pwc-archive/papers-with-abstracts huggingface dataset:
From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood
Authors: Kelvin Guu, Panupong Pasupat, Evan Zheran Liu, Percy Liang
Date: 2017-04-25 00:00:00
Proceeding: ACL 2017 7
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Semantic Parsing

Our goal is to learn a semantic parser that maps natural language utterances
into executable programs when only indirect supervision is available: examples
are labeled with the correct execution result, but not the program itself.
Consequently, we must search the space of programs for those that output the
correct result, while not being misled by spurious programs: incorrect programs
that coincidentally output the correct result. We connect two common learning
paradigms, reinforcement learning (RL) and maximum marginal likelihood (MML),
and then present a new learning algorithm that combines the strengths of both.
The new algorithm guards against spurious programs by combining the systematic
search traditionally employed in MML with the randomized exploration of RL, and
by updating parameters such that probability is spread more evenly across
consistent programs. We apply our learning algorithm to a new neural semantic
parser and show significant gains over existing state-of-the-art results on a
recent context-dependent semantic parsing task.
Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car
Authors: Mariusz Bojarski, Philip Yeres, Anna Choromanska, Krzysztof Choromanski, Bernhard Firner, Lawrence Jackel, Urs Muller
Date: 2017-04-25 00:00:00
Tasks: Autonomous Driving, Self-Driving Cars

As part of a complete software stack for autonomous driving, NVIDIA has
created a neural-network-based system, known as PilotNet, which outputs
steering angles given images of the road ahead. PilotNet is trained using road
images paired with the steering angles generated by a human driving a
data-collection car. It derives the necessary domain knowledge by observing
human drivers. This eliminates the need for human engineers to anticipate what
is important in an image and foresee all the necessary rules for safe driving.
Road tests demonstrated that PilotNet can successfully perform lane keeping in
a wide variety of driving conditions, regardless of whether lane markings are
present or not.
  The goal of the work described here is to explain what PilotNet learns and
how it makes its decisions. To this end we developed a method for determining
which elements in the road image most influence PilotNet's steering decision.
Results show that PilotNet indeed learns to recognize relevant objects on the
road.
  In addition to learning the obvious features such as lane markings, edges of
roads, and other cars, PilotNet learns more subtle features that would be hard
to anticipate and program by engineers, for example, bushes lining the edge of
the road and atypical vehicle classes.
Adaptive Cost Function for Pointcloud Registration
Authors: Johan Ekekrantz, John Folkesson, Patric Jensfelt
Date: 2017-04-25 00:00:00

In this paper we introduce an adaptive cost function for pointcloud
registration. The algorithm automatically estimates the sensor noise, which is
important for generalization across different sensors and environments. Through
experiments on real and synthetic data, we show significant improvements in
accuracy and robustness over state-of-the-art solutions.
Fisher consistency for prior probability shift
Authors: Dirk Tasche
Date: 2017-01-19 00:00:00

We introduce Fisher consistency in the sense of unbiasedness as a desirable
property for estimators of class prior probabilities. Lack of Fisher
consistency could be used as a criterion to dismiss estimators that are
unlikely to deliver precise estimates in test datasets under prior probability
and more general dataset shift. The usefulness of this unbiasedness concept is
demonstrated with three examples of classifiers used for quantification:
Adjusted Classify & Count, EM-algorithm and CDE-Iterate. We find that Adjusted
Classify & Count and EM-algorithm are Fisher consistent. A counter-example
shows that CDE-Iterate is not Fisher consistent and, therefore, cannot be
trusted to deliver reliable estimates of class probabilities.
Small-footprint Highway Deep Neural Networks for Speech Recognition
Authors: Liang Lu, Steve Renals
Date: 2016-10-18 00:00:00
Tasks: speech-recognition, Speech Recognition

State-of-the-art speech recognition systems typically employ neural network
acoustic models. However, compared to Gaussian mixture models, deep neural
network (DNN) based acoustic models often have many more model parameters,
making it challenging for them to be deployed on resource-constrained
platforms, such as mobile devices. In this paper, we study the application of
the recently proposed highway deep neural network (HDNN) for training
small-footprint acoustic models. HDNNs are a depth-gated feedforward neural
network, which include two types of gate functions to facilitate the
information flow through different layers. Our study demonstrates that HDNNs
are more compact than regular DNNs for acoustic modeling, i.e., they can
achieve comparable recognition accuracy with many fewer model parameters.
Furthermore, HDNNs are more controllable than DNNs: the gate functions of an
HDNN can control the behavior of the whole network using a very small number of
model parameters. Finally, we show that HDNNs are more adaptable than DNNs. For
example, simply updating the gate functions using adaptation data can result in
considerable gains in accuracy. We demonstrate these aspects by experiments
using the publicly available AMI corpus, which has around 80 hours of training
data.
Automatic Compositor Attribution in the First Folio of Shakespeare
Authors: Maria Ryskina, Hannah Alpert-Abrams, Dan Garrette, Taylor Berg-Kirkpatrick
Date: 2017-04-25 00:00:00
Proceeding: ACL 2017 7
Tasks: Clustering, Optical Character Recognition (OCR)

Compositor attribution, the clustering of pages in a historical printed
document by the individual who set the type, is a bibliographic task that
relies on analysis of orthographic variation and inspection of visual details
of the printed page. In this paper, we introduce a novel unsupervised model
that jointly describes the textual and visual features needed to distinguish
compositors. Applied to images of Shakespeare's First Folio, our model predicts
attributions that agree with the manual judgements of bibliographers with an
accuracy of 87%, even on text that is the output of OCR.
Deep Multitask Learning for Semantic Dependency Parsing
Authors: Hao Peng, Sam Thomson, Noah A. Smith
Date: 2017-04-22 00:00:00
Proceeding: ACL 2017 7
Tasks: ARC, Dependency Parsing, Semantic Dependency Parsing

We present a deep neural architecture that parses sentences into three
semantic dependency graph formalisms. By using efficient, nearly arc-factored
inference and a bidirectional-LSTM composed with a multi-layer perceptron, our
base system is able to significantly improve the state of the art for semantic
dependency parsing, without using hand-engineered features or syntax. We then
explore two multitask learning approaches---one that shares parameters across
formalisms, and one that uses higher-order structures to predict the graphs
jointly. We find that both approaches improve performance across formalisms on
average, achieving a new state of the art. Our code is open-source and
available at https://github.com/Noahs-ARK/NeurboParser.
Multi-View Dynamic Facial Action Unit Detection
Authors: Andres Romero, Juan Leon, Pablo Arbelaez
Date: 2017-04-25 00:00:00
Tasks: Action Unit Detection, Facial Action Unit Detection

We propose a novel convolutional neural network approach to address the
fine-grained recognition problem of multi-view dynamic facial action unit
detection. We leverage recent gains in large-scale object recognition by
formulating the task of predicting the presence or absence of a specific action
unit in a still image of a human face as holistic classification. We then
explore the design space of our approach by considering both shared and
independent representations for separate action units, and also different CNN
architectures for combining color and motion information. We then move to the
novel setup of the FERA 2017 Challenge, in which we propose a multi-view
extension of our approach that operates by first predicting the viewpoint from
which the video was taken, and then evaluating an ensemble of action unit
detectors that were trained for that specific viewpoint. Our approach is
holistic, efficient, and modular, since new action units can be easily included
in the overall system. Our approach significantly outperforms the baseline of
the FERA 2017 Challenge, with an absolute improvement of 14% on the F1-metric.
Additionally, it compares favorably against the winner of the FERA 2017
challenge. Code source is available at https://github.com/BCV-Uniandes/AUNets.
Linguistically Regularized LSTMs for Sentiment Classification
Authors: Qiao Qian, Minlie Huang, Jinhao Lei, Xiaoyan Zhu
Date: 2016-11-12 00:00:00
Tasks: Classification, General Classification, Negation, Sentence, Sentiment Analysis, Sentiment Classification

Sentiment understanding has been a long-term goal of AI in the past decades.
This paper deals with sentence-level sentiment classification. Though a variety
of neural network models have been proposed very recently, however, previous
models either depend on expensive phrase-level annotation, whose performance
drops substantially when trained with only sentence-level annotation; or do not
fully employ linguistic resources (e.g., sentiment lexicons, negation words,
intensity words), thus not being able to produce linguistically coherent
representations. In this paper, we propose simple models trained with
sentence-level annotation, but also attempt to generating linguistically
coherent representations by employing regularizers that model the linguistic
role of sentiment lexicons, negation words, and intensity words. Results show
that our models are effective to capture the sentiment shifting effect of
sentiment, negation, and intensity words, while still obtain competitive
results without sacrificing the models' simplicity.
Introspective Generative Modeling: Decide Discriminatively
Authors: Justin Lazarow, Long Jin, Zhuowen Tu
Date: 2017-04-25 00:00:00
Tasks: General Classification

We study unsupervised learning by developing introspective generative
modeling (IGM) that attains a generator using progressively learned deep
convolutional neural networks. The generator is itself a discriminator, capable
of introspection: being able to self-evaluate the difference between its
generated samples and the given training data. When followed by repeated
discriminative learning, desirable properties of modern discriminative
classifiers are directly inherited by the generator. IGM learns a cascade of
CNN classifiers using a synthesis-by-classification algorithm. In the
experiments, we observe encouraging results on a number of applications
including texture modeling, artistic style transferring, face modeling, and
semi-supervised learning.
Hand Keypoint Detection in Single Images using Multiview Bootstrapping
Authors: Tomas Simon, Hanbyul Joo, Iain Matthews, Yaser Sheikh
Date: 2017-04-25 00:00:00
Proceeding: CVPR 2017 7
Tasks: Keypoint Detection

We present an approach that uses a multi-camera system to train fine-grained
detectors for keypoints that are prone to occlusion, such as the joints of a
hand. We call this procedure multiview bootstrapping: first, an initial
keypoint detector is used to produce noisy labels in multiple views of the
hand. The noisy detections are then triangulated in 3D using multiview geometry
or marked as outliers. Finally, the reprojected triangulations are used as new
labeled training data to improve the detector. We repeat this process,
generating more labeled data in each iteration. We derive a result analytically
relating the minimum number of views to achieve target true and false positive
rates for a given detector. The method is used to train a hand keypoint
detector for single images. The resulting keypoint detector runs in realtime on
RGB images and has accuracy comparable to methods that use depth sensors. The
single view detector, triangulated over multiple views, enables 3D markerless
hand motion capture with complex object interactions.
A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates
Authors: Zhi Li, Wei Shi, Ming Yan
Date: 2017-04-25 00:00:00

This paper proposes a novel proximal-gradient algorithm for a decentralized optimization problem with a composite objective containing smooth and non-smooth terms. Specifically, the smooth and nonsmooth terms are dealt with by gradient and proximal updates, respectively. The proposed algorithm is closely related to a previous algorithm, PG-EXTRA \cite{shi2015proximal}, but has a few advantages. First of all, agents use uncoordinated step-sizes, and the stable upper bounds on step-sizes are independent of network topologies. The step-sizes depend on local objective functions, and they can be as large as those of the gradient descent. Secondly, for the special case without non-smooth terms, linear convergence can be achieved under the strong convexity assumption. The dependence of the convergence rate on the objective functions and the network are separated, and the convergence rate of the new algorithm is as good as one of the two convergence rates that match the typical rates for the general gradient descent and the consensus averaging. We provide numerical experiments to demonstrate the efficacy of the introduced algorithm and validate our theoretical discoveries.
SfM-Net: Learning of Structure and Motion from Video
Authors: Sudheendra Vijayanarasimhan, Susanna Ricco, Cordelia Schmid, Rahul Sukthankar, Katerina Fragkiadaki
Date: 2017-04-25 00:00:00
Tasks: Motion Estimation, Object, Optical Flow Estimation

We propose SfM-Net, a geometry-aware neural network for motion estimation in
videos that decomposes frame-to-frame pixel motion in terms of scene and object
depth, camera motion and 3D object rotations and translations. Given a sequence
of frames, SfM-Net predicts depth, segmentation, camera and rigid object
motions, converts those into a dense frame-to-frame motion field (optical
flow), differentiably warps frames in time to match pixels and back-propagates.
The model can be trained with various degrees of supervision: 1)
self-supervised by the re-projection photometric error (completely
unsupervised), 2) supervised by ego-motion (camera motion), or 3) supervised by
depth (e.g., as provided by RGBD sensors). SfM-Net extracts meaningful depth
estimates and successfully estimates frame-to-frame camera rotations and
translations. It often successfully segments the moving objects in the scene,
even though such supervision is never provided.
Arabidopsis roots segmentation based on morphological operations and CRFs
Authors: José Ignacio Orlando, Hugo Luis Manterola, Enzo Ferrante, Federico Ariel
Date: 2017-04-25 00:00:00

Arabidopsis thaliana is a plant species widely utilized by scientists to
estimate the impact of genetic differences in root morphological features. For
this purpose, images of this plant after genetic modifications are taken to
study differences in the root architecture. This task requires manual
segmentations of radicular structures, although this is a particularly tedious
and time-consuming labor. In this work, we present an unsupervised method for
Arabidopsis thaliana root segmentation based on morphological operations and
fully-connected Conditional Random Fields. Although other approaches have been
proposed to this purpose, all of them are based on more complex and expensive
imaging modalities. Our results prove that our method can be easily applied
over images taken using conventional scanners, with a minor user intervention.
A first data set, our results and a fully open source implementation are
available online.
FWDA: a Fast Wishart Discriminant Analysis with its Application to Electronic Health Records Data Classification
Authors: Haoyi Xiong, Wei Cheng, Wenqing Hu, Jiang Bian, Zhishan Guo
Date: 2017-04-25 00:00:00
Tasks: Classification, General Classification

Linear Discriminant Analysis (LDA) on Electronic Health Records (EHR) data is
widely-used for early detection of diseases. Classical LDA for EHR data
classification, however, suffers from two handicaps: the ill-posed estimation
of LDA parameters (e.g., covariance matrix), and the "linear inseparability" of
EHR data. To handle these two issues, in this paper, we propose a novel
classifier FWDA -- Fast Wishart Discriminant Analysis, that makes predictions
in an ensemble way. Specifically, FWDA first surrogates the distribution of
inverse covariance matrices using a Wishart distribution estimated from the
training data, then "weighted-averages" the classification results of multiple
LDA classifiers parameterized by the sampled inverse covariance matrices via a
Bayesian Voting scheme. The weights for voting are optimally updated to adapt
each new input data, so as to enable the nonlinear classification. Theoretical
analysis indicates that FWDA possesses a fast convergence rate and a robust
performance on high dimensional data. Extensive experiments on large-scale EHR
dataset show that our approach outperforms state-of-the-art algorithms by a
large margin.
Exploiting Domain Knowledge via Grouped Weight Sharing with Application to Text Categorization
Authors: Ye Zhang, Matthew Lease, Byron C. Wallace
Date: 2017-02-08 00:00:00
Proceeding: ACL 2017 7
Tasks: General Classification, Model Compression, Text Categorization

A fundamental advantage of neural models for NLP is their ability to learn
representations from scratch. However, in practice this often means ignoring
existing external linguistic resources, e.g., WordNet or domain specific
ontologies such as the Unified Medical Language System (UMLS). We propose a
general, novel method for exploiting such resources via weight sharing. Prior
work on weight sharing in neural networks has considered it largely as a means
of model compression. In contrast, we treat weight sharing as a flexible
mechanism for incorporating prior knowledge into neural models. We show that
this approach consistently yields improved performance on classification tasks
compared to baseline strategies that do not exploit weight sharing.
Email Babel: Does Language Affect Criminal Activity in Compromised Webmail Accounts?
Authors: Emeric Bernard-Jones, Jeremiah Onaolapo, Gianluca Stringhini
Date: 2017-04-25 00:00:00
Tasks: Navigate

We set out to understand the effects of differing language on the ability of
cybercriminals to navigate webmail accounts and locate sensitive information in
them. To this end, we configured thirty Gmail honeypot accounts with English,
Romanian, and Greek language settings. We populated the accounts with email
messages in those languages by subscribing them to selected online newsletters.
We hid email messages about fake bank accounts in fifteen of the accounts to
mimic real-world webmail users that sometimes store sensitive information in
their accounts. We then leaked credentials to the honey accounts via paste
sites on the Surface Web and the Dark Web, and collected data for fifteen days.
Our statistical analyses on the data show that cybercriminals are more likely
to discover sensitive information (bank account information) in the Greek
accounts than the remaining accounts, contrary to the expectation that Greek
ought to constitute a barrier to the understanding of non-Greek visitors to the
Greek accounts. We also extracted the important words among the emails that
cybercriminals accessed (as an approximation of the keywords that they searched
for within the honey accounts), and found that financial terms featured among
the top words. In summary, we show that language plays a significant role in
the ability of cybercriminals to access sensitive information hidden in
compromised webmail accounts.
Joint Sequence Learning and Cross-Modality Convolution for 3D Biomedical Segmentation
Authors: Kuan-Lun Tseng, Yen-Liang Lin, Winston Hsu, Chung-Yang Huang
Date: 2017-04-25 00:00:00
Proceeding: CVPR 2017 7
Tasks: Decoder, Segmentation

Deep learning models such as convolutional neural net- work have been widely
used in 3D biomedical segmentation and achieve state-of-the-art performance.
However, most of them often adapt a single modality or stack multiple
modalities as different input channels. To better leverage the multi-
modalities, we propose a deep encoder-decoder structure with cross-modality
convolution layers to incorporate different modalities of MRI data. In
addition, we exploit convolutional LSTM to model a sequence of 2D slices, and
jointly learn the multi-modalities and convolutional LSTM in an end-to-end
manner. To avoid converging to the certain labels, we adopt a re-weighting
scheme and two-phase training to handle the label imbalance. Experimental
results on BRATS-2015 show that our method outperforms state-of-the-art
biomedical segmentation approaches.
Fine-Grained Entity Typing with High-Multiplicity Assignments
Authors: Maxim Rabinovich, Dan Klein
Date: 2017-04-25 00:00:00
Proceeding: ACL 2017 7
Tasks: Entity Typing, Vocal Bursts Intensity Prediction, Vocal Bursts Type Prediction

As entity type systems become richer and more fine-grained, we expect the
number of types assigned to a given entity to increase. However, most
fine-grained typing work has focused on datasets that exhibit a low degree of
type multiplicity. In this paper, we consider the high-multiplicity regime
inherent in data sources such as Wikipedia that have semi-open type systems. We
introduce a set-prediction approach to this problem and show that our model
outperforms unstructured baselines on a new Wikipedia-based fine-grained typing
corpus.
DeepAM: Migrate APIs with Multi-modal Sequence to Sequence Learning
Authors: Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, Sunghun Kim
Date: 2017-04-25 00:00:00

Computer programs written in one language are often required to be ported to
other languages to support multiple devices and environments. When programs use
language specific APIs (Application Programming Interfaces), it is very
challenging to migrate these APIs to the corresponding APIs written in other
languages. Existing approaches mine API mappings from projects that have
corresponding versions in two languages. They rely on the sparse availability
of bilingual projects, thus producing a limited number of API mappings. In this
paper, we propose an intelligent system called DeepAM for automatically mining
API mappings from a large-scale code corpus without bilingual projects. The key
component of DeepAM is based on the multimodal sequence to sequence learning
architecture that aims to learn joint semantic representations of bilingual API
sequences from big source code data. Experimental results indicate that DeepAM
significantly increases the accuracy of API mappings as well as the number of
API mappings, when compared with the state-of-the-art approaches.