pwc-archive/papers-with-abstracts huggingface dataset:
Harmonic Grammar, Optimality Theory, and Syntax Learnability: An Empirical Exploration of Czech Word Order
Authors: Ann Irvine, Mark Dredze
Date: 2017-02-19 00:00:00

This work presents a systematic theoretical and empirical comparison of the
major algorithms that have been proposed for learning Harmonic and Optimality
Theory grammars (HG and OT, respectively). By comparing learning algorithms, we
are also able to compare the closely related OT and HG frameworks themselves.
Experimental results show that the additional expressivity of the HG framework
over OT affords performance gains in the task of predicting the surface word
order of Czech sentences. We compare the perceptron with the classic Gradual
Learning Algorithm (GLA), which learns OT grammars, as well as the popular
Maximum Entropy model. In addition to showing that the perceptron is
theoretically appealing, our work shows that the performance of the HG model it
learns approaches that of the upper bound in prediction accuracy on a held out
test set and that it is capable of accurately modeling observed variation.
The Absent-Minded Driver Problem Redux
Authors: Subhash Kak
Date: 2017-02-19 00:00:00

This paper reconsiders the problem of the absent-minded driver who must
choose between alternatives with different payoff with imperfect recall and
varying degrees of knowledge of the system. The classical absent-minded driver
problem represents the case with limited information and it has bearing on the
general area of communication and learning, social choice, mechanism design,
auctions, theories of knowledge, belief, and rational agency. Within the
framework of extensive games, this problem has applications to many artificial
intelligence scenarios. It is obvious that the performance of the agent
improves as information available increases. It is shown that a non-uniform
assignment strategy for successive choices does better than a fixed probability
strategy. We consider both classical and quantum approaches to the problem. We
argue that the superior performance of quantum decisions with access to
entanglement cannot be fairly compared to a classical algorithm. If the
cognitive systems of agents are taken to have access to quantum resources, or
have a quantum mechanical basis, then that can be leveraged into superior
performance.
Variational Boosting: Iteratively Refining Posterior Approximations
Authors: Andrew C. Miller, Nicholas Foti, Ryan P. Adams
Date: 2016-11-20 00:00:00
Proceeding: ICML 2017 8
Tasks: Variational Inference

We propose a black-box variational inference method to approximate
intractable distributions with an increasingly rich approximating class. Our
method, termed variational boosting, iteratively refines an existing
variational approximation by solving a sequence of optimization problems,
allowing the practitioner to trade computation time for accuracy. We show how
to expand the variational approximating class by incorporating additional
covariance structure and by introducing new components to form a mixture. We
apply variational boosting to synthetic and real statistical models, and show
that resulting posterior inferences compare favorably to existing posterior
approximation algorithms in both accuracy and efficiency.
Derivative Based Focal Plane Array Nonuniformity Correction
Authors: G. Ness, A. Oved, I. Kakon
Date: 2017-02-19 00:00:00

This paper presents a fast and robust method for fixed pattern noise
nonuniformity correction of infrared focal plane arrays. The proposed method
requires neither shutter nor elaborate calibrations and therefore enables a
real time correction with no interruptions. Based on derivative estimation of
the fixed pattern noise from pixel sized translations of the focal plane array,
the proposed method has the advantages of being invariant to the noise
magnitude and robust to unknown camera and inter-scene movements while being
virtually transparent to the end-user.
Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World
Authors: Sahil Garg, Irina Rish, Guillermo Cecchi, Aurelie Lozano
Date: 2017-01-22 00:00:00
Tasks: Dictionary Learning, Hippocampus, L2 Regularization, Representation Learning

In this paper, we focus on online representation learning in non-stationary
environments which may require continuous adaptation of model architecture. We
propose a novel online dictionary-learning (sparse-coding) framework which
incorporates the addition and deletion of hidden units (dictionary elements),
and is inspired by the adult neurogenesis phenomenon in the dentate gyrus of
the hippocampus, known to be associated with improved cognitive function and
adaptation to new environments. In the online learning setting, where new input
instances arrive sequentially in batches, the neuronal-birth is implemented by
adding new units with random initial weights (random dictionary elements); the
number of new units is determined by the current performance (representation
error) of the dictionary, higher error causing an increase in the birth rate.
Neuronal-death is implemented by imposing l1/l2-regularization (group sparsity)
on the dictionary within the block-coordinate descent optimization at each
iteration of our online alternating minimization scheme, which iterates between
the code and dictionary updates. Finally, hidden unit connectivity adaptation
is facilitated by introducing sparsity in dictionary elements. Our empirical
evaluation on several real-life datasets (images and language) as well as on
synthetic data demonstrates that the proposed approach can considerably
outperform the state-of-art fixed-size (nonadaptive) online sparse coding of
Mairal et al. (2009) in the presence of nonstationary data. Moreover, we
identify certain properties of the data (e.g., sparse inputs with nearly
non-overlapping supports) and of the model (e.g., dictionary sparsity)
associated with such improvements.
Zoom Out-and-In Network with Recursive Training for Object Proposal
Authors: Hongyang Li, Yu Liu, Wanli Ouyang, Xiaogang Wang
Date: 2017-02-19 00:00:00

In this paper, we propose a zoom-out-and-in network for generating object
proposals. We utilize different resolutions of feature maps in the network to
detect object instances of various sizes. Specifically, we divide the anchor
candidates into three clusters based on the scale size and place them on
feature maps of distinct strides to detect small, medium and large objects,
respectively. Deeper feature maps contain region-level semantics which can help
shallow counterparts to identify small objects. Therefore we design a zoom-in
sub-network to increase the resolution of high level features via a
deconvolution operation. The high-level features with high resolution are then
combined and merged with low-level features to detect objects. Furthermore, we
devise a recursive training pipeline to consecutively regress region proposals
at the training stage in order to match the iterative regression at the testing
stage. We demonstrate the effectiveness of the proposed method on ILSVRC DET
and MS COCO datasets, where our algorithm performs better than the
state-of-the-arts in various evaluation metrics. It also increases average
precision by around 2% in the detection system.
CNN as Guided Multi-layer RECOS Transform
Authors: C. -C. Jay Kuo
Date: 2017-01-30 00:00:00
Tasks: Clustering

There is a resurging interest in developing a neural-network-based solution
to the supervised machine learning problem. The convolutional neural network
(CNN) will be studied in this note. To begin with, we introduce a RECOS
transform as a basic building block of CNNs. The "RECOS" is an acronym for
"REctified-COrrelations on a Sphere". It consists of two main concepts: 1) data
clustering on a sphere and 2) rectification. Afterwards, we interpret a CNN as
a network that implements the guided multi-layer RECOS transform with three
highlights. First, we compare the traditional single-layer and modern
multi-layer signal analysis approaches, point out key ingredients that enable
the multi-layer approach, and provide a full explanation to the operating
principle of CNNs. Second, we discuss how guidance is provided by labels
through backpropagation (BP) in the training. Third, we show that a trained
network can be greatly simplified in the testing stage demanding only one-bit
representation for both filter weights and inputs.
Polynomial Time Efficient Construction Heuristics for Vertex Separation Minimization Problem
Authors: Pallavi Jain, Gur Saran, Kamal Srivastava
Date: 2017-02-19 00:00:00

Vertex Separation Minimization Problem (VSMP) consists of finding a layout of
a graph G = (V,E) which minimizes the maximum vertex cut or separation of a
layout. It is an NP-complete problem in general for which metaheuristic
techniques can be applied to find near optimal solution. VSMP has applications
in VLSI design, graph drawing and computer language compiler design. VSMP is
polynomially solvable for grids, trees, permutation graphs and cographs.
Construction heuristics play a very important role in the metaheuristic
techniques as they are responsible for generating initial solutions which lead
to fast convergence. In this paper, we have proposed three construction
heuristics H1, H2 and H3 and performed experiments on Grids, Small graphs,
Trees and Harwell Boeing graphs, totaling 248 instances of graphs. Experiments
reveal that H1, H2 and H3 are able to achieve best results for 88.71%, 43.5%
and 37.1% of the total instances respectively while the best construction
heuristic in the literature achieves the best solution for 39.9% of the total
instances. We have also compared the results with the state-of-the-art
metaheuristic GVNS and observed that the proposed construction heuristics
improves the results for some of the input instances. It was found that GVNS
obtained best results for 82.9% instances of all input instances and the
heuristic H1 obtained best results for 82.3% of all input instances.
Cross Device Matching for Online Advertising with Neural Feature Ensembles : First Place Solution at CIKM Cup 2016
Authors: Minh C. Phan, Yi Tay, Tuan-Anh Nguyen Pham
Date: 2016-10-23 00:00:00
Tasks: Classification, General Classification

We describe the 1st place winning approach for the CIKM Cup 2016 Challenge.
In this paper, we provide an approach to reasonably identify same users across
multiple devices based on browsing logs. Our approach regards a candidate
ranking problem as pairwise classification and utilizes an unsupervised neural
feature ensemble approach to learn latent features of users. Combined with
traditional hand crafted features, each user pair feature is fed into a
supervised classifier in order to perform pairwise classification. Lastly, we
propose supervised and unsupervised inference techniques.
Non-negative Tensor Factorization for Human Behavioral Pattern Mining in Online Games
Authors: Anna Sapienza, Alessandro Bessi, Emilio Ferrara
Date: 2017-02-19 00:00:00
Tasks: Tensor Decomposition

Multiplayer online battle arena has become a popular game genre. It also
received increasing attention from our research community because they provide
a wealth of information about human interactions and behaviors. A major problem
is extracting meaningful patterns of activity from this type of data, in a way
that is also easy to interpret. Here, we propose to exploit tensor
decomposition techniques, and in particular Non-negative Tensor Factorization,
to discover hidden correlated behavioral patterns of play in a popular game:
League of Legends. We first collect the entire gaming history of a group of
about one thousand players, totaling roughly $100K$ matches. By applying our
methodological framework, we then separate players into groups that exhibit
similar features and playing strategies, as well as similar temporal
trajectories, i.e., behavioral progressions over the course of their gaming
history: this will allow us to investigate how players learn and improve their
skills.
CityPersons: A Diverse Dataset for Pedestrian Detection
Authors: Shanshan Zhang, Rodrigo Benenson, Bernt Schiele
Date: 2017-02-19 00:00:00
Proceeding: CVPR 2017 7
Tasks: Diversity, Pedestrian Detection

Convnets have enabled significant progress in pedestrian detection recently,
but there are still open questions regarding suitable architectures and
training data. We revisit CNN design and point out key adaptations, enabling
plain FasterRCNN to obtain state-of-the-art results on the Caltech dataset.
  To achieve further improvement from more and better data, we introduce
CityPersons, a new set of person annotations on top of the Cityscapes dataset.
The diversity of CityPersons allows us for the first time to train one single
CNN model that generalizes well over multiple benchmarks. Moreover, with
additional training with CityPersons, we obtain top results using FasterRCNN on
Caltech, improving especially for more difficult cases (heavy occlusion and
small scale) and providing higher localization quality.
An Adaptivity Hierarchy Theorem for Property Testing
Authors: Clement Canonne, Tom Gur
Date: 2017-02-19 00:00:00

Adaptivity is known to play a crucial role in property testing. In
particular, there exist properties for which there is an exponential gap
between the power of \emph{adaptive} testing algorithms, wherein each query may
be determined by the answers received to prior queries, and their
\emph{non-adaptive} counterparts, in which all queries are independent of
answers obtained from previous queries.
  In this work, we investigate the role of adaptivity in property testing at a
finer level. We first quantify the degree of adaptivity of a testing algorithm
by considering the number of "rounds of adaptivity" it uses. More accurately,
we say that a tester is $k$-(round) adaptive if it makes queries in $k+1$
rounds, where the queries in the $i$'th round may depend on the answers
obtained in the previous $i-1$ rounds. Then, we ask the following question:
  Does the power of testing algorithms smoothly grow with the number of rounds
of adaptivity?
  We provide a positive answer to the foregoing question by proving an
adaptivity hierarchy theorem for property testing. Specifically, our main
result shows that for every $n\in \mathbb{N}$ and $0 \le k \le n^{0.99}$ there
exists a property $\mathcal{P}_{n,k}$ of functions for which (1) there exists a
$k$-adaptive tester for $\mathcal{P}_{n,k}$ with query complexity
$\tilde{O}(k)$, yet (2) any $(k-1)$-adaptive tester for $\mathcal{P}_{n,k}$
must make $\Omega(n)$ queries. In addition, we show that such a qualitative
adaptivity hierarchy can be witnessed for testing natural properties of graphs.
Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes
Authors: Lunjia Hu, Ruihan Wu, Tianhong Li, Li-Wei Wang
Date: 2017-02-18 00:00:00

In this work we study the quantitative relation between the recursive
teaching dimension (RTD) and the VC dimension (VCD) of concept classes of
finite sizes. The RTD of a concept class $\mathcal C \subseteq \{0, 1\}^n$,
introduced by Zilles et al. (2011), is a combinatorial complexity measure
characterized by the worst-case number of examples necessary to identify a
concept in $\mathcal C$ according to the recursive teaching model.
  For any finite concept class $\mathcal C \subseteq \{0,1\}^n$ with
$\mathrm{VCD}(\mathcal C)=d$, Simon & Zilles (2015) posed an open problem
$\mathrm{RTD}(\mathcal C) = O(d)$, i.e., is RTD linearly upper bounded by VCD?
Previously, the best known result is an exponential upper bound
$\mathrm{RTD}(\mathcal C) = O(d \cdot 2^d)$, due to Chen et al. (2016). In this
paper, we show a quadratic upper bound: $\mathrm{RTD}(\mathcal C) = O(d^2)$,
much closer to an answer to the open problem. We also discuss the challenges in
fully solving the problem.
Robust Shape Registration using Fuzzy Correspondences
Authors: Abhishek Kolagunda, Scott Sorensen, Philip Saponaro, Wayne Treible, Chandra Kambhamettu
Date: 2017-02-18 00:00:00

Shape registration is the process of aligning one 3D model to another. Most
previous methods to align shapes with no known correspondences attempt to solve
for both the transformation and correspondences iteratively. We present a shape
registration approach that solves for the transformation using fuzzy
correspondences to maximize the overlap between the given shape and the target
shape. A coarse to fine approach with Levenberg-Marquardt method is used for
optimization. Real and synthetic experiments show our approach is robust and
outperforms other state of the art methods when point clouds are noisy, sparse,
and have non-uniform density. Experiments show our method is more robust to
initialization and can handle larger scale changes and rotation than other
methods. We also show that the approach can be used for 2D-3D alignment via
ray-point alignment.
The Game Imitation: Deep Supervised Convolutional Networks for Quick Video Game AI
Authors: Zhao Chen, Darvin Yi
Date: 2017-02-18 00:00:00
Tasks: Decision Making, Imitation Learning, Q-Learning

We present a vision-only model for gaming AI which uses a late integration
deep convolutional network architecture trained in a purely supervised
imitation learning context. Although state-of-the-art deep learning models for
video game tasks generally rely on more complex methods such as deep-Q
learning, we show that a supervised model which requires substantially fewer
resources and training time can already perform well at human reaction speeds
on the N64 classic game Super Smash Bros. We frame our learning task as a
30-class classification problem, and our CNN model achieves 80% top-1 and 95%
top-3 validation accuracy. With slight test-time fine-tuning, our model is also
competitive during live simulation with the highest-level AI built into the
game. We will further show evidence through network visualizations that the
network is successfully leveraging temporal information during inference to aid
in decision making. Our work demonstrates that supervised CNN models can
provide good performance in challenging policy prediction tasks while being
significantly simpler and more lightweight than alternatives.
On Loss Functions for Deep Neural Networks in Classification
Authors: Katarzyna Janocha, Wojciech Marian Czarnecki
Date: 2017-02-18 00:00:00
Tasks: Classification, General Classification

Deep neural networks are currently among the most commonly used classifiers.
Despite easily achieving very good performance, one of the best selling points
of these models is their modular design - one can conveniently adapt their
architecture to specific needs, change connectivity patterns, attach
specialised layers, experiment with a large amount of activation functions,
normalisation schemes and many others. While one can find impressively wide
spread of various configurations of almost every aspect of the deep nets, one
element is, in authors' opinion, underrepresented - while solving
classification problems, vast majority of papers and applications simply use
log loss. In this paper we try to investigate how particular choices of loss
functions affect deep models and their learning dynamics, as well as resulting
classifiers robustness to various effects. We perform experiments on classical
datasets, as well as provide some additional, theoretical insights into the
problem. In particular we show that L1 and L2 losses are, quite surprisingly,
justified classification objectives for deep nets, by providing probabilistic
interpretation in terms of expected misclassification. We also introduce two
losses which are not typically used as deep nets objectives and show that they
are viable alternatives to the existing ones.
Learning to predict where to look in interactive environments using deep recurrent q-learning
Authors: Sajad Mousavi, Michael Schukat, Enda Howley, Ali Borji, Nasser Mozayani
Date: 2016-12-17 00:00:00
Tasks: Atari Games, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Bottom-Up (BU) saliency models do not perform well in complex interactive
environments where humans are actively engaged in tasks (e.g., sandwich making
and playing the video games). In this paper, we leverage Reinforcement Learning
(RL) to highlight task-relevant locations of input frames. We propose a soft
attention mechanism combined with the Deep Q-Network (DQN) model to teach an RL
agent how to play a game and where to look by focusing on the most pertinent
parts of its visual input. Our evaluations on several Atari 2600 games show
that the soft attention based model could predict fixation locations
significantly better than bottom-up models such as Itti-Kochs saliency and
Graph-Based Visual Saliency (GBVS) models.
Causal inference via algebraic geometry: feasibility tests for functional causal structures with two binary observed variables
Authors: Ciarán M. Lee, Robert W. Spekkens
Date: 2015-06-12 00:00:00
Tasks: Causal Inference

We provide a scheme for inferring causal relations from uncontrolled
statistical data based on tools from computational algebraic geometry, in
particular, the computation of Groebner bases. We focus on causal structures
containing just two observed variables, each of which is binary. We consider
the consequences of imposing different restrictions on the number and
cardinality of latent variables and of assuming different functional
dependences of the observed variables on the latent ones (in particular, the
noise need not be additive). We provide an inductive scheme for classifying
functional causal structures into distinct observational equivalence classes.
For each observational equivalence class, we provide a procedure for deriving
constraints on the joint distribution that are necessary and sufficient
conditions for it to arise from a model in that class. We also demonstrate how
this sort of approach provides a means of determining which causal parameters
are identifiable and how to solve for these. Prospects for expanding the scope
of our scheme, in particular to the problem of quantum causal inference, are
also discussed.
A Stylometric Inquiry into Hyperpartisan and Fake News
Authors: Martin Potthast, Johannes Kiesel, Kevin Reinartz, Janek Bevendorff, Benno Stein
Date: 2017-02-18 00:00:00
Proceeding: ACL 2018 7
Tasks: Articles, Authorship Verification, Fake News Detection, Meta-Learning

This paper reports on a writing style analysis of hyperpartisan (i.e.,
extremely one-sided) news in connection to fake news. It presents a large
corpus of 1,627 articles that were manually fact-checked by professional
journalists from BuzzFeed. The articles originated from 9 well-known political
publishers, 3 each from the mainstream, the hyperpartisan left-wing, and the
hyperpartisan right-wing. In sum, the corpus contains 299 fake news, 97% of
which originated from hyperpartisan publishers.
  We propose and demonstrate a new way of assessing style similarity between
text categories via Unmasking---a meta-learning approach originally devised for
authorship verification---, revealing that the style of left-wing and
right-wing news have a lot more in common than any of the two have with the
mainstream. Furthermore, we show that hyperpartisan news can be discriminated
well by its style from the mainstream (F1=0.78), as can be satire from both
(F1=0.81). Unsurprisingly, style-based fake news detection does not live up to
scratch (F1=0.46). Nevertheless, the former results are important to implement
pre-screening for fake news detectors.