pwc-archive/papers-with-abstracts huggingface dataset:
Continuous online sequence learning with an unsupervised neural network model
Authors: Yuwei Cui, Subutai Ahmad, Jeff Hawkins
Date: 2015-12-17 00:00:00
Tasks: Anomaly Detection, Temporal Sequences

The ability to recognize and predict temporal sequences of sensory inputs is
vital for survival in natural environments. Based on many known properties of
cortical neurons, hierarchical temporal memory (HTM) sequence memory is
recently proposed as a theoretical framework for sequence learning in the
cortex. In this paper, we analyze properties of HTM sequence memory and apply
it to sequence learning and prediction problems with streaming data. We show
the model is able to continuously learn a large number of variable-order
temporal sequences using an unsupervised Hebbian-like learning rule. The sparse
temporal codes formed by the model can robustly handle branching temporal
sequences by maintaining multiple predictions until there is sufficient
disambiguating evidence. We compare the HTM sequence memory with other sequence
learning algorithms, including statistical methods: autoregressive integrated
moving average (ARIMA), feedforward neural networks: online sequential extreme
learning machine (ELM), and recurrent neural networks: long short-term memory
(LSTM) and echo-state networks (ESN), on sequence prediction problems with both
artificial and real-world data. The HTM model achieves comparable accuracy to
other state-of-the-art algorithms. The model also exhibits properties that are
critical for sequence learning, including continuous online learning, the
ability to handle multiple predictions and branching sequences with high order
statistics, robustness to sensor noise and fault tolerance, and good
performance without task-specific hyper- parameters tuning. Therefore the HTM
sequence memory not only advances our understanding of how the brain may solve
the sequence learning problem, but is also applicable to a wide range of
real-world problems such as discrete and continuous sequence prediction,
anomaly detection, and sequence classification.
Comparing Fifty Natural Languages and Twelve Genetic Languages Using Word Embedding Language Divergence (WELD) as a Quantitative Measure of Language Distance
Authors: Ehsaneddin Asgari, Mohammad R. K. Mofrad
Date: 2016-04-28 00:00:00
Proceeding: WS 2016 6
Tasks: Dialect Identification, Language Modeling, Language Modelling, Sentence

We introduce a new measure of distance between languages based on word
embedding, called word embedding language divergence (WELD). WELD is defined as
divergence between unified similarity distribution of words between languages.
Using such a measure, we perform language comparison for fifty natural
languages and twelve genetic languages. Our natural language dataset is a
collection of sentence-aligned parallel corpora from bible translations for
fifty languages spanning a variety of language families. Although we use
parallel corpora, which guarantees having the same content in all languages,
interestingly in many cases languages within the same family cluster together.
In addition to natural languages, we perform language comparison for the coding
regions in the genomes of 12 different organisms (4 plants, 6 animals, and two
human subjects). Our result confirms a significant high-level difference in the
genetic language model of humans/animals versus plants. The proposed method is
a step toward defining a quantitative measure of similarity between languages,
with applications in languages classification, genre identification, dialect
identification, and evaluation of translations.
AGI and Reflexivity
Authors: Pascal Faudemay
Date: 2016-04-15 00:00:00

We define a property of intelligent systems, which we call Reflexivity. In
human beings, it is one aspect of consciousness, and an element of
deliberation. We propose a conjecture, that this property is conditioned by a
topological property of the processes which implement this reflexivity. These
processes may be symbolic, or non symbolic e.g. connexionnist. An architecture
which implements reflexivity may be based on the interaction of one or several
modules of deep learning, which may be specialized or not, and interconnected
in a relevant way. A necessary condition of reflexivity is the existence of
recurrence in its processes, we will examine in which cases this condition may
be sufficient. We will then examine how this topology and this property make
possible the expression of a second property, the deliberation. In a final
paragraph, we propose an evaluation of intelligent systems, based on the
fulfillment of all or some of these properties.
A Probabilistic Adaptive Search System for Exploring the Face Space
Authors: Andres G. Abad, Luis I. Reyes Castro
Date: 2016-04-28 00:00:00
Tasks: Bayesian Optimization

Face recall is a basic human cognitive process performed routinely, e.g.,
when meeting someone and determining if we have met that person before.
Assisting a subject during face recall by suggesting candidate faces can be
challenging. One of the reasons is that the search space - the face space - is
quite large and lacks structure. A commercial application of face recall is
facial composite systems - such as Identikit, PhotoFIT, and CD-FIT - where a
witness searches for an image of a face that resembles his memory of a
particular offender. The inherent uncertainty and cost in the evaluation of the
objective function, the large size and lack of structure of the search space,
and the unavailability of the gradient concept makes this problem inappropriate
for traditional optimization methods. In this paper we propose a novel
evolutionary approach for searching the face space that can be used as a facial
composite system. The approach is inspired by methods of Bayesian optimization
and differs from other applications in the use of the skew-normal distribution
as its acquisition function. This choice of acquisition function provides
greater granularity, with regularized, conservative, and realistic results.
Fast Simulation of Probabilistic Boolean Networks (Technical Report)
Authors: Andrzej Mizera, Jun Pang, Qixia Yuan
Date: 2016-04-28 00:00:00

Probabilistic Boolean networks (PBNs) is an important mathematical framework
widely used for modelling and analysing biological systems. PBNs are suited for
modelling large biological systems, which more and more often arise in systems
biology. However, the large system size poses a~significant challenge to the
analysis of PBNs, in particular, to the crucial analysis of their steady-state
behaviour. Numerical methods for performing steady-state analyses suffer from
the state-space explosion problem, which makes the utilisation of statistical
methods the only viable approach. However, such methods require long
simulations of PBNs, rendering the simulation speed a crucial efficiency
factor. For large PBNs and high estimation precision requirements, a slow
simulation speed becomes an obstacle. In this paper, we propose a
structure-based method for fast simulation of PBNs. This method first performs
a network reduction operation and then divides nodes into groups for parallel
simulation. Experimental results show that our method can lead to an
approximately 10 times speedup for computing steady-state probabilities of a
real-life biological network.
Robust subspace recovery by Tyler's M-estimator
Authors: Teng Zhang
Date: 2012-06-07 00:00:00
Tasks: Position

This paper considers the problem of robust subspace recovery: given a set of $N$ points in $\mathbb{R}^D$, if many lie in a $d$-dimensional subspace, then can we recover the underlying subspace? We show that Tyler's M-estimator can be used to recover the underlying subspace, if the percentage of the inliers is larger than $d/D$ and the data points lie in general position. Empirically, Tyler's M-estimator compares favorably with other convex subspace recovery algorithms in both simulations and experiments on real data sets.
Sparseness Meets Deepness: 3D Human Pose Estimation from Monocular Video
Authors: Xiaowei Zhou, Menglong Zhu, Spyridon Leonardos, Kosta Derpanis, Kostas Daniilidis
Date: 2015-11-30 00:00:00
Proceeding: CVPR 2016 6
Tasks: 2D Pose Estimation, 3D Human Pose Estimation, 3D Pose Estimation, Monocular 3D Human Pose Estimation, Pose Estimation

This paper addresses the challenge of 3D full-body human pose estimation from
a monocular image sequence. Here, two cases are considered: (i) the image
locations of the human joints are provided and (ii) the image locations of
joints are unknown. In the former case, a novel approach is introduced that
integrates a sparsity-driven 3D geometric prior and temporal smoothness. In the
latter case, the former case is extended by treating the image locations of the
joints as latent variables. A deep fully convolutional network is trained to
predict the uncertainty maps of the 2D joint locations. The 3D pose estimates
are realized via an Expectation-Maximization algorithm over the entire
sequence, where it is shown that the 2D joint location uncertainties can be
conveniently marginalized out during inference. Empirical evaluation on the
Human3.6M dataset shows that the proposed approaches achieve greater 3D pose
estimation accuracy over state-of-the-art baselines. Further, the proposed
approach outperforms a publicly available 2D pose estimation baseline on the
challenging PennAction dataset.
Two Differentially Private Rating Collection Mechanisms for Recommender Systems
Authors: Wenjie Zheng
Date: 2016-04-28 00:00:00
Tasks: Recommendation Systems, Vocal Bursts Valence Prediction

We design two mechanisms for the recommender system to collect user ratings.
One is modified Laplace mechanism, and the other is randomized response
mechanism. We prove that they are both differentially private and preserve the
data utility.
Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition
Authors: Théodore Bluche
Date: 2016-04-28 00:00:00
Proceeding: NeurIPS 2016
Tasks: Handwriting Recognition, Segmentation

Offline handwriting recognition systems require cropped text line images for
both training and recognition. On the one hand, the annotation of position and
transcript at line level is costly to obtain. On the other hand, automatic line
segmentation algorithms are prone to errors, compromising the subsequent
recognition. In this paper, we propose a modification of the popular and
efficient multi-dimensional long short-term memory recurrent neural networks
(MDLSTM-RNNs) to enable end-to-end processing of handwritten paragraphs. More
particularly, we replace the collapse layer transforming the two-dimensional
representation into a sequence of predictions by a recurrent version which can
recognize one line at a time. In the proposed model, a neural network performs
a kind of implicit line segmentation by computing attention weights on the
image representation. The experiments on paragraphs of Rimes and IAM database
yield results that are competitive with those of networks trained at line
level, and constitute a significant step towards end-to-end transcription of
full documents.
Large-scale Analysis of Chess Games with Chess Engines: A Preliminary Report
Authors: Mathieu Acher, François Esnault
Date: 2016-04-28 00:00:00
Tasks: Decision Making

The strength of chess engines together with the availability of numerous
chess games have attracted the attention of chess players, data scientists, and
researchers during the last decades. State-of-the-art engines now provide an
authoritative judgement that can be used in many applications like cheating
detection, intrinsic ratings computation, skill assessment, or the study of
human decision-making. A key issue for the research community is to gather a
large dataset of chess games together with the judgement of chess engines.
Unfortunately the analysis of each move takes lots of times. In this paper, we
report our effort to analyse almost 5 millions chess games with a computing
grid. During summer 2015, we processed 270 millions unique played positions
using the Stockfish engine with a quite high depth (20). We populated a
database of 1+ tera-octets of chess evaluations, representing an estimated time
of 50 years of computation on a single machine. Our effort is a first step
towards the replication of research results, the supply of open data and
procedures for exploring new directions, and the investigation of software
engineering/scalability issues when computing billions of moves.
Semantic Reasoning for Context-aware Internet of Things Applications
Authors: Altti Ilari Maarala, Xiang Su, Jukka Riekki
Date: 2016-04-28 00:00:00

Advances in ICT are bringing into reality the vision of a large number of
uniquely identifiable, interconnected objects and things that gather
information from diverse physical environments and deliver the information to a
variety of innovative applications and services. These sensing objects and
things form the Internet of Things (IoT) that can improve energy and cost
efficiency and automation in many different industry fields such as
transportation and logistics, health care and manufacturing, and facilitate our
everyday lives as well. IoT applications rely on real-time context data and
allow sending information for driving the behaviors of users in intelligent
environments.
An Enhanced Deep Feature Representation for Person Re-identification
Authors: Shangxuan Wu, Ying-Cong Chen, Xiang Li, An-Cong Wu, Jin-Jie You, Wei-Shi Zheng
Date: 2016-04-26 00:00:00
Tasks: Metric Learning, Person Re-Identification

Feature representation and metric learning are two critical components in
person re-identification models. In this paper, we focus on the feature
representation and claim that hand-crafted histogram features can be
complementary to Convolutional Neural Network (CNN) features. We propose a
novel feature extraction model called Feature Fusion Net (FFN) for pedestrian
image representation. In FFN, back propagation makes CNN features constrained
by the handcrafted features. Utilizing color histogram features (RGB, HSV,
YCbCr, Lab and YIQ) and texture features (multi-scale and multi-orientation
Gabor features), we get a new deep feature representation that is more
discriminative and compact. Experiments on three challenging datasets (VIPeR,
CUHK01, PRID450s) validates the effectiveness of our proposal.
RATM: Recurrent Attentive Tracking Model
Authors: Samira Ebrahimi Kahou, Vincent Michalski, Roland Memisevic
Date: 2015-10-29 00:00:00
Tasks: model, Object Tracking

We present an attention-based modular neural framework for computer vision.
The framework uses a soft attention mechanism allowing models to be trained
with gradient descent. It consists of three modules: a recurrent attention
module controlling where to look in an image or video frame, a
feature-extraction module providing a representation of what is seen, and an
objective module formalizing why the model learns its attentive behavior. The
attention module allows the model to focus computation on task-related
information in the input. We apply the framework to several object tracking
tasks and explore various design choices. We experiment with three data sets,
bouncing ball, moving digits and the real-world KTH data set. The proposed
Recurrent Attentive Tracking Model performs well on all three tasks and can
generalize to related but previously unseen sequences from a challenging
tracking data set.
Sequential Bayesian optimal experimental design via approximate dynamic programming
Authors: Xun Huan, Youssef M. Marzouk
Date: 2016-04-28 00:00:00
Tasks: Experimental Design

The design of multiple experiments is commonly undertaken via suboptimal
strategies, such as batch (open-loop) design that omits feedback or greedy
(myopic) design that does not account for future effects. This paper introduces
new strategies for the optimal design of sequential experiments. First, we
rigorously formulate the general sequential optimal experimental design (sOED)
problem as a dynamic program. Batch and greedy designs are shown to result from
special cases of this formulation. We then focus on sOED for parameter
inference, adopting a Bayesian formulation with an information theoretic design
objective. To make the problem tractable, we develop new numerical approaches
for nonlinear design with continuous parameter, design, and observation spaces.
We approximate the optimal policy by using backward induction with regression
to construct and refine value function approximations in the dynamic program.
The proposed algorithm iteratively generates trajectories via exploration and
exploitation to improve approximation accuracy in frequently visited regions of
the state space. Numerical results are verified against analytical solutions in
a linear-Gaussian setting. Advantages over batch and greedy design are then
demonstrated on a nonlinear source inversion problem where we seek an optimal
policy for sequential sensing.
An Empirical Evaluation of Current Convolutional Architectures' Ability to Manage Nuisance Location and Scale Variability
Authors: Nikolaos Karianakis, Jingming Dong, Stefano Soatto
Date: 2015-05-26 00:00:00
Proceeding: CVPR 2016 6
Tasks: General Classification

We conduct an empirical study to test the ability of Convolutional Neural
Networks (CNNs) to reduce the effects of nuisance transformations of the input
data, such as location, scale and aspect ratio. We isolate factors by adopting
a common convolutional architecture either deployed globally on the image to
compute class posterior distributions, or restricted locally to compute class
conditional distributions given location, scale and aspect ratios of bounding
boxes determined by proposal heuristics. In theory, averaging the latter should
yield inferior performance compared to proper marginalization. Yet empirical
evidence suggests the converse, leading us to conclude that - at the current
level of complexity of convolutional architectures and scale of the data sets
used to train them - CNNs are not very effective at marginalizing nuisance
variability. We also quantify the effects of context on the overall
classification task and its impact on the performance of CNNs, and propose
improved sampling techniques for heuristic proposal schemes that improve
end-to-end performance to state-of-the-art levels. We test our hypothesis on a
classification task using the ImageNet Challenge benchmark and on a
wide-baseline matching task using the Oxford and Fischer's datasets.
What value do explicit high level concepts have in vision to language problems?
Authors: Qi Wu, Chunhua Shen, Lingqiao Liu, Anthony Dick, Anton Van Den Hengel
Date: 2015-06-03 00:00:00
Proceeding: CVPR 2016 6
Tasks: Image Captioning, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Much of the recent progress in Vision-to-Language (V2L) problems has been
achieved through a combination of Convolutional Neural Networks (CNNs) and
Recurrent Neural Networks (RNNs). This approach does not explicitly represent
high-level semantic concepts, but rather seeks to progress directly from image
features to text. We propose here a method of incorporating high-level concepts
into the very successful CNN-RNN approach, and show that it achieves a
significant improvement on the state-of-the-art performance in both image
captioning and visual question answering. We also show that the same mechanism
can be used to introduce external semantic information and that doing so
further improves performance. In doing so we provide an analysis of the value
of high level semantic information in V2L problems.
Recognizing Activities of Daily Living with a Wrist-mounted Camera
Authors: Katsunori Ohnishi, Atsushi Kanehira, Asako Kanezaki, Tatsuya Harada
Date: 2015-11-20 00:00:00
Proceeding: CVPR 2016 6
Tasks: object-detection, Object Detection

We present a novel dataset and a novel algorithm for recognizing activities
of daily living (ADL) from a first-person wearable camera. Handled objects are
crucially important for egocentric ADL recognition. For specific examination of
objects related to users' actions separately from other objects in an
environment, many previous works have addressed the detection of handled
objects in images captured from head-mounted and chest-mounted cameras.
Nevertheless, detecting handled objects is not always easy because they tend to
appear small in images. They can be occluded by a user's body. As described
herein, we mount a camera on a user's wrist. A wrist-mounted camera can capture
handled objects at a large scale, and thus it enables us to skip object
detection process. To compare a wrist-mounted camera and a head-mounted camera,
we also develop a novel and publicly available dataset that includes videos and
annotations of daily activities captured simultaneously by both cameras.
Additionally, we propose a discriminative video representation that retains
spatial and temporal information after encoding frame descriptors extracted by
Convolutional Neural Networks (CNN).
Streaming View Learning
Authors: Chang Xu, DaCheng Tao, Chao Xu
Date: 2016-04-28 00:00:00
Tasks: MULTI-VIEW LEARNING

An underlying assumption in conventional multi-view learning algorithms is
that all views can be simultaneously accessed. However, due to various factors
when collecting and pre-processing data from different views, the streaming
view setting, in which views arrive in a streaming manner, is becoming more
common. By assuming that the subspaces of a multi-view model trained over past
views are stable, here we fine tune their combination weights such that the
well-trained multi-view model is compatible with new views. This largely
overcomes the burden of learning new view functions and updating past view
functions. We theoretically examine convergence issues and the influence of
streaming views in the proposed algorithm. Experimental results on real-world
datasets suggest that studying the streaming views problem in multi-view
learning is significant and that the proposed algorithm can effectively handle
streaming views in different applications.
Crafting Adversarial Input Sequences for Recurrent Neural Networks
Authors: Nicolas Papernot, Patrick McDaniel, Ananthram Swami, Richard Harang
Date: 2016-04-28 00:00:00
Tasks: Autonomous Vehicles, BIG-bench Machine Learning, General Classification

Machine learning models are frequently used to solve complex security
problems, as well as to make decisions in sensitive situations like guiding
autonomous vehicles or predicting financial market behaviors. Previous efforts
have shown that numerous machine learning models were vulnerable to adversarial
manipulations of their inputs taking the form of adversarial samples. Such
inputs are crafted by adding carefully selected perturbations to legitimate
inputs so as to force the machine learning model to misbehave, for instance by
outputting a wrong class if the machine learning task of interest is
classification. In fact, to the best of our knowledge, all previous work on
adversarial samples crafting for neural network considered models used to solve
classification tasks, most frequently in computer vision applications. In this
paper, we contribute to the field of adversarial machine learning by
investigating adversarial input sequences for recurrent neural networks
processing sequential data. We show that the classes of algorithms introduced
previously to craft adversarial samples misclassified by feed-forward neural
networks can be adapted to recurrent neural networks. In a experiment, we show
that adversaries can craft adversarial sequences misleading both categorical
and sequential recurrent neural networks.