pwc-archive/papers-with-abstracts huggingface dataset:
Kernel Manifold Alignment
Authors: Devis Tuia, Gustau Camps-Valls
Date: 2015-04-09 00:00:00
Tasks: Computational Efficiency, Domain Adaptation, Object Recognition

We introduce a kernel method for manifold alignment (KEMA) and domain
adaptation that can match an arbitrary number of data sources without needing
corresponding pairs, just few labeled examples in all domains. KEMA has
interesting properties: 1) it generalizes other manifold alignment methods, 2)
it can align manifolds of very different complexities, performing a sort of
manifold unfolding plus alignment, 3) it can define a domain-specific metric to
cope with multimodal specificities, 4) it can align data spaces of different
dimensionality, 5) it is robust to strong nonlinear feature deformations, and
6) it is closed-form invertible which allows transfer across-domains and data
synthesis. We also present a reduced-rank version for computational efficiency
and discuss the generalization performance of KEMA under Rademacher principles
of stability. KEMA exhibits very good performance over competing methods in
synthetic examples, visual object recognition and recognition of facial
expressions tasks.
Scalable Iterative Algorithm for Robust Subspace Clustering
Authors: Sanghyuk Chun, Yung-Kyun Noh, Jinwoo Shin
Date: 2015-03-05 00:00:00
Tasks: Clustering, Dimensionality Reduction

Subspace clustering (SC) is a popular method for dimensionality reduction of
high-dimensional data, where it generalizes Principal Component Analysis (PCA).
Recently, several methods have been proposed to enhance the robustness of PCA
and SC, while most of them are computationally very expensive, in particular,
for high dimensional large-scale data. In this paper, we develop much faster
iterative algorithms for SC, incorporating robustness using a {\em non-squared}
$\ell_2$-norm objective. The known implementations for optimizing the objective
would be costly due to the alternative optimization of two separate objectives:
optimal cluster-membership assignment and robust subspace selection, while the
substitution of one process to a faster surrogate can cause failure in
convergence. To address the issue, we use a simplified procedure requiring
efficient matrix-vector multiplications for subspace update instead of solving
an expensive eigenvector problem at each iteration, in addition to release
nested robust PCA loops. We prove that the proposed algorithm monotonically
converges to a local minimum with approximation guarantees, e.g., it achieves
2-approximation for the robust PCA objective. In our experiments, the proposed
algorithm is shown to converge at an order of magnitude faster than known
algorithms optimizing the same objective, and have outperforms prior subspace
clustering methods in accuracy and running time for MNIST dataset.
Multilingual Open Relation Extraction Using Cross-lingual Projection
Authors: Manaal Faruqui, Shankar Kumar
Date: 2015-03-22 00:00:00
Proceeding: HLT 2015 5
Tasks: Relation, Relation Extraction, Sentence

Open domain relation extraction systems identify relation and argument phrases in a sentence without relying on any underlying schema. However, current state-of-the-art relation extraction systems are available only for English because of their heavy reliance on linguistic tools such as part-of-speech taggers and dependency parsers. We present a cross-lingual annotation projection method for language independent relation extraction. We evaluate our method on a manually annotated test set and present results on three typologically different languages. We release these manual annotations and extracted relations in 61 languages from Wikipedia.
Sparse Overcomplete Word Vector Representations
Authors: Manaal Faruqui, Yulia Tsvetkov, Dani Yogatama, Chris Dyer, Noah Smith
Date: 2015-06-05 00:00:00
Proceeding: IJCNLP 2015 7

Current distributed representations of words show little resemblance to
theories of lexical semantics. The former are dense and uninterpretable, the
latter largely based on familiar, discrete classes (e.g., supersenses) and
relations (e.g., synonymy and hypernymy). We propose methods that transform
word vectors into sparse (and optionally binary) vectors. The resulting
representations are more similar to the interpretable features typically used
in NLP, though they are discovered automatically from raw corpora. Because the
vectors are highly sparse, they are computationally easy to work with. Most
importantly, we find that they outperform the original vectors on benchmark
tasks.
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes
Authors: Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash K. Mansinghka
Date: 2015-03-01 00:00:00

Markov jump processes (MJPs) are used to model a wide range of phenomena from
disease progression to RNA path folding. However, maximum likelihood estimation
of parametric models leads to degenerate trajectories and inferential
performance is poor in nonparametric models. We take a small-variance
asymptotics (SVA) approach to overcome these limitations. We derive the
small-variance asymptotics for parametric and nonparametric MJPs for both
directly observed and hidden state models. In the parametric case we obtain a
novel objective function which leads to non-degenerate trajectories. To derive
the nonparametric version we introduce the gamma-gamma process, a novel
extension to the gamma-exponential process. We propose algorithms for each of
these formulations, which we call \emph{JUMP-means}. Our experiments
demonstrate that JUMP-means is competitive with or outperforms widely used MJP
inference approaches in terms of both speed and reconstruction accuracy.
Abstractive Multi-Document Summarization via Phrase Selection and Merging
Authors: Lidong Bing, Piji Li, Yi Liao, Wai Lam, Weiwei Guo, Rebecca J. Passonneau
Date: 2015-06-04 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Document Summarization, Multi-Document Summarization, Sentence

We propose an abstraction-based multi-document summarization framework that
can construct new sentences by exploring more fine-grained syntactic units than
sentences, namely, noun/verb phrases. Different from existing abstraction-based
approaches, our method first constructs a pool of concepts and facts
represented by phrases from the input documents. Then new sentences are
generated by selecting and merging informative phrases to maximize the salience
of phrases and meanwhile satisfy the sentence construction constraints. We
employ integer linear optimization for conducting phrase selection and merging
simultaneously in order to achieve the global optimal solution for a summary.
Experimental results on the benchmark data set TAC 2011 show that our framework
outperforms the state-of-the-art models under automated pyramid evaluation
metric, and achieves reasonably well results on manual linguistic quality
evaluation.
BayesPy: Variational Bayesian Inference in Python
Authors: Jaakko Luttinen
Date: 2014-10-03 00:00:00
Tasks: Bayesian Inference, Variational Inference

BayesPy is an open-source Python software package for performing variational
Bayesian inference. It is based on the variational message passing framework
and supports conjugate exponential family models. By removing the tedious task
of implementing the variational Bayesian update equations, the user can
construct models faster and in a less error-prone way. Simple syntax, flexible
model construction and efficient inference make BayesPy suitable for both
average and expert Bayesian users. It also supports some advanced methods such
as stochastic and collapsed variational inference.
MADE: Masked Autoencoder for Distribution Estimation
Authors: Mathieu Germain, Karol Gregor, Iain Murray, Hugo Larochelle
Date: 2015-02-12 00:00:00
Tasks: Density Estimation, Image Generation

There has been a lot of recent interest in designing neural network models to
estimate a distribution from a set of examples. We introduce a simple
modification for autoencoder neural networks that yields powerful generative
models. Our method masks the autoencoder's parameters to respect autoregressive
constraints: each input is reconstructed only from previous inputs in a given
ordering. Constrained this way, the autoencoder outputs can be interpreted as a
set of conditional probabilities, and their product, the full joint
probability. We can also train a single network that can decompose the joint
probability in multiple different orderings. Our simple framework can be
applied to multiple architectures, including deep ones. Vectorized
implementations, such as on GPUs, are simple and fast. Experiments demonstrate
that this approach is competitive with state-of-the-art tractable distribution
estimators. At test time, the method is significantly faster and scales better
than other autoregressive estimators.
Content Translation: Computer-assisted translation tool for Wikipedia articles
Authors: Niklas Laxström, Pau Giner, Santhosh Thottingal
Date: 2015-06-05 00:00:00
Tasks: Articles, Translation

The quality and quantity of articles in each Wikipedia language varies
greatly. Translating from another Wikipedia is a natural way to add more
content, but the translation process is not properly supported in the software
used by Wikipedia. Past computer-assisted translation tools built for Wikipedia
are not commonly used. We created a tool that adapts to the specific needs of
an open community and to the kind of content in Wikipedia. Qualitative and
quantitative data indicates that the new tool helps users translate articles
easier and faster.
Idioms-Proverbs Lexicon for Modern Standard Arabic and Colloquial Sentiment Analysis
Authors: Hossam S. Ibrahim, Sherif M. Abdou, Mervat Gheith
Date: 2015-06-05 00:00:00
Tasks: Opinion Mining, Sentence, Sentiment Analysis

Although, the fair amount of works in sentiment analysis (SA) and opinion
mining (OM) systems in the last decade and with respect to the performance of
these systems, but it still not desired performance, especially for
morphologically-Rich Language (MRL) such as Arabic, due to the complexities and
challenges exist in the nature of the languages itself. One of these challenges
is the detection of idioms or proverbs phrases within the writer text or
comment. An idiom or proverb is a form of speech or an expression that is
peculiar to itself. Grammatically, it cannot be understood from the individual
meanings of its elements and can yield different sentiment when treats as
separate words. Consequently, In order to facilitate the task of detection and
classification of lexical phrases for automated SA systems, this paper presents
AIPSeLEX a novel idioms/ proverbs sentiment lexicon for modern standard Arabic
(MSA) and colloquial. AIPSeLEX is manually collected and annotated at sentence
level with semantic orientation (positive or negative). The efforts of manually
building and annotating the lexicon are reported. Moreover, we build a
classifier that extracts idioms and proverbs, phrases from text using n-gram
and similarity measure methods. Finally, several experiments were carried out
on various data, including Arabic tweets and Arabic microblogs (hotel
reservation, product reviews, and TV program comments) from publicly available
Arabic online reviews websites (social media, blogs, forums, e-commerce web
sites) to evaluate the coverage and accuracy of AIPSeLEX.
Semidefinite and Spectral Relaxations for Multi-Label Classification
Authors: Rémi Lajugie, Piotr Bojanowski, Sylvain Arlot, Francis Bach
Date: 2015-06-05 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Structured Prediction

In this paper, we address the problem of multi-label classification. We
consider linear classifiers and propose to learn a prior over the space of
labels to directly leverage the performance of such methods. This prior takes
the form of a quadratic function of the labels and permits to encode both
attractive and repulsive relations between labels. We cast this problem as a
structured prediction one aiming at optimizing either the accuracies of the
predictors or the F 1-score. This leads to an optimization problem closely
related to the max-cut problem, which naturally leads to semidefinite and
spectral relaxations. We show on standard datasets how such a general prior can
improve the performances of multi-label techniques.
High-dimensional Ordinary Least-squares Projection for Screening Variables
Authors: Xiangyu Wang, Chenlei Leng
Date: 2015-06-05 00:00:00
Tasks: Variable Selection, Vocal Bursts Intensity Prediction

Variable selection is a challenging issue in statistical applications when
the number of predictors $p$ far exceeds the number of observations $n$. In
this ultra-high dimensional setting, the sure independence screening (SIS)
procedure was introduced to significantly reduce the dimensionality by
preserving the true model with overwhelming probability, before a refined
second stage analysis. However, the aforementioned sure screening property
strongly relies on the assumption that the important variables in the model
have large marginal correlations with the response, which rarely holds in
reality. To overcome this, we propose a novel and simple screening technique
called the high-dimensional ordinary least-squares projection (HOLP). We show
that HOLP possesses the sure screening property and gives consistent variable
selection without the strong correlation assumption, and has a low
computational complexity. A ridge type HOLP procedure is also discussed.
Simulation study shows that HOLP performs competitively compared to many other
marginal correlation based methods. An application to a mammalian eye disease
data illustrates the attractiveness of HOLP.
Text to 3D Scene Generation with Rich Lexical Grounding
Authors: Angel Chang, Will Monroe, Manolis Savva, Christopher Potts, Christopher D. Manning
Date: 2015-05-23 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Scene Generation, Text to 3D

The ability to map descriptions of scenes to 3D geometric representations has
many applications in areas such as art, education, and robotics. However, prior
work on the text to 3D scene generation task has used manually specified object
categories and language that identifies them. We introduce a dataset of 3D
scenes annotated with natural language descriptions and learn from this data
how to ground textual descriptions to physical objects. Our method successfully
grounds a variety of lexical terms to concrete referents, and we show
quantitatively that our method improves 3D scene generation over previous work
using purely rule-based methods. We evaluate the fidelity and plausibility of
3D scenes generated with our grounding approach through human judgments. To
ease evaluation on this task, we also introduce an automated metric that
strongly correlates with human judgments.
Spectral Learning of Large Structured HMMs for Comparative Epigenomics
Authors: Chicheng Zhang, Jimin Song, Kevin C Chen, Kamalika Chaudhuri
Date: 2015-06-04 00:00:00
Proceeding: NeurIPS 2015 12

We develop a latent variable model and an efficient spectral algorithm
motivated by the recent emergence of very large data sets of chromatin marks
from multiple human cell types. A natural model for chromatin data in one cell
type is a Hidden Markov Model (HMM); we model the relationship between multiple
cell types by connecting their hidden states by a fixed tree of known
structure. The main challenge with learning parameters of such models is that
iterative methods such as EM are very slow, while naive spectral methods result
in time and space complexity exponential in the number of cell types. We
exploit properties of the tree structure of the hidden states to provide
spectral algorithms that are more computationally efficient for current
biological datasets. We provide sample complexity bounds for our algorithm and
evaluate it experimentally on biological data from nine human cell types.
Finally, we show that beyond our specific model, some of our algorithmic ideas
can be applied to other graphical models.
Visual Causal Feature Learning
Authors: Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt
Date: 2014-12-07 00:00:00
Tasks: Active Learning

We provide a rigorous definition of the visual cause of a behavior that is
broadly applicable to the visually driven behavior in humans, animals, neurons,
robots and other perceiving systems. Our framework generalizes standard
accounts of causal learning to settings in which the causal variables need to
be constructed from micro-variables. We prove the Causal Coarsening Theorem,
which allows us to gain causal knowledge from observational data with minimal
experimental effort. The theorem provides a connection to standard inference
techniques in machine learning that identify features of an image that
correlate with, but may not cause, the target behavior. Finally, we propose an
active learning scheme to learn a manipulator function that performs optimal
manipulations on the image to automatically identify the visual cause of a
target behavior. We illustrate our inference and learning algorithms in
experiments based on both synthetic and real data.
Monocular SLAM Supported Object Recognition
Authors: Sudeep Pillai, John Leonard
Date: 2015-06-04 00:00:00
Tasks: Object, Object Recognition

In this work, we develop a monocular SLAM-aware object recognition system
that is able to achieve considerably stronger recognition performance, as
compared to classical object recognition systems that function on a
frame-by-frame basis. By incorporating several key ideas including multi-view
object proposals and efficient feature encoding methods, our proposed system is
able to detect and robustly recognize objects in its environment using a single
RGB camera in near-constant time. Through experiments, we illustrate the
utility of using such a system to effectively detect and recognize objects,
incorporating multiple object viewpoint detections into a unified prediction
hypothesis. The performance of the proposed recognition system is evaluated on
the UW RGB-D Dataset, showing strong recognition performance and scalable
run-time performance compared to current state-of-the-art recognition systems.
The Preference Learning Toolbox
Authors: Vincent E. Farrugia, Héctor P. Martínez, Georgios N. Yannakakis
Date: 2015-06-04 00:00:00
Tasks: BIG-bench Machine Learning, feature selection

Preference learning (PL) is a core area of machine learning that handles
datasets with ordinal relations. As the number of generated data of ordinal
nature is increasing, the importance and role of the PL field becomes central
within machine learning research and practice. This paper introduces an open
source, scalable, efficient and accessible preference learning toolbox that
supports the key phases of the data training process incorporating various
popular data preprocessing, feature selection and preference learning methods.
The Long-Short Story of Movie Description
Authors: Anna Rohrbach, Marcus Rohrbach, Bernt Schiele
Date: 2015-06-04 00:00:00
Tasks: Image Captioning, Image Description, Sentence

Generating descriptions for videos has many applications including assisting
blind people and human-robot interaction. The recent advances in image
captioning as well as the release of large-scale movie description datasets
such as MPII Movie Description allow to study this task in more depth. Many of
the proposed methods for image captioning rely on pre-trained object classifier
CNNs and Long-Short Term Memory recurrent networks (LSTMs) for generating
descriptions. While image description focuses on objects, we argue that it is
important to distinguish verbs, objects, and places in the challenging setting
of movie description. In this work we show how to learn robust visual
classifiers from the weak annotations of the sentence descriptions. Based on
these visual classifiers we learn how to generate a description using an LSTM.
We explore different design choices to build and train the LSTM and achieve the
best performance to date on the challenging MPII-MD dataset. We compare and
analyze our approach and prior work along various dimensions to better
understand the key challenges of the movie description task.
Fine-Grained Visual Categorization via Multi-stage Metric Learning
Authors: Qi Qian, Rong Jin, Shenghuo Zhu, Yuanqing Lin
Date: 2014-02-03 00:00:00
Proceeding: CVPR 2015 6
Tasks: Fine-Grained Visual Categorization, Metric Learning

Fine-grained visual categorization (FGVC) is to categorize objects into
subordinate classes instead of basic classes. One major challenge in FGVC is
the co-occurrence of two issues: 1) many subordinate classes are highly
correlated and are difficult to distinguish, and 2) there exists the large
intra-class variation (e.g., due to object pose). This paper proposes to
explicitly address the above two issues via distance metric learning (DML). DML
addresses the first issue by learning an embedding so that data points from the
same class will be pulled together while those from different classes should be
pushed apart from each other; and it addresses the second issue by allowing the
flexibility that only a portion of the neighbors (not all data points) from the
same class need to be pulled together. However, feature representation of an
image is often high dimensional, and DML is known to have difficulty in dealing
with high dimensional feature vectors since it would require $\mathcal{O}(d^2)$
for storage and $\mathcal{O}(d^3)$ for optimization. To this end, we proposed a
multi-stage metric learning framework that divides the large-scale high
dimensional learning problem to a series of simple subproblems, achieving
$\mathcal{O}(d)$ computational complexity. The empirical study with FVGC
benchmark datasets verifies that our method is both effective and efficient
compared to the state-of-the-art FGVC approaches.
Verification of Generalized Inconsistency-Aware Knowledge and Action Bases (Extended Version)
Authors: Diego Calvanese, Marco Montali, Ario Santoso
Date: 2015-04-30 00:00:00

Knowledge and Action Bases (KABs) have been put forward as a semantically
rich representation of a domain, using a DL KB to account for its static
aspects, and actions to evolve its extensional part over time, possibly
introducing new objects. Recently, KABs have been extended to manage
inconsistency, with ad-hoc verification techniques geared towards specific
semantics. This work provides a twofold contribution along this line of
research. On the one hand, we enrich KABs with a high-level, compact action
language inspired by Golog, obtaining so called Golog-KABs (GKABs). On the
other hand, we introduce a parametric execution semantics for GKABs, so as to
elegantly accomodate a plethora of inconsistency-aware semantics based on the
notion of repair. We then provide several reductions for the verification of
sophisticated first-order temporal properties over inconsistency-aware GKABs,
and show that it can be addressed using known techniques, developed for
standard KABs.
Remarks on pointed digital homotopy
Authors: Laurence Boxer, P. Christopher Staecker
Date: 2015-03-10 00:00:00

We present and explore in detail a pair of digital images with
$c_u$-adjacencies that are homotopic but not pointed homotopic. For two digital
loops $f,g: [0,m]_Z \rightarrow X$ with the same basepoint, we introduce the
notion of {\em tight at the basepoint (TAB)} pointed homotopy, which is more
restrictive than ordinary pointed homotopy and yields some different results.
  We present a variant form of the digital fundamental group. Based on what we
call {\em eventually constant} loops, this version of the fundamental group is
equivalent to that of Boxer (1999), but offers the advantage that eventually
constant maps are often easier to work with than the trivial extensions that
are key to the development of the fundamental group in Boxer (1999) and many
subsequent papers.
  We show that homotopy equivalent digital images have isomorphic fundamental
groups, even when the homotopy equivalence does not preserve the basepoint.
This assertion appeared in Boxer (2005), but there was an error in the proof;
here, we correct the error.