pwc-archive/papers-with-abstracts huggingface dataset:
A Rotation Invariant Latent Factor Model for Moveme Discovery from Static Poses
Authors: Matteo Ruggero Ronchi, Joon Sik Kim, Yisong Yue
Date: 2016-09-23 00:00:00

We tackle the problem of learning a rotation invariant latent factor model
when the training data is comprised of lower-dimensional projections of the
original feature space. The main goal is the discovery of a set of 3-D bases
poses that can characterize the manifold of primitive human motions, or
movemes, from a training set of 2-D projected poses obtained from still images
taken at various camera angles. The proposed technique for basis discovery is
data-driven rather than hand-designed. The learned representation is rotation
invariant, and can reconstruct any training instance from multiple viewing
angles. We apply our method to modeling human poses in sports (via the Leeds
Sports Dataset), and demonstrate the effectiveness of the learned bases in a
range of applications such as activity classification, inference of dynamics
from a single frame, and synthetic representation of movements.
Screening Rules for Convex Problems
Authors: Anant Raj, Jakob Olbrich, Bernd Gärtner, Bernhard Schölkopf, Martin Jaggi
Date: 2016-09-23 00:00:00

We propose a new framework for deriving screening rules for convex
optimization problems. Our approach covers a large class of constrained and
penalized optimization formulations, and works in two steps. First, given any
approximate point, the structure of the objective function and the duality gap
is used to gather information on the optimal solution. In the second step, this
information is used to produce screening rules, i.e. safely identifying
unimportant weight variables of the optimal solution. Our general framework
leads to a large variety of useful existing as well as new screening rules for
many applications. For example, we provide new screening rules for general
simplex and $L_1$-constrained problems, Elastic Net, squared-loss Support
Vector Machines, minimum enclosing ball, as well as structured norm regularized
problems, such as group lasso.
AMR-to-text generation as a Traveling Salesman Problem
Authors: Linfeng Song, Yue Zhang, Xiaochang Peng, Zhiguo Wang, Daniel Gildea
Date: 2016-09-23 00:00:00
Proceeding: EMNLP 2016 11
Tasks: AMR-to-Text Generation, Text Generation, Translation, Traveling Salesman Problem

The task of AMR-to-text generation is to generate grammatical text that
sustains the semantic meaning for a given AMR graph. We at- tack the task by
first partitioning the AMR graph into smaller fragments, and then generating
the translation for each fragment, before finally deciding the order by solving
an asymmetric generalized traveling salesman problem (AGTSP). A Maximum Entropy
classifier is trained to estimate the traveling costs, and a TSP solver is used
to find the optimized solution. The final model reports a BLEU score of 22.44
on the SemEval-2016 Task8 dataset.
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
Authors: Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P. Aitken, Rob Bishop, Daniel Rueckert, Zehan Wang
Date: 2016-09-16 00:00:00
Proceeding: CVPR 2016 6
Tasks: GPU, Image Super-Resolution, Super-Resolution, Video Super-Resolution

Recently, several models based on deep neural networks have achieved great
success in terms of both reconstruction accuracy and computational performance
for single image super-resolution. In these methods, the low resolution (LR)
input image is upscaled to the high resolution (HR) space using a single
filter, commonly bicubic interpolation, before reconstruction. This means that
the super-resolution (SR) operation is performed in HR space. We demonstrate
that this is sub-optimal and adds computational complexity. In this paper, we
present the first convolutional neural network (CNN) capable of real-time SR of
1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN
architecture where the feature maps are extracted in the LR space. In addition,
we introduce an efficient sub-pixel convolution layer which learns an array of
upscaling filters to upscale the final LR feature maps into the HR output. By
doing so, we effectively replace the handcrafted bicubic filter in the SR
pipeline with more complex upscaling filters specifically trained for each
feature map, whilst also reducing the computational complexity of the overall
SR operation. We evaluate the proposed approach using images and videos from
publicly available datasets and show that it performs significantly better
(+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster
than previous CNN-based methods.
Regulating Reward Training by Means of Certainty Prediction in a Neural Network-Implemented Pong Game
Authors: Matt Oberdorfer, Matt Abuzalaf
Date: 2016-09-23 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present the first reinforcement-learning model to self-improve its
reward-modulated training implemented through a continuously improving
"intuition" neural network. An agent was trained how to play the arcade video
game Pong with two reward-based alternatives, one where the paddle was placed
randomly during training, and a second where the paddle was simultaneously
trained on three additional neural networks such that it could develop a sense
of "certainty" as to how probable its own predicted paddle position will be to
return the ball. If the agent was less than 95% certain to return the ball, the
policy used an intuition neural network to place the paddle. We trained both
architectures for an equivalent number of epochs and tested learning
performance by letting the trained programs play against a near-perfect
opponent. Through this, we found that the reinforcement learning model that
uses an intuition neural network for placing the paddle during reward training
quickly overtakes the simple architecture in its ability to outplay the
near-perfect opponent, additionally outscoring that opponent by an increasingly
wide margin after additional epochs of training.
Building an Evaluation Scale using Item Response Theory
Authors: John P. Lalor, Hao Wu, Hong Yu
Date: 2016-05-28 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Natural Language Inference

Evaluation of NLP methods requires testing against a previously vetted
gold-standard test set and reporting standard metrics
(accuracy/precision/recall/F1). The current assumption is that all items in a
given test set are equal with regards to difficulty and discriminating power.
We propose Item Response Theory (IRT) from psychometrics as an alternative
means for gold-standard test-set generation and NLP system evaluation. IRT is
able to describe characteristics of individual items - their difficulty and
discriminating power - and can account for these characteristics in its
estimation of human intelligence or ability for an NLP task. In this paper, we
demonstrate IRT by generating a gold-standard test set for Recognizing Textual
Entailment. By collecting a large number of human responses and fitting our IRT
model, we show that our IRT model compares NLP systems with the performance in
a human population and is able to provide more insight into system performance
than standard evaluation metrics. We show that a high accuracy score does not
always imply a high IRT score, which depends on the item characteristics and
the response pattern.
Incremental Learning for Fully Unsupervised Word Segmentation Using Penalized Likelihood and Model Selection
Authors: Ruey-Cheng Chen
Date: 2016-07-20 00:00:00
Tasks: Incremental Learning, Model Selection, Segmentation

We present a novel incremental learning approach for unsupervised word
segmentation that combines features from probabilistic modeling and model
selection. This includes super-additive penalties for addressing the cognitive
burden imposed by long word formation, and new model selection criteria based
on higher-order generative assumptions. Our approach is fully unsupervised; it
relies on a small number of parameters that permits flexible modeling and a
mechanism that automatically learns parameters from the data. Through
experimentation, we show that this intricate design has led to top-tier
performance in both phonemic and orthographic word segmentation.
Real-time Human Pose Estimation from Video with Convolutional Neural Networks
Authors: Marko Linna, Juho Kannala, Esa Rahtu
Date: 2016-09-23 00:00:00
Tasks: Action Recognition, Pose Estimation, Temporal Action Localization

In this paper, we present a method for real-time multi-person human pose
estimation from video by utilizing convolutional neural networks. Our method is
aimed for use case specific applications, where good accuracy is essential and
variation of the background and poses is limited. This enables us to use a
generic network architecture, which is both accurate and fast. We divide the
problem into two phases: (1) pre-training and (2) finetuning. In pre-training,
the network is learned with highly diverse input data from publicly available
datasets, while in finetuning we train with application specific data, which we
record with Kinect. Our method differs from most of the state-of-the-art
methods in that we consider the whole system, including person detector, pose
estimator and an automatic way to record application specific training material
for finetuning. Our method is considerably faster than many of the
state-of-the-art methods. Our method can be thought of as a replacement for
Kinect, and it can be used for higher level tasks, such as gesture control,
games, person tracking, action recognition and action tracking. We achieved
accuracy of 96.8\% (PCK@0.2) with application specific data.
Active Ranking from Pairwise Comparisons and when Parametric Assumptions Don't Help
Authors: Reinhard Heckel, Nihar B. Shah, Kannan Ramchandran, Martin J. Wainwright
Date: 2016-06-28 00:00:00
Tasks: Open-Ended Question Answering

We consider sequential or active ranking of a set of n items based on noisy
pairwise comparisons. Items are ranked according to the probability that a
given item beats a randomly chosen item, and ranking refers to partitioning the
items into sets of pre-specified sizes according to their scores. This notion
of ranking includes as special cases the identification of the top-k items and
the total ordering of the items. We first analyze a sequential ranking
algorithm that counts the number of comparisons won, and uses these counts to
decide whether to stop, or to compare another pair of items, chosen based on
confidence intervals specified by the data collected up to that point. We prove
that this algorithm succeeds in recovering the ranking using a number of
comparisons that is optimal up to logarithmic factors. This guarantee does not
require any structural properties of the underlying pairwise probability
matrix, unlike a significant body of past work on pairwise ranking based on
parametric models such as the Thurstone or Bradley-Terry-Luce models. It has
been a long-standing open question as to whether or not imposing these
parametric assumptions allows for improved ranking algorithms. For stochastic
comparison models, in which the pairwise probabilities are bounded away from
zero, our second contribution is to resolve this issue by proving a lower bound
for parametric models. This shows, perhaps surprisingly, that these popular
parametric modeling choices offer at most logarithmic gains for stochastic
comparisons.
Neural Text Generation from Structured Data with Application to the Biography Domain
Authors: Remi Lebret, David Grangier, Michael Auli
Date: 2016-03-24 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Concept-To-Text Generation, Language Modeling, Language Modelling, Sentence, Table-to-Text Generation, Text Generation

This paper introduces a neural model for concept-to-text generation that
scales to large, rich domains. We experiment with a new dataset of biographies
from Wikipedia that is an order of magnitude larger than existing resources
with over 700k samples. The dataset is also vastly more diverse with a 400k
vocabulary, compared to a few hundred words for Weathergov or Robocup. Our
model builds upon recent work on conditional neural language model for text
generation. To deal with the large vocabulary, we extend these models to mix a
fixed vocabulary with copy actions that transfer sample-specific words from the
input database to the generated output sentence. Our neural model significantly
out-performs a classical Kneser-Ney language model adapted to this task by
nearly 15 BLEU.
Graph clustering, variational image segmentation methods and Hough transform scale detection for object measurement in images
Authors: Luca Calatroni, Yves van Gennip, Carola-Bibiane Schönlieb, Hannah Rowland, Arjuna Flenner
Date: 2016-02-27 00:00:00
Tasks: Clustering, Graph Clustering, Image Segmentation, Matrix Completion, Semantic Segmentation

We consider the problem of scale detection in images where a region of
interest is present together with a measurement tool (e.g. a ruler). For the
segmentation part, we focus on the graph based method by Flenner and Bertozzi
which reinterprets classical continuous Ginzburg-Landau minimisation models in
a totally discrete framework. To overcome the numerical difficulties due to the
large size of the images considered we use matrix completion and splitting
techniques. The scale on the measurement tool is detected via a Hough transform
based algorithm. The method is then applied to some measurement tasks arising
in real-world applications such as zoology, medicine and archaeology.
Multi-Output Artificial Neural Network for Storm Surge Prediction in North Carolina
Authors: Anton Bezuglov, Brian Blanton, Reinaldo Santiago
Date: 2016-09-23 00:00:00

During hurricane seasons, emergency managers and other decision makers need
accurate and `on-time' information on potential storm surge impacts. Fully
dynamical computer models, such as the ADCIRC tide, storm surge, and wind-wave
model take several hours to complete a forecast when configured at high spatial
resolution. Additionally, statically meaningful ensembles of high-resolution
models (needed for uncertainty estimation) cannot easily be computed in near
real-time. This paper discusses an artificial neural network model for storm
surge prediction in North Carolina. The network model provides fast, real-time
storm surge estimates at coastal locations in North Carolina. The paper studies
the performance of the neural network model vs. other models on synthetic and
real hurricane data.
The face-space duality hypothesis: a computational model
Authors: Jonathan Vitale, Mary-Anne Williams, Benjamin Johnston
Date: 2016-09-23 00:00:00
Tasks: Dimensionality Reduction, model

Valentine's face-space suggests that faces are represented in a psychological
multidimensional space according to their perceived properties. However, the
proposed framework was initially designed as an account of invariant facial
features only, and explanations for dynamic features representation were
neglected. In this paper we propose, develop and evaluate a computational model
for a twofold structure of the face-space, able to unify both identity and
expression representations in a single implemented model. To capture both
invariant and dynamic facial features we introduce the face-space duality
hypothesis and subsequently validate it through a mathematical presentation
using a general approach to dimensionality reduction. Two experiments with real
facial images show that the proposed face-space: (1) supports both identity and
expression recognition, and (2) has a twofold structure anticipated by our
formal argument.
Example-Based Image Synthesis via Randomized Patch-Matching
Authors: Yi Ren, Yaniv Romano, Michael Elad
Date: 2016-09-23 00:00:00
Tasks: Image Generation, Patch Matching, Texture Synthesis

Image and texture synthesis is a challenging task that has long been drawing
attention in the fields of image processing, graphics, and machine learning.
This problem consists of modelling the desired type of images, either through
training examples or via a parametric modeling, and then generating images that
belong to the same statistical origin.
  This work addresses the image synthesis task, focusing on two specific
families of images -- handwritten digits and face images. This paper offers two
main contributions. First, we suggest a simple and intuitive algorithm capable
of generating such images in a unified way. The proposed approach taken is
pyramidal, consisting of upscaling and refining the estimated image several
times. For each upscaling stage, the algorithm randomly draws small patches
from a patch database, and merges these to form a coherent and novel image with
high visual quality. The second contribution is a general framework for the
evaluation of the generation performance, which combines three aspects: the
likelihood, the originality and the spread of the synthesized images. We assess
the proposed synthesis scheme and show that the results are similar in nature,
and yet different from the ones found in the training set, suggesting that true
synthesis effect has been obtained.
A Locally Adaptive Normal Distribution
Authors: Georgios Arvanitidis, Lars Kai Hansen, Søren Hauberg
Date: 2016-06-08 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: EEG, Electroencephalogram (EEG)

The multivariate normal density is a monotonic function of the distance to
the mean, and its ellipsoidal shape is due to the underlying Euclidean metric.
We suggest to replace this metric with a locally adaptive, smoothly changing
(Riemannian) metric that favors regions of high local density. The resulting
locally adaptive normal distribution (LAND) is a generalization of the normal
distribution to the "manifold" setting, where data is assumed to lie near a
potentially low-dimensional manifold embedded in $\mathbb{R}^D$. The LAND is
parametric, depending only on a mean and a covariance, and is the maximum
entropy distribution under the given metric. The underlying metric is, however,
non-parametric. We develop a maximum likelihood algorithm to infer the
distribution parameters that relies on a combination of gradient descent and
Monte Carlo integration. We further extend the LAND to mixture models, and
provide the corresponding EM algorithm. We demonstrate the efficiency of the
LAND to fit non-trivial probability distributions over both synthetic data, and
EEG measurements of human sleep.
Estimating Probability Distributions using "Dirac" Kernels (via Rademacher-Walsh Polynomial Basis Functions)
Authors: Hamse Y. Mussa, Avid M. Afzal
Date: 2016-09-23 00:00:00

In many applications (in particular information systems, such as pattern
recognition, machine learning, cheminformatics, bioinformatics to name but a
few) the assessment of uncertainty is essential - i.e., the estimation of the
underlying probability distribution function. More often than not, the form of
this function is unknown and it becomes necessary to non-parametrically
construct/estimate it from a given sample. One of the methods of choice to
non-parametrically estimate the unknown probability distribution function for a
given random variable (defined on binary space) has been the expansion of the
estimation function in Rademacher-Walsh Polynomial basis functions. In this
paper we demonstrate that the expansion of the probability distribution
function estimation in Rademacher-Walsh Polynomial basis functions is
equivalent to the expansion of the function estimation in a set of "Dirac
kernel" functions. The latter approach can ameliorate the computational
bottleneck and notational awkwardness often associated with the
Rademacher-Walsh Polynomial basis functions approach, in particular when the
binary input space is large.
EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras
Authors: Helge Rhodin, Christian Richardt, Dan Casas, Eldar Insafutdinov, Mohammad Shafiei, Hans-Peter Seidel, Bernt Schiele, Christian Theobalt
Date: 2016-09-23 00:00:00
Tasks: Pose Estimation, Vocal Bursts Valence Prediction

Marker-based and marker-less optical skeletal motion-capture methods use an
outside-in arrangement of cameras placed around a scene, with viewpoints
converging on the center. They often create discomfort by possibly needed
marker suits, and their recording volume is severely restricted and often
constrained to indoor scenes with controlled backgrounds. Alternative
suit-based systems use several inertial measurement units or an exoskeleton to
capture motion. This makes capturing independent of a confined volume, but
requires substantial, often constraining, and hard to set up body
instrumentation. We therefore propose a new method for real-time, marker-less
and egocentric motion capture which estimates the full-body skeleton pose from
a lightweight stereo pair of fisheye cameras that are attached to a helmet or
virtual reality headset. It combines the strength of a new generative pose
estimation framework for fisheye views with a ConvNet-based body-part detector
trained on a large new dataset. Our inside-in method captures full-body motion
in general indoor and outdoor scenes, and also crowded scenes with many people
in close vicinity. The captured user can freely move around, which enables
reconstruction of larger-scale activities and is particularly useful in virtual
reality to freely roam and interact, while seeing the fully motion-captured
virtual body.
Funnel-Structured Cascade for Multi-View Face Detection with Alignment-Awareness
Authors: Shuzhe Wu, Meina Kan, Zhenliang He, Shiguang Shan, Xilin Chen
Date: 2016-09-23 00:00:00
Tasks: Face Alignment, Face Detection

Multi-view face detection in open environment is a challenging task due to
diverse variations of face appearances and shapes. Most multi-view face
detectors depend on multiple models and organize them in parallel, pyramid or
tree structure, which compromise between the accuracy and time-cost. Aiming at
a more favorable multi-view face detector, we propose a novel funnel-structured
cascade (FuSt) detection framework. In a coarse-to-fine flavor, our FuSt
consists of, from top to bottom, 1) multiple view-specific fast LAB cascade for
extremely quick face proposal, 2) multiple coarse MLP cascade for further
candidate window verification, and 3) a unified fine MLP cascade with
shape-indexed features for accurate face detection. Compared with other
structures, on the one hand, the proposed one uses multiple computationally
efficient distributed classifiers to propose a small number of candidate
windows but with a high recall of multi-view faces. On the other hand, by using
a unified MLP cascade to examine proposals of all views in a centralized style,
it provides a favorable solution for multi-view face detection with high
accuracy and low time-cost. Besides, the FuSt detector is alignment-aware and
performs a coarse facial part prediction which is beneficial for subsequent
face alignment. Extensive experiments on two challenging datasets, FDDB and
AFW, demonstrate the effectiveness of our FuSt detector in both accuracy and
speed.