pwc-archive/papers-with-abstracts huggingface dataset:
Predicting Human Interaction via Relative Attention Model
Authors: Yichao Yan, Bingbing Ni, Xiaokang Yang
Date: 2017-05-26 00:00:00
Tasks: model

Predicting human interaction is challenging as the on-going activity has to
be inferred based on a partially observed video. Essentially, a good algorithm
should effectively model the mutual influence between the two interacting
subjects. Also, only a small region in the scene is discriminative for
identifying the on-going interaction. In this work, we propose a relative
attention model to explicitly address these difficulties. Built on a
tri-coupled deep recurrent structure representing both interacting subjects and
global interaction status, the proposed network collects spatio-temporal
information from each subject, rectified with global interaction information,
yielding effective interaction representation. Moreover, the proposed network
also unifies an attention module to assign higher importance to the regions
which are relevant to the on-going action. Extensive experiments have been
conducted on two public datasets, and the results demonstrate that the proposed
relative attention network successfully predicts informative regions between
interacting subjects, which in turn yields superior human interaction
prediction accuracy.
A WL-SPPIM Semantic Model for Document Classification
Authors: Ming Li, Peilun Xiao, Ju Zhang
Date: 2017-05-26 00:00:00
Tasks: Classification, Document Classification, General Classification, model, text-classification, Text Classification

In this paper, we explore SPPIM-based text classification method, and the
experiment reveals that the SPPIM method is equal to or even superior than SGNS
method in text classification task on three international and standard text
datasets, namely 20newsgroups, Reuters52 and WebKB. Comparing to SGNS, although
SPPMI provides a better solution, it is not necessarily better than SGNS in
text classification tasks. Based on our analysis, SGNS takes into the
consideration of weight calculation during decomposition process, so it has
better performance than SPPIM in some standard datasets. Inspired by this, we
propose a WL-SPPIM semantic model based on SPPIM model, and experiment shows
that WL-SPPIM approach has better classification and higher scalability in the
text classification task compared with LDA, SGNS and SPPIM approaches.
Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135)
Authors: Xiaolin Wu, Xi Zhang
Date: 2016-11-13 00:00:00
Tasks: BIG-bench Machine Learning

In November 2016 we submitted to arXiv our paper "Automated Inference on
Criminality Using Face Images". It generated a great deal of discussions in the
Internet and some media outlets. Our work is only intended for pure academic
discussions; how it has become a media consumption is a total surprise to us.
Although in agreement with our critics on the need and importance of policing
AI research for the general good of the society, we are deeply baffled by the
ways some of them mispresented our work, in particular the motive and objective
of our research.
Taste or Addiction?: Using Play Logs to Infer Song Selection Motivation
Authors: Kosetsu Tsukuda, Masataka Goto
Date: 2017-05-26 00:00:00

Online music services are increasing in popularity. They enable us to analyze
people's music listening behavior based on play logs. Although it is known that
people listen to music based on topic (e.g., rock or jazz), we assume that when
a user is addicted to an artist, s/he chooses the artist's songs regardless of
topic. Based on this assumption, in this paper, we propose a probabilistic
model to analyze people's music listening behavior. Our main contributions are
three-fold. First, to the best of our knowledge, this is the first study
modeling music listening behavior by taking into account the influence of
addiction to artists. Second, by using real-world datasets of play logs, we
showed the effectiveness of our proposed model. Third, we carried out
qualitative experiments and showed that taking addiction into account enables
us to analyze music listening behavior from a new viewpoint in terms of how
people listen to music according to the time of day, how an artist's songs are
listened to by people, etc. We also discuss the possibility of applying the
analysis results to applications such as artist similarity computation and song
recommendation.
Effective Sampling: Fast Segmentation Using Robust Geometric Model Fitting
Authors: Ruwan Tennakoon, Alireza Sadri, Reza Hoseinnezhad, Alireza Bab-Hadiashar
Date: 2017-05-26 00:00:00
Tasks: Clustering

Identifying the underlying models in a set of data points contaminated by
noise and outliers, leads to a highly complex multi-model fitting problem. This
problem can be posed as a clustering problem by the projection of higher order
affinities between data points into a graph, which can then be clustered using
spectral clustering. Calculating all possible higher order affinities is
computationally expensive. Hence in most cases only a subset is used. In this
paper, we propose an effective sampling method to obtain a highly accurate
approximation of the full graph required to solve multi-structural model
fitting problems in computer vision. The proposed method is based on the
observation that the usefulness of a graph for segmentation improves as the
distribution of hypotheses (used to build the graph) approaches the
distribution of actual parameters for the given data. In this paper, we
approximate this actual parameter distribution using a k-th order statistics
based cost function and the samples are generated using a greedy algorithm
coupled with a data sub-sampling strategy. The experimental analysis shows that
the proposed method is both accurate and computationally efficient compared to
the state-of-the-art robust multi-model fitting techniques. The code is
publicly available from https://github.com/RuwanT/model-fitting-cbs.
Human Trajectory Prediction using Spatially aware Deep Attention Models
Authors: Daksh Varshneya, G. Srinivasaraghavan
Date: 2017-05-26 00:00:00
Tasks: Deep Attention, Trajectory Prediction

Trajectory Prediction of dynamic objects is a widely studied topic in the
field of artificial intelligence. Thanks to a large number of applications like
predicting abnormal events, navigation system for the blind, etc. there have
been many approaches to attempt learning patterns of motion directly from data
using a wide variety of techniques ranging from hand-crafted features to
sophisticated deep learning models for unsupervised feature learning. All these
approaches have been limited by problems like inefficient features in the case
of hand crafted features, large error propagation across the predicted
trajectory and no information of static artefacts around the dynamic moving
objects. We propose an end to end deep learning model to learn the motion
patterns of humans using different navigational modes directly from data using
the much popular sequence to sequence model coupled with a soft attention
mechanism. We also propose a novel approach to model the static artefacts in a
scene and using these to predict the dynamic trajectories. The proposed method,
tested on trajectories of pedestrians, consistently outperforms previously
proposed state of the art approaches on a variety of large scale data sets. We
also show how our architecture can be naturally extended to handle multiple
modes of movement (say pedestrians, skaters, bikers and buses) simultaneously.
Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge
Authors: Kingsley Kuan, Mathieu Ravaut, Gaurav Manek, Huiling Chen, Jie Lin, Babar Nazir, Cen Chen, Tse Chiang Howe, Zeng Zeng, Vijay Chandrasekhar
Date: 2017-05-26 00:00:00
Tasks: Lung Cancer Diagnosis

We present a deep learning framework for computer-aided lung cancer
diagnosis. Our multi-stage framework detects nodules in 3D lung CAT scans,
determines if each nodule is malignant, and finally assigns a cancer
probability based on these results. We discuss the challenges and advantages of
our framework. In the Kaggle Data Science Bowl 2017, our framework ranked 41st
out of 1972 teams.
Hierarchical Cellular Automata for Visual Saliency
Authors: Yao Qin, Mengyang Feng, Huchuan Lu, Garrison W. Cottrell
Date: 2017-05-26 00:00:00
Tasks: Saliency Detection

Saliency detection, finding the most important parts of an image, has become
increasingly popular in computer vision. In this paper, we introduce
Hierarchical Cellular Automata (HCA) -- a temporally evolving model to
intelligently detect salient objects. HCA consists of two main components:
Single-layer Cellular Automata (SCA) and Cuboid Cellular Automata (CCA). As an
unsupervised propagation mechanism, Single-layer Cellular Automata can exploit
the intrinsic relevance of similar regions through interactions with neighbors.
Low-level image features as well as high-level semantic information extracted
from deep neural networks are incorporated into the SCA to measure the
correlation between different image patches. With these hierarchical deep
features, an impact factor matrix and a coherence matrix are constructed to
balance the influences on each cell's next state. The saliency values of all
cells are iteratively updated according to a well-defined update rule.
Furthermore, we propose CCA to integrate multiple saliency maps generated by
SCA at different scales in a Bayesian framework. Therefore, single-layer
propagation and multi-layer integration are jointly modeled in our unified HCA.
Surprisingly, we find that the SCA can improve all existing methods that we
applied it to, resulting in a similar precision level regardless of the
original results. The CCA can act as an efficient pixel-wise aggregation
algorithm that can integrate state-of-the-art methods, resulting in even better
results. Extensive experiments on four challenging datasets demonstrate that
the proposed algorithm outperforms state-of-the-art conventional methods and is
competitive with deep learning based approaches.
Fuzzy Approach Topic Discovery in Health and Medical Corpora
Authors: Amir Karami, Aryya Gangopadhyay, Bin Zhou, Hadi Kharrazi
Date: 2017-05-02 00:00:00

The majority of medical documents and electronic health records (EHRs) are in
text format that poses a challenge for data processing and finding relevant
documents. Looking for ways to automatically retrieve the enormous amount of
health and medical knowledge has always been an intriguing topic. Powerful
methods have been developed in recent years to make the text processing
automatic. One of the popular approaches to retrieve information based on
discovering the themes in health & medical corpora is topic modeling, however,
this approach still needs new perspectives. In this research we describe fuzzy
latent semantic analysis (FLSA), a novel approach in topic modeling using fuzzy
perspective. FLSA can handle health & medical corpora redundancy issue and
provides a new method to estimate the number of topics. The quantitative
evaluations show that FLSA produces superior performance and features to latent
Dirichlet allocation (LDA), the most popular topic model.
Generative and Discriminative Text Classification with Recurrent Neural Networks
Authors: Dani Yogatama, Chris Dyer, Wang Ling, Phil Blunsom
Date: 2017-03-06 00:00:00
Tasks: Classification, Continual Learning, General Classification, text-classification, Text Classification

We empirically characterize the performance of discriminative and generative
LSTM models for text classification. We find that although RNN-based generative
models are more powerful than their bag-of-words ancestors (e.g., they account
for conditional dependencies across words in a document), they have higher
asymptotic error rates than discriminatively trained RNN models. However we
also find that generative models approach their asymptotic error rate more
rapidly than their discriminative counterparts---the same pattern that Ng &
Jordan (2001) proved holds for linear classification models that make more
naive conditional independence assumptions. Building on this finding, we
hypothesize that RNN-based generative classification models will be more robust
to shifts in the data distribution. This hypothesis is confirmed in a series of
experiments in zero-shot and continual learning settings that show that
generative models substantially outperform discriminative models.
Adapting Deep Visuomotor Representations with Weak Pairwise Constraints
Authors: Eric Tzeng, Coline Devin, Judy Hoffman, Chelsea Finn, Pieter Abbeel, Sergey Levine, Kate Saenko, Trevor Darrell
Date: 2015-11-23 00:00:00
Tasks: Domain Adaptation

Real-world robotics problems often occur in domains that differ significantly
from the robot's prior training environment. For many robotic control tasks,
real world experience is expensive to obtain, but data is easy to collect in
either an instrumented environment or in simulation. We propose a novel domain
adaptation approach for robot perception that adapts visual representations
learned on a large easy-to-obtain source dataset (e.g. synthetic images) to a
target real-world domain, without requiring expensive manual data annotation of
real world data before policy search. Supervised domain adaptation methods
minimize cross-domain differences using pairs of aligned images that contain
the same object or scene in both the source and target domains, thus learning a
domain-invariant representation. However, they require manual alignment of such
image pairs. Fully unsupervised adaptation methods rely on minimizing the
discrepancy between the feature distributions across domains. We propose a
novel, more powerful combination of both distribution and pairwise image
alignment, and remove the requirement for expensive annotation by using weakly
aligned pairs of images in the source and target domains. Focusing on adapting
from simulation to real world data using a PR2 robot, we evaluate our approach
on a manipulation task and show that by using weakly paired images, our method
compensates for domain shift more effectively than previous techniques,
enabling better robot performance in the real world.
NCBO Ontology Recommender 2.0: An Enhanced Approach for Biomedical Ontology Recommendation
Authors: Marcos Martinez-Romero, Clement Jonquet, Martin J. O'Connor, John Graybeal, Alejandro Pazos, Mark A. Musen
Date: 2016-11-18 00:00:00
Tasks: Data Integration

Biomedical researchers use ontologies to annotate their data with ontology
terms, enabling better data integration and interoperability. However, the
number, variety and complexity of current biomedical ontologies make it
cumbersome for researchers to determine which ones to reuse for their specific
needs. To overcome this problem, in 2010 the National Center for Biomedical
Ontology (NCBO) released the Ontology Recommender, which is a service that
receives a biomedical text corpus or a list of keywords and suggests ontologies
appropriate for referencing the indicated terms. We developed a new version of
the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a new
recommendation approach that evaluates the relevance of an ontology to
biomedical text data according to four criteria: (1) the extent to which the
ontology covers the input data; (2) the acceptance of the ontology in the
biomedical community; (3) the level of detail of the ontology classes that
cover the input data; and (4) the specialization of the ontology to the domain
of the input data. Our evaluation shows that the enhanced recommender provides
higher quality suggestions than the original approach, providing better
coverage of the input data, more detailed information about their concepts,
increased specialization for the domain of the input data, and greater
acceptance and use in the community. In addition, it provides users with more
explanatory information, along with suggestions of not only individual
ontologies but also groups of ontologies. It also can be customized to fit the
needs of different scenarios. Ontology Recommender 2.0 combines the strengths
of its predecessor with a range of adjustments and new features that improve
its reliability and usefulness. Ontology Recommender 2.0 recommends over 500
biomedical ontologies from the NCBO BioPortal platform, where it is openly
available.
Visually grounded learning of keyword prediction from untranscribed speech
Authors: Herman Kamper, Shane Settle, Gregory Shakhnarovich, Karen Livescu
Date: 2017-03-23 00:00:00
Tasks: Language Acquisition, TAG

During language acquisition, infants have the benefit of visual cues to
ground spoken language. Robots similarly have access to audio and visual
sensors. Recent work has shown that images and spoken captions can be mapped
into a meaningful common space, allowing images to be retrieved using speech
and vice versa. In this setting of images paired with untranscribed spoken
captions, we consider whether computer vision systems can be used to obtain
textual labels for the speech. Concretely, we use an image-to-words multi-label
visual classifier to tag images with soft textual labels, and then train a
neural network to map from the speech to these soft targets. We show that the
resulting speech system is able to predict which words occur in an
utterance---acting as a spoken bag-of-words classifier---without seeing any
parallel speech and text. We find that the model often confuses semantically
related words, e.g. "man" and "person", making it even more effective as a
semantic keyword spotter.
Mining Process Model Descriptions of Daily Life through Event Abstraction
Authors: Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst
Date: 2017-05-25 00:00:00

Process mining techniques focus on extracting insight in processes from event
logs. Process mining has the potential to provide valuable insights in
(un)healthy habits and to contribute to ambient assisted living solutions when
applied on data from smart home environments. However, events recorded in smart
home environments are on the level of sensor triggers, at which process
discovery algorithms produce overgeneralizing process models that allow for too
much behavior and that are difficult to interpret for human experts. We show
that abstracting the events to a higher-level interpretation can enable
discovery of more precise and more comprehensible models. We present a
framework for the extraction of features that can be used for abstraction with
supervised learning methods that is based on the XES IEEE standard for event
logs. This framework can automatically abstract sensor-level events to their
interpretation at the human activity level, after training it on training data
for which both the sensor and human activity events are known. We demonstrate
our abstraction framework on three real-life smart home event logs and show
that the process models that can be discovered after abstraction are more
precise indeed.
Rejection-Cascade of Gaussians: Real-time adaptive background subtraction framework
Authors: B Ravi Kiran, Arindam Das, Senthil Yogamani
Date: 2017-05-25 00:00:00

Background-Foreground classification is a well-studied problem in computer vision. Due to the pixel-wise nature of modeling and processing in the algorithm, it is usually difficult to satisfy real-time constraints. There is a trade-off between the speed (because of model complexity) and accuracy. Inspired by the rejection cascade of Viola-Jones classifier, we decompose the Gaussian Mixture Model (GMM) into an adaptive cascade of Gaussians(CoG). We achieve a good improvement in speed without compromising the accuracy with respect to the baseline GMM model. We demonstrate a speed-up factor of 4-5x and 17 percent average improvement in accuracy over Wallflowers surveillance datasets. The CoG is then demonstrated to over the latent space representation of images of a convolutional variational autoencoder(VAE). We provide initial results over CDW-2014 dataset, which could speed up background subtraction for deep architectures.
Operation Frames and Clubs in Kidney Exchange
Authors: Gabriele Farina, John P. Dickerson, Tuomas Sandholm
Date: 2017-05-25 00:00:00

A kidney exchange is a centrally-administered barter market where patients
swap their willing yet incompatible donors. Modern kidney exchanges use
2-cycles, 3-cycles, and chains initiated by non-directed donors (altruists who
are willing to give a kidney to anyone) as the means for swapping.
  We propose significant generalizations to kidney exchange. We allow more than
one donor to donate in exchange for their desired patient receiving a kidney.
We also allow for the possibility of a donor willing to donate if any of a
number of patients receive kidneys. Furthermore, we combine these notions and
generalize them. The generalization is to exchange among organ clubs, where a
club is willing to donate organs outside the club if and only if the club
receives organs from outside the club according to given specifications. We
prove that unlike in the standard model, the uncapped clearing problem is
NP-complete.
  We also present the notion of operation frames that can be used to sequence
the operations across batches, and present integer programming formulations for
the market clearing problems for these new types of organ exchanges.
  Experiments show that in the single-donation setting, operation frames
improve planning by 34%--51%. Allowing up to two donors to donate in exchange
for one kidney donated to their designated patient yields a further increase in
social welfare.
Diagonal Rescaling For Neural Networks
Authors: Jean Lafond, Nicolas Vasilache, Léon Bottou
Date: 2017-05-25 00:00:00

We define a second-order neural network stochastic gradient training
algorithm whose block-diagonal structure effectively amounts to normalizing the
unit activations. Investigating why this algorithm lacks in robustness then
reveals two interesting insights. The first insight suggests a new way to scale
the stepsizes, clarifying popular algorithms such as RMSProp as well as old
neural network tricks such as fanin stepsize scaling. The second insight
stresses the practical importance of dealing with fast changes of the curvature
of the cost.
Object Recognition with and without Objects
Authors: Zhuotun Zhu, Lingxi Xie, Alan L. Yuille
Date: 2016-11-20 00:00:00
Tasks: Object, Object Recognition

While recent deep neural networks have achieved a promising performance on
object recognition, they rely implicitly on the visual contents of the whole
image. In this paper, we train deep neural net- works on the foreground
(object) and background (context) regions of images respectively. Consider- ing
human recognition in the same situations, net- works trained on the pure
background without ob- jects achieves highly reasonable recognition performance
that beats humans by a large margin if only given context. However, humans
still outperform networks with pure object available, which indicates networks
and human beings have different mechanisms in understanding an image.
Furthermore, we straightforwardly combine multiple trained networks to explore
different visual cues learned by different networks. Experiments show that
useful visual hints can be explicitly learned separately and then combined to
achieve higher performance, which verifies the advantages of the proposed
framework.