pwc-archive/papers-with-abstracts huggingface dataset:
General factorization framework for context-aware recommendations
Authors: Balázs Hidasi, Domonkos Tikk
Date: 2014-01-18 00:00:00

Context-aware recommendation algorithms focus on refining recommendations by
considering additional information, available to the system. This topic has
gained a lot of attention recently. Among others, several factorization methods
were proposed to solve the problem, although most of them assume explicit
feedback which strongly limits their real-world applicability. While these
algorithms apply various loss functions and optimization strategies, the
preference modeling under context is less explored due to the lack of tools
allowing for easy experimentation with various models. As context dimensions
are introduced beyond users and items, the space of possible preference models
and the importance of proper modeling largely increases.
  In this paper we propose a General Factorization Framework (GFF), a single
flexible algorithm that takes the preference model as an input and computes
latent feature matrices for the input dimensions. GFF allows us to easily
experiment with various linear models on any context-aware recommendation task,
be it explicit or implicit feedback based. The scaling properties makes it
usable under real life circumstances as well.
  We demonstrate the framework's potential by exploring various preference
models on a 4-dimensional context-aware problem with contexts that are
available for almost any real life datasets. We show in our experiments --
performed on five real life, implicit feedback datasets -- that proper
preference modelling significantly increases recommendation accuracy, and
previously unused models outperform the traditional ones. Novel models in GFF
also outperform state-of-the-art factorization algorithms.
  We also extend the method to be fully compliant to the Multidimensional
Dataspace Model, one of the most extensive data models of context-enriched
data. Extended GFF allows the seamless incorporation of information into the
fac[truncated]
High Performance Offline Handwritten Chinese Character Recognition Using GoogLeNet and Directional Feature Maps
Authors: Zhuoyao Zhong, Lianwen Jin, Zecheng Xie
Date: 2015-05-19 00:00:00
Tasks: image-classification, Image Classification, Offline Handwritten Chinese Character Recognition

Just like its great success in solving many computer vision problems, the
convolutional neural networks (CNN) provided new end-to-end approach to
handwritten Chinese character recognition (HCCR) with very promising results in
recent years. However, previous CNNs so far proposed for HCCR were neither deep
enough nor slim enough. We show in this paper that, a deeper architecture can
benefit HCCR a lot to achieve higher performance, meanwhile can be designed
with less parameters. We also show that the traditional feature extraction
methods, such as Gabor or gradient feature maps, are still useful for enhancing
the performance of CNN. We design a streamlined version of GoogLeNet [13],
which was original proposed for image classification in recent years with very
deep architecture, for HCCR (denoted as HCCR-GoogLeNet). The HCCR-GoogLeNet we
used is 19 layers deep but involves with only 7.26 million parameters.
Experiments were conducted using the ICDAR 2013 offline HCCR competition
dataset. It has been shown that with the proper incorporation with traditional
directional feature maps, the proposed single and ensemble HCCR-GoogLeNet
models achieve new state of the art recognition accuracy of 96.35% and 96.74%,
respectively, outperforming previous best result with significant gap.
Character-level Chinese Writer Identification using Path Signature Feature, DropStroke and Deep CNN
Authors: Weixin Yang, Lianwen Jin, Manfei Liu
Date: 2015-05-19 00:00:00
Tasks: Data Augmentation

Most existing online writer-identification systems require that the text
content is supplied in advance and rely on separately designed features and
classifiers. The identifications are based on lines of text, entire paragraphs,
or entire documents; however, these materials are not always available. In this
paper, we introduce a path-signature feature to an end-to-end text-independent
writer-identification system with a deep convolutional neural network (DCNN).
Because deep models require a considerable amount of data to achieve good
performance, we propose a data-augmentation method named DropStroke to enrich
personal handwriting. Experiments were conducted on online handwritten Chinese
characters from the CASIA-OLHWDB1.0 dataset, which consists of 3,866 classes
from 420 writers. For each writer, we only used 200 samples for training and
the remaining 3,666. The results reveal that the path-signature feature is
useful for writer identification, and the proposed DropStroke technique
enhances the generalization and significantly improves performance.
Have a Look at What I See
Authors: Lior Talker, Yael Moses, Ilan Shimshoni
Date: 2015-05-19 00:00:00
Tasks: 3D Scene Reconstruction

We propose a method for guiding a photographer to rotate her/his smartphone
camera to obtain an image that overlaps with another image of the same scene.
The other image is taken by another photographer from a different viewpoint.
Our method is applicable even when the images do not have overlapping fields of
view. Straightforward applications of our method include sharing attention to
regions of interest for social purposes, or adding missing images to improve
structure for motion results. Our solution uses additional images of the scene,
which are often available since many people use their smartphone cameras
regularly. These images may be available online from other photographers who
are present at the scene. Our method avoids 3D scene reconstruction; it relies
instead on a new representation that consists of the spatial orders of the
scene points on two axes, x and y. This representation allows a sequence of
points to be chosen efficiently and projected onto the photographers images,
using epipolar point transfer. Overlaying these epipolar lines on the live
preview of the camera produces a convenient interface to guide the user. The
method was tested on challenging datasets of images and succeeded in guiding a
photographer from one view to a non-overlapping destination view.
Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors
Authors: Limin Wang, Yu Qiao, Xiaoou Tang
Date: 2015-05-19 00:00:00
Proceeding: CVPR 2015 6
Tasks: Action Recognition, Action Understanding, Activity Recognition In Videos, Temporal Action Localization

Visual features are of vital importance for human action understanding in
videos. This paper presents a new video representation, called
trajectory-pooled deep-convolutional descriptor (TDD), which shares the merits
of both hand-crafted features and deep-learned features. Specifically, we
utilize deep architectures to learn discriminative convolutional feature maps,
and conduct trajectory-constrained pooling to aggregate these convolutional
features into effective descriptors. To enhance the robustness of TDDs, we
design two normalization methods to transform convolutional feature maps,
namely spatiotemporal normalization and channel normalization. The advantages
of our features come from (i) TDDs are automatically learned and contain high
discriminative capacity compared with those hand-crafted features; (ii) TDDs
take account of the intrinsic characteristics of temporal dimension and
introduce the strategies of trajectory-constrained sampling and pooling for
aggregating deep-learned features. We conduct experiments on two challenging
datasets: HMDB51 and UCF101. Experimental results show that TDDs outperform
previous hand-crafted features and deep-learned features. Our method also
achieves superior performance to the state of the art on these datasets (HMDB51
65.9%, UCF101 91.5%).
What is Learning? A primary discussion about information and Representation
Authors: Hao Wu
Date: 2015-05-19 00:00:00
Tasks: BIG-bench Machine Learning

Nowadays, represented by Deep Learning techniques, the field of machine
learning is experiencing unprecedented prosperity and its influence is
demonstrated in academia, industry and civil society. "Intelligent" has become
a label which could not be neglected for most applications; celebrities and
scientists also warned that the development of full artificial intelligence may
spell the end of the human race. It seems that the answer to building a
computer system that could automatically improve with experience is right on
the next corner. While for AI and machine learning researchers, it is a
consensus that we are not anywhere near the core technique which could bring
the Terminator, Number 5 or R2D2 into real life, and there is not even a formal
definition about what is intelligence, or one of its basic properties:
Learning. Therefore, even though researchers know these concerns are not
necessary currently, there is no generalized explanation about why these
concerns are not necessary, and what properties people should take into account
that would make these concerns to be necessary. In this paper, starts from
analysing the relation between information and its representation, a necessary
condition for a model to be a learning model is proposed. This condition and
related future works could be used to verify whether a system is able to learn
or not, and enrich our understanding of learning: one important property of
Intelligence.
Logarithmic Time Online Multiclass prediction
Authors: Anna Choromanska, John Langford
Date: 2014-06-06 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Prediction

We study the problem of multiclass classification with an extremely large
number of classes (k), with the goal of obtaining train and test time
complexity logarithmic in the number of classes. We develop top-down tree
construction approaches for constructing logarithmic depth trees. On the
theoretical front, we formulate a new objective function, which is optimized at
each node of the tree and creates dynamic partitions of the data which are both
pure (in terms of class labels) and balanced. We demonstrate that under
favorable conditions, we can construct logarithmic depth trees that have leaves
with low label entropy. However, the objective function at the nodes is
challenging to optimize computationally. We address the empirical problem with
a new online decision tree construction procedure. Experiments demonstrate that
this online algorithm quickly achieves improvement in test error compared to
more common logarithmic training time approaches, which makes it a plausible
method in computationally constrained large-k applications.
Tensor Factorization via Matrix Factorization
Authors: Volodymyr Kuleshov, Arun Tejasvi Chaganty, Percy Liang
Date: 2015-01-29 00:00:00
Tasks: parameter estimation

Tensor factorization arises in many machine learning applications, such
knowledge base modeling and parameter estimation in latent variable models.
However, numerical methods for tensor factorization have not reached the level
of maturity of matrix factorization methods. In this paper, we propose a new
method for CP tensor factorization that uses random projections to reduce the
problem to simultaneous matrix diagonalization. Our method is conceptually
simple and also applies to non-orthogonal and asymmetric tensors of arbitrary
order. We prove that a small number random projections essentially preserves
the spectral information in the tensor, allowing us to remove the dependence on
the eigengap that plagued earlier tensor-to-matrix reductions. Experimentally,
our method outperforms existing tensor factorization methods on both simulated
data and two real datasets.
Towards a Learning Theory of Cause-Effect Inference
Authors: David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, Ilya Tolstikhin
Date: 2015-02-09 00:00:00
Tasks: Causal Inference, Generalization Bounds, Learning Theory

We pose causal inference as the problem of learning to classify probability
distributions. In particular, we assume access to a collection
$\{(S_i,l_i)\}_{i=1}^n$, where each $S_i$ is a sample drawn from the
probability distribution of $X_i \times Y_i$, and $l_i$ is a binary label
indicating whether "$X_i \to Y_i$" or "$X_i \leftarrow Y_i$". Given these data,
we build a causal inference rule in two steps. First, we featurize each $S_i$
using the kernel mean embedding associated with some characteristic kernel.
Second, we train a binary classifier on such embeddings to distinguish between
causal directions. We present generalization bounds showing the statistical
consistency and learning rates of the proposed approach, and provide a simple
implementation that achieves state-of-the-art cause-effect inference.
Furthermore, we extend our ideas to infer causal relationships between more
than two variables.
An Asynchronous Mini-Batch Algorithm for Regularized Stochastic Optimization
Authors: Hamid Reza Feyzmahdavian, Arda Aytekin, Mikael Johansson
Date: 2015-05-18 00:00:00
Tasks: Distributed Computing, Stochastic Optimization

Mini-batch optimization has proven to be a powerful paradigm for large-scale
learning. However, the state of the art parallel mini-batch algorithms assume
synchronous operation or cyclic update orders. When worker nodes are
heterogeneous (due to different computational capabilities or different
communication delays), synchronous and cyclic operations are inefficient since
they will leave workers idle waiting for the slower nodes to complete their
computations. In this paper, we propose an asynchronous mini-batch algorithm
for regularized stochastic optimization problems with smooth loss functions
that eliminates idle waiting and allows workers to run at their maximal update
rates. We show that by suitably choosing the step-size values, the algorithm
achieves a rate of the order $O(1/\sqrt{T})$ for general convex regularization
functions, and the rate $O(1/T)$ for strongly convex regularization functions,
where $T$ is the number of iterations. In both cases, the impact of asynchrony
on the convergence rate of our algorithm is asymptotically negligible, and a
near-linear speedup in the number of workers can be expected. Theoretical
results are confirmed in real implementations on a distributed computing
infrastructure.
Predicting Important Objects for Egocentric Video Summarization
Authors: Yong Jae Lee, Kristen Grauman
Date: 2015-05-18 00:00:00
Tasks: Event Detection, Video Summarization

We present a video summarization approach for egocentric or "wearable" camera
data. Given hours of video, the proposed method produces a compact storyboard
summary of the camera wearer's day. In contrast to traditional keyframe
selection techniques, the resulting summary focuses on the most important
objects and people with which the camera wearer interacts. To accomplish this,
we develop region cues indicative of high-level saliency in egocentric
video---such as the nearness to hands, gaze, and frequency of occurrence---and
learn a regressor to predict the relative importance of any new region based on
these cues. Using these predictions and a simple form of temporal event
detection, our method selects frames for the storyboard that reflect the key
object-driven happenings. We adjust the compactness of the final summary given
either an importance selection criterion or a length budget; for the latter, we
design an efficient dynamic programming solution that accounts for importance,
visual uniqueness, and temporal displacement. Critically, the approach is
neither camera-wearer-specific nor object-specific; that means the learned
importance metric need not be trained for a given user or context, and it can
predict the importance of objects and people that have never been seen
previously. Our results on two egocentric video datasets show the method's
promise relative to existing techniques for saliency and summarization.
On the tightness of an SDP relaxation of k-means
Authors: Takayuki Iguchi, Dustin G. Mixon, Jesse Peterson, Soledad Villar
Date: 2015-05-18 00:00:00

Recently, Awasthi et al. introduced an SDP relaxation of the $k$-means
problem in $\mathbb R^m$. In this work, we consider a random model for the data
points in which $k$ balls of unit radius are deterministically distributed
throughout $\mathbb R^m$, and then in each ball, $n$ points are drawn according
to a common rotationally invariant probability distribution. For any fixed ball
configuration and probability distribution, we prove that the SDP relaxation of
the $k$-means problem exactly recovers these planted clusters with probability
$1-e^{-\Omega(n)}$ provided the distance between any two of the ball centers is
$>2+\epsilon$, where $\epsilon$ is an explicit function of the configuration of
the ball centers, and can be arbitrarily small when $m$ is large.
Cascading Bandits: Learning to Rank in the Cascade Model
Authors: Branislav Kveton, Csaba Szepesvari, Zheng Wen, Azin Ashkan
Date: 2015-02-10 00:00:00
Tasks: Learning-To-Rank

A search engine usually outputs a list of $K$ web pages. The user examines
this list, from the first web page to the last, and chooses the first
attractive page. This model of user behavior is known as the cascade model. In
this paper, we propose cascading bandits, a learning variant of the cascade
model where the objective is to identify $K$ most attractive items. We
formulate our problem as a stochastic combinatorial partial monitoring problem.
We propose two algorithms for solving it, CascadeUCB1 and CascadeKL-UCB. We
also prove gap-dependent upper bounds on the regret of these algorithms and
derive a lower bound on the regret in cascading bandits. The lower bound
matches the upper bound of CascadeKL-UCB up to a logarithmic factor. We
experiment with our algorithms on several problems. The algorithms perform
surprisingly well even when our modeling assumptions are violated.
Foundational principles for large scale inference: Illustrations through correlation mining
Authors: Alfred O. Hero, Bala Rajaratnam
Date: 2015-05-11 00:00:00

When can reliable inference be drawn in the "Big Data" context? This paper
presents a framework for answering this fundamental question in the context of
correlation mining, with implications for general large scale inference. In
large scale data applications like genomics, connectomics, and eco-informatics
the dataset is often variable-rich but sample-starved: a regime where the
number $n$ of acquired samples (statistical replicates) is far fewer than the
number $p$ of observed variables (genes, neurons, voxels, or chemical
constituents). Much of recent work has focused on understanding the
computational complexity of proposed methods for "Big Data." Sample complexity
however has received relatively less attention, especially in the setting when
the sample size $n$ is fixed, and the dimension $p$ grows without bound. To
address this gap, we develop a unified statistical framework that explicitly
quantifies the sample complexity of various inferential tasks. Sampling regimes
can be divided into several categories: 1) the classical asymptotic regime
where the variable dimension is fixed and the sample size goes to infinity; 2)
the mixed asymptotic regime where both variable dimension and sample size go to
infinity at comparable rates; 3) the purely high dimensional asymptotic regime
where the variable dimension goes to infinity and the sample size is fixed.
Each regime has its niche but only the latter regime applies to exa-scale data
dimension. We illustrate this high dimensional framework for the problem of
correlation mining, where it is the matrix of pairwise and partial correlations
among the variables that are of interest. We demonstrate various regimes of
correlation mining based on the unifying perspective of high dimensional
learning rates and sample complexity for different structured covariance models
and different inference tasks.
Extraction of Pharmacokinetic Evidence of Drug-drug Interactions from the Literature
Authors: Artemy Kolchinsky, Anália Lourenço, Heng-Yi Wu, Lang Li, Luis M. Rocha
Date: 2014-12-02 00:00:00
Tasks: Epidemiology, General Classification, Literature Mining, Sentence, Sentence Classification

Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a
subject of intense scientific interest. Biomedical literature mining can aid
DDI research by extracting evidence for large numbers of potential interactions
from published literature and clinical databases. Though DDI is investigated in
domains ranging in scale from intracellular biochemistry to human populations,
literature mining has not been used to extract specific types of experimental
evidence, which are reported differently for distinct experimental goals. We
focus on pharmacokinetic evidence for DDI, essential for identifying causal
mechanisms of putative interactions and as input for further pharmacological
and pharmaco-epidemiology investigations. We used manually curated corpora of
PubMed abstracts and annotated sentences to evaluate the efficacy of literature
mining on two tasks: first, identifying PubMed abstracts containing
pharmacokinetic evidence of DDIs; second, extracting sentences containing such
evidence from abstracts. We implemented a text mining pipeline and evaluated it
using several linear classifiers and a variety of feature transforms. The most
important textual features in the abstract and sentence classification tasks
were analyzed. We also investigated the performance benefits of using features
derived from PubMed metadata fields, various publicly available named entity
recognizers, and pharmacokinetic dictionaries. Several classifiers performed
very well in distinguishing relevant and irrelevant abstracts (reaching
F1~=0.93, MCC~=0.74, iAUC~=0.99) and sentences (F1~=0.76, MCC~=0.65,
iAUC~=0.83). We found that word bigram features were important for achieving
optimal classifier performance and that features derived from Medical Subject
Headings (MeSH) terms significantly improved abstract classification. ...
On sets of graded attribute implications with witnessed non-redundancy
Authors: Vilem Vychodil
Date: 2015-05-18 00:00:00
Tasks: Attribute

We study properties of particular non-redundant sets of if-then rules
describing dependencies between graded attributes. We introduce notions of
saturation and witnessed non-redundancy of sets of graded attribute
implications are show that bases of graded attribute implications given by
systems of pseudo-intents correspond to non-redundant sets of graded attribute
implications with saturated consequents where the non-redundancy is witnessed
by antecedents of the contained graded attribute implications. We introduce an
algorithm which transforms any complete set of graded attribute implications
parameterized by globalization into a base given by pseudo-intents.
Experimental evaluation is provided to compare the method of obtaining bases
for general parameterizations by hedges with earlier graph-based approaches.