pwc-archive/papers-with-abstracts huggingface dataset:
Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data
Authors: Lisa Anne Hendricks, Subhashini Venugopalan, Marcus Rohrbach, Raymond Mooney, Kate Saenko, Trevor Darrell
Date: 2015-11-17 00:00:00
Proceeding: CVPR 2016 6
Tasks: Image Captioning, Novel Concepts, Object Recognition, Sentence, Video Captioning

While recent deep neural network models have achieved promising results on
the image captioning task, they rely largely on the availability of corpora
with paired image and sentence captions to describe objects in context. In this
work, we propose the Deep Compositional Captioner (DCC) to address the task of
generating descriptions of novel objects which are not present in paired
image-sentence datasets. Our method achieves this by leveraging large object
recognition datasets and external text corpora and by transferring knowledge
between semantically similar concepts. Current deep caption models can only
describe objects contained in paired image-sentence corpora, despite the fact
that they are pre-trained with large object recognition datasets, namely
ImageNet. In contrast, our model can compose sentences that describe novel
objects and their interactions with other objects. We demonstrate our model's
ability to describe novel concepts by empirically evaluating its performance on
MSCOCO and show qualitative results on ImageNet images of objects for which no
paired image-caption data exist. Further, we extend our approach to generate
descriptions of objects in video clips. Our results show that DCC has distinct
advantages over existing image and video captioning approaches for generating
descriptions of new objects in context.
Quantum cognition beyond Hilbert space II: Applications
Authors: Diederik Aerts, Lyneth Beltran, Massimiliano Sassoli de Bianchi, Sandro Sozzo, Tomas Veloz
Date: 2016-04-27 00:00:00

The research on human cognition has recently benefited from the use of the
mathematical formalism of quantum theory in Hilbert space. However, cognitive
situations exist which indicate that the Hilbert space structure, and the
associated Born rule, would be insufficient to provide a satisfactory modeling
of the collected data, so that one needs to go beyond Hilbert space. In Part I
of this paper we follow this direction and present a general tension-reduction
(GTR) model, in the ambit of an operational and realistic framework for human
cognition. In this Part II we apply this non-Hilbertian quantum-like model to
faithfully reproduce the probabilities of the 'Clinton/Gore' and 'Rose/Jackson'
experiments on question order effects. We also explain why the GTR-model is
needed if one wants to deal, in a fully consistent way, with response
replicability and unpacking effects.
Quantum Cognition Beyond Hilbert Space I: Fundamentals
Authors: Diederik Aerts, Lyneth Beltran, Massimiliano Sassoli de Bianchi, Sandro Sozzo, Tomas Veloz
Date: 2016-04-27 00:00:00

The formalism of quantum theory in Hilbert space has been applied with
success to the modeling and explanation of several cognitive phenomena, whereas
traditional cognitive approaches were problematical. However, this 'quantum
cognition paradigm' was recently challenged by its proven impossibility to
simultaneously model 'question order effects' and 'response replicability'. In
Part I of this paper we describe sequential dichotomic measurements within an
operational and realistic framework for human cognition elaborated by
ourselves, and represent them in a quantum-like 'extended Bloch representation'
where the Born rule of quantum probability does not necessarily hold. In Part
II we apply this mathematical framework to successfully model question order
effects, response replicability and unpacking effects, thus opening the way
toward quantum cognition beyond Hilbert space.
Multiview Differential Geometry of Curves
Authors: Ricardo Fabbri, Benjamin Kimia
Date: 2016-04-27 00:00:00
Tasks: 3D Reconstruction

The field of multiple view geometry has seen tremendous progress in
reconstruction and calibration due to methods for extracting reliable point
features and key developments in projective geometry. Point features, however,
are not available in certain applications and result in unstructured point
cloud reconstructions. General image curves provide a complementary feature
when keypoints are scarce, and result in 3D curve geometry, but face challenges
not addressed by the usual projective geometry of points and algebraic curves.
We address these challenges by laying the theoretical foundations of a
framework based on the differential geometry of general curves, including
stationary curves, occluding contours, and non-rigid curves, aiming at stereo
correspondence, camera estimation (including calibration, pose, and multiview
epipolar geometry), and 3D reconstruction given measured image curves. By
gathering previous results into a cohesive theory, novel results were made
possible, yielding three contributions. First we derive the differential
geometry of an image curve (tangent, curvature, curvature derivative) from that
of the underlying space curve (tangent, curvature, curvature derivative,
torsion). Second, we derive the differential geometry of a space curve from
that of two corresponding image curves. Third, the differential motion of an
image curve is derived from camera motion and the differential geometry and
motion of the space curve. The availability of such a theory enables novel
curve-based multiview reconstruction and camera estimation systems to augment
existing point-based approaches. This theory has been used to reconstruct a "3D
curve sketch", to determine camera pose from local curve geometry, and
tracking; other developments are underway.
Scalable Discrete Sampling as a Multi-Armed Bandit Problem
Authors: Yutian Chen, Zoubin Ghahramani
Date: 2015-06-30 00:00:00
Tasks: Bayesian Inference, Multi-Armed Bandits

Drawing a sample from a discrete distribution is one of the building
components for Monte Carlo methods. Like other sampling algorithms, discrete
sampling suffers from the high computational burden in large-scale inference
problems. We study the problem of sampling a discrete random variable with a
high degree of dependency that is typical in large-scale Bayesian inference and
graphical models, and propose an efficient approximate solution with a
subsampling approach. We make a novel connection between the discrete sampling
and Multi-Armed Bandits problems with a finite reward population and provide
three algorithms with theoretical guarantees. Empirical evaluations show the
robustness and efficiency of the approximate algorithms in both synthetic and
real-world large-scale problems.
Propositional Abduction with Implicit Hitting Sets
Authors: Alexey Ignatiev, Antonio Morgado, Joao Marques-Silva
Date: 2016-04-27 00:00:00

Logic-based abduction finds important applications in artificial intelligence
and related areas. One application example is in finding explanations for
observed phenomena. Propositional abduction is a restriction of abduction to
the propositional domain, and complexity-wise is in the second level of the
polynomial hierarchy. Recent work has shown that exploiting implicit hitting
sets and propositional satisfiability (SAT) solvers provides an efficient
approach for propositional abduction. This paper investigates this earlier work
and proposes a number of algorithmic improvements. These improvements are shown
to yield exponential reductions in the number of SAT solver calls. More
importantly, the experimental results show significant performance improvements
compared to the the best approaches for propositional abduction.
Diving deeper into mentee networks
Authors: Ragav Venkatesan, Baoxin Li
Date: 2016-04-27 00:00:00

Modern computer vision is all about the possession of powerful image
representations. Deeper and deeper convolutional neural networks have been
built using larger and larger datasets and are made publicly available. A large
swath of computer vision scientists use these pre-trained networks with varying
degrees of successes in various tasks. Even though there is tremendous success
in copying these networks, the representational space is not learnt from the
target dataset in a traditional manner. One of the reasons for opting to use a
pre-trained network over a network learnt from scratch is that small datasets
provide less supervision and require meticulous regularization, smaller and
careful tweaking of learning rates to even achieve stable learning without
weight explosion. It is often the case that large deep networks are not
portable, which necessitates the ability to learn mid-sized networks from
scratch.
  In this article, we dive deeper into training these mid-sized networks on
small datasets from scratch by drawing additional supervision from a large
pre-trained network. Such learning also provides better generalization
accuracies than networks trained with common regularization techniques such as
l2, l1 and dropouts. We show that features learnt thus, are more general than
those learnt independently. We studied various characteristics of such networks
and found some interesting behaviors.
Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle
Authors: Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg
Date: 2015-02-23 00:00:00

While machine learning has proven to be a powerful data-driven solution to
many real-life problems, its use in sensitive domains has been limited due to
privacy concerns. A popular approach known as **differential privacy** offers
provable privacy guarantees, but it is often observed in practice that it could
substantially hamper learning accuracy. In this paper we study the learnability
(whether a problem can be learned by any algorithm) under Vapnik's general
learning setting with differential privacy constraint, and reveal some
intricate relationships between privacy, stability and learnability.
  In particular, we show that a problem is privately learnable **if an only
if** there is a private algorithm that asymptotically minimizes the empirical
risk (AERM). In contrast, for non-private learning AERM alone is not sufficient
for learnability. This result suggests that when searching for private learning
algorithms, we can restrict the search to algorithms that are AERM. In light of
this, we propose a conceptual procedure that always finds a universally
consistent algorithm whenever the problem is learnable under privacy
constraint. We also propose a generic and practical algorithm and show that
under very general conditions it privately learns a wide class of learning
problems. Lastly, we extend some of the results to the more practical
$(\epsilon,\delta)$-differential privacy and establish the existence of a
phase-transition on the class of problems that are approximately privately
learnable with respect to how small $\delta$ needs to be.
Interpretable Deep Neural Networks for Single-Trial EEG Classification
Authors: Irene Sturm, Sebastian Bach, Wojciech Samek, Klaus-Robert Müller
Date: 2016-04-27 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification, Specificity, Subject Transfer

Background: In cognitive neuroscience the potential of Deep Neural Networks
(DNNs) for solving complex classification tasks is yet to be fully exploited.
The most limiting factor is that DNNs as notorious 'black boxes' do not provide
insight into neurophysiological phenomena underlying a decision. Layer-wise
Relevance Propagation (LRP) has been introduced as a novel method to explain
individual network decisions. New Method: We propose the application of DNNs
with LRP for the first time for EEG data analysis. Through LRP the single-trial
DNN decisions are transformed into heatmaps indicating each data point's
relevance for the outcome of the decision. Results: DNN achieves classification
accuracies comparable to those of CSP-LDA. In subjects with low performance
subject-to-subject transfer of trained DNNs can improve the results. The
single-trial LRP heatmaps reveal neurophysiologically plausible patterns,
resembling CSP-derived scalp maps. Critically, while CSP patterns represent
class-wise aggregated information, LRP heatmaps pinpoint neural patterns to
single time points in single trials. Comparison with Existing Method(s): We
compare the classification performance of DNNs to that of linear CSP-LDA on two
data sets related to motor-imaginery BCI. Conclusion: We have demonstrated that
DNN is a powerful non-linear tool for EEG analysis. With LRP a new quality of
high-resolution assessment of neural activity can be reached. LRP is a
potential remedy for the lack of interpretability of DNNs that has limited
their utility in neuroscientific applications. The extreme specificity of the
LRP-derived heatmaps opens up new avenues for investigating neural activity
underlying complex perception or decision-related processes.
Classifying Syntactic Regularities for Hundreds of Languages
Authors: Reed Coke, Ben King, Dragomir Radev
Date: 2016-03-25 00:00:00
Tasks: General Classification, regression

This paper presents a comparison of classification methods for linguistic
typology for the purpose of expanding an extensive, but sparse language
resource: the World Atlas of Language Structures (WALS) (Dryer and Haspelmath,
2013). We experimented with a variety of regression and nearest-neighbor
methods for use in classification over a set of 325 languages and six syntactic
rules drawn from WALS. To classify each rule, we consider the typological
features of the other five rules; linguistic features extracted from a
word-aligned Bible in each language; and genealogical features (genus and
family) of each language. In general, we find that propagating the majority
label among all languages of the same genus achieves the best accuracy in label
pre- diction. Following this, a logistic regression model that combines
typological and linguistic features offers the next best performance.
Interestingly, this model actually outperforms the majority labels among all
languages of the same family.
Influence Maximization with Bandits
Authors: Sharan Vaswani, Laks. V. S. Lakshmanan, Mark Schmidt
Date: 2015-02-27 00:00:00

We consider the problem of \emph{influence maximization}, the problem of
maximizing the number of people that become aware of a product by finding the
`best' set of `seed' users to expose the product to. Most prior work on this
topic assumes that we know the probability of each user influencing each other
user, or we have data that lets us estimate these influences. However, this
information is typically not initially available or is difficult to obtain. To
avoid this assumption, we adopt a combinatorial multi-armed bandit paradigm
that estimates the influence probabilities as we sequentially try different
seed sets. We establish bounds on the performance of this procedure under the
existing edge-level feedback as well as a novel and more realistic node-level
feedback. Beyond our theoretical results, we describe a practical
implementation and experimentally demonstrate its efficiency and effectiveness
on four real datasets.
Extracting Temporal and Causal Relations between Events
Authors: Paramita Mirza
Date: 2016-04-27 00:00:00
Proceeding: ACL 2014 6
Tasks: Relation, Relation Extraction, Temporal Relation Extraction, Timeline Summarization, Word Embeddings

Structured information resulting from temporal information processing is
crucial for a variety of natural language processing tasks, for instance to
generate timeline summarization of events from news documents, or to answer
temporal/causal-related questions about some events. In this thesis we present
a framework for an integrated temporal and causal relation extraction system.
We first develop a robust extraction component for each type of relations, i.e.
temporal order and causality. We then combine the two extraction components
into an integrated relation extraction system, CATENA---CAusal and Temporal
relation Extraction from NAtural language texts---, by utilizing the
presumption about event precedence in causality, that causing events must
happened BEFORE resulting events. Several resources and techniques to improve
our relation extraction systems are also discussed, including word embeddings
and training data expansion. Finally, we report our adaptation efforts of
temporal information processing for languages other than English, namely
Italian and Indonesian.
Global-Local Face Upsampling Network
Authors: Oncel Tuzel, Yuichi Taguchi, John R. Hershey
Date: 2016-03-23 00:00:00
Tasks: Face Hallucination, Face Reconstruction, Hallucination, Super-Resolution

Face hallucination, which is the task of generating a high-resolution face
image from a low-resolution input image, is a well-studied problem that is
useful in widespread application areas. Face hallucination is particularly
challenging when the input face resolution is very low (e.g., 10 x 12 pixels)
and/or the image is captured in an uncontrolled setting with large pose and
illumination variations. In this paper, we revisit the algorithm introduced in
[1] and present a deep interpretation of this framework that achieves
state-of-the-art under such challenging scenarios. In our deep network
architecture the global and local constraints that define a face can be
efficiently modeled and learned end-to-end using training data. Conceptually
our network design can be partitioned into two sub-networks: the first one
implements the holistic face reconstruction according to global constraints,
and the second one enhances face-specific details and enforces local patch
statistics. We optimize the deep network using a new loss function for
super-resolution that combines reconstruction error with a learned face quality
measure in adversarial setting, producing improved visual results. We conduct
extensive experiments in both controlled and uncontrolled setups and show that
our algorithm improves the state of the art both numerically and visually.
An ABC interpretation of the multiple auxiliary variable method
Authors: Dennis Prangle, Richard G. Everitt
Date: 2016-04-27 00:00:00

We show that the auxiliary variable method (M{\o}ller et al., 2006; Murray et
al., 2006) for inference of Markov random fields can be viewed as an
approximate Bayesian computation method for likelihood estimation.
Detecting Violence in Video using Subclasses
Authors: Xirong Li, Yujia Huo, Jieping Xu, Qin Jin
Date: 2016-04-27 00:00:00

This paper attacks the challenging problem of violence detection in videos.
Different from existing works focusing on combining multi-modal features, we go
one step further by adding and exploiting subclasses visually related to
violence. We enrich the MediaEval 2015 violence dataset by \emph{manually}
labeling violence videos with respect to the subclasses. Such fine-grained
annotations not only help understand what have impeded previous efforts on
learning to fuse the multi-modal features, but also enhance the generalization
ability of the learned fusion to novel test data. The new subclass based
solution, with AP of 0.303 and P100 of 0.55 on the MediaEval 2015 test set,
outperforms several state-of-the-art alternatives. Notice that our solution
does not require fine-grained annotations on the test set, so it can be
directly applied on novel and fully unlabeled videos. Interestingly, our study
shows that motion related features, though being essential part in previous
systems, are dispensable.
Expressive Completeness of Existential Rule Languages for Ontology-based Query Answering
Authors: Heng Zhang, Yan Zhang, Jia-Huai You
Date: 2016-04-18 00:00:00

Existential rules, also known as data dependencies in Databases, have been
recently rediscovered as a promising family of languages for Ontology-based
Query Answering. In this paper, we prove that disjunctive embedded dependencies
exactly capture the class of recursively enumerable ontologies in
Ontology-based Conjunctive Query Answering (OCQA). Our expressive completeness
result does not rely on any built-in linear order on the database. To establish
the expressive completeness, we introduce a novel semantic definition for OCQA
ontologies. We also show that neither the class of disjunctive tuple-generating
dependencies nor the class of embedded dependencies is expressively complete
for recursively enumerable OCQA ontologies.
Selecting the Selection
Authors: Giles Reger, Martin Suda, Andrei Voronkov, Krystof Hoder
Date: 2016-04-27 00:00:00

Modern saturation-based Automated Theorem Provers typically implement the
superposition calculus for reasoning about first-order logic with or without
equality. Practical implementations of this calculus use a variety of literal
selections and term orderings to tame the growth of the search space and help
steer proof search. This paper introduces the notion of lookahead selection
that estimates (looks ahead) the effect on the search space of selecting a
literal. There is also a case made for the use of incomplete selection
functions that attempt to restrict the search space instead of satisfying some
completeness criteria. Experimental evaluation in the \Vampire\ theorem prover
shows that both lookahead selection and incomplete selection significantly
contribute to solving hard problems unsolvable by other methods.
Incremental Reconstruction of Urban Environments by Edge-Points Delaunay Triangulation
Authors: Andrea Romanoni, Matteo Matteucci
Date: 2016-04-21 00:00:00

Urban reconstruction from a video captured by a surveying vehicle constitutes
a core module of automated mapping. When computational power represents a
limited resource and, a detailed map is not the primary goal, the
reconstruction can be performed incrementally, from a monocular video, carving
a 3D Delaunay triangulation of sparse points; this allows online incremental
mapping for tasks such as traversability analysis or obstacle avoidance. To
exploit the sharp edges of urban landscape, we propose to use a Delaunay
triangulation of Edge-Points, which are the 3D points corresponding to image
edges. These points constrain the edges of the 3D Delaunay triangulation to
real-world edges. Besides the use of the Edge-Points, a second contribution of
this paper is the Inverse Cone Heuristic that preemptively avoids the creation
of artifacts in the reconstructed manifold surface. We force the reconstruction
of a manifold surface since it makes it possible to apply computer graphics or
photometric refinement algorithms to the output mesh. We evaluated our approach
on four real sequences of the public available KITTI dataset by comparing the
incremental reconstruction against Velodyne measurements.