pwc-archive/papers-with-abstracts huggingface dataset:
On Prediction and Tolerance Intervals for Dynamic Treatment Regimes
Authors: Daniel J. Lizotte, Arezoo Tahmasebi
Date: 2017-04-24 00:00:00

We develop and evaluate tolerance interval methods for dynamic treatment
regimes (DTRs) that can provide more detailed prognostic information to
patients who will follow an estimated optimal regime. Although the problem of
constructing confidence intervals for DTRs has been extensively studied,
prediction and tolerance intervals have received little attention. We begin by
reviewing in detail different interval estimation and prediction methods and
then adapting them to the DTR setting. We illustrate some of the challenges
associated with tolerance interval estimation stemming from the fact that we do
not typically have data that were generated from the estimated optimal regime.
We give an extensive empirical evaluation of the methods and discussed several
practical aspects of method choice, and we present an example application using
data from a clinical trial. Finally, we discuss future directions within this
important emerging area of DTR research.
Transformation-Based Models of Video Sequences
Authors: Joost van Amersfoort, Anitha Kannan, Marc'Aurelio Ranzato, Arthur Szlam, Du Tran, Soumith Chintala
Date: 2017-01-29 00:00:00
Tasks: Prediction

In this work we propose a simple unsupervised approach for next frame prediction in video. Instead of directly predicting the pixels in a frame given past frames, we predict the transformations needed for generating the next frame in a sequence, given the transformations of the past frames. This leads to sharper results, while using a smaller prediction model. In order to enable a fair comparison between different video frame prediction models, we also propose a new evaluation protocol. We use generated frames as input to a classifier trained with ground truth sequences. This criterion guarantees that models scoring high are those producing sequences which preserve discriminative features, as opposed to merely penalizing any deviation, plausible or not, from the ground truth. Our proposed approach compares favourably against more sophisticated ones on the UCF-101 data set, while also being more efficient in terms of the number of parameters and computational cost.
Detecting English Writing Styles For Non Native Speakers
Authors: Yanging Chen, Rami Al-Rfou', Yejin Choi
Date: 2017-04-24 00:00:00

This paper presents the first attempt, up to our knowledge, to classify
English writing styles on this scale with the challenge of classifying day to
day language written by writers with different backgrounds covering various
areas of topics.The paper proposes simple machine learning algorithms and
simple to generate features to solve hard problems. Relying on the scale of the
data available from large sources of knowledge like Wikipedia. We believe such
sources of data are crucial to generate robust solutions for the web with high
accuracy and easy to deploy in practice. The paper achieves 74\% accuracy
classifying native versus non native speakers writing styles.
  Moreover, the paper shows some interesting observations on the similarity
between different languages measured by the similarity of their users English
writing styles. This technique could be used to show some well known facts
about languages as in grouping them into families, which our experiments
support.
Faster Principal Component Regression and Stable Matrix Chebyshev Approximation
Authors: Zeyuan Allen-Zhu, Yuanzhi Li
Date: 2016-08-16 00:00:00
Proceeding: ICML 2017 8
Tasks: regression

We solve principal component regression (PCR), up to a multiplicative
accuracy $1+\gamma$, by reducing the problem to $\tilde{O}(\gamma^{-1})$
black-box calls of ridge regression. Therefore, our algorithm does not require
any explicit construction of the top principal components, and is suitable for
large-scale PCR instances. In contrast, previous result requires
$\tilde{O}(\gamma^{-2})$ such black-box calls.
  We obtain this result by developing a general stable recurrence formula for
matrix Chebyshev polynomials, and a degree-optimal polynomial approximation to
the matrix sign function. Our techniques may be of independent interests,
especially when designing iterative methods.
Recognizing Descriptive Wikipedia Categories for Historical Figures
Authors: Yanqing Chen, Steven Skiena
Date: 2017-04-24 00:00:00
Tasks: Descriptive, Information Retrieval, Retrieval, TAG

Wikipedia is a useful knowledge source that benefits many applications in
language processing and knowledge representation. An important feature of
Wikipedia is that of categories. Wikipedia pages are assigned different
categories according to their contents as human-annotated labels which can be
used in information retrieval, ad hoc search improvements, entity ranking and
tag recommendations. However, important pages are usually assigned too many
categories, which makes it difficult to recognize the most important ones that
give the best descriptions.
  In this paper, we propose an approach to recognize the most descriptive
Wikipedia categories. We observe that historical figures in a precise category
presumably are mutually similar and such categorical coherence could be
evaluated via texts or Wikipedia links of corresponding members in the
category. We rank descriptive level of Wikipedia categories according to their
coherence and our ranking yield an overall agreement of 88.27% compared with
human wisdom.
Finding Approximate Local Minima Faster than Gradient Descent
Authors: Naman Agarwal, Zeyuan Allen-Zhu, Brian Bullins, Elad Hazan, Tengyu Ma
Date: 2016-11-03 00:00:00
Tasks: BIG-bench Machine Learning

We design a non-convex second-order optimization algorithm that is guaranteed
to return an approximate local minimum in time which scales linearly in the
underlying dimension and the number of training examples. The time complexity
of our algorithm to find an approximate local minimum is even faster than that
of gradient descent to find a critical point. Our algorithm applies to a
general class of optimization problems including training a neural network and
other non-convex objectives arising in machine learning.
Ruminating Reader: Reasoning with Gated Multi-Hop Attention
Authors: Yichen Gong, Samuel R. Bowman
Date: 2017-04-24 00:00:00
Proceeding: WS 2018 7
Tasks: Question Answering, Reading Comprehension

To answer the question in machine comprehension (MC) task, the models need to
establish the interaction between the question and the context. To tackle the
problem that the single-pass model cannot reflect on and correct its answer, we
present Ruminating Reader. Ruminating Reader adds a second pass of attention
and a novel information fusion component to the Bi-Directional Attention Flow
model (BiDAF). We propose novel layer structures that construct an query-aware
context vector representation and fuse encoding representation with
intermediate representation on top of BiDAF model. We show that a multi-hop
attention mechanism can be applied to a bi-directional attention structure. In
experiments on SQuAD, we find that the Reader outperforms the BiDAF baseline by
a substantial margin, and matches or surpasses the performance of all other
published systems.
Finding News Citations for Wikipedia
Authors: Besnik Fetahu, Katja Markert, Wolfgang Nejdl, Avishek Anand
Date: 2017-03-30 00:00:00
Tasks: Articles, Sentence

An important editing policy in Wikipedia is to provide citations for added
statements in Wikipedia pages, where statements can be arbitrary pieces of
text, ranging from a sentence to a paragraph. In many cases citations are
either outdated or missing altogether.
  In this work we address the problem of finding and updating news citations
for statements in entity pages. We propose a two-stage supervised approach for
this problem. In the first step, we construct a classifier to find out whether
statements need a news citation or other kinds of citations (web, book,
journal, etc.). In the second step, we develop a news citation algorithm for
Wikipedia statements, which recommends appropriate citations from a given news
collection. Apart from IR techniques that use the statement to query the news
collection, we also formalize three properties of an appropriate citation,
namely: (i) the citation should entail the Wikipedia statement, (ii) the
statement should be central to the citation, and (iii) the citation should be
from an authoritative source.
  We perform an extensive evaluation of both steps, using 20 million articles
from a real-world news collection. Our results are quite promising, and show
that we can perform this task with high precision and at scale.
Accelerated Nearest Neighbor Search with Quick ADC
Authors: Fabien André, Anne-Marie Kermarrec, Nicolas Le Scouarnec
Date: 2017-04-24 00:00:00
Tasks: Quantization, Retrieval

Efficient Nearest Neighbor (NN) search in high-dimensional spaces is a
foundation of many multimedia retrieval systems. Because it offers low
responses times, Product Quantization (PQ) is a popular solution. PQ compresses
high-dimensional vectors into short codes using several sub-quantizers, which
enables in-RAM storage of large databases. This allows fast answers to NN
queries, without accessing the SSD or HDD. The key feature of PQ is that it can
compute distances between short codes and high-dimensional vectors using
cache-resident lookup tables. The efficiency of this technique, named
Asymmetric Distance Computation (ADC), remains limited because it performs many
cache accesses.
  In this paper, we introduce Quick ADC, a novel technique that achieves a 3 to
6 times speedup over ADC by exploiting Single Instruction Multiple Data (SIMD)
units available in current CPUs. Efficiently exploiting SIMD requires
algorithmic changes to the ADC procedure. Namely, Quick ADC relies on two key
modifications of ADC: (i) the use 4-bit sub-quantizers instead of the standard
8-bit sub-quantizers and (ii) the quantization of floating-point distances.
This allows Quick ADC to exceed the performance of state-of-the-art systems,
e.g., it achieves a Recall@100 of 0.94 in 3.4 ms on 1 billion SIFT descriptors
(128-bit codes).
A Trie-Structured Bayesian Model for Unsupervised Morphological Segmentation
Authors: Murathan Kurfali, Ahmet Üstün, Burcu Can
Date: 2017-04-24 00:00:00
Tasks: Segmentation, Word Embeddings

In this paper, we introduce a trie-structured Bayesian model for unsupervised
morphological segmentation. We adopt prior information from different sources
in the model. We use neural word embeddings to discover words that are
morphologically derived from each other and thereby that are semantically
similar. We use letter successor variety counts obtained from tries that are
built by neural word embeddings. Our results show that using different
information sources such as neural word embeddings and letter successor variety
as prior information improves morphological segmentation in a Bayesian model.
Our model outperforms other unsupervised morphological segmentation models on
Turkish and gives promising results on English and German for scarce resources.
Accurate Optical Flow via Direct Cost Volume Processing
Authors: Jia Xu, René Ranftl, Vladlen Koltun
Date: 2017-04-24 00:00:00
Proceeding: CVPR 2017 7
Tasks: Optical Flow Estimation, Stereo Matching, Stereo Matching Hand

We present an optical flow estimation approach that operates on the full
four-dimensional cost volume. This direct approach shares the structural
benefits of leading stereo matching pipelines, which are known to yield high
accuracy. To this day, such approaches have been considered impractical due to
the size of the cost volume. We show that the full four-dimensional cost volume
can be constructed in a fraction of a second due to its regularity. We then
exploit this regularity further by adapting semi-global matching to the
four-dimensional setting. This yields a pipeline that achieves significantly
higher accuracy than state-of-the-art optical flow methods while being faster
than most. Our approach outperforms all published general-purpose optical flow
methods on both Sintel and KITTI 2015 benchmarks.
Multitask Evolution with Cartesian Genetic Programming
Authors: Eric O. Scott, Kenneth A. De Jong
Date: 2017-02-07 00:00:00

We introduce a genetic programming method for solving multiple Boolean
circuit synthesis tasks simultaneously. This allows us to solve a set of
elementary logic functions twice as easily as with a direct, single-task
approach.
DeepVel: deep learning for the estimation of horizontal velocities at the solar surface
Authors: A. Asensio Ramos, I. S. Requerey, N. Vitas
Date: 2017-03-15 00:00:00
Tasks: 3D Depth Estimation

Many phenomena taking place in the solar photosphere are controlled by plasma
motions. Although the line-of-sight component of the velocity can be estimated
using the Doppler effect, we do not have direct spectroscopic access to the
components that are perpendicular to the line-of-sight. These components are
typically estimated using methods based on local correlation tracking. We have
designed DeepVel, an end-to-end deep neural network that produces an estimation
of the velocity at every single pixel and at every time step and at three
different heights in the atmosphere from just two consecutive continuum images.
We confront DeepVel with local correlation tracking, pointing out that they
give very similar results in the time- and spatially-averaged cases. We use the
network to study the evolution in height of the horizontal velocity field in
fragmenting granules, supporting the buoyancy-braking mechanism for the
formation of integranular lanes in these granules. We also show that DeepVel
can capture very small vortices, so that we can potentially expand the scaling
cascade of vortices to very small sizes and durations.
A Real-time Hand Gesture Recognition and Human-Computer Interaction System
Authors: Pei Xu
Date: 2017-04-24 00:00:00
Tasks: Gesture Recognition, Hand Detection, Hand Gesture Recognition, Hand-Gesture Recognition

In this project, we design a real-time human-computer interaction system
based on hand gesture. The whole system consists of three components: hand
detection, gesture recognition and human-computer interaction (HCI) based on
recognition; and realizes the robust control of mouse and keyboard events with
a higher accuracy of gesture recognition. Specifically, we use the
convolutional neural network (CNN) to recognize gestures and makes it
attainable to identify relatively complex gestures using only one cheap
monocular camera. We introduce the Kalman filter to estimate the hand position
based on which the mouse cursor control is realized in a stable and smooth way.
During the HCI stage, we develop a simple strategy to avoid the false
recognition caused by noises - mostly transient, false gestures, and thus to
improve the reliability of interaction. The developed system is highly
extendable and can be used in human-robotic or other human-machine interaction
scenarios with more complex command formats rather than just mouse and keyboard
events.
Measuring the Accuracy of Object Detectors and Trackers
Authors: Tobias Bottger, Patrick Follmann, Michael Fauser
Date: 2017-04-24 00:00:00
Tasks: Object, object-detection, Object Detection, Object Tracking

The accuracy of object detectors and trackers is most commonly evaluated by
the Intersection over Union (IoU) criterion. To date, most approaches are
restricted to axis-aligned or oriented boxes and, as a consequence, many
datasets are only labeled with boxes. Nevertheless, axis-aligned or oriented
boxes cannot accurately capture an object's shape. To address this, a number of
densely segmented datasets has started to emerge in both the object detection
and the object tracking communities. However, evaluating the accuracy of object
detectors and trackers that are restricted to boxes on densely segmented data
is not straightforward. To close this gap, we introduce the relative
Intersection over Union (rIoU) accuracy measure. The measure normalizes the IoU
with the optimal box for the segmentation to generate an accuracy measure that
ranges between 0 and 1 and allows a more precise measurement of accuracies.
Furthermore, it enables an efficient and easy way to understand scenes and the
strengths and weaknesses of an object detection or tracking approach. We
display how the new measure can be efficiently calculated and present an
easy-to-use evaluation framework. The framework is tested on the DAVIS and the
VOT2016 segmentations and has been made available to the community.
Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling
Authors: Zhe Gan, Chunyuan Li, Changyou Chen, Yunchen Pu, Qinliang Su, Lawrence Carin
Date: 2016-11-23 00:00:00
Proceeding: ACL 2017 7
Tasks: Language Modeling, Language Modelling, Stochastic Optimization

Recurrent neural networks (RNNs) have shown promising performance for
language modeling. However, traditional training of RNNs using back-propagation
through time often suffers from overfitting. One reason for this is that
stochastic optimization (used for large training sets) does not provide good
estimates of model uncertainty. This paper leverages recent advances in
stochastic gradient Markov Chain Monte Carlo (also appropriate for large
training sets) to learn weight uncertainty in RNNs. It yields a principled
Bayesian learning algorithm, adding gradient noise during training (enhancing
exploration of the model-parameter space) and model averaging when testing.
Extensive experiments on various RNN models and across a broad range of
applications demonstrate the superiority of the proposed approach over
stochastic optimization.
Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning
Authors: Jason D. Williams, Kavosh Asadi, Geoffrey Zweig
Date: 2017-02-10 00:00:00
Proceeding: ACL 2017 7
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

End-to-end learning of recurrent neural networks (RNNs) is an attractive
solution for dialog systems; however, current techniques are data-intensive and
require thousands of dialogs to learn simple behaviors. We introduce Hybrid
Code Networks (HCNs), which combine an RNN with domain-specific knowledge
encoded as software and system action templates. Compared to existing
end-to-end approaches, HCNs considerably reduce the amount of training data
required, while retaining the key benefit of inferring a latent representation
of dialog state. In addition, HCNs can be optimized with supervised learning,
reinforcement learning, or a mixture of both. HCNs attain state-of-the-art
performance on the bAbI dialog dataset, and outperform two commercially
deployed customer-facing dialog systems.
BranchConnect: Large-Scale Visual Recognition with Learned Branch Connections
Authors: Karim Ahmed, Lorenzo Torresani
Date: 2017-04-20 00:00:00
Tasks: Image Categorization, Object Recognition

We introduce an architecture for large-scale image categorization that
enables the end-to-end learning of separate visual features for the different
classes to distinguish. The proposed model consists of a deep CNN shaped like a
tree. The stem of the tree includes a sequence of convolutional layers common
to all classes. The stem then splits into multiple branches implementing
parallel feature extractors, which are ultimately connected to the final
classification layer via learned gated connections. These learned gates
determine for each individual class the subset of features to use. Such a
scheme naturally encourages the learning of a heterogeneous set of specialized
features through the separate branches and it allows each class to use the
subset of features that are optimal for its recognition. We show the generality
of our proposed method by reshaping several popular CNNs from the literature
into our proposed architecture. Our experiments on the CIFAR100, CIFAR10, and
Synth datasets show that in each case our resulting model yields a substantial
improvement in accuracy over the original CNN. Our empirical analysis also
suggests that our scheme acts as a form of beneficial regularization improving
generalization performance.
Fast PET reconstruction using Multi-scale Fully Convolutional Neural Networks
Authors: Jieqing Jiao, Sebastien Ourselin
Date: 2017-04-24 00:00:00
Tasks: Computational Efficiency, Image Reconstruction

Reconstruction of PET images is an ill-posed inverse problem and often
requires iterative algorithms to achieve good image quality for reliable
clinical use in practice, at huge computational costs. In this paper, we
consider the PET reconstruction a dense prediction problem where the large
scale contextual information is essential, and propose a novel architecture of
multi-scale fully convolutional neural networks (msfCNN) for fast PET image
reconstruction. The proposed msfCNN gains large receptive fields with both
memory and computational efficiency, by using a downscaling-upscaling structure
and dilated convolutions. Instead of pooling and deconvolution, we propose to
use the periodic shuffling operation from sub-pixel convolution and its inverse
to scale the size of feature maps without losing resolution. Residual
connections were added to improve training. We trained the proposed msfCNN
model with simulated data, and applied it to clinical PET data acquired on a
Siemens mMR scanner. The results from real oncological and neurodegenerative
cases show that the proposed msfCNN-based reconstruction outperforms the
iterative approaches in terms of computational time while achieving comparable
image quality for quantification. The proposed msfCNN model can be applied to
other dense prediction tasks, and fast msfCNN-based PET reconstruction could
facilitate the potential use of molecular imaging in interventional/surgical
procedures, where cancer surgery can particularly benefit.
Cross-lingual and cross-domain discourse segmentation of entire documents
Authors: Chloé Braud, Ophélie Lacroix, Anders Søgaard
Date: 2017-04-13 00:00:00
Tasks: Discourse Segmentation, Segmentation, Sentence

Discourse segmentation is a crucial step in building end-to-end discourse
parsers. However, discourse segmenters only exist for a few languages and
domains. Typically they only detect intra-sentential segment boundaries,
assuming gold standard sentence and token segmentation, and relying on
high-quality syntactic parses and rich heuristics that are not generally
available across languages and domains. In this paper, we propose statistical
discourse segmenters for five languages and three domains that do not rely on
gold pre-annotations. We also consider the problem of learning discourse
segmenters when no labeled data is available for a language. Our fully
supervised system obtains 89.5% F1 for English newswire, with slight drops in
performance on other domains, and we report supervised and unsupervised
(cross-lingual) results for five languages in total.
Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method
Authors: Xiao Han
Date: 2017-04-24 00:00:00
Tasks: Lesion Segmentation, Segmentation, Tumor Segmentation

Liver lesion segmentation is an important step for liver cancer diagnosis,
treatment planning and treatment evaluation. LiTS (Liver Tumor Segmentation
Challenge) provides a common testbed for comparing different automatic liver
lesion segmentation methods. We participate in this challenge by developing a
deep convolutional neural network (DCNN) method. The particular DCNN model
works in 2.5D in that it takes a stack of adjacent slices as input and produces
the segmentation map corresponding to the center slice. The model has 32 layers
in total and makes use of both long range concatenation connections of U-Net
[1] and short-range residual connections from ResNet [2]. The model was trained
using the 130 LiTS training datasets and achieved an average Dice score of 0.67
when evaluated on the 70 test CT scans, which ranked first for the LiTS
challenge at the time of the ISBI 2017 conference.