pwc-archive/papers-with-abstracts huggingface dataset:
A Primer on the Signature Method in Machine Learning
Authors: Ilya Chevyrev, Andrey Kormilitzin
Date: 2016-03-11 00:00:00
Tasks: BIG-bench Machine Learning, Dimensionality Reduction

We provide an introduction to the signature method, focusing on its theoretical properties and machine learning applications. Our presentation is divided into two parts. In the first part, we present the definition and fundamental properties of the signature of a path. The signature is a sequence of numbers associated with a path that captures many of its important analytic and geometric properties. As a sequence of numbers, the signature serves as a compact description (dimension reduction) of a path. In presenting its theoretical properties, we assume only familiarity with classical real analysis and integration, and supplement theory with straightforward examples. We also mention several advanced topics, including the role of the signature in rough path theory. In the second part, we present practical applications of the signature to the area of machine learning. The signature method is a non-parametric way of transforming data into a set of features that can be used in machine learning tasks. In this method, data are converted into multi-dimensional paths, by means of embedding algorithms, of which the signature is then computed. We describe this pipeline in detail, making a link with the properties of the signature presented in the first part. We furthermore review some of the developments of the signature method in machine learning and, as an illustrative example, present a detailed application of the method to handwritten digit classification.
Towards using social media to identify individuals at risk for preventable chronic illness
Authors: Dane Bell, Daniel Fried, Luwen Huangfu, Mihai Surdeanu, Stephen Kobourov
Date: 2016-03-11 00:00:00
Proceeding: LREC 2016 5

We describe a strategy for the acquisition of training data necessary to
build a social-media-driven early detection system for individuals at risk for
(preventable) type 2 diabetes mellitus (T2DM). The strategy uses a game-like
quiz with data and questions acquired semi-automatically from Twitter. The
questions are designed to inspire participant engagement and collect relevant
data to train a public-health model applied to individuals. Prior systems
designed to use social media such as Twitter to predict obesity (a risk factor
for T2DM) operate on entire communities such as states, counties, or cities,
based on statistics gathered by government agencies. Because there is
considerable variation among individuals within these groups, training data on
the individual level would be more effective, but this data is difficult to
acquire. The approach proposed here aims to address this issue. Our strategy
has two steps. First, we trained a random forest classifier on data gathered
from (public) Twitter statuses and state-level statistics with state-of-the-art
accuracy. We then converted this classifier into a 20-questions-style quiz and
made it available online. In doing so, we achieved high engagement with
individuals that took the quiz, while also building a training set of
voluntarily supplied individual-level data for future classification.
Region Graph Based Method for Multi-Object Detection and Tracking using Depth Cameras
Authors: Sachin Mehta, Balakrishnan Prabhakaran
Date: 2016-03-11 00:00:00
Tasks: Object, object-detection, Object Detection

In this paper, we propose a multi-object detection and tracking method using
depth cameras. Depth maps are very noisy and obscure in object detection. We
first propose a region-based method to suppress high magnitude noise which
cannot be filtered using spatial filters. Second, the proposed method detect
Region of Interests by temporal learning which are then tracked using weighted
graph-based approach. We demonstrate the performance of the proposed method on
standard depth camera datasets with and without object occlusions. Experimental
results show that the proposed method is able to suppress high magnitude noise
in depth maps and detect/track the objects (with and without occlusion).
Efficient Clustering of Correlated Variables and Variable Selection in High-Dimensional Linear Models
Authors: Niharika Gauraha, Swapan K. Parui
Date: 2016-03-11 00:00:00
Tasks: Clustering, Variable Selection

In this paper, we introduce Adaptive Cluster Lasso(ACL) method for variable
selection in high dimensional sparse regression models with strongly correlated
variables. To handle correlated variables, the concept of clustering or
grouping variables and then pursuing model fitting is widely accepted. When the
dimension is very high, finding an appropriate group structure is as difficult
as the original problem. The ACL is a three-stage procedure where, at the first
stage, we use the Lasso(or its adaptive or thresholded version) to do initial
selection, then we also include those variables which are not selected by the
Lasso but are strongly correlated with the variables selected by the Lasso. At
the second stage we cluster the variables based on the reduced set of
predictors and in the third stage we perform sparse estimation such as Lasso on
cluster representatives or the group Lasso based on the structures generated by
clustering procedure. We show that our procedure is consistent and efficient in
finding true underlying population group structure(under assumption of
irrepresentable and beta-min conditions). We also study the group selection
consistency of our method and we support the theory using simulated and
pseudo-real dataset examples.
Computing Strong Game-Theoretic Strategies in Jotto
Authors: Sam Ganzfried
Date: 2011-07-17 00:00:00

We develop a new approach that computes approximate equilibrium strategies in
Jotto, a popular word game. Jotto is an extremely large two-player game of
imperfect information; its game tree has many orders of magnitude more states
than games previously studied, including no-limit Texas hold 'em. To address
the fact that the game is so large, we propose a novel strategy representation
called oracular form, in which we do not explicitly represent a strategy, but
rather appeal to an oracle that quickly outputs a sample move from the
strategy's distribution. Our overall approach is based on an extension of the
fictitious play algorithm to this oracular setting. We demonstrate the
superiority of our computed strategies over the strategies computed by a
benchmark algorithm, both in terms of head-to-head and worst-case performance.
Distribution Free Learning with Local Queries
Authors: Galit Bary-Weisberg, Amit Daniely, Shai Shalev-Shwartz
Date: 2016-03-11 00:00:00

The model of learning with \emph{local membership queries} interpolates
between the PAC model and the membership queries model by allowing the learner
to query the label of any example that is similar to an example in the training
set. This model, recently proposed and studied by Awasthi, Feldman and Kanade,
aims to facilitate practical use of membership queries.
  We continue this line of work, proving both positive and negative results in
the {\em distribution free} setting. We restrict to the boolean cube $\{-1,
1\}^n$, and say that a query is $q$-local if it is of a hamming distance $\le
q$ from some training example. On the positive side, we show that $1$-local
queries already give an additional strength, and allow to learn a certain type
of DNF formulas. On the negative side, we show that even
$\left(n^{0.99}\right)$-local queries cannot help to learn various classes
including Automata, DNFs and more. Likewise, $q$-local queries for any constant
$q$ cannot help to learn Juntas, Decision Trees, Sparse Polynomials and more.
Moreover, for these classes, an algorithm that uses
$\left(\log^{0.99}(n)\right)$-local queries would lead to a breakthrough in the
best known running times.
Searching for Topological Symmetry in Data Haystack
Authors: Kallol Roy, Anh Tong, Jaesik Choi
Date: 2016-03-11 00:00:00

Finding interesting symmetrical topological structures in high-dimensional
systems is an important problem in statistical machine learning. Limited amount
of available high-dimensional data and its sensitivity to noise pose
computational challenges to find symmetry. Our paper presents a new method to
find local symmetries in a low-dimensional 2-D grid structure which is embedded
in high-dimensional structure. To compute the symmetry in a grid structure, we
introduce three legal grid moves (i) Commutation (ii) Cyclic Permutation (iii)
Stabilization on sets of local grid squares, grid blocks. The three grid moves
are legal transformations as they preserve the statistical distribution of
hamming distances in each grid block. We propose and coin the term of grid
symmetry of data on the 2-D data grid as the invariance of statistical
distributions of hamming distance are preserved after a sequence of grid moves.
We have computed and analyzed the grid symmetry of data on multivariate
Gaussian distributions and Gamma distributions with noise.
Learning Gaze Transitions from Depth to Improve Video Saliency Estimation
Authors: G. Leifman, D. Rudoy, T. Swedish, E. Bayro-Corrochano, R. Raskar
Date: 2016-03-11 00:00:00
Proceeding: ICCV 2017
Tasks: Saliency Prediction

In this paper we introduce a novel Depth-Aware Video Saliency approach to
predict human focus of attention when viewing RGBD videos on regular 2D
screens. We train a generative convolutional neural network which predicts a
saliency map for a frame, given the fixation map of the previous frame.
Saliency estimation in this scenario is highly important since in the near
future 3D video content will be easily acquired and yet hard to display. This
can be explained, on the one hand, by the dramatic improvement of 3D-capable
acquisition equipment. On the other hand, despite the considerable progress in
3D display technologies, most of the 3D displays are still expensive and
require wearing special glasses. To evaluate the performance of our approach,
we present a new comprehensive database of eye-fixation ground-truth for RGBD
videos. Our experiments indicate that integrating depth into video saliency
calculation is beneficial. We demonstrate that our approach outperforms
state-of-the-art methods for video saliency, achieving 15% relative
improvement.
Efficient forward propagation of time-sequences in convolutional neural networks using Deep Shifting
Authors: Koen Groenland, Sander Bohte
Date: 2016-03-11 00:00:00

When a Convolutional Neural Network is used for on-the-fly evaluation of
continuously updating time-sequences, many redundant convolution operations are
performed. We propose the method of Deep Shifting, which remembers previously
calculated results of convolution operations in order to minimize the number of
calculations. The reduction in complexity is at least a constant and in the
best case quadratic. We demonstrate that this method does indeed save
significant computation time in a practical implementation, especially when the
networks receives a large number of time-frames.
Learning from Imbalanced Multiclass Sequential Data Streams Using Dynamically Weighted Conditional Random Fields
Authors: Roberto L. Shinmoto Torres, Damith C. Ranasinghe, Qinfeng Shi, Anton Van Den Hengel
Date: 2016-03-11 00:00:00
Tasks: Activity Recognition, General Classification

The present study introduces a method for improving the classification
performance of imbalanced multiclass data streams from wireless body worn
sensors. Data imbalance is an inherent problem in activity recognition caused
by the irregular time distribution of activities, which are sequential and
dependent on previous movements. We use conditional random fields (CRF), a
graphical model for structured classification, to take advantage of
dependencies between activities in a sequence. However, CRFs do not consider
the negative effects of class imbalance during training. We propose a
class-wise dynamically weighted CRF (dWCRF) where weights are automatically
determined during training by maximizing the expected overall F-score. Our
results based on three case studies from a healthcare application using a
batteryless body worn sensor, demonstrate that our method, in general, improves
overall and minority class F-score when compared to other CRF based classifiers
and achieves similar or better overall and class-wise performance when compared
to SVM based classifiers under conditions of limited training data. We also
confirm the performance of our approach using an additional battery powered
body worn sensor dataset, achieving similar results in cases of high class
imbalance.
A Recursive Born Approach to Nonlinear Inverse Scattering
Authors: Ulugbek S. Kamilov, Dehong Liu, Hassan Mansour, Petros T. Boufounos
Date: 2016-03-11 00:00:00
Tasks: Transparent objects

The Iterative Born Approximation (IBA) is a well-known method for describing
waves scattered by semi-transparent objects. In this paper, we present a novel
nonlinear inverse scattering method that combines IBA with an edge-preserving
total variation (TV) regularizer. The proposed method is obtained by relating
iterations of IBA to layers of a feedforward neural network and developing a
corresponding error backpropagation algorithm for efficiently estimating the
permittivity of the object. Simulations illustrate that, by accounting for
multiple scattering, the method successfully recovers the permittivity
distribution where the traditional linear inverse scattering fails.
A short proof that $O_2$ is an MCFL
Authors: Mark-Jan Nederhof
Date: 2016-03-11 00:00:00

We present a new proof that $O_2$ is a multiple context-free language. It
contrasts with a recent proof by Salvati (2015) in its avoidance of concepts
that seem specific to two-dimensional geometry, such as the complex exponential
function. Our simple proof creates realistic prospects of widening the results
to higher dimensions. This finding is of central importance to the relation
between extreme free word order and classes of grammars used to describe the
syntax of natural language.
Optimized Polynomial Evaluation with Semantic Annotations
Authors: Daniel Rubio Bonilla, Colin W. Glass, Jan Kuper
Date: 2016-03-04 00:00:00

In this paper we discuss how semantic annotations can be used to introduce
mathematical algorithmic information of the underlying imperative code to
enable compilers to produce code transformations that will enable better
performance. By using this approaches not only good performance is achieved,
but also better programmability, maintainability and portability across
different hardware architectures. To exemplify this we will use polynomial
equations of different degrees.
Maximum Entropy Deep Inverse Reinforcement Learning
Authors: Markus Wulfmeier, Peter Ondruska, Ingmar Posner
Date: 2015-07-17 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper presents a general framework for exploiting the representational
capacity of neural networks to approximate complex, nonlinear reward functions
in the context of solving the inverse reinforcement learning (IRL) problem. We
show in this context that the Maximum Entropy paradigm for IRL lends itself
naturally to the efficient training of deep architectures. At test time, the
approach leads to a computational complexity independent of the number of
demonstrations, which makes it especially well-suited for applications in
life-long learning scenarios. Our approach achieves performance commensurate to
the state-of-the-art on existing benchmarks while exceeding on an alternative
benchmark based on highly varying reward structures. Finally, we extend the
basic architecture - which is equivalent to a simplified subclass of Fully
Convolutional Neural Networks (FCNNs) with width one - to include larger
convolutions in order to eliminate dependency on precomputed spatial features
and work on raw input representations.
Fast Optical Flow using Dense Inverse Search
Authors: Till Kroeger, Radu Timofte, Dengxin Dai, Luc van Gool
Date: 2016-03-11 00:00:00
Tasks: Action Detection, Activity Detection, CPU, Optical Flow Estimation

Most recent works in optical flow extraction focus on the accuracy and
neglect the time complexity. However, in real-life visual applications, such as
tracking, activity detection and recognition, the time complexity is critical.
  We propose a solution with very low time complexity and competitive accuracy
for the computation of dense optical flow. It consists of three parts: 1)
inverse search for patch correspondences; 2) dense displacement field creation
through patch aggregation along multiple scales; 3) variational refinement. At
the core of our Dense Inverse Search-based method (DIS) is the efficient search
of correspondences inspired by the inverse compositional image alignment
proposed by Baker and Matthews in 2001.
  DIS is competitive on standard optical flow benchmarks with large
displacements. DIS runs at 300Hz up to 600Hz on a single CPU core, reaching the
temporal resolution of human's biological vision system. It is order(s) of
magnitude faster than state-of-the-art methods in the same range of accuracy,
making DIS ideal for visual applications.
Watch-n-Patch: Unsupervised Learning of Actions and Relations
Authors: Chenxia Wu, Jiemi Zhang, Ozan Sener, Bart Selman, Silvio Savarese, Ashutosh Saxena
Date: 2016-03-11 00:00:00
Tasks: Action Segmentation, Clustering, Unsupervised Action Segmentation

There is a large variation in the activities that humans perform in their
everyday lives. We consider modeling these composite human activities which
comprises multiple basic level actions in a completely unsupervised setting.
Our model learns high-level co-occurrence and temporal relations between the
actions. We consider the video as a sequence of short-term action clips, which
contains human-words and object-words. An activity is about a set of
action-topics and object-topics indicating which actions are present and which
objects are interacting with. We then propose a new probabilistic model
relating the words and the topics. It allows us to model long-range action
relations that commonly exist in the composite activities, which is challenging
in previous works. We apply our model to the unsupervised action segmentation
and clustering, and to a novel application that detects forgotten actions,
which we call action patching. For evaluation, we contribute a new challenging
RGB-D activity video dataset recorded by the new Kinect v2, which contains
several human daily activities as compositions of multiple actions interacting
with different objects. Moreover, we develop a robotic system that watches
people and reminds people by applying our action patching algorithm. Our
robotic setup can be easily deployed on any assistive robot.
A Set Theoretic Approach for Knowledge Representation: the Representation Part
Authors: Yi Zhou
Date: 2016-03-11 00:00:00
Tasks: Form

In this paper, we propose a set theoretic approach for knowledge
representation. While the syntax of an application domain is captured by set
theoretic constructs including individuals, concepts and operators, knowledge
is formalized by equality assertions. We first present a primitive form that
uses minimal assumed knowledge and constructs. Then, assuming naive set theory,
we extend it by definitions, which are special kinds of knowledge.
Interestingly, we show that the primitive form is expressive enough to define
logic operators, not only propositional connectives but also quantifiers.
Embedding Label Structures for Fine-Grained Feature Representation
Authors: Xiaofan Zhang, Feng Zhou, Yuanqing Lin, Shaoting Zhang
Date: 2015-12-09 00:00:00
Proceeding: CVPR 2016 6
Tasks: Fine-Grained Image Classification, General Classification, image-classification, Image Classification, Image Retrieval, Multi-Task Learning, Retrieval, Triplet

Recent algorithms in convolutional neural networks (CNN) considerably advance
the fine-grained image classification, which aims to differentiate subtle
differences among subordinate classes. However, previous studies have rarely
focused on learning a fined-grained and structured feature representation that
is able to locate similar images at different levels of relevance, e.g.,
discovering cars from the same make or the same model, both of which require
high precision. In this paper, we propose two main contributions to tackle this
problem. 1) A multi-task learning framework is designed to effectively learn
fine-grained feature representations by jointly optimizing both classification
and similarity constraints. 2) To model the multi-level relevance, label
structures such as hierarchy or shared attributes are seamlessly embedded into
the framework by generalizing the triplet loss. Extensive and thorough
experiments have been conducted on three fine-grained datasets, i.e., the
Stanford car, the car-333, and the food datasets, which contain either
hierarchical labels or shared attributes. Our proposed method has achieved very
competitive performance, i.e., among state-of-the-art classification accuracy.
More importantly, it significantly outperforms previous fine-grained feature
representations for image retrieval at different levels of relevance.
Temporal Convolutional Neural Networks for Diagnosis from Lab Tests
Authors: Narges Razavian, David Sontag
Date: 2015-11-25 00:00:00
Tasks: regression

Early diagnosis of treatable diseases is essential for improving healthcare,
and many diseases' onsets are predictable from annual lab tests and their
temporal trends. We introduce a multi-resolution convolutional neural network
for early detection of multiple diseases from irregularly measured sparse lab
values. Our novel architecture takes as input both an imputed version of the
data and a binary observation matrix. For imputing the temporal sparse
observations, we develop a flexible, fast to train method for differentiable
multivariate kernel regression. Our experiments on data from 298K individuals
over 8 years, 18 common lab measurements, and 171 diseases show that the
temporal signatures learned via convolution are significantly more predictive
than baselines commonly used for early disease diagnosis.
The Algorithm of Islamic Jurisprudence (Fiqh) with Validation of an Entscheidungsproblem
Authors: Elnaserledinellah Mahmood Abdelwahab, Karim Daghbouche, Nadra Ahmad Shannan
Date: 2016-03-10 00:00:00
Tasks: Formal Logic, Jurisprudence

The historic background of algorithmic processing with regard to etymology
and methodology is translated into terms of mathematical logic and Computer
Science. A formal logic structure is introduced by exemplaryquestions posed to
Fiqh-chapters to define alogic query language. As a foundation, ageneric
algorithm for deciding Fiqh-rulings is designed to enable and further leverage
rule of law (vs. rule by law) with full transparency and complete algorithmic
coverage of Islamic law eventually providing legal security, legal equality,
and full legal accountability.This is implemented by disentangling and
reinstating classic Fiqh-methodology (usul al-Fiqh) with the expressive power
of subsets of First Order Logic (FOL)sustainably substituting ad hoc reasoning
with falsifiable rational argumentation. The results are discussed in formal
terms of completeness, decidability and complexity of formal Fiqh-systems.
AnEntscheidungsproblem for formal Fiqh-Systems is formulated and validated.