pwc-archive/papers-with-abstracts huggingface dataset:
The SP theory of intelligence: distinctive features and advantages
Authors: J. G. Wolff
Date: 2015-08-17 00:00:00

This paper highlights distinctive features of the "SP theory of intelligence"
and its apparent advantages compared with some AI-related alternatives.
Distinctive features and advantages are: simplification and integration of
observations and concepts; simplification and integration of structures and
processes in computing systems; the theory is itself a theory of computing; it
can be the basis for new architectures for computers; information compression
via the matching and unification of patterns and, more specifically, via
multiple alignment, is fundamental; transparency in the representation and
processing of knowledge; the discovery of 'natural' structures via information
compression (DONSVIC); interpretations of mathematics; interpretations in human
perception and cognition; and realisation of abstract concepts in terms of
neurons and their inter-connections ("SP-neural"). These things relate to
AI-related alternatives: minimum length encoding and related concepts; deep
learning in neural networks; unified theories of cognition and related
research; universal search; Bayesian networks and more; pattern recognition and
vision; the analysis, production, and translation of natural language;
Unsupervised learning of natural language; exact and inexact forms of
reasoning; representation and processing of diverse forms of knowledge; IBM's
Watson; software engineering; solving problems associated with big data, and in
the development of intelligence in autonomous robots. In conclusion, the SP
system can provide a firm foundation for the long-term development of AI, with
many potential benefits and applications. It may also deliver useful results on
relatively short timescales. A high-parallel, open-source version of the SP
machine, derived from the SP computer model, would be a means for researchers
everywhere to explore what can be done with the system, and to create new
versions of it.
Online Learning to Sample
Authors: Guillaume Bouchard, Théo Trouillon, Julien Perez, Adrien Gaidon
Date: 2015-06-30 00:00:00
Tasks: image-classification, Image Classification, Reinforcement Learning

Stochastic Gradient Descent (SGD) is one of the most widely used techniques
for online optimization in machine learning. In this work, we accelerate SGD by
adaptively learning how to sample the most useful training examples at each
time step. First, we show that SGD can be used to learn the best possible
sampling distribution of an importance sampling estimator. Second, we show that
the sampling distribution of an SGD algorithm can be estimated online by
incrementally minimizing the variance of the gradient. The resulting algorithm
- called Adaptive Weighted SGD (AW-SGD) - maintains a set of parameters to
optimize, as well as a set of parameters to sample learning examples. We show
that AWSGD yields faster convergence in three different applications: (i) image
classification with deep features, where the sampling of images depends on
their labels, (ii) matrix factorization, where rows and columns are not sampled
uniformly, and (iii) reinforcement learning, where the optimized and
exploration policies are estimated at the same time, where our approach
corresponds to an off-policy gradient algorithm.
Modeling Time Series Similarity with Siamese Recurrent Networks
Authors: Wenjie Pei, David M. J. Tax, Laurens van der Maaten
Date: 2016-03-15 00:00:00
Tasks: domain classification, General Classification, Metric Learning, One-Shot Learning, Sign Language Recognition, Time Series, Time Series Analysis

Traditional techniques for measuring similarities between time series are
based on handcrafted similarity measures, whereas more recent learning-based
approaches cannot exploit external supervision. We combine ideas from
time-series modeling and metric learning, and study siamese recurrent networks
(SRNs) that minimize a classification loss to learn a good similarity measure
between time series. Specifically, our approach learns a vectorial
representation for each time series in such a way that similar time series are
modeled by similar representations, and dissimilar time series by dissimilar
representations. Because it is a similarity prediction models, SRNs are
particularly well-suited to challenging scenarios such as signature
recognition, in which each person is a separate class and very few examples per
class are available. We demonstrate the potential merits of SRNs in
within-domain and out-of-domain classification experiments and in one-shot
learning experiments on tasks such as signature, voice, and sign language
recognition.
Evolving Boolean Regulatory Networks with Variable Gene Expression Times
Authors: Larry Bull
Date: 2016-03-02 00:00:00

The time taken for gene expression varies not least because proteins vary in
length considerably. This paper uses an abstract, tuneable Boolean regulatory
network model to explore gene expression time variation. In particular, it is
shown how non-uniform expression times can emerge under certain conditions
through simulated evolution. That is, gene expression time variance appears
beneficial in the shaping of the dynamical behaviour of the regulatory network
without explicit consideration of protein function.
Zero-sum repeated games: Counterexamples to the existence of the asymptotic value and the conjecture $\operatorname{maxmin}=\operatorname{lim}v_n$
Authors: Bruno Ziliotto
Date: 2013-05-21 00:00:00
Tasks: Math

Mertens [In Proceedings of the International Congress of Mathematicians
(Berkeley, Calif., 1986) (1987) 1528-1577 Amer. Math. Soc.] proposed two
general conjectures about repeated games: the first one is that, in any
two-person zero-sum repeated game, the asymptotic value exists, and the second
one is that, when Player 1 is more informed than Player 2, in the long run
Player 1 is able to guarantee the asymptotic value. We disprove these two
long-standing conjectures by providing an example of a zero-sum repeated game
with public signals and perfect observation of the actions, where the value of
the $\lambda$-discounted game does not converge when $\lambda$ goes to 0. The
aforementioned example involves seven states, two actions and two signals for
each player. Remarkably, players observe the payoffs, and play in turn.
Accelerating a hybrid continuum-atomistic fluidic model with on-the-fly machine learning
Authors: David Stephenson, James R. Kermode, Duncan A Lockerby
Date: 2016-03-15 00:00:00
Tasks: Bayesian Inference, BIG-bench Machine Learning

We present a hybrid continuum-atomistic scheme which combines molecular
dynamics (MD) simulations with on-the-fly machine learning techniques for the
accurate and efficient prediction of multiscale fluidic systems. By using a
Gaussian process as a surrogate model for the computationally expensive MD
simulations, we use Bayesian inference to predict the system behaviour at the
atomistic scale, purely by consideration of the macroscopic inputs and outputs.
Whenever the uncertainty of this prediction is greater than a predetermined
acceptable threshold, a new MD simulation is performed to continually augment
the database, which is never required to be complete. This provides a
substantial enhancement to the current generation of hybrid methods, which
often require many similar atomistic simulations to be performed, discarding
information after it is used once.
  We apply our hybrid scheme to nano-confined unsteady flow through a
high-aspect-ratio converging-diverging channel, and make comparisons between
the new scheme and full MD simulations for a range of uncertainty thresholds
and initial databases. For low thresholds, our hybrid solution is highly
accurate\,---\,within the thermal noise of a full MD simulation. As the
uncertainty threshold is raised, the accuracy of our scheme decreases and the
computational speed-up increases (relative to a full MD simulation), enabling
the compromise between precision and efficiency to be tuned. The speed-up of
our hybrid solution ranges from an order of magnitude, with no initial
database, to cases where an extensive initial database ensures no new MD
simulations are required.
Scalable Image Retrieval by Sparse Product Quantization
Authors: Qingqun Ning, Jianke Zhu, Zhiyuan Zhong, Steven C. H. Hoi, Chun Chen
Date: 2016-03-15 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Quantization, Retrieval

Fast Approximate Nearest Neighbor (ANN) search technique for high-dimensional
feature indexing and retrieval is the crux of large-scale image retrieval. A
recent promising technique is Product Quantization, which attempts to index
high-dimensional image features by decomposing the feature space into a
Cartesian product of low dimensional subspaces and quantizing each of them
separately. Despite the promising results reported, their quantization approach
follows the typical hard assignment of traditional quantization methods, which
may result in large quantization errors and thus inferior search performance.
Unlike the existing approaches, in this paper, we propose a novel approach
called Sparse Product Quantization (SPQ) to encoding the high-dimensional
feature vectors into sparse representation. We optimize the sparse
representations of the feature vectors by minimizing their quantization errors,
making the resulting representation is essentially close to the original data
in practice. Experiments show that the proposed SPQ technique is not only able
to compress data, but also an effective encoding technique. We obtain
state-of-the-art results for ANN search on four public image datasets and the
promising results of content-based image retrieval further validate the
efficacy of our proposed method.
Classification with Repulsion Tensors: A Case Study on Face Recognition
Authors: Hawren Fang
Date: 2016-03-15 00:00:00
Tasks: Classification, Dimensionality Reduction, Face Recognition, General Classification

We consider dimensionality reduction methods for face recognition in a
supervised setting, using an image-as-matrix representation. A common procedure
is to project image matrices into a smaller space in which the recognition is
performed. These methods are often called "two-dimensional" in the literature
and there exist counterparts that use an image-as-vector representation. When
two face images are close to each other in the input space they may remain
close after projection - but this is not desirable in the situation when these
two images are from different classes, and this often affects the recognition
performance. We extend a previously developed `repulsion Laplacean' technique
based on adding terms to the objective function with the goal or creation a
repulsion energy between such images in the projected space. This scheme, which
relies on a repulsion graph, is generic and can be incorporated into various
two-dimensional methods. It can be regarded as a multilinear generalization of
the repulsion strategy by Kokiopoulou and Saad [Pattern Recog., 42 (2009), pp.
2392--2402]. Experimental results demonstrate that the proposed methodology
offers significant recognition improvement relative to the underlying
two-dimensional methods.
Optimal Sensing via Multi-armed Bandit Relaxations in Mixed Observability Domains
Authors: Mikko Lauri, Risto Ritala
Date: 2016-03-15 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, Sequential Decision Making

Sequential decision making under uncertainty is studied in a mixed
observability domain. The goal is to maximize the amount of information
obtained on a partially observable stochastic process under constraints imposed
by a fully observable internal state. An upper bound for the optimal value
function is derived by relaxing constraints. We identify conditions under which
the relaxed problem is a multi-armed bandit whose optimal policy is easily
computable. The upper bound is applied to prune the search space in the
original problem, and the effect on solution quality is assessed via simulation
experiments. Empirical results show effective pruning of the search space in a
target monitoring domain.
Model-based Reinforcement Learning with Parametrized Physical Models and Optimism-Driven Exploration
Authors: Christopher Xie, Sachin Patil, Teodor Moldovan, Sergey Levine, Pieter Abbeel
Date: 2015-09-23 00:00:00
Tasks: Model-based Reinforcement Learning, Model Predictive Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we present a robotic model-based reinforcement learning method
that combines ideas from model identification and model predictive control. We
use a feature-based representation of the dynamics that allows the dynamics
model to be fitted with a simple least squares procedure, and the features are
identified from a high-level specification of the robot's morphology,
consisting of the number and connectivity structure of its links. Model
predictive control is then used to choose the actions under an optimistic model
of the dynamics, which produces an efficient and goal-directed exploration
strategy. We present real time experimental results on standard benchmark
problems involving the pendulum, cartpole, and double pendulum systems.
Experiments indicate that our method is able to learn a range of benchmark
tasks substantially faster than the previous best methods. To evaluate our
approach on a realistic robotic control task, we also demonstrate real time
control of a simulated 7 degree of freedom arm.
On the exact recovery of sparse signals via conic relaxations
Authors: Hongbo Dong
Date: 2016-03-15 00:00:00
Tasks: Sparse Learning, Variable Selection

In this note we compare two recently proposed semidefinite relaxations for
the sparse linear regression problem by Pilanci, Wainwright and El Ghaoui
(Sparse learning via boolean relaxations, 2015) and Dong, Chen and Linderoth
(Relaxation vs. Regularization A conic optimization perspective of statistical
variable selection, 2015). We focus on the cardinality constrained formulation,
and prove that the relaxation proposed by Dong, etc. is theoretically no weaker
than the one proposed by Pilanci, etc. Therefore any sufficient condition of
exact recovery derived by Pilanci can be readily applied to the other
relaxation, including their results on high probability recovery for Gaussian
ensemble. Finally we provide empirical evidence that the relaxation by Dong,
etc. requires much fewer observations to guarantee the recovery of true
support.
Optimized Kernel-based Projection Space of Riemannian Manifolds
Authors: Azadeh Alavi, Vishal M. Patel, Rama Chellappa
Date: 2016-02-10 00:00:00
Tasks: Classification, Clustering, Dictionary Learning, General Classification

It is proven that encoding images and videos through Symmetric Positive
Definite (SPD) matrices, and considering the Riemannian geometry of the
resulting space, can lead to increased classification performance. Taking into
account manifold geometry is typically done via embedding the manifolds in
tangent spaces, or Reproducing Kernel Hilbert Spaces (RKHS). Recently, it was
shown that embedding such manifolds into a Random Projection Spaces (RPS),
rather than RKHS or tangent space, leads to higher classification and
clustering performance. However, based on structure and dimensionality of the
randomly generated hyperplanes, the classification performance over RPS may
vary significantly. In addition, fine-tuning RPS is data expensive (as it
requires validation-data), time consuming, and resource demanding. In this
paper, we introduce an approach to learn an optimized kernel-based projection
(with fixed dimensionality), by employing the concept of subspace clustering.
As such, we encode the association of data points to the underlying subspace of
each point, to generate meaningful hyperplanes. Further, we adopt the concept
of dictionary learning and sparse coding, and discriminative analysis, for the
optimized kernel-based projection space (OPS) on SPD manifolds. We validate our
algorithm on several classification tasks. The experiment results also
demonstrate that the proposed method outperforms state-of-the-art methods on
such manifolds.
Unsupervised Ranking Model for Entity Coreference Resolution
Authors: Xuezhe Ma, Zhengzhong Liu, Eduard Hovy
Date: 2016-03-15 00:00:00
Proceeding: NAACL 2016 6
Tasks: coreference-resolution, Coreference Resolution, model

Coreference resolution is one of the first stages in deep language
understanding and its importance has been well recognized in the natural
language processing community. In this paper, we propose a generative,
unsupervised ranking model for entity coreference resolution by introducing
resolution mode variables. Our unsupervised system achieves 58.44% F1 score of
the CoNLL metric on the English data from the CoNLL-2012 shared task (Pradhan
et al., 2012), outperforming the Stanford deterministic system (Lee et al.,
2013) by 3.01%.
Effective Computer Model For Recognizing Nationality From Frontal Image
Authors: Bat-Erdene Batsukh, Ganbat Tsend
Date: 2016-03-15 00:00:00

We are introducing new effective computer model for extracting nationality
from frontal image candidate using face part color, size and distances based on
deep research. Determining face part size, color, and distances is depending on
a variety of factors including image quality, lighting condition, rotation
angle, occlusion and facial emotion. Therefore, first we need to detect a face
on the image then convert an image into the real input. After that, we can
determine image candidate gender, face shape, key points and face parts.
Finally, we will return the result, based on the comparison of sizes and
distances with the sample measurement table database. While we were measuring
samples, there were big differences between images by their gender and face
shapes. Input images must be the frontal face image that has smooth lighting
and does not have any rotation angle. The model can be used in military,
police, defense, healthcare, and technology sectors. Finally, Computer can
distinguish nationality from the face image.
Brain-Inspired Deep Networks for Image Aesthetics Assessment
Authors: Zhangyang Wang, Shiyu Chang, Florin Dolcos, Diane Beck, Ding Liu, Thomas S. Huang
Date: 2016-01-16 00:00:00
Tasks: Data Augmentation, High-Level Synthesis

Image aesthetics assessment has been challenging due to its subjective
nature. Inspired by the scientific advances in the human visual perception and
neuroaesthetics, we design Brain-Inspired Deep Networks (BDN) for this task.
BDN first learns attributes through the parallel supervised pathways, on a
variety of selected feature dimensions. A high-level synthesis network is
trained to associate and transform those attributes into the overall aesthetics
rating. We then extend BDN to predicting the distribution of human ratings,
since aesthetics ratings are often subjective. Another highlight is our
first-of-its-kind study of label-preserving transformations in the context of
aesthetics assessment, which leads to an effective data augmentation approach.
Experimental results on the AVA dataset show that our biological inspired and
task-specific BDN model gains significantly performance improvement, compared
to other state-of-the-art models with the same or higher parameter capacity.
Robust Multi-body Feature Tracker: A Segmentation-free Approach
Authors: Pan Ji, Hongdong Li, Mathieu Salzmann, Yiran Zhong
Date: 2016-03-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Action Recognition, Motion Segmentation, Segmentation, Temporal Action Localization

Feature tracking is a fundamental problem in computer vision, with
applications in many computer vision tasks, such as visual SLAM and action
recognition. This paper introduces a novel multi-body feature tracker that
exploits a multi-body rigidity assumption to improve tracking robustness under
a general perspective camera model. A conventional approach to addressing this
problem would consist of alternating between solving two subtasks: motion
segmentation and feature tracking under rigidity constraints for each segment.
This approach, however, requires knowing the number of motions, as well as
assigning points to motion groups, which is typically sensitive to the motion
estimates. By contrast, here, we introduce a segmentation-free solution to
multi-body feature tracking that bypasses the motion assignment step and
reduces to solving a series of subproblems with closed-form solutions. Our
experiments demonstrate the benefits of our approach in terms of tracking
accuracy and robustness to noise.
Object Contour Detection with a Fully Convolutional Encoder-Decoder Network
Authors: Jimei Yang, Brian Price, Scott Cohen, Honglak Lee, Ming-Hsuan Yang
Date: 2016-03-15 00:00:00
Proceeding: CVPR 2016 6
Tasks: Contour Detection, Decoder, Edge Detection, Object

We develop a deep learning algorithm for contour detection with a fully
convolutional encoder-decoder network. Different from previous low-level edge
detection, our algorithm focuses on detecting higher-level object contours. Our
network is trained end-to-end on PASCAL VOC with refined ground truth from
inaccurate polygon annotations, yielding much higher precision in object
contour detection than previous methods. We find that the learned model
generalizes well to unseen object classes from the same super-categories on MS
COCO and can match state-of-the-art edge detection on BSDS500 with fine-tuning.
By combining with the multiscale combinatorial grouping algorithm, our method
can generate high-quality segmented object proposals, which significantly
advance the state-of-the-art on PASCAL VOC (improving average recall from 0.62
to 0.67) with a relatively small amount of candidates ($\sim$1660 per image).
Sample and Filter: Nonparametric Scene Parsing via Efficient Filtering
Authors: Mohammad Najafi, Sarah Taghavi Namin, Mathieu Salzmann, Lars Petersson
Date: 2015-11-16 00:00:00
Proceeding: CVPR 2016 6
Tasks: Scene Parsing, Superpixels

Scene parsing has attracted a lot of attention in computer vision. While
parametric models have proven effective for this task, they cannot easily
incorporate new training data. By contrast, nonparametric approaches, which
bypass any learning phase and directly transfer the labels from the training
data to the query images, can readily exploit new labeled samples as they
become available. Unfortunately, because of the computational cost of their
label transfer procedures, state-of-the-art nonparametric methods typically
filter out most training images to only keep a few relevant ones to label the
query. As such, these methods throw away many images that still contain
valuable information and generally obtain an unbalanced set of labeled samples.
In this paper, we introduce a nonparametric approach to scene parsing that
follows a sample-and-filter strategy. More specifically, we propose to sample
labeled superpixels according to an image similarity score, which allows us to
obtain a balanced set of samples. We then formulate label transfer as an
efficient filtering procedure, which lets us exploit more labeled samples than
existing techniques. Our experiments evidence the benefits of our approach over
state-of-the-art nonparametric methods on two benchmark datasets.
Multichannel Variable-Size Convolution for Sentence Classification
Authors: Wenpeng Yin, Hinrich Schütze
Date: 2016-03-15 00:00:00
Proceeding: CONLL 2015 7
Tasks: Classification, General Classification, Sentence, Sentence Classification, Word Embeddings

We propose MVCNN, a convolution neural network (CNN) architecture for
sentence classification. It (i) combines diverse versions of pretrained word
embeddings and (ii) extracts features of multigranular phrases with
variable-size convolution filters. We also show that pretraining MVCNN is
critical for good performance. MVCNN achieves state-of-the-art performance on
four tasks: on small-scale binary, small-scale multi-class and largescale
Twitter sentiment prediction and on subjectivity classification.