pwc-archive/papers-with-abstracts huggingface dataset:
Lattice-Based Recurrent Neural Network Encoders for Neural Machine Translation
Authors: Jinsong Su, Zhixing Tan, Deyi Xiong, Rongrong Ji, Xiaodong Shi, Yang Liu
Date: 2016-09-25 00:00:00
Tasks: Machine Translation, NMT, Sentence, Translation

Neural machine translation (NMT) heavily relies on word-level modelling to
learn semantic representations of input sentences. However, for languages
without natural word delimiters (e.g., Chinese) where input sentences have to
be tokenized first, conventional NMT is confronted with two issues: 1) it is
difficult to find an optimal tokenization granularity for source sentence
modelling, and 2) errors in 1-best tokenizations may propagate to the encoder
of NMT. To handle these issues, we propose word-lattice based Recurrent Neural
Network (RNN) encoders for NMT, which generalize the standard RNN to word
lattice topology. The proposed encoders take as input a word lattice that
compactly encodes multiple tokenizations, and learn to generate new hidden
states from arbitrarily many inputs and hidden states in preceding time steps.
As such, the word-lattice based encoders not only alleviate the negative impact
of tokenization errors but also are more expressive and flexible to embed input
sentences. Experiment results on Chinese-English translation demonstrate the
superiorities of the proposed encoders over the conventional encoder.
Kinematic-Layout-aware Random Forests for Depth-based Action Recognition
Authors: Seungryul Baek, Zhiyuan Shi, Masato Kawade, Tae-Kyun Kim
Date: 2016-07-23 00:00:00
Tasks: Action Recognition, Temporal Action Localization

In this paper, we tackle the problem of 24 hours-monitoring patient actions
in a ward such as "stretching an arm out of the bed", "falling out of the bed",
where temporal movements are subtle or significant. In the concerned scenarios,
the relations between scene layouts and body kinematics (skeletons) become
important cues to recognize actions; however they are hard to be secured at a
testing stage. To address this problem, we propose a kinematic-layout-aware
random forest which takes into account the kinematic-layout (\ie layout and
skeletons), to maximize the discriminative power of depth image appearance. We
integrate the kinematic-layout in the split criteria of random forests to guide
the learning process by 1) determining the switch to either the depth
appearance or the kinematic-layout information, and 2) implicitly closing the
gap between two distributions obtained by the kinematic-layout and the
appearance, when the kinematic-layout appears useful. The kinematic-layout
information is not required for the test data, thus called "privileged
information prior". The proposed method has also been testified in cross-view
settings, by the use of view-invariant features and enforcing the consistency
among synthetic-view data. Experimental evaluations on our new dataset PATIENT,
CAD-60 and UWA3D (multiview) demonstrate that our method outperforms various
state-of-the-arts.
A series of maximum entropy upper bounds of the differential entropy
Authors: Frank Nielsen, Richard Nock
Date: 2016-12-09 00:00:00
Tasks: BIG-bench Machine Learning, Form

We present a series of closed-form maximum entropy upper bounds for the
differential entropy of a continuous univariate random variable and study the
properties of that series. We then show how to use those generic bounds for
upper bounding the differential entropy of Gaussian mixture models. This
requires to calculate the raw moments and raw absolute moments of Gaussian
mixtures in closed-form that may also be handy in statistical machine learning
and information theory. We report on our experiments and discuss on the
tightness of those bounds.
GOTM: a Goal-Oriented Framework for Capturing Uncertainty of Medical Treatments
Authors: Davoud Mougouei, David Powers
Date: 2016-12-09 00:00:00

It has been widely recognized that uncertainty is an inevitable aspect of diagnosis and treatment of medical disorders. Such uncertainties hence, need to be considered in computerized medical models. The existing medical modeling techniques however, have mainly focused on capturing uncertainty associated with diagnosis of medical disorders while ignoring uncertainty of treatments. To tackle this issue, we have proposed using a fuzzy-based modeling and description technique for capturing uncertainties in treatment plans. We have further contributed a formal framework which allows for goal-oriented modeling and analysis of medical treatments.
Facial Expression Recognition using Convolutional Neural Networks: State of the Art
Authors: Christopher Pramerdorfer, Martin Kampel
Date: 2016-12-09 00:00:00
Tasks: Facial Expression Recognition

The ability to recognize facial expressions automatically enables novel
applications in human-computer interaction and other areas. Consequently, there
has been active research in this field, with several recent works utilizing
Convolutional Neural Networks (CNNs) for feature extraction and inference.
These works differ significantly in terms of CNN architectures and other
factors. Based on the reported results alone, the performance impact of these
factors is unclear. In this paper, we review the state of the art in
image-based facial expression recognition using CNNs and highlight algorithmic
differences and their performance impact. On this basis, we identify existing
bottlenecks and consequently directions for advancing this research field.
Furthermore, we demonstrate that overcoming one of these bottlenecks - the
comparatively basic architectures of the CNNs utilized in this field - leads to
a substantial performance increase. By forming an ensemble of modern deep CNNs,
we obtain a FER2013 test accuracy of 75.2%, outperforming previous works
without requiring auxiliary training data or face registration.
Matrix Neural Networks
Authors: Junbin Gao, Yi Guo, Zhiyong Wang
Date: 2016-01-15 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Traditional neural networks assume vectorial inputs as the network is
arranged as layers of single line of computing units called neurons. This
special structure requires the non-vectorial inputs such as matrices to be
converted into vectors. This process can be problematic. Firstly, the spatial
information among elements of the data may be lost during vectorisation.
Secondly, the solution space becomes very large which demands very special
treatments to the network parameters and high computational cost. To address
these issues, we propose matrix neural networks (MatNet), which takes matrices
directly as inputs. Each neuron senses summarised information through bilinear
mapping from lower layer units in exactly the same way as the classic feed
forward neural networks. Under this structure, back prorogation and gradient
descent combination can be utilised to obtain network parameters efficiently.
Furthermore, it can be conveniently extended for multimodal inputs. We apply
MatNet to MNIST handwritten digits classification and image super resolution
tasks to show its effectiveness. Without too much tweaking MatNet achieves
comparable performance as the state-of-the-art methods in both tasks with
considerably reduced complexity.
Fast Fourier single-pixel imaging using binary illumination
Authors: Zibang Zhang, Xueying Wang, Jingang Zhong
Date: 2016-12-09 00:00:00

Fourier single-pixel imaging (FSI) has proven capable of reconstructing
high-quality two-dimensional and three-dimensional images. The utilization of
the sparsity of natural images in Fourier domain allows high-resolution images
to be reconstructed from far fewer measurements than effective image pixels.
However, applying original FSI in digital micro-mirror device (DMD) based
high-speed imaging system turns out to be challenging, because the original FSI
uses grayscale Fourier basis patterns for illumination while DMDs generate
grayscale patterns at a relatively low rate. DMDs are a binary device which can
only generate a black-and-white pattern at each instance. In this paper, we
adopt binary Fourier patterns for illumination to achieve DMD-based high-speed
single-pixel imaging. Binary Fourier patterns are generated by upsampling and
then applying error diffusion based dithering to the grayscale patterns.
Experiments demonstrate the proposed technique able to achieve static imaging
with high quality and dynamic imaging in real time. The proposed technique
potentially allows high-quality and high-speed imaging over broad wavebands.
Exploiting 2D Floorplan for Building-scale Panorama RGBD Alignment
Authors: Erik Wijmans, Yasutaka Furukawa
Date: 2016-12-08 00:00:00
Proceeding: CVPR 2017 7

This paper presents a novel algorithm that utilizes a 2D floorplan to align
panorama RGBD scans. While effective panorama RGBD alignment techniques exist,
such a system requires extremely dense RGBD image sampling. Our approach can
significantly reduce the number of necessary scans with the aid of a floorplan
image. We formulate a novel Markov Random Field inference problem as a scan
placement over the floorplan, as opposed to the conventional scan-to-scan
alignment. The technical contributions lie in multi-modal image correspondence
cues (between scans and schematic floorplan) as well as a novel coverage
potential avoiding an inherent stacking bias. The proposed approach has been
evaluated on five challenging large indoor spaces. To the best of our
knowledge, we present the first effective system that utilizes a 2D floorplan
image for building-scale 3D pointcloud alignment. The source code and the data
will be shared with the community to further enhance indoor mapping research.
Deep TEN: Texture Encoding Network
Authors: Hang Zhang, Jia Xue, Kristin Dana
Date: 2016-12-08 00:00:00
Proceeding: CVPR 2017 7
Tasks: Dictionary Learning, Material Recognition

We propose a Deep Texture Encoding Network (Deep-TEN) with a novel Encoding
Layer integrated on top of convolutional layers, which ports the entire
dictionary learning and encoding pipeline into a single model. Current methods
build from distinct components, using standard encoders with separate
off-the-shelf features such as SIFT descriptors or pre-trained CNN features for
material recognition. Our new approach provides an end-to-end learning
framework, where the inherent visual vocabularies are learned directly from the
loss function. The features, dictionaries and the encoding representation for
the classifier are all learned simultaneously. The representation is orderless
and therefore is particularly useful for material and texture recognition. The
Encoding Layer generalizes robust residual encoders such as VLAD and Fisher
Vectors, and has the property of discarding domain specific information which
makes the learned convolutional features easier to transfer. Additionally,
joint training using multiple datasets of varied sizes and class labels is
supported resulting in increased recognition performance. The experimental
results show superior performance as compared to state-of-the-art methods using
gold-standard databases such as MINC-2500, Flickr Material Database,
KTH-TIPS-2b, and two recent databases 4D-Light-Field-Material and GTOS. The
source code for the complete system are publicly available.
Discovering Conversational Dependencies between Messages in Dialogs
Authors: Wenchao Du, Pascal Poupart, Wei Xu
Date: 2016-12-08 00:00:00

We investigate the task of inferring conversational dependencies between
messages in one-on-one online chat, which has become one of the most popular
forms of customer service. We propose a novel probabilistic classifier that
leverages conversational, lexical and semantic information. The approach is
evaluated empirically on a set of customer service chat logs from a Chinese
e-commerce website. It outperforms heuristic baselines.
Safety Verification and Control for Collision Avoidance at Road Intersections
Authors: Heejin Ahn, Domitilla Del Vecchio
Date: 2016-12-08 00:00:00
Tasks: Blocking, Collision Avoidance, Scheduling

This paper presents the design of a supervisory algorithm that monitors
safety at road intersections and overrides drivers with a safe input when
necessary. The design of the supervisor consists of two parts: safety
verification and control design. Safety verification is the problem to
determine if vehicles will be able to cross the intersection without colliding
with current drivers' inputs. We translate this safety verification problem
into a jobshop scheduling problem, which minimizes the maximum lateness and
evaluates if the optimal cost is zero. The zero optimal cost corresponds to the
case in which all vehicles can cross each conflict area without collisions.
Computing the optimal cost requires solving a Mixed Integer Nonlinear
Programming (MINLP) problem due to the nonlinear second-order dynamics of the
vehicles. We therefore estimate this optimal cost by formulating two related
Mixed Integer Linear Programming (MILP) problems that assume simpler vehicle
dynamics. We prove that these two MILP problems yield lower and upper bounds of
the optimal cost. We also quantify the worst case approximation errors of these
MILP problems. We design the supervisor to override the vehicles with a safe
control input if the MILP problem that computes the upper bound yields a
positive optimal cost. We theoretically demonstrate that the supervisor keeps
the intersection safe and is non-blocking. Computer simulations further
validate that the algorithms can run in real time for problems of realistic
size.
Nearly-optimal Robust Matrix Completion
Authors: Yeshwanth Cherapanamjeri, Kartik Gupta, Prateek Jain
Date: 2016-06-23 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion

In this paper, we consider the problem of Robust Matrix Completion (RMC)
where the goal is to recover a low-rank matrix by observing a small number of
its entries out of which a few can be arbitrarily corrupted. We propose a
simple projected gradient descent method to estimate the low-rank matrix that
alternately performs a projected gradient descent step and cleans up a few of
the corrupted entries using hard-thresholding. Our algorithm solves RMC using
nearly optimal number of observations as well as nearly optimal number of
corruptions. Our result also implies significant improvement over the existing
time complexity bounds for the low-rank matrix completion problem. Finally, an
application of our result to the robust PCA problem (low-rank+sparse matrix
separation) leads to nearly linear time (in matrix dimensions) algorithm for
the same; existing state-of-the-art methods require quadratic time. Our
empirical results corroborate our theoretical results and show that even for
moderate sized problems, our method for robust PCA is an an order of magnitude
faster than the existing methods.
Improved generator objectives for GANs
Authors: Ben Poole, Alexander A. Alemi, Jascha Sohl-Dickstein, Anelia Angelova
Date: 2016-12-08 00:00:00
Tasks: Density Ratio Estimation, Diversity

We present a framework to understand GAN training as alternating density
ratio estimation and approximate divergence minimization. This provides an
interpretation for the mismatched GAN generator and discriminator objectives
often used in practice, and explains the problem of poor sample diversity. We
also derive a family of generator objectives that target arbitrary
$f$-divergences without minimizing a lower bound, and use them to train
generative image models that target either improved sample quality or greater
sample diversity.
Recurrent neural network training with preconditioned stochastic gradient descent
Authors: Xi-Lin Li
Date: 2016-06-14 00:00:00
Tasks: Handwritten Digit Recognition

This paper studies the performance of a recently proposed preconditioned
stochastic gradient descent (PSGD) algorithm on recurrent neural network (RNN)
training. PSGD adaptively estimates a preconditioner to accelerate gradient
descent, and is designed to be simple, general and easy to use, as stochastic
gradient descent (SGD). RNNs, especially the ones requiring extremely long term
memories, are difficult to train. We have tested PSGD on a set of synthetic
pathological RNN learning problems and the real world MNIST handwritten digit
recognition task. Experimental results suggest that PSGD is able to achieve
highly competitive performance without using any trick like preprocessing,
pretraining or parameter tweaking.
Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation
Authors: Xianming Liu, Amy Zhang, Tobias Tiecke, Andreas Gros, Thomas S. Huang
Date: 2016-12-08 00:00:00
Tasks: Semantic Segmentation, Weakly-supervised Learning

Learning from weakly-supervised data is one of the main challenges in machine
learning and computer vision, especially for tasks such as image semantic
segmentation where labeling is extremely expensive and subjective. In this
paper, we propose a novel neural network architecture to perform
weakly-supervised learning by suppressing irrelevant neuron activations. It
localizes objects of interest by learning from image-level categorical labels
in an end-to-end manner. We apply this algorithm to a practical challenge of
transforming satellite images into a map of settlements and individual
buildings. Experimental results show that the proposed algorithm achieves
superior performance and efficiency when compared with various baseline models.
A Maximum A Posteriori Estimation Framework for Robust High Dynamic Range Video Synthesis
Authors: Yuelong Li, Chul Lee, Vishal Monga
Date: 2016-12-08 00:00:00
Tasks: Image Generation, Optical Flow Estimation

High dynamic range (HDR) image synthesis from multiple low dynamic range
(LDR) exposures continues to be actively researched. The extension to HDR video
synthesis is a topic of significant current interest due to potential cost
benefits. For HDR video, a stiff practical challenge presents itself in the
form of accurate correspondence estimation of objects between video frames. In
particular, loss of data resulting from poor exposures and varying intensity
make conventional optical flow methods highly inaccurate. We avoid exact
correspondence estimation by proposing a statistical approach via maximum a
posterior (MAP) estimation, and under appropriate statistical assumptions and
choice of priors and models, we reduce it to an optimization problem of solving
for the foreground and background of the target frame. We obtain the background
through rank minimization and estimate the foreground via a novel multiscale
adaptive kernel regression technique, which implicitly captures local structure
and temporal motion by solving an unconstrained optimization problem. Extensive
experimental results on both real and synthetic datasets demonstrate that our
algorithm is more capable of delivering high-quality HDR videos than current
state-of-the-art methods, under both subjective and objective assessments.
Furthermore, a thorough complexity analysis reveals that our algorithm achieves
better complexity-performance trade-off than conventional methods.
Hierarchy through Composition with Linearly Solvable Markov Decision Processes
Authors: Andrew M. Saxe, Adam Earle, Benjamin Rosman
Date: 2016-12-08 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Hierarchical architectures are critical to the scalability of reinforcement
learning methods. Current hierarchical frameworks execute actions serially,
with macro-actions comprising sequences of primitive actions. We propose a
novel alternative to these control hierarchies based on concurrent execution of
many actions in parallel. Our scheme uses the concurrent compositionality
provided by the linearly solvable Markov decision process (LMDP) framework,
which naturally enables a learning agent to draw on several macro-actions
simultaneously to solve new tasks. We introduce the Multitask LMDP module,
which maintains a parallel distributed representation of tasks and may be
stacked to form deep hierarchies abstracted in space and time.
Protein-Ligand Scoring with Convolutional Neural Networks
Authors: Matthew Ragoza, Joshua Hochuli, Elisa Idrobo, Jocelyn Sunseri, David Ryan Koes
Date: 2016-12-08 00:00:00
Tasks: Drug Design, Drug Discovery, Pose Prediction

Computational approaches to drug discovery can reduce the time and cost
associated with experimental assays and enable the screening of novel
chemotypes. Structure-based drug design methods rely on scoring functions to
rank and predict binding affinities and poses. The ever-expanding amount of
protein-ligand binding and structural data enables the use of deep machine
learning techniques for protein-ligand scoring.
  We describe convolutional neural network (CNN) scoring functions that take as
input a comprehensive 3D representation of a protein-ligand interaction. A CNN
scoring function automatically learns the key features of protein-ligand
interactions that correlate with binding. We train and optimize our CNN scoring
functions to discriminate between correct and incorrect binding poses and known
binders and non-binders. We find that our CNN scoring function outperforms the
AutoDock Vina scoring function when ranking poses both for pose prediction and
virtual screening.
Joint Hand Detection and Rotation Estimation by Using CNN
Authors: Xiaoming Deng, Ye Yuan, Yinda Zhang, Ping Tan, Liang Chang, Shuo Yang, Hongan Wang
Date: 2016-12-08 00:00:00
Tasks: General Classification, Hand Detection, object-detection, Object Detection

Hand detection is essential for many hand related tasks, e.g. parsing hand
pose, understanding gesture, which are extremely useful for robotics and
human-computer interaction. However, hand detection in uncontrolled
environments is challenging due to the flexibility of wrist joint and cluttered
background. We propose a deep learning based approach which detects hands and
calibrates in-plane rotation under supervision at the same time. To guarantee
the recall, we propose a context aware proposal generation algorithm which
significantly outperforms the selective search. We then design a convolutional
neural network(CNN) which handles object rotation explicitly to jointly solve
the object detection and rotation estimation tasks. Experiments show that our
method achieves better results than state-of-the-art detection models on
widely-used benchmarks such as Oxford and Egohands database. We further show
that rotation estimation and classification can mutually benefit each other.
A Stackelberg Game Perspective on the Conflict Between Machine Learning and Data Obfuscation
Authors: Jeffrey Pawlick, Quanyan Zhu
Date: 2016-08-08 00:00:00
Tasks: BIG-bench Machine Learning

Data is the new oil; this refrain is repeated extensively in the age of
internet tracking, machine learning, and data analytics. As data collection
becomes more personal and pervasive, however, public pressure is mounting for
privacy protection. In this atmosphere, developers have created applications to
add noise to user attributes visible to tracking algorithms. This creates a
strategic interaction between trackers and users when incentives to maintain
privacy and improve accuracy are misaligned. In this paper, we conceptualize
this conflict through an N+1-player, augmented Stackelberg game. First a
machine learner declares a privacy protection level, and then users respond by
choosing their own perturbation amounts. We use the general frameworks of
differential privacy and empirical risk minimization to quantify the utility
components due to privacy and accuracy, respectively. In equilibrium, each user
perturbs her data independently, which leads to a high net loss in accuracy. To
remedy this scenario, we show that the learner improves his utility by
proactively perturbing the data himself. While other work in this area has
studied privacy markets and mechanism design for truthful reporting of user
information, we take a different viewpoint by considering both user and learner
perturbation.