pwc-archive/papers-with-abstracts huggingface dataset:
Matrix Variate RBM and Its Applications
Authors: Guanglei Qi, Yanfeng Sun, Junbin Gao, Yongli Hu, Jinghua Li
Date: 2016-01-05 00:00:00
Tasks: Handwritten Digit Recognition, Image Super-Resolution, Super-Resolution

Restricted Boltzmann Machine (RBM) is an importan- t generative model
modeling vectorial data. While applying an RBM in practice to images, the data
have to be vec- torized. This results in high-dimensional data and valu- able
spatial information has got lost in vectorization. In this paper, a
Matrix-Variate Restricted Boltzmann Machine (MVRBM) model is proposed by
generalizing the classic RBM to explicitly model matrix data. In the new RBM
model, both input and hidden variables are in matrix forms which are connected
by bilinear transforms. The MVRBM has much less model parameters, resulting in
a faster train- ing algorithm while retaining comparable performance as the
classic RBM. The advantages of the MVRBM have been demonstrated on two
real-world applications: Image super- resolution and handwritten digit
recognition.
Feedforward Sequential Memory Networks: A New Structure to Learn Long-term Dependency
Authors: Shiliang Zhang, Cong Liu, Hui Jiang, Si Wei, Li-Rong Dai, Yu Hu
Date: 2015-12-28 00:00:00
Tasks: Language Modelling, speech-recognition, Speech Recognition, Time Series, Time Series Analysis

In this paper, we propose a novel neural network structure, namely
\emph{feedforward sequential memory networks (FSMN)}, to model long-term
dependency in time series without using recurrent feedback. The proposed FSMN
is a standard fully-connected feedforward neural network equipped with some
learnable memory blocks in its hidden layers. The memory blocks use a
tapped-delay line structure to encode the long context information into a
fixed-size representation as short-term memory mechanism. We have evaluated the
proposed FSMNs in several standard benchmark tasks, including speech
recognition and language modelling. Experimental results have shown FSMNs
significantly outperform the conventional recurrent neural networks (RNN),
including LSTMs, in modeling sequential signals like speech or language.
Moreover, FSMNs can be learned much more reliably and faster than RNNs or LSTMs
due to the inherent non-recurrent model structure.
Event Specific Multimodal Pattern Mining with Image-Caption Pairs
Authors: Hongzhi Li, Joseph G. Ellis, Shih-Fu Chang
Date: 2015-12-31 00:00:00
Tasks: Descriptive, Image Captioning

In this paper we describe a novel framework and algorithms for discovering
image patch patterns from a large corpus of weakly supervised image-caption
pairs generated from news events. Current pattern mining techniques attempt to
find patterns that are representative and discriminative, we stipulate that our
discovered patterns must also be recognizable by humans and preferably with
meaningful names. We propose a new multimodal pattern mining approach that
leverages the descriptive captions often accompanying news images to learn
semantically meaningful image patch patterns. The mutltimodal patterns are then
named using words mined from the associated image captions for each pattern. A
novel evaluation framework is provided that demonstrates our patterns are 26.2%
more semantically meaningful than those discovered by the state of the art
vision only pipeline, and that we can provide tags for the discovered images
patches with 54.5% accuracy with no direct supervision. Our methods also
discover named patterns beyond those covered by the existing image datasets
like ImageNet. To the best of our knowledge this is the first algorithm
developed to automatically mine image patch patterns that have strong semantic
meaning specific to high-level news events, and then evaluate these patterns
based on that criteria.
Multi-Source Neural Translation
Authors: Barret Zoph, Kevin Knight
Date: 2016-01-05 00:00:00
Proceeding: NAACL 2016 6
Tasks: Decoder, Machine Translation, Translation

We build a multi-source machine translation model and train it to maximize
the probability of a target English string given French and German sources.
Using the neural encoder-decoder framework, we explore several combination
methods and report up to +4.8 Bleu increases on top of a very strong
attention-based neural translation model.
Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis
Authors: Jimei Yang, Scott Reed, Ming-Hsuan Yang, Honglak Lee
Date: 2016-01-05 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Decoder, Object

An important problem for both graphics and vision is to synthesize novel
views of a 3D object from a single image. This is particularly challenging due
to the partial observability inherent in projecting a 3D object onto the image
space, and the ill-posedness of inferring object shape and pose. However, we
can train a neural network to address the problem if we restrict our attention
to specific object categories (in our case faces and chairs) for which we can
gather ample training data. In this paper, we propose a novel recurrent
convolutional encoder-decoder network that is trained end-to-end on the task of
rendering rotated objects starting from a single image. The recurrent structure
allows our model to capture long-term dependencies along a sequence of
transformations. We demonstrate the quality of its predictions for human faces
on the Multi-PIE dataset and for a dataset of 3D chair models, and also show
its ability to disentangle latent factors of variation (e.g., identity and
pose) without using full supervision.
Nonlinear Hebbian learning as a unifying principle in receptive field formation
Authors: Carlos S. N. Brito, Wulfram Gerstner
Date: 2016-01-04 00:00:00

The development of sensory receptive fields has been modeled in the past by a
variety of models including normative models such as sparse coding or
independent component analysis and bottom-up models such as spike-timing
dependent plasticity or the Bienenstock-Cooper-Munro model of synaptic
plasticity. Here we show that the above variety of approaches can all be
unified into a single common principle, namely Nonlinear Hebbian Learning. When
Nonlinear Hebbian Learning is applied to natural images, receptive field shapes
were strongly constrained by the input statistics and preprocessing, but
exhibited only modest variation across different choices of nonlinearities in
neuron models or synaptic plasticity rules. Neither overcompleteness nor sparse
network activity are necessary for the development of localized receptive
fields. The analysis of alternative sensory modalities such as auditory models
or V2 development lead to the same conclusions. In all examples, receptive
fields can be predicted a priori by reformulating an abstract model as
nonlinear Hebbian learning. Thus nonlinear Hebbian learning and natural
statistics can account for many aspects of receptive field formation across
models and sensory modalities.
Artwork creation by a cognitive architecture integrating computational creativity and dual process approaches
Authors: Agnese Augello, Ignazio Infantino, Antonio Lieto, Giovanni Pilato, Riccardo Rizzo, Filippo Vella
Date: 2016-01-04 00:00:00
Tasks: Descriptive

The paper proposes a novel cognitive architecture (CA) for computational
creativity based on the Psi model and on the mechanisms inspired by dual
process theories of reasoning and rationality. In recent years, many cognitive
models have focused on dual process theories to better describe and implement
complex cognitive skills in artificial agents, but creativity has been
approached only at a descriptive level. In previous works we have described
various modules of the cognitive architecture that allows a robot to execute
creative paintings. By means of dual process theories we refine some relevant
mechanisms to obtain artworks, and in particular we explain details about the
resolution level of the CA dealing with different strategies of access to the
Long Term Memory (LTM) and managing the interaction between S1 and S2 processes
of the dual process theory. The creative process involves both divergent and
convergent processes in either implicit or explicit manner. This leads to four
activities (exploratory, reflective, tacit, and analytic) that, triggered by
urges and motivations, generate creative acts. These creative acts exploit both
the LTM and the WM in order to make novel substitutions to a perceived image by
properly mixing parts of pictures coming from different domains. The paper
highlights the role of the interaction between S1 and S2 processes, modulated
by the resolution level, which focuses the attention of the creative agent by
broadening or narrowing the exploration of novel solutions, or even drawing the
solution from a set of already made associations. An example of artificial
painter is described in some experimentations by using a robotic platform.
Scalable Models for Computing Hierarchies in Information Networks
Authors: Baoxu Shi, Tim Weninger
Date: 2016-01-04 00:00:00

Information hierarchies are organizational structures that often used to
organize and present large and complex information as well as provide a
mechanism for effective human navigation. Fortunately, many statistical and
computational models exist that automatically generate hierarchies; however,
the existing approaches do not consider linkages in information {\em networks}
that are increasingly common in real-world scenarios. Current approaches also
tend to present topics as an abstract probably distribution over words, etc
rather than as tangible nodes from the original network. Furthermore, the
statistical techniques present in many previous works are not yet capable of
processing data at Web-scale. In this paper we present the Hierarchical
Document Topic Model (HDTM), which uses a distributed vertex-programming
process to calculate a nonparametric Bayesian generative model. Experiments on
three medium size data sets and the entire Wikipedia dataset show that HDTM can
infer accurate hierarchies even over large information networks.
Distant IE by Bootstrapping Using Lists and Document Structure
Authors: Lidong Bing, Mingyang Ling, Richard C. Wang, William W. Cohen
Date: 2016-01-04 00:00:00

Distant labeling for information extraction (IE) suffers from noisy training
data. We describe a way of reducing the noise associated with distant IE by
identifying coupling constraints between potential instance labels. As one
example of coupling, items in a list are likely to have the same label. A
second example of coupling comes from analysis of document structure: in some
corpora, sections can be identified such that items in the same section are
likely to have the same label. Such sections do not exist in all corpora, but
we show that augmenting a large corpus with coupling constraints from even a
small, well-structured corpus can improve performance substantially, doubling
F1 on one task.
Multimodal Classification of Events in Social Media
Authors: Matthias Zeppelzauer, Daniel Schopfhauser
Date: 2016-01-04 00:00:00
Tasks: Classification, General Classification

A large amount of social media hosted on platforms like Flickr and Instagram
is related to social events. The task of social event classification refers to
the distinction of event and non-event-related content as well as the
classification of event types (e.g. sports events, concerts, etc.). In this
paper, we provide an extensive study of textual, visual, as well as multimodal
representations for social event classification. We investigate strengths and
weaknesses of the modalities and study synergy effects between the modalities.
Experimental results obtained with our multimodal representation outperform
state-of-the-art methods and provide a new baseline for future research.
Interacting Behavior and Emerging Complexity
Authors: Alyssa Adams, Hector Zenil, Eduardo Hermo Reyes, Joost Joosten
Date: 2015-12-23 00:00:00

Can we quantify the change of complexity throughout evolutionary processes?
We attempt to address this question through an empirical approach. In very
general terms, we simulate two simple organisms on a computer that compete over
limited available resources. We implement Global Rules that determine the
interaction between two Elementary Cellular Automata on the same grid. Global
Rules change the complexity of the state evolution output which suggests that
some complexity is intrinsic to the interaction rules themselves. The largest
increases in complexity occurred when the interacting elementary rules had very
little complexity, suggesting that they are able to accept complexity through
interaction only. We also found that some Class 3 or 4 CA rules are more
fragile than others to Global Rules, while others are more robust, hence
suggesting some intrinsic properties of the rules independent of the Global
Rule choice. We provide statistical mappings of Elementary Cellular Automata
exposed to Global Rules and different initial conditions onto different
complexity classes.
NFL Play Prediction
Authors: Brendan Teich, Roman Lutz, Valentin Kassarnig
Date: 2016-01-04 00:00:00
Tasks: Prediction

Based on NFL game data we try to predict the outcome of a play in multiple
different ways. An application of this is the following: by plugging in various
play options one could determine the best play for a given situation in real
time. While the outcome of a play can be described in many ways we had the most
promising results with a newly defined measure that we call "progress". We see
this work as a first step to include predictive analysis into NFL playcalling.
Semi-supervised Tuning from Temporal Coherence
Authors: Davide Maltoni, Vincenzo Lomonaco
Date: 2015-11-10 00:00:00
Tasks: General Classification

Recent works demonstrated the usefulness of temporal coherence to regularize
supervised training or to learn invariant features with deep architectures. In
particular, enforcing smooth output changes while presenting temporally-closed
frames from video sequences, proved to be an effective strategy. In this paper
we prove the efficacy of temporal coherence for semi-supervised incremental
tuning. We show that a deep architecture, just mildly trained in a supervised
manner, can progressively improve its classification accuracy, if exposed to
video sequences of unlabeled data. The extent to which, in some cases, a
semi-supervised tuning allows to improve classification accuracy (approaching
the supervised one) is somewhat surprising. A number of control experiments
pointed out the fundamental role of temporal coherence.
Approximate Message Passing with Nearest Neighbor Sparsity Pattern Learning
Authors: Xiangming Meng, Sheng Wu, Linling Kuang, Defeng, Huang, Jianhua Lu
Date: 2016-01-04 00:00:00

We consider the problem of recovering clustered sparse signals with no prior
knowledge of the sparsity pattern. Beyond simple sparsity, signals of interest
often exhibits an underlying sparsity pattern which, if leveraged, can improve
the reconstruction performance. However, the sparsity pattern is usually
unknown a priori. Inspired by the idea of k-nearest neighbor (k-NN) algorithm,
we propose an efficient algorithm termed approximate message passing with
nearest neighbor sparsity pattern learning (AMP-NNSPL), which learns the
sparsity pattern adaptively. AMP-NNSPL specifies a flexible spike and slab
prior on the unknown signal and, after each AMP iteration, sets the sparse
ratios as the average of the nearest neighbor estimates via expectation
maximization (EM). Experimental results on both synthetic and real data
demonstrate the superiority of our proposed algorithm both in terms of
reconstruction performance and computational complexity.
Denoising Criterion for Variational Auto-Encoding Framework
Authors: Daniel Jiwoong Im, Sungjin Ahn, Roland Memisevic, Yoshua Bengio
Date: 2015-11-19 00:00:00
Tasks: Denoising

Denoising autoencoders (DAE) are trained to reconstruct their clean inputs
with noise injected at the input level, while variational autoencoders (VAE)
are trained with noise injected in their stochastic hidden layer, with a
regularizer that encourages this noise injection. In this paper, we show that
injecting noise both in input and in the stochastic hidden layer can be
advantageous and we propose a modified variational lower bound as an improved
objective function in this setup. When input is corrupted, then the standard
VAE lower bound involves marginalizing the encoder conditional distribution
over the input noise, which makes the training criterion intractable. Instead,
we propose a modified training criterion which corresponds to a tractable bound
when input is corrupted. Experimentally, we find that the proposed denoising
variational autoencoder (DVAE) yields better average log-likelihood than the
VAE and the importance weighted autoencoder on the MNIST and Frey Face
datasets.
Modelling-based experiment retrieval: A case study with gene expression clustering
Authors: Paul Blomstedt, Ritabrata Dutta, Sohan Seth, Alvis Brazma, Samuel Kaski
Date: 2015-05-19 00:00:00
Tasks: Clustering, Retrieval

Motivation: Public and private repositories of experimental data are growing
to sizes that require dedicated methods for finding relevant data. To improve
on the state of the art of keyword searches from annotations, methods for
content-based retrieval have been proposed. In the context of gene expression
experiments, most methods retrieve gene expression profiles, requiring each
experiment to be expressed as a single profile, typically of case vs. control.
A more general, recently suggested alternative is to retrieve experiments whose
models are good for modelling the query dataset. However, for very noisy and
high-dimensional query data, this retrieval criterion turns out to be very
noisy as well.
  Results: We propose doing retrieval using a denoised model of the query
dataset, instead of the original noisy dataset itself. To this end, we
introduce a general probabilistic framework, where each experiment is modelled
separately and the retrieval is done by finding related models. For retrieval
of gene expression experiments, we use a probabilistic model called product
partition model, which induces a clustering of genes that show similar
expression patterns across a number of samples. The suggested metric for
retrieval using clusterings is the normalized information distance. Empirical
results finally suggest that inference for the full probabilistic model can be
approximated with good performance using computationally faster heuristic
clustering approaches (e.g. $k$-means). The method is highly scalable and
straightforward to apply to construct a general-purpose gene expression
experiment retrieval method.
  Availability: The method can be implemented using standard clustering
algorithms and normalized information distance, available in many statistical
software packages.
Learning relationships between data obtained independently
Authors: Alexandra Carpentier, Teresa Schlueter
Date: 2016-01-04 00:00:00

The aim of this paper is to provide a new method for learning the
relationships between data that have been obtained independently. Unlike
existing methods like matching, the proposed technique does not require any
contextual information, provided that the dependency between the variables of
interest is monotone. It can therefore be easily combined with matching in
order to exploit the advantages of both methods. This technique can be
described as a mix between quantile matching, and deconvolution. We provide for
it a theoretical and an empirical validation.
Fitting Spectral Decay with the $k$-Support Norm
Authors: Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos
Date: 2016-01-04 00:00:00
Tasks: Matrix Completion

The spectral $k$-support norm enjoys good estimation properties in low rank
matrix learning problems, empirically outperforming the trace norm. Its unit
ball is the convex hull of rank $k$ matrices with unit Frobenius norm. In this
paper we generalize the norm to the spectral $(k,p)$-support norm, whose
additional parameter $p$ can be used to tailor the norm to the decay of the
spectrum of the underlying model. We characterize the unit ball and we
explicitly compute the norm. We further provide a conditional gradient method
to solve regularization problems with the norm, and we derive an efficient
algorithm to compute the Euclidean projection on the unit ball in the case
$p=\infty$. In numerical experiments, we show that allowing $p$ to vary
significantly improves performance over the spectral $k$-support norm on
various matrix completion benchmarks, and better captures the spectral decay of
the underlying model.
Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds
Authors: Ming Yin, Yi Guo, Junbin Gao, Zhaoshui He, Shengli Xie
Date: 2016-01-04 00:00:00
Proceeding: CVPR 2016 6
Tasks: Clustering

Sparse subspace clustering (SSC), as one of the most successful subspace
clustering methods, has achieved notable clustering accuracy in computer vision
tasks. However, SSC applies only to vector data in Euclidean space. As such,
there is still no satisfactory approach to solve subspace clustering by ${\it
self-expressive}$ principle for symmetric positive definite (SPD) matrices
which is very useful in computer vision. In this paper, by embedding the SPD
matrices into a Reproducing Kernel Hilbert Space (RKHS), a kernel subspace
clustering method is constructed on the SPD manifold through an appropriate
Log-Euclidean kernel, termed as kernel sparse subspace clustering on the SPD
Riemannian manifold (KSSCR). By exploiting the intrinsic Riemannian geometry
within data, KSSCR can effectively characterize the geodesic distance between
SPD matrices to uncover the underlying subspace structure. Experimental results
on two famous database demonstrate that the proposed method achieves better
clustering results than the state-of-the-art approaches.
Convergence of Stochastic Gradient Descent for PCA
Authors: Ohad Shamir
Date: 2015-09-30 00:00:00

We consider the problem of principal component analysis (PCA) in a streaming
stochastic setting, where our goal is to find a direction of approximate
maximal variance, based on a stream of i.i.d. data points in $\reals^d$. A
simple and computationally cheap algorithm for this is stochastic gradient
descent (SGD), which incrementally updates its estimate based on each new data
point. However, due to the non-convex nature of the problem, analyzing its
performance has been a challenge. In particular, existing guarantees rely on a
non-trivial eigengap assumption on the covariance matrix, which is intuitively
unnecessary. In this paper, we provide (to the best of our knowledge) the first
eigengap-free convergence guarantees for SGD in the context of PCA. This also
partially resolves an open problem posed in \cite{hardt2014noisy}. Moreover,
under an eigengap assumption, we show that the same techniques lead to new SGD
convergence guarantees with better dependence on the eigengap.
Multi-task CNN Model for Attribute Prediction
Authors: Abrar H. Abdulnabi, Gang Wang, Jiwen Lu, Kui Jia
Date: 2016-01-04 00:00:00
Tasks: Attribute, Clothing Attribute Recognition, model, Multi-Task Learning, Prediction

This paper proposes a joint multi-task learning algorithm to better predict
attributes in images using deep convolutional neural networks (CNN). We
consider learning binary semantic attributes through a multi-task CNN model,
where each CNN will predict one binary attribute. The multi-task learning
allows CNN models to simultaneously share visual knowledge among different
attribute categories. Each CNN will generate attribute-specific feature
representations, and then we apply multi-task learning on the features to
predict their attributes. In our multi-task framework, we propose a method to
decompose the overall model's parameters into a latent task matrix and
combination matrix. Furthermore, under-sampled classifiers can leverage shared
statistics from other classifiers to improve their performance. Natural
grouping of attributes is applied such that attributes in the same group are
encouraged to share more knowledge. Meanwhile, attributes in different groups
will generally compete with each other, and consequently share less knowledge.
We show the effectiveness of our method on two popular attribute datasets.