amount of infected
individuals over time. Preliminary experiments with synthetic data show that
our control strategy consistently outperforms several alternatives. Looking
into the future, we believe our methodology provides a promising step towards
the development of practical data-driven control strategies of epidemic
processes.
Application of Deep Learning on Predicting Prognosis of Acute Myeloid Leukemia with Cytogenetics, Age, and Mutations
Authors: Mei Lin, Vanya Jaitly, Iris Wang, Zhihong Hu, Lei Chen, Md. Amer Wahed, Zeyad Kanaan, Adan Rios, Andy N. D. Nguyen
Date: 2018-10-30 00:00:00
Tasks: Prognosis

We explore how Deep Learning (DL) can be utilized to predict prognosis of
acute myeloid leukemia (AML). Out of TCGA (The Cancer Genome Atlas) database,
94 AML cases are used in this study. Input data include age, 10 common
cytogenetic and 23 most common mutation results; output is the prognosis
(diagnosis to death, DTD). In our DL network, autoencoders are stacked to form
a hierarchical DL model from which raw data are compressed and organized and
high-level features are extracted. The network is written in R language and is
designed to predict prognosis of AML for a given case (DTD of more than or less
than 730 days). The DL network achieves an excellent accuracy of 83% in
predicting prognosis. As a proof-of-concept study, our preliminary results
demonstrate a practical application of DL in future practice of prognostic
prediction using next-gen sequencing (NGS) data.
DeepHTTP: Semantics-Structure Model with Attention for Anomalous HTTP Traffic Detection and Pattern Mining
Authors: Yuqi Yu, Hanbing Yan, Hongchao Guan, Hao Zhou
Date: 2018-10-30 00:00:00

In the Internet age, cyber-attacks occur frequently with complex types.
Traffic generated by access activities can record website status and user
request information, which brings a great opportunity for network attack
detection. Among diverse network protocols, Hypertext Transfer Protocol (HTTP)
is widely used in government, organizations and enterprises. In this work, we
propose DeepHTTP, a semantics structure integration model utilizing
Bidirectional Long Short-Term Memory (Bi-LSTM) with attention mechanism to
model HTTP traffic as a natural language sequence. In addition to extracting
traffic content information, we integrate structural information to enhance the
generalization capabilities of the model. Moreover, the application of
attention mechanism can assist in discovering critical parts of anomalous
traffic and further mining attack patterns. Additionally, we demonstrate how to
incrementally update the data set and retrain model so that it can be adapted
to new anomalous traffic. Extensive experimental evaluations over large traffic
data have illustrated that DeepHTTP has outstanding performance in traffic
detection and pattern discovery.
Minimap2: pairwise alignment for nucleotide sequences
Authors: Heng Li
Date: 2017-08-04 00:00:00

Motivation: Recent advances in sequencing technologies promise ultra-long
reads of $\sim$100 kilo bases (kb) in average, full-length mRNA or cDNA reads
in high throughput and genomic contigs over 100 mega bases (Mb) in length.
Existing alignment programs are unable or inefficient to process such data at
scale, which presses for the development of new alignment algorithms.
  Results: Minimap2 is a general-purpose alignment program to map DNA or long
mRNA sequences against a large reference database. It works with accurate short
reads of $\ge$100bp in length, $\ge$1kb genomic reads at error rate $\sim$15%,
full-length noisy Direct RNA or cDNA reads, and assembly contigs or closely
related full chromosomes of hundreds of megabases in length. Minimap2 does
split-read alignment, employs concave gap cost for long insertions and
deletions (INDELs) and introduces new heuristics to reduce spurious alignments.
It is 3-4 times faster than mainstream short-read mappers at comparable
accuracy and $\ge$30 times faster at higher accuracy for both genomic and mRNA
reads, surpassing most aligners specialized in one type of alignment.
  Availability and implementation: https://github.com/lh3/minimap2
  Contact: hengli@broadinstitute.org
A Streamlined Encoder/Decoder Architecture for Melody Extraction
Authors: Tsung-Han Hsieh, Li Su, Yi-Hsuan Yang
Date: 2018-10-30 00:00:00
Tasks: Decoder, Melody Extraction

Melody extraction in polyphonic musical audio is important for music signal
processing. In this paper, we propose a novel streamlined encoder/decoder
network that is designed for the task. We make two technical contributions.
First, drawing inspiration from a state-of-the-art model for semantic
pixel-wise segmentation, we pass through the pooling indices between pooling
and un-pooling layers to localize the melody in frequency. We can achieve
result close to the state-of-the-art with much fewer convolutional layers and
simpler convolution modules. Second, we propose a way to use the bottleneck
layer of the network to estimate the existence of a melody line for each time
frame, and make it possible to use a simple argmax function instead of ad-hoc
thresholding to get the final estimation of the melody line. Our experiments on
both vocal melody extraction and general melody extraction validate the
effectiveness of the proposed model.
Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network
Authors: Subeesh Vasu, Nimisha Thekke Madam, Rajagopalan A. N
Date: 2018-11-01 00:00:00
Tasks: Generative Adversarial Network, Image Super-Resolution, SSIM, Super-Resolution

Convolutional neural network (CNN) based methods have recently achieved great
success for image super-resolution (SR). However, most deep CNN based SR models
attempt to improve distortion measures (e.g. PSNR, SSIM, IFC, VIF) while
resulting in poor quantified perceptual quality (e.g. human opinion score,
no-reference quality measures such as NIQE). Few works have attempted to
improve the perceptual quality at the cost of performance reduction in
distortion measures. A very recent study has revealed that distortion and
perceptual quality are at odds with each other and there is always a trade-off
between the two. Often the restoration algorithms that are superior in terms of
perceptual quality, are inferior in terms of distortion measures. Our work
attempts to analyze the trade-off between distortion and perceptual quality for
the problem of single image SR. To this end, we use the well-known SR
architecture-enhanced deep super-resolution (EDSR) network and show that it can
be adapted to achieve better perceptual quality for a specific range of the
distortion measure. While the original network of EDSR was trained to minimize
the error defined based on per-pixel accuracy alone, we train our network using
a generative adversarial network framework with EDSR as the generator module.
Our proposed network, called enhanced perceptual super-resolution network
(EPSR), is trained with a combination of mean squared error loss, perceptual
loss, and adversarial loss. Our experiments reveal that EPSR achieves the
state-of-the-art trade-off between distortion and perceptual quality while the
existing methods perform well in either of these measures alone.
Deep Structured Prediction with Nonlinear Output Transformations
Authors: Colin Graber, Ofer Meshi, Alexander Schwing
Date: 2018-11-01 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Prediction, Semantic Segmentation, Structured Prediction

Deep structured models are widely used for tasks like semantic segmentation,
where explicit correlations between variables provide important prior
information which generally helps to reduce the data needs of deep nets.
However, current deep structured models are restricted by oftentimes very local
neighborhood structure, which cannot be increased for computational complexity
reasons, and by the fact that the output configuration, or a representation
thereof, cannot be transformed further. Very recent approaches which address
those issues include graphical model inference inside deep nets so as to permit
subsequent non-linear output space transformations. However, optimization of
those formulations is challenging and not well understood. Here, we develop a
novel model which generalizes existing approaches, such as structured
prediction energy networks, and discuss a formulation which maintains
applicability of existing inference techniques.
Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering
Authors: Medhini Narasimhan, Svetlana Lazebnik, Alexander G. Schwing
Date: 2018-11-01 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Factual Visual Question Answering, General Knowledge, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Accurately answering a question about a given image requires combining
observations with general knowledge. While this is effortless for humans,
reasoning with general knowledge remains an algorithmic challenge. To advance
research in this direction a novel `fact-based' visual question answering
(FVQA) task has been introduced recently along with a large set of curated
facts which link two entities, i.e., two possible answers, via a relation.
Given a question-image pair, deep network techniques have been employed to
successively reduce the large set of facts until one of the two entities of the
final remaining fact is predicted as the answer. We observe that a successive
process which considers one fact at a time to form a local decision is
sub-optimal. Instead, we develop an entity graph and use a graph convolutional
network to `reason' about the correct answer by jointly considering all
entities. We show on the challenging FVQA dataset that this leads to an
improvement in accuracy of around 7% compared to the state of the art.