pwc-archive/papers-with-abstracts huggingface dataset:
Modeling the Ellsberg Paradox by Argument Strength
Authors: Niki Pfeifer, Hanna Pankka
Date: 2017-03-09 00:00:00

We present a formal measure of argument strength, which combines the ideas
that conclusions of strong arguments are (i) highly probable and (ii) their
uncertainty is relatively precise. Likewise, arguments are weak when their
conclusion probability is low or when it is highly imprecise. We show how the
proposed measure provides a new model of the Ellsberg paradox. Moreover, we
further substantiate the psychological plausibility of our approach by an
experiment (N = 60). The data show that the proposed measure predicts human
inferences in the original Ellsberg task and in corresponding argument strength
tasks. Finally, we report qualitative data taken from structured interviews on
folk psychological conceptions on what argument strength means.
Compressed Sensing using Generative Models
Authors: Ashish Bora, Ajil Jalal, Eric Price, Alexandros G. Dimakis
Date: 2017-03-09 00:00:00
Proceeding: ICML 2017 8
Tasks: compressed sensing

The goal of compressed sensing is to estimate a vector from an
underdetermined system of noisy linear measurements, by making use of prior
knowledge on the structure of vectors in the relevant domain. For almost all
results in this literature, the structure is represented by sparsity in a
well-chosen basis. We show how to achieve guarantees similar to standard
compressed sensing but without employing sparsity at all. Instead, we suppose
that vectors lie near the range of a generative model $G: \mathbb{R}^k \to
\mathbb{R}^n$. Our main theorem is that, if $G$ is $L$-Lipschitz, then roughly
$O(k \log L)$ random Gaussian measurements suffice for an $\ell_2/\ell_2$
recovery guarantee. We demonstrate our results using generative models from
published variational autoencoder and generative adversarial networks. Our
method can use $5$-$10$x fewer measurements than Lasso for the same accuracy.
Prior-based Hierarchical Segmentation Highlighting Structures of Interest
Authors: Amin Fehri, Santiago Velasco-Forero, Fernand Meyer
Date: 2017-03-09 00:00:00
Tasks: Image Segmentation, Position, Segmentation, Semantic Segmentation

Image segmentation is the process of partitioning an image into a set of
meaningful regions according to some criteria. Hierarchical segmentation has
emerged as a major trend in this regard as it favors the emergence of important
regions at different scales. On the other hand, many methods allow us to have
prior information on the position of structures of interest in the images. In
this paper, we present a versatile hierarchical segmentation method that takes
into account any prior spatial information and outputs a hierarchical
segmentation that emphasizes the contours or regions of interest while
preserving the important structures in the image. Several applications are
presented that illustrate the method versatility and efficiency.
Embedding Tarskian Semantics in Vector Spaces
Authors: Taisuke Sato
Date: 2017-03-09 00:00:00

We propose a new linear algebraic approach to the computation of Tarskian
semantics in logic. We embed a finite model M in first-order logic with N
entities in N-dimensional Euclidean space R^N by mapping entities of M to N
dimensional one-hot vectors and k-ary relations to order-k adjacency tensors
(multi-way arrays). Second given a logical formula F in prenex normal form, we
compile F into a set Sigma_F of algebraic formulas in multi-linear algebra with
a nonlinear operation. In this compilation, existential quantifiers are
compiled into a specific type of tensors, e.g., identity matrices in the case
of quantifying two occurrences of a variable. It is shown that a systematic
evaluation of Sigma_F in R^N gives the truth value, 1(true) or 0(false), of F
in M. Based on this framework, we also propose an unprecedented way of
computing the least models defined by Datalog programs in linear spaces via
matrix equations and empirically show its effectiveness compared to
state-of-the-art approaches.
Segmenting Dermoscopic Images
Authors: Mario Rosario Guarracino, Lucia Maddalena
Date: 2017-03-09 00:00:00
Tasks: Lesion Segmentation, Segmentation

We propose an automatic algorithm, named SDI, for the segmentation of skin
lesions in dermoscopic images, articulated into three main steps: selection of
the image ROI, selection of the segmentation band, and segmentation. We present
extensive experimental results achieved by the SDI algorithm on the lesion
segmentation dataset made available for the ISIC 2017 challenge on Skin Lesion
Analysis Towards Melanoma Detection, highlighting its advantages and
disadvantages.
Distribution-Specific Hardness of Learning Neural Networks
Authors: Ohad Shamir
Date: 2016-09-05 00:00:00

Although neural networks are routinely and successfully trained in practice
using simple gradient-based methods, most existing theoretical results are
negative, showing that learning such networks is difficult, in a worst-case
sense over all data distributions. In this paper, we take a more nuanced view,
and consider whether specific assumptions on the "niceness" of the input
distribution, or "niceness" of the target function (e.g. in terms of
smoothness, non-degeneracy, incoherence, random choice of parameters etc.), are
sufficient to guarantee learnability using gradient-based methods. We provide
evidence that neither class of assumptions alone is sufficient: On the one
hand, for any member of a class of "nice" target functions, there are difficult
input distributions. On the other hand, we identify a family of simple target
functions, which are difficult to learn even if the input distribution is
"nice". To prove our results, we develop some tools which may be of independent
interest, such as extending Fourier-based hardness techniques developed in the
context of statistical queries \cite{blum1994weakly}, from the Boolean cube to
Euclidean space and to more general classes of functions.
Cross-validation
Authors: Sylvain Arlot
Date: 2017-03-09 00:00:00

This text is a survey on cross-validation. We define all classical
cross-validation procedures, and we study their properties for two different
goals: estimating the risk of a given estimator, and selecting the best
estimator among a given family. For the risk estimation problem, we compute the
bias (which can also be corrected) and the variance of cross-validation
methods. For estimator selection, we first provide a first-order analysis
(based on expectations). Then, we explain how to take into account second-order
terms (from variance computations, and by taking into account the usefulness of
overpenalization). This allows, in the end, to provide some guidelines for
choosing the best cross-validation method for a given learning problem.
Behavior-based Navigation of Mobile Robot in Unknown Environments Using Fuzzy Logic and Multi-Objective Optimization
Authors: Thi Thanh Van Nguyen, Manh Duong Phung, Quang Vinh Tran
Date: 2017-03-09 00:00:00

This study proposes behavior-based navigation architecture, named BBFM, to
deal with the problem of navigating the mobile robot in unknown environments in
the presence of obstacles and local minimum regions. In the architecture, the
complex navigation task is split into principal sub-tasks or behaviors. Each
behavior is implemented by a fuzzy controller and executed independently to
deal with a specific problem of navigation. The fuzzy controller is modified to
contain only the fuzzification and inference procedures so that its output is a
membership function representing the behavior's objective. The membership
functions of all controllers are then used as the objective functions for a
multi-objective optimization process to coordinate all behaviors. The result of
this process is an overall control signal, which is Pareto-optimal, used to
control the robot. A number of simulations, comparisons, and experiments were
conducted. The results show that the proposed architecture outperforms some
popular behavior-based architectures in term of accuracy, smoothness, traveled
distance, and time response.
Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media
Authors: Enes Kocabey, Mustafa Camurcu, Ferda Ofli, Yusuf Aytar, Javier Marin, Antonio Torralba, Ingmar Weber
Date: 2017-03-09 00:00:00
Tasks: Body Mass Index (BMI) Prediction

A person's weight status can have profound implications on their life,
ranging from mental health, to longevity, to financial income. At the societal
level, "fat shaming" and other forms of "sizeism" are a growing concern, while
increasing obesity rates are linked to ever raising healthcare costs. For these
reasons, researchers from a variety of backgrounds are interested in studying
obesity from all angles. To obtain data, traditionally, a person would have to
accurately self-report their body-mass index (BMI) or would have to see a
doctor to have it measured. In this paper, we show how computer vision can be
used to infer a person's BMI from social media images. We hope that our tool,
which we release, helps to advance the study of social aspects related to body
weight.
Counterfactual Control for Free from Generative Models
Authors: Nicholas Guttenberg, Yen Yu, Ryota Kanai
Date: 2017-02-22 00:00:00
Tasks: counterfactual

We introduce a method by which a generative model learning the joint
distribution between actions and future states can be used to automatically
infer a control scheme for any desired reward function, which may be altered on
the fly without retraining the model. In this method, the problem of action
selection is reduced to one of gradient descent on the latent space of the
generative model, with the model itself providing the means of evaluating
outcomes and finding the gradient, much like how the reward network in Deep
Q-Networks (DQN) provides gradient information for the action generator. Unlike
DQN or Actor-Critic, which are conditional models for a specific reward, using
a generative model of the full joint distribution permits the reward to be
changed on the fly. In addition, the generated futures can be inspected to gain
insight in to what the network 'thinks' will happen, and to what went wrong
when the outcomes deviate from prediction.
Detecting Sockpuppets in Deceptive Opinion Spam
Authors: Marjan Hosseinia, Arjun Mukherjee
Date: 2017-03-09 00:00:00
Tasks: Authorship Attribution, Diversity

This paper explores the problem of sockpuppet detection in deceptive opinion
spam using authorship attribution and verification approaches. Two methods are
explored. The first is a feature subsampling scheme that uses the KL-Divergence
on stylistic language models of an author to find discriminative features. The
second is a transduction scheme, spy induction that leverages the diversity of
authors in the unlabeled test set by sending a set of spies (positive samples)
from the training set to retrieve hidden samples in the unlabeled test set
using nearest and farthest neighbors. Experiments using ground truth sockpuppet
data show the effectiveness of the proposed schemes.
Hierarchical Multiscale Recurrent Neural Networks
Authors: Junyoung Chung, Sungjin Ahn, Yoshua Bengio
Date: 2016-09-06 00:00:00
Tasks: Language Modelling

Learning both hierarchical and temporal representation has been among the
long-standing challenges of recurrent neural networks. Multiscale recurrent
neural networks have been considered as a promising approach to resolve this
issue, yet there has been a lack of empirical evidence showing that this type
of models can actually capture the temporal dependencies by discovering the
latent hierarchical structure of the sequence. In this paper, we propose a
novel multiscale approach, called the hierarchical multiscale recurrent neural
networks, which can capture the latent hierarchical structure in the sequence
by encoding the temporal dependencies with different timescales using a novel
update mechanism. We show some evidence that our proposed multiscale
architecture can discover underlying hierarchical structure in the sequences
without using explicit boundary information. We evaluate our proposed model on
character-level language modelling and handwriting sequence modelling.
A Structured Self-attentive Sentence Embedding
Authors: Zhouhan Lin, Minwei Feng, Cicero Nogueira dos santos, Mo Yu, Bing Xiang, Bo-Wen Zhou, Yoshua Bengio
Date: 2017-03-09 00:00:00
Tasks: Author Profiling, General Classification, Natural Language Inference, Sentence, Sentence Embedding, Sentence-Embedding, Sentiment Analysis, Sentiment Classification

This paper proposes a new model for extracting an interpretable sentence
embedding by introducing self-attention. Instead of using a vector, we use a
2-D matrix to represent the embedding, with each row of the matrix attending on
a different part of the sentence. We also propose a self-attention mechanism
and a special regularization term for the model. As a side effect, the
embedding comes with an easy way of visualizing what specific parts of the
sentence are encoded into the embedding. We evaluate our model on 3 different
tasks: author profiling, sentiment classification, and textual entailment.
Results show that our model yields a significant performance gain compared to
other sentence embedding methods in all of the 3 tasks.
Learning to Remember Rare Events
Authors: Łukasz Kaiser, Ofir Nachum, Aurko Roy, Samy Bengio
Date: 2017-03-09 00:00:00
Tasks: Few-Shot Image Classification, image-classification, Image Classification, Machine Translation, One-Shot Learning

Despite recent advances, memory-augmented deep neural networks are still
limited when it comes to life-long and one-shot learning, especially in
remembering rare events. We present a large-scale life-long memory module for
use in deep learning. The module exploits fast nearest-neighbor algorithms for
efficiency and thus scales to large memory sizes. Except for the
nearest-neighbor query, the module is fully differentiable and trained
end-to-end with no extra supervision. It operates in a life-long manner, i.e.,
without the need to reset it during training.
  Our memory module can be easily added to any part of a supervised neural
network. To show its versatility we add it to a number of networks, from simple
convolutional ones tested on image classification to deep sequence-to-sequence
and recurrent-convolutional models. In all cases, the enhanced network gains
the ability to remember and do life-long one-shot learning. Our module
remembers training examples shown many thousands of steps in the past and it
can successfully generalize from them. We set new state-of-the-art for one-shot
learning on the Omniglot dataset and demonstrate, for the first time, life-long
one-shot learning in recurrent neural networks on a large-scale machine
translation task.
DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution
Authors: Thomas Vandal, Evan Kodra, Sangram Ganguly, Andrew Michaelis, Ramakrishna Nemani, Auroop R. Ganguly
Date: 2017-03-09 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

The impacts of climate change are felt by most critical systems, such as
infrastructure, ecological systems, and power-plants. However, contemporary
Earth System Models (ESM) are run at spatial resolutions too coarse for
assessing effects this localized. Local scale projections can be obtained using
statistical downscaling, a technique which uses historical climate observations
to learn a low-resolution to high-resolution mapping. Depending on statistical
modeling choices, downscaled projections have been shown to vary significantly
terms of accuracy and reliability. The spatio-temporal nature of the climate
system motivates the adaptation of super-resolution image processing techniques
to statistical downscaling. In our work, we present DeepSD, a generalized
stacked super resolution convolutional neural network (SRCNN) framework for
statistical downscaling of climate variables. DeepSD augments SRCNN with
multi-scale input channels to maximize predictability in statistical
downscaling. We provide a comparison with Bias Correction Spatial
Disaggregation as well as three Automated-Statistical Downscaling approaches in
downscaling daily precipitation from 1 degree (~100km) to 1/8 degrees (~12.5km)
over the Continental United States. Furthermore, a framework using the NASA
Earth Exchange (NEX) platform is discussed for downscaling more than 20 ESM
models with multiple emission scenarios.
Statistical Cost Sharing
Authors: Eric Balkanski, Umar Syed, Sergei Vassilvitskii
Date: 2017-03-09 00:00:00
Proceeding: NeurIPS 2017 12

We study the cost sharing problem for cooperative games in situations where
the cost function $C$ is not available via oracle queries, but must instead be
derived from data, represented as tuples $(S, C(S))$, for different subsets $S$
of players. We formalize this approach, which we call statistical cost sharing,
and consider the computation of the core and the Shapley value, when the tuples
are drawn from some distribution $\mathcal{D}$.
  Previous work by Balcan et al. in this setting showed how to compute cost
shares that satisfy the core property with high probability for limited classes
of functions. We expand on their work and give an algorithm that computes such
cost shares for any function with a non-empty core. We complement these results
by proving an inapproximability lower bound for a weaker relaxation.
  We then turn our attention to the Shapley value. We first show that when cost
functions come from the family of submodular functions with bounded curvature,
$\kappa$, the Shapley value can be approximated from samples up to a $\sqrt{1 -
\kappa}$ factor, and that the bound is tight. We then define statistical
analogues of the Shapley axioms, and derive a notion of statistical Shapley
value. We show that these can always be approximated arbitrarily well for
general functions over any distribution $\mathcal{D}$.
Image Classification of Melanoma, Nevus and Seborrheic Keratosis by Deep Neural Network Ensemble
Authors: Kazuhisa Matsunaga, Akira Hamada, Akane Minagawa, Hiroshi Koga
Date: 2017-03-09 00:00:00
Tasks: General Classification, image-classification, Image Classification, Lesion Classification

This short paper reports the method and the evaluation results of Casio and
Shinshu University joint team for the ISBI Challenge 2017 - Skin Lesion
Analysis Towards Melanoma Detection - Part 3: Lesion Classification hosted by
ISIC. Our online validation score was 0.958 with melanoma classifier AUC 0.924
and seborrheic keratosis classifier AUC 0.993.
Similarity Preserving Representation Learning for Time Series Clustering
Authors: Qi Lei, Jin-Feng Yi, Roman Vaculin, Lingfei Wu, Inderjit S. Dhillon
Date: 2017-02-12 00:00:00
Tasks: Clustering, Representation Learning, Time Series, Time Series Analysis, Time Series Clustering

A considerable amount of clustering algorithms take instance-feature matrices as their inputs. As such, they cannot directly analyze time series data due to its temporal nature, usually unequal lengths, and complex properties. This is a great pity since many of these algorithms are effective, robust, efficient, and easy to use. In this paper, we bridge this gap by proposing an efficient representation learning framework that is able to convert a set of time series with various lengths to an instance-feature matrix. In particular, we guarantee that the pairwise similarities between time series are well preserved after the transformation, thus the learned feature representation is particularly suitable for the time series clustering task. Given a set of $n$ time series, we first construct an $n\times n$ partially-observed similarity matrix by randomly sampling $\mathcal{O}(n \log n)$ pairs of time series and computing their pairwise similarities. We then propose an efficient algorithm that solves a non-convex and NP-hard problem to learn new features based on the partially-observed similarity matrix. By conducting extensive empirical studies, we show that the proposed framework is more effective, efficient, and flexible, compared to other state-of-the-art time series clustering methods.
Information Extraction in Illicit Domains
Authors: Mayank Kejriwal, Pedro Szekely
Date: 2017-03-09 00:00:00
Tasks: Attribute

Extracting useful entities and attribute values from illicit domains such as
human trafficking is a challenging problem with the potential for widespread
social impact. Such domains employ atypical language models, have `long tails'
and suffer from the problem of concept drift. In this paper, we propose a
lightweight, feature-agnostic Information Extraction (IE) paradigm specifically
designed for such domains. Our approach uses raw, unlabeled text from an
initial corpus, and a few (12-120) seed annotations per domain-specific
attribute, to learn robust IE models for unobserved pages and websites.
Empirically, we demonstrate that our approach can outperform feature-centric
Conditional Random Field baselines by over 18\% F-Measure on five annotated
sets of real-world human trafficking datasets in both low-supervision and
high-supervision settings. We also show that our approach is demonstrably
robust to concept drift, and can be efficiently bootstrapped even in a serial
computing environment.
Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection
Authors: Yu Xiang, Wongun Choi, Yuanqing Lin, Silvio Savarese
Date: 2016-04-16 00:00:00
Tasks: 2D Object Detection, General Classification, Object, object-detection, Object Detection, Pose Estimation, Region Proposal, Vehicle Pose Estimation

In CNN-based object detection methods, region proposal becomes a bottleneck
when objects exhibit significant scale variation, occlusion or truncation. In
addition, these methods mainly focus on 2D object detection and cannot estimate
detailed properties of objects. In this paper, we propose subcategory-aware
CNNs for object detection. We introduce a novel region proposal network that
uses subcategory information to guide the proposal generating process, and a
new detection network for joint detection and subcategory classification. By
using subcategories related to object pose, we achieve state-of-the-art
performance on both detection and pose estimation on commonly used benchmarks.
Deep Learning applied to NLP
Authors: Marc Moreno Lopez, Jugal Kalita
Date: 2017-03-09 00:00:00
Tasks: Deep Learning, General Classification, image-classification, Image Classification

Convolutional Neural Network (CNNs) are typically associated with Computer
Vision. CNNs are responsible for major breakthroughs in Image Classification
and are the core of most Computer Vision systems today. More recently CNNs have
been applied to problems in Natural Language Processing and gotten some
interesting results. In this paper, we will try to explain the basics of CNNs,
its different variations and how they have been applied to NLP.
Deep Convolutional Neural Network Inference with Floating-point Weights and Fixed-point Activations
Authors: Liangzhen Lai, Naveen Suda, Vikas Chandra
Date: 2017-03-08 00:00:00

Deep convolutional neural network (CNN) inference requires significant amount
of memory and computation, which limits its deployment on embedded devices. To
alleviate these problems to some extent, prior research utilize low precision
fixed-point numbers to represent the CNN weights and activations. However, the
minimum required data precision of fixed-point weights varies across different
networks and also across different layers of the same network. In this work, we
propose using floating-point numbers for representing the weights and
fixed-point numbers for representing the activations. We show that using
floating-point representation for weights is more efficient than fixed-point
representation for the same bit-width and demonstrate it on popular large-scale
CNNs such as AlexNet, SqueezeNet, GoogLeNet and VGG-16. We also show that such
a representation scheme enables compact hardware multiply-and-accumulate (MAC)
unit design. Experimental results show that the proposed scheme reduces the
weight storage by up to 36% and power consumption of the hardware multiplier by
up to 50%.