pwc-archive/papers-with-abstracts huggingface dataset:
Learning Locomotion Skills Using DeepRL: Does the Choice of Action Space Matter?
Authors: Xue Bin Peng, Michiel Van de Panne
Date: 2016-11-03 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The use of deep reinforcement learning allows for high-dimensional state
descriptors, but little is known about how the choice of action representation
impacts the learning difficulty and the resulting performance. We compare the
impact of four different action parameterizations (torques, muscle-activations,
target joint angles, and target joint-angle velocities) in terms of learning
time, policy robustness, motion quality, and policy query rates. Our results
are evaluated on a gait-cycle imitation task for multiple planar articulated
figures and multiple gaits. We demonstrate that the local feedback provided by
higher-level action parameterizations can significantly impact the learning,
robustness, and quality of the resulting policies.
An empirical study for Vietnamese dependency parsing
Authors: Dat Quoc Nguyen, Mark Dras, Mark Johnson
Date: 2016-11-03 00:00:00
Proceeding: ALTA 2016 12
Tasks: Dependency Parsing

This paper presents an empirical comparison of different dependency parsers
for Vietnamese, which has some unusual characteristics such as copula drop and
verb serialization. Experimental results show that the neural network-based
parsers perform significantly better than the traditional parsers. We report
the highest parsing scores published to date for Vietnamese with the labeled
attachment score (LAS) at 73.53% and the unlabeled attachment score (UAS) at
80.66%.
Multitask Protein Function Prediction Through Task Dissimilarity
Authors: Marco Frasca, Nicolò Cesa Bianchi
Date: 2016-11-03 00:00:00
Tasks: Prediction, Protein Function Prediction

Automated protein function prediction is a challenging problem with
distinctive features, such as the hierarchical organization of protein
functions and the scarcity of annotated proteins for most biological functions.
We propose a multitask learning algorithm addressing both issues. Unlike
standard multitask algorithms, which use task (protein functions) similarity
information as a bias to speed up learning, we show that dissimilarity
information enforces separation of rare class labels from frequent class
labels, and for this reason is better suited for solving unbalanced protein
function prediction problems. We support our claim by showing that a multitask
extension of the label propagation algorithm empirically works best when the
task relatedness information is represented using a dissimilarity matrix as
opposed to a similarity matrix. Moreover, the experimental comparison carried
out on three model organism shows that our method has a more stable performance
in both "protein-centric" and "function-centric" evaluation settings.
Adaptive mixed norm optical flow estimation
Authors: Vania V. Estrela, Matthias O. Franz, Ricardo T. Lopes, G. P. De Araujo
Date: 2016-11-03 00:00:00
Tasks: Optical Flow Estimation

The pel-recursive computation of 2-D optical flow has been extensively
studied in computer vision to estimate motion from image sequences, but it
still raises a wealth of issues, such as the treatment of outliers, motion
discontinuities and occlusion. It relies on spatio-temporal brightness
variations due to motion. Our proposed adaptive regularized approach deals with
these issues within a common framework. It relies on the use of a data-driven
technique called Mixed Norm (MN) to estimate the best motion vector for a given
pixel. In our model, various types of noise can be handled, representing
different sources of error. The motion vector estimation takes into
consideration local image properties and it results from the minimization of a
mixed norm functional with a regularization parameter depending on the
kurtosis. This parameter determines the relative importance of the fourth norm
and makes the functional convex. The main advantage of the developed procedure
is that no knowledge of the noise distribution is necessary. Experiments
indicate that this approach provides robust estimates of the optical flow.
Stochastic Variance Reduction Methods for Saddle-Point Problems
Authors: P. Balamurugan, Francis Bach
Date: 2016-05-20 00:00:00
Proceeding: NeurIPS 2016 12

We consider convex-concave saddle-point problems where the objective
functions may be split in many components, and extend recent stochastic
variance reduction methods (such as SVRG or SAGA) to provide the first
large-scale linearly convergent algorithms for this class of problems which is
common in machine learning. While the algorithmic extension is straightforward,
it comes with challenges and opportunities: (a) the convex minimization
analysis does not apply and we use the notion of monotone operators to prove
convergence, showing in particular that the same algorithm applies to a larger
class of problems, such as variational inequalities, (b) there are two notions
of splits, in terms of functions, or in terms of partial derivatives, (c) the
split does need to be done with convex-concave terms, (d) non-uniform sampling
is key to an efficient algorithm, both in theory and practice, and (e) these
incremental algorithms can be easily accelerated using a simple extension of
the "catalyst" framework, leading to an algorithm which is always superior to
accelerated batch algorithms.
Recent Advances in Transient Imaging: A Computer Graphics and Vision Perspective
Authors: Adrian Jarabo, Belen Masia, Julio Marco, Diego Gutierrez
Date: 2016-11-03 00:00:00
Tasks: PICO

Transient imaging has recently made a huge impact in the computer graphics
and computer vision fields. By capturing, reconstructing, or simulating light
transport at extreme temporal resolutions, researchers have proposed novel
techniques to show movies of light in motion, see around corners, detect
objects in highly-scattering media, or infer material properties from a
distance, to name a few. The key idea is to leverage the wealth of information
in the temporal domain at the pico or nanosecond resolution, information
usually lost during the capture-time temporal integration. This paper presents
recent advances in this field of transient imaging from a graphics and vision
perspective, including capture techniques, analysis, applications and
simulation.
Rough Set Based Color Channel Selection
Authors: Soumyabrata Dev, Florian M. Savoy, Yee Hui Lee, Stefan Winkler
Date: 2016-11-03 00:00:00
Tasks: channel selection, Segmentation

Color channel selection is essential for accurate segmentation of sky and
clouds in images obtained from ground-based sky cameras. Most prior works in
cloud segmentation use threshold based methods on color channels selected in an
ad-hoc manner. In this letter, we propose the use of rough sets for color
channel selection in visible-light images. Our proposed approach assesses color
channels with respect to their contribution for segmentation, and identifies
the most effective ones.
Detecting Context Dependent Messages in a Conversational Environment
Authors: Chaozhuo Li, Yu Wu, Wei Wu, Chen Xing, Zhoujun Li, Ming Zhou
Date: 2016-11-02 00:00:00
Proceeding: COLING 2016 12
Tasks: Chatbot, Response Generation

While automatic response generation for building chatbot systems has drawn a
lot of attention recently, there is limited understanding on when we need to
consider the linguistic context of an input text in the generation process. The
task is challenging, as messages in a conversational environment are short and
informal, and evidence that can indicate a message is context dependent is
scarce. After a study of social conversation data crawled from the web, we
observed that some characteristics estimated from the responses of messages are
discriminative for identifying context dependent messages. With the
characteristics as weak supervision, we propose using a Long Short Term Memory
(LSTM) network to learn a classifier. Our method carries out text
representation and classifier learning in a unified framework. Experimental
results show that the proposed method can significantly outperform baseline
methods on accuracy of classification.
Low Rank Approximation with Entrywise $\ell_1$-Norm Error
Authors: Zhao Song, David P. Woodruff, Peilin Zhong
Date: 2016-11-03 00:00:00

We study the $\ell_1$-low rank approximation problem, where for a given $n \times d$ matrix $A$ and approximation factor $\alpha \geq 1$, the goal is to output a rank-$k$ matrix $\widehat{A}$ for which $$\|A-\widehat{A}\|_1 \leq \alpha \cdot \min_{\textrm{rank-}k\textrm{ matrices}~A'}\|A-A'\|_1,$$ where for an $n \times d$ matrix $C$, we let $\|C\|_1 = \sum_{i=1}^n \sum_{j=1}^d |C_{i,j}|$. This error measure is known to be more robust than the Frobenius norm in the presence of outliers and is indicated in models where Gaussian assumptions on the noise may not apply. The problem was shown to be NP-hard by Gillis and Vavasis and a number of heuristics have been proposed. It was asked in multiple places if there are any approximation algorithms. We give the first provable approximation algorithms for $\ell_1$-low rank approximation, showing that it is possible to achieve approximation factor $\alpha = (\log d) \cdot \mathrm{poly}(k)$ in $\mathrm{nnz}(A) + (n+d) \mathrm{poly}(k)$ time, where $\mathrm{nnz}(A)$ denotes the number of non-zero entries of $A$. If $k$ is constant, we further improve the approximation ratio to $O(1)$ with a $\mathrm{poly}(nd)$-time algorithm. Under the Exponential Time Hypothesis, we show there is no $\mathrm{poly}(nd)$-time algorithm achieving a $(1+\frac{1}{\log^{1+\gamma}(nd)})$-approximation, for $\gamma > 0$ an arbitrarily small constant, even when $k = 1$. We give a number of additional results for $\ell_1$-low rank approximation: nearly tight upper and lower bounds for column subset selection, CUR decompositions, extensions to low rank approximation with respect to $\ell_p$-norms for $1 \leq p < 2$ and earthmover distance, low-communication distributed protocols and low-memory streaming algorithms, algorithms with limited randomness, and bicriteria algorithms. We also give a preliminary empirical evaluation.
Maximizing Investment Value of Small-Scale PV in a Smart Grid Environment
Authors: Jeremy Every, Li Li, Youguang G. Guo, David G. Dorrell
Date: 2016-11-03 00:00:00

Determining the optimal size and orientation of small-scale residential based
PV arrays will become increasingly complex in the future smart grid environment
with the introduction of smart meters and dynamic tariffs. However consumers
can leverage the availability of smart meter data to conduct a more detailed
exploration of PV investment options for their particular circumstances. In
this paper, an optimization method for PV orientation and sizing is proposed
whereby maximizing the PV investment value is set as the defining objective.
Solar insolation and PV array models are described to form the basis of the PV
array optimization strategy. A constrained particle swarm optimization
algorithm is selected due to its strong performance in non-linear applications.
The optimization algorithm is applied to real-world metered data to quantify
the possible investment value of a PV installation under different energy
retailers and tariff structures. The arrangement with the highest value is
determined to enable prospective small-scale PV investors to select the most
cost-effective system.
Extracting Actionability from Machine Learning Models by Sub-optimal Deterministic Planning
Authors: Qiang Lyu, Yixin Chen, Zhaorong Li, Zhicheng Cui, Ling Chen, Xing Zhang, Haihua Shen
Date: 2016-11-03 00:00:00
Tasks: Attribute, BIG-bench Machine Learning, Management

A main focus of machine learning research has been improving the
generalization accuracy and efficiency of prediction models. Many models such
as SVM, random forest, and deep neural nets have been proposed and achieved
great success. However, what emerges as missing in many applications is
actionability, i.e., the ability to turn prediction results into actions. For
example, in applications such as customer relationship management, clinical
prediction, and advertisement, the users need not only accurate prediction, but
also actionable instructions which can transfer an input to a desirable goal
(e.g., higher profit repays, lower morbidity rates, higher ads hit rates).
Existing effort in deriving such actionable knowledge is few and limited to
simple action models which restricted to only change one attribute for each
action. The dilemma is that in many real applications those action models are
often more complex and harder to extract an optimal solution.
  In this paper, we propose a novel approach that achieves actionability by
combining learning with planning, two core areas of AI. In particular, we
propose a framework to extract actionable knowledge from random forest, one of
the most widely used and best off-the-shelf classifiers. We formulate the
actionability problem to a sub-optimal action planning (SOAP) problem, which is
to find a plan to alter certain features of a given input so that the random
forest would yield a desirable output, while minimizing the total costs of
actions. Technically, the SOAP problem is formulated in the SAS+ planning
formalism, and solved using a Max-SAT based approach. Our experimental results
demonstrate the effectiveness and efficiency of the proposed approach on a
personal credit dataset and other benchmarks. Our work represents a new
application of automated planning on an emerging and challenging machine
learning paradigm.
Tensor Decomposition via Variational Auto-Encoder
Authors: Bin Liu, Zenglin Xu, Yingming Li
Date: 2016-11-03 00:00:00
Tasks: Missing Values, Tensor Decomposition

Tensor decomposition is an important technique for capturing the high-order
interactions among multiway data. Multi-linear tensor composition methods, such
as the Tucker decomposition and the CANDECOMP/PARAFAC (CP), assume that the
complex interactions among objects are multi-linear, and are thus insufficient
to represent nonlinear relationships in data. Another assumption of these
methods is that a predefined rank should be known. However, the rank of tensors
is hard to estimate, especially for cases with missing values. To address these
issues, we design a Bayesian generative model for tensor decomposition.
Different from the traditional Bayesian methods, the high-order interactions of
tensor entries are modeled with variational auto-encoder. The proposed model
takes advantages of Neural Networks and nonparametric Bayesian models, by
replacing the multi-linear product in traditional Bayesian tensor decomposition
with a complex nonlinear function (via Neural Networks) whose parameters can be
learned from data. Experimental results on synthetic data and real-world
chemometrics tensor data have demonstrated that our new model can achieve
significantly higher prediction performance than the state-of-the-art tensor
decomposition approaches.
Spatio-temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural Networks
Authors: R. Devon Hjelm, Eswar Damaraju, Kyunghyun Cho, Helmut Laufs, Sergey M. Plis, Vince Calhoun
Date: 2016-11-03 00:00:00
Tasks: blind source separation

We introduce a novel recurrent neural network (RNN) approach to account for
temporal dynamics and dependencies in brain networks observed via functional
magnetic resonance imaging (fMRI). Our approach directly parameterizes temporal
dynamics through recurrent connections, which can be used to formulate blind
source separation with a conditional (rather than marginal) independence
assumption, which we call RNN-ICA. This formulation enables us to visualize the
temporal dynamics of both first order (activity) and second order (directed
connectivity) information in brain networks that are widely studied in a static
sense, but not well-characterized dynamically. RNN-ICA predicts dynamics
directly from the recurrent states of the RNN in both task and resting state
fMRI. Our results show both task-related and group-differentiating directed
connectivity.
Quantile Reinforcement Learning
Authors: Hugo Gilbert, Paul Weng
Date: 2016-11-03 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In reinforcement learning, the standard criterion to evaluate policies in a
state is the expectation of (discounted) sum of rewards. However, this
criterion may not always be suitable, we consider an alternative criterion
based on the notion of quantiles. In the case of episodic reinforcement
learning problems, we propose an algorithm based on stochastic approximation
with two timescales. We evaluate our proposition on a simple model of the TV
show, Who wants to be a millionaire.
An All-In-One Convolutional Neural Network for Face Analysis
Authors: Rajeev Ranjan, Swami Sankaranarayanan, Carlos D. Castillo, Rama Chellappa
Date: 2016-11-03 00:00:00
Tasks: Age Estimation, All, Face Alignment, Face Detection, Face Recognition, Face Verification, Multi-Task Learning, Pose Estimation

We present a multi-purpose algorithm for simultaneous face detection, face
alignment, pose estimation, gender recognition, smile detection, age estimation
and face recognition using a single deep convolutional neural network (CNN).
The proposed method employs a multi-task learning framework that regularizes
the shared parameters of CNN and builds a synergy among different domains and
tasks. Extensive experiments show that the network has a better understanding
of face and achieves state-of-the-art result for most of these tasks.
Understanding Convolutional Neural Networks with A Mathematical Model
Authors: C. -C. Jay Kuo
Date: 2016-09-14 00:00:00
Tasks: model

This work attempts to address two fundamental questions about the structure
of the convolutional neural networks (CNN): 1) why a non-linear activation
function is essential at the filter output of every convolutional layer? 2)
what is the advantage of the two-layer cascade system over the one-layer
system? A mathematical model called the "REctified-COrrelations on a Sphere"
(RECOS) is proposed to answer these two questions. After the CNN training
process, the converged filter weights define a set of anchor vectors in the
RECOS model. Anchor vectors represent the frequently occurring patterns (or the
spectral components). The necessity of rectification is explained using the
RECOS model. Then, the behavior of a two-layer RECOS system is analyzed and
compared with its one-layer counterpart. The LeNet-5 and the MNIST dataset are
used to illustrate discussion points. Finally, the RECOS model is generalized
to a multi-layer system with the AlexNet as an example.
  Keywords: Convolutional Neural Network (CNN), Nonlinear Activation, RECOS
Model, Rectified Linear Unit (ReLU), MNIST Dataset.
Learning Deep Embeddings with Histogram Loss
Authors: Evgeniya Ustinova, Victor Lempitsky
Date: 2016-11-02 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Stochastic Optimization

We suggest a loss for learning deep embeddings. The new loss does not
introduce parameters that need to be tuned and results in very good embeddings
across a range of datasets and problems. The loss is computed by estimating two
distribution of similarities for positive (matching) and negative
(non-matching) sample pairs, and then computing the probability of a positive
pair to have a lower similarity score than a negative pair based on the
estimated similarity distributions. We show that such operations can be
performed in a simple and piecewise-differentiable manner using 1D histograms
with soft assignment operations. This makes the proposed loss suitable for
learning deep embeddings using stochastic optimization. In the experiments, the
new loss performs favourably compared to recently proposed alternatives.
Gaussian Processes for Survival Analysis
Authors: Tamara Fernández, Nicolás Rivera, Yee Whye Teh
Date: 2016-11-02 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Gaussian Processes, Survival Analysis

We introduce a semi-parametric Bayesian model for survival analysis. The
model is centred on a parametric baseline hazard, and uses a Gaussian process
to model variations away from it nonparametrically, as well as dependence on
covariates. As opposed to many other methods in survival analysis, our
framework does not impose unnecessary constraints in the hazard rate or in the
survival function. Furthermore, our model handles left, right and interval
censoring mechanisms common in survival analysis. We propose a MCMC algorithm
to perform inference and an approximation scheme based on random Fourier
features to make computations faster. We report experimental results on
synthetic and real data, showing that our model performs better than competing
models such as Cox proportional hazards, ANOVA-DDP and random survival forests.
Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures
Authors: Jonathan Vacher, Andrew Isaac Meso, Laurent U. Perrinet, Gabriel Peyré
Date: 2016-11-02 00:00:00
Tasks: Bayesian Inference, Texture Synthesis

A common practice to account for psychophysical biases in vision is to frame
them as consequences of a dynamic process relying on optimal inference with
respect to a generative model. The present study details the complete
formulation of such a generative model intended to probe visual motion
perception with a dynamic texture model. It is first derived in a set of
axiomatic steps constrained by biological plausibility. We extend previous
contributions by detailing three equivalent formulations of this texture model.
First, the composite dynamic textures are constructed by the random aggregation
of warped patterns, which can be viewed as 3D Gaussian fields. Secondly, these
textures are cast as solutions to a stochastic partial differential equation
(sPDE). This essential step enables real time, on-the-fly texture synthesis
using time-discretized auto-regressive processes. It also allows for the
derivation of a local motion-energy model, which corresponds to the
log-likelihood of the probability density. The log-likelihoods are essential
for the construction of a Bayesian inference framework. We use the dynamic
texture model to psychophysically probe speed perception in humans using
zoom-like changes in the spatial frequency content of the stimulus. The human
data replicates previous findings showing perceived speed to be positively
biased by spatial frequency increments. A Bayesian observer who combines a
Gaussian likelihood centered at the true speed and a spatial frequency
dependent width with a "slow speed prior" successfully accounts for the
perceptual bias. More precisely, the bias arises from a decrease in the
observer's likelihood width estimated from the experiments as the spatial
frequency increases. Such a trend is compatible with the trend of the dynamic
texture likelihood width.
A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection
Authors: Mingbin Xu, Hui Jiang
Date: 2016-11-02 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Sentence

In this paper, we study a novel approach for named entity recognition (NER)
and mention detection in natural language processing. Instead of treating NER
as a sequence labelling problem, we propose a new local detection approach,
which rely on the recent fixed-size ordinally forgetting encoding (FOFE) method
to fully encode each sentence fragment and its left/right contexts into a
fixed-size representation. Afterwards, a simple feedforward neural network is
used to reject or predict entity label for each individual fragment. The
proposed method has been evaluated in several popular NER and mention detection
tasks, including the CoNLL 2003 NER task and TAC-KBP2015 and TAC-KBP2016
Tri-lingual Entity Discovery and Linking (EDL) tasks. Our methods have yielded
pretty strong performance in all of these examined tasks. This local detection
approach has shown many advantages over the traditional sequence labelling
methods.