pwc-archive/papers-with-abstracts huggingface dataset:
A Comparative Study for Predicting Heart Diseases Using Data Mining Classification Methods
Authors: Israa Ahmed Zriqat, Ahmad Mousa Altamimi, Mohammad Azzeh
Date: 2017-04-10 00:00:00
Tasks: Ensemble Learning, General Classification

Improving the precision of heart diseases detection has been investigated by
many researchers in the literature. Such improvement induced by the
overwhelming health care expenditures and erroneous diagnosis. As a result,
various methodologies have been proposed to analyze the disease factors aiming
to decrease the physicians practice variation and reduce medical costs and
errors. In this paper, our main motivation is to develop an effective
intelligent medical decision support system based on data mining techniques. In
this context, five data mining classifying algorithms, with large datasets,
have been utilized to assess and analyze the risk factors statistically related
to heart diseases in order to compare the performance of the implemented
classifiers (e.g., Na\"ive Bayes, Decision Tree, Discriminant, Random Forest,
and Support Vector Machine). To underscore the practical viability of our
approach, the selected classifiers have been implemented using MATLAB tool with
two datasets. Results of the conducted experiments showed that all
classification algorithms are predictive and can give relatively correct
answer. However, the decision tree outperforms other classifiers with an
accuracy rate of 99.0% followed by Random forest. That is the case because both
of them have relatively same mechanism but the Random forest can build ensemble
of decision tree. Although ensemble learning has been proved to produce
superior results, but in our case the decision tree has outperformed its
ensemble version.
Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation
Authors: Bugra Tekin, Pablo Márquez-Neila, Mathieu Salzmann, Pascal Fua
Date: 2016-11-17 00:00:00
Proceeding: ICCV 2017 10
Tasks: 3D Human Pose Estimation, Monocular 3D Human Pose Estimation, Pose Estimation

Most recent approaches to monocular 3D human pose estimation rely on Deep
Learning. They typically involve regressing from an image to either 3D joint
coordinates directly or 2D joint locations from which 3D coordinates are
inferred. Both approaches have their strengths and weaknesses and we therefore
propose a novel architecture designed to deliver the best of both worlds by
performing both simultaneously and fusing the information along the way. At the
heart of our framework is a trainable fusion scheme that learns how to fuse the
information optimally instead of being hand-designed. This yields significant
improvements upon the state-of-the-art on standard 3D human pose estimation
benchmarks.
EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for kEyphrase ClassificaTION
Authors: Steffen Eger, Erik-Lân Do Dinh, Ilia Kuznetsov, Masoud Kiaeeha, Iryna Gurevych
Date: 2017-04-07 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: General Classification

This paper describes our approach to the SemEval 2017 Task 10: "Extracting
Keyphrases and Relations from Scientific Publications", specifically to Subtask
(B): "Classification of identified keyphrases". We explored three different
deep learning approaches: a character-level convolutional neural network (CNN),
a stacked learner with an MLP meta-classifier, and an attention based Bi-LSTM.
From these approaches, we created an ensemble of differently
hyper-parameterized systems, achieving a micro-F1-score of 0.63 on the test
data. Our approach ranks 2nd (score of 1st placed system: 0.64) out of four
according to this official score. However, we erroneously trained 2 out of 3
neural nets (the stacker and the CNN) on only roughly 15% of the full data,
namely, the original development set. When trained on the full data
(training+development), our ensemble has a micro-F1-score of 0.69. Our code is
available from https://github.com/UKPLab/semeval2017-scienceie.
Deep Affordance-grounded Sensorimotor Object Recognition
Authors: Spyridon Thermos, Georgios Th. Papadopoulos, Petros Daras, Gerasimos Potamianos
Date: 2017-04-10 00:00:00
Proceeding: CVPR 2017 7
Tasks: Object, Object Recognition

It is well-established by cognitive neuroscience that human perception of
objects constitutes a complex process, where object appearance information is
combined with evidence about the so-called object "affordances", namely the
types of actions that humans typically perform when interacting with them. This
fact has recently motivated the "sensorimotor" approach to the challenging task
of automatic object recognition, where both information sources are fused to
improve robustness. In this work, the aforementioned paradigm is adopted,
surpassing current limitations of sensorimotor object recognition research.
Specifically, the deep learning paradigm is introduced to the problem for the
first time, developing a number of novel neuro-biologically and
neuro-physiologically inspired architectures that utilize state-of-the-art
neural networks for fusing the available information sources in multiple ways.
The proposed methods are evaluated using a large RGB-D corpus, which is
specifically collected for the task of sensorimotor object recognition and is
made publicly available. Experimental results demonstrate the utility of
affordance information to object recognition, achieving an up to 29% relative
error reduction by its inclusion.
Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking
Authors: Laura Leal-Taixé, Anton Milan, Konrad Schindler, Daniel Cremers, Ian Reid, Stefan Roth
Date: 2017-04-10 00:00:00
Tasks: Multiple Object Tracking, Multiple People Tracking, Object, Object Tracking

Standardized benchmarks are crucial for the majority of computer vision
applications. Although leaderboards and ranking tables should not be
over-claimed, benchmarks often provide the most objective measure of
performance and are therefore important guides for research. We present a
benchmark for Multiple Object Tracking launched in the late 2014, with the goal
of creating a framework for the standardized evaluation of multiple object
tracking methods. This paper collects the two releases of the benchmark made so
far, and provides an in-depth analysis of almost 50 state-of-the-art trackers
that were tested on over 11000 frames. We show the current trends and
weaknesses of multiple people tracking methods, and provide pointers of what
researchers should be focusing on to push the field forward.
Fully Convolutional Instance-aware Semantic Segmentation
Authors: Yi Li, Haozhi Qi, Jifeng Dai, Xiangyang Ji, Yichen Wei
Date: 2016-11-23 00:00:00
Proceeding: CVPR 2017 7
Tasks: General Classification, Instance Segmentation, Segmentation, Semantic Segmentation

We present the first fully convolutional end-to-end solution for
instance-aware semantic segmentation task. It inherits all the merits of FCNs
for semantic segmentation and instance mask proposal. It performs instance mask
prediction and classification jointly. The underlying convolutional
representation is fully shared between the two sub-tasks, as well as between
all regions of interest. The proposed network is highly integrated and achieves
state-of-the-art performance in both accuracy and efficiency. It wins the COCO
2016 segmentation competition by a large margin. Code would be released at
\url{https://github.com/daijifeng001/TA-FCN}.
OctNet: Learning Deep 3D Representations at High Resolutions
Authors: Gernot Riegler, Ali Osman Ulusoy, Andreas Geiger
Date: 2016-11-15 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D Object Classification, General Classification, Vocal Bursts Intensity Prediction

We present OctNet, a representation for deep learning with sparse 3D data. In
contrast to existing models, our representation enables 3D convolutional
networks which are both deep and high resolution. Towards this goal, we exploit
the sparsity in the input data to hierarchically partition the space using a
set of unbalanced octrees where each leaf node stores a pooled feature
representation. This allows to focus memory allocation and computation to the
relevant dense regions and enables deeper networks without compromising
resolution. We demonstrate the utility of our OctNet representation by
analyzing the impact of resolution on several 3D tasks including 3D object
classification, orientation estimation and point cloud labeling.
Mapping Between fMRI Responses to Movies and their Natural Language Annotations
Authors: Kiran Vodrahalli, Po-Hsuan Chen, YIngyu Liang, Christopher Baldassano, Janice Chen, Esther Yong, Christopher Honey, Uri Hasson, Peter Ramadge, Ken Norman, Sanjeev Arora
Date: 2016-10-13 00:00:00
Tasks: Scene Classification, Sentence, Sentence Embedding, Sentence-Embedding

Several research groups have shown how to correlate fMRI responses to the
meanings of presented stimuli. This paper presents new methods for doing so
when only a natural language annotation is available as the description of the
stimulus. We study fMRI data gathered from subjects watching an episode of BBCs
Sherlock [1], and learn bidirectional mappings between fMRI responses and
natural language representations. We show how to leverage data from multiple
subjects watching the same movie to improve the accuracy of the mappings,
allowing us to succeed at a scene classification task with 72% accuracy (random
guessing would give 4%) and at a scene ranking task with average rank in the
top 4% (random guessing would give 50%). The key ingredients are (a) the use of
the Shared Response Model (SRM) and its variant SRM-ICA [2, 3] to aggregate
fMRI data from multiple subjects, both of which are shown to be superior to
standard PCA in producing low-dimensional representations for the tasks in this
paper; (b) a sentence embedding technique adapted from the natural language
processing (NLP) literature [4] that produces semantic vector representation of
the annotations; (c) using previous timestep information in the featurization
of the predictor data.
Object Detection in Videos with Tubelet Proposal Networks
Authors: Kai Kang, Hongsheng Li, Tong Xiao, Wanli Ouyang, Junjie Yan, Xihui Liu, Xiaogang Wang
Date: 2017-02-21 00:00:00
Proceeding: CVPR 2017 7
Tasks: Object, object-detection, Object Detection, Object Tracking

Object detection in videos has drawn increasing attention recently with the
introduction of the large-scale ImageNet VID dataset. Different from object
detection in static images, temporal information in videos is vital for object
detection. To fully utilize temporal information, state-of-the-art methods are
based on spatiotemporal tubelets, which are essentially sequences of associated
bounding boxes across time. However, the existing methods have major
limitations in generating tubelets in terms of quality and efficiency.
Motion-based methods are able to obtain dense tubelets efficiently, but the
lengths are generally only several frames, which is not optimal for
incorporating long-term temporal information. Appearance-based methods, usually
involving generic object tracking, could generate long tubelets, but are
usually computationally expensive. In this work, we propose a framework for
object detection in videos, which consists of a novel tubelet proposal network
to efficiently generate spatiotemporal proposals, and a Long Short-term Memory
(LSTM) network that incorporates temporal information from tubelet proposals
for achieving high object detection accuracy in videos. Experiments on the
large-scale ImageNet VID dataset demonstrate the effectiveness of the proposed
framework for object detection in videos.
Detail-revealing Deep Video Super-resolution
Authors: Xin Tao, Hongyun Gao, Renjie Liao, Jue Wang, Jiaya Jia
Date: 2017-04-10 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image Super-Resolution, Motion Compensation, Super-Resolution, Video Super-Resolution

Previous CNN-based video super-resolution approaches need to align multiple
frames to the reference. In this paper, we show that proper frame alignment and
motion compensation is crucial for achieving high quality results. We
accordingly propose a `sub-pixel motion compensation' (SPMC) layer in a CNN
framework. Analysis and experiments show the suitability of this layer in video
SR. The final end-to-end, scalable CNN framework effectively incorporates the
SPMC layer and fuses multiple frames to reveal image details. Our
implementation can generate visually and quantitatively high-quality results,
superior to current state-of-the-arts, without the need of parameter tuning.
DeepPermNet: Visual Permutation Learning
Authors: Rodrigo Santa Cruz, Basura Fernando, Anoop Cherian, Stephen Gould
Date: 2017-04-10 00:00:00
Proceeding: CVPR 2017 7
Tasks: Representation Learning, Rolling Shutter Correction

We present a principled approach to uncover the structure of visual data by
solving a novel deep learning task coined visual permutation learning. The goal
of this task is to find the permutation that recovers the structure of data
from shuffled versions of it. In the case of natural images, this task boils
down to recovering the original image from patches shuffled by an unknown
permutation matrix. Unfortunately, permutation matrices are discrete, thereby
posing difficulties for gradient-based methods. To this end, we resort to a
continuous approximation of these matrices using doubly-stochastic matrices
which we generate from standard CNN predictions using Sinkhorn iterations.
Unrolling these iterations in a Sinkhorn network layer, we propose DeepPermNet,
an end-to-end CNN model for this task. The utility of DeepPermNet is
demonstrated on two challenging computer vision problems, namely, (i) relative
attributes learning and (ii) self-supervised representation learning. Our
results show state-of-the-art performance on the Public Figures and OSR
benchmarks for (i) and on the classification and segmentation tasks on the
PASCAL VOC dataset for (ii).
Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction
Authors: Xiaolei Ma, Zhuang Dai, Zhengbing He, Jihui Na, Yong Wang, Yunpeng Wang
Date: 2017-01-16 00:00:00

This paper proposes a convolutional neural network (CNN)-based method that
learns traffic as images and predicts large-scale, network-wide traffic speed
with a high accuracy. Spatiotemporal traffic dynamics are converted to images
describing the time and space relations of traffic flow via a two-dimensional
time-space matrix. A CNN is applied to the image following two consecutive
steps: abstract traffic feature extraction and network-wide traffic speed
prediction. The effectiveness of the proposed method is evaluated by taking two
real-world transportation networks, the second ring road and north-east
transportation network in Beijing, as examples, and comparing the method with
four prevailing algorithms, namely, ordinary least squares, k-nearest
neighbors, artificial neural network, and random forest, and three deep
learning architectures, namely, stacked autoencoder, recurrent neural network,
and long-short-term memory network. The results show that the proposed method
outperforms other algorithms by an average accuracy improvement of 42.91%
within an acceptable execution time. The CNN can train the model in a
reasonable time and, thus, is suitable for large-scale transportation networks.
Distributed Learning for Cooperative Inference
Authors: Angelia Nedić, Alex Olshevsky, César A. Uribe
Date: 2017-04-10 00:00:00

We study the problem of cooperative inference where a group of agents
interact over a network and seek to estimate a joint parameter that best
explains a set of observations. Agents do not know the network topology or the
observations of other agents. We explore a variational interpretation of the
Bayesian posterior density, and its relation to the stochastic mirror descent
algorithm, to propose a new distributed learning algorithm. We show that, under
appropriate assumptions, the beliefs generated by the proposed algorithm
concentrate around the true parameter exponentially fast. We provide explicit
non-asymptotic bounds for the convergence rate. Moreover, we develop explicit
and computationally efficient algorithms for observation models belonging to
exponential families.
Formal approaches to a definition of agents
Authors: Martin Biehl
Date: 2017-04-10 00:00:00

This thesis contributes to the formalisation of the notion of an agent within
the class of finite multivariate Markov chains. Agents are seen as entities
that act, perceive, and are goal-directed.
  We present a new measure that can be used to identify entities (called
$\iota$-entities), some general requirements for entities in multivariate
Markov chains, as well as formal definitions of actions and perceptions
suitable for such entities.
  The intuition behind $\iota$-entities is that entities are spatiotemporal
patterns for which every part makes every other part more probable. The
measure, complete local integration (CLI), is formally investigated in general
Bayesian networks. It is based on the specific local integration (SLI) which is
measured with respect to a partition. CLI is the minimum value of SLI over all
partitions. We prove that $\iota$-entities are blocks in specific partitions of
the global trajectory. These partitions are the finest partitions that achieve
a given SLI value. We also establish the transformation behaviour of SLI under
permutations of nodes in the network.
  We go on to present three conditions on general definitions of entities.
These are not fulfilled by sets of random variables i.e.\ the perception-action
loop, which is often used to model agents, is too restrictive. We propose that
any general entity definition should in effect specify a subset (called an an
entity-set) of the set of all spatiotemporal patterns of a given multivariate
Markov chain. The set of $\iota$-entities is such a set. Importantly the
perception-action loop also induces an entity-set.
  We then propose formal definitions of actions and perceptions for arbitrary
entity-sets. These specialise to standard notions in case of the
perception-action loop entity-set.
  Finally we look at some very simple examples.
Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation
Authors: Zheng Xu, Mario A. T. Figueiredo, Xiaoming Yuan, Christoph Studer, Tom Goldstein
Date: 2017-04-10 00:00:00
Proceeding: CVPR 2017 7

Many modern computer vision and machine learning applications rely on solving
difficult optimization problems that involve non-differentiable objective
functions and constraints. The alternating direction method of multipliers
(ADMM) is a widely used approach to solve such problems. Relaxed ADMM is a
generalization of ADMM that often achieves better performance, but its
efficiency depends strongly on algorithm parameters that must be chosen by an
expert user. We propose an adaptive method that automatically tunes the key
algorithm parameters to achieve optimal performance without user oversight.
Inspired by recent work on adaptivity, the proposed adaptive relaxed ADMM
(ARADMM) is derived by assuming a Barzilai-Borwein style linear gradient. A
detailed convergence analysis of ARADMM is provided, and numerical results on
several applications demonstrate fast practical convergence.
Learning Important Features Through Propagating Activation Differences
Authors: Avanti Shrikumar, Peyton Greenside, Anshul Kundaje
Date: 2017-04-10 00:00:00
Proceeding: ICML 2017 8
Tasks: Interpretable Machine Learning

The purported "black box" nature of neural networks is a barrier to adoption in applications where interpretability is essential. Here we present DeepLIFT (Deep Learning Important FeaTures), a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input. DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. By optionally giving separate consideration to positive and negative contributions, DeepLIFT can also reveal dependencies which are missed by other approaches. Scores can be computed efficiently in a single backward pass. We apply DeepLIFT to models trained on MNIST and simulated genomic data, and show significant advantages over gradient-based methods. Video tutorial: http://goo.gl/qKb7pL, ICML slides: bit.ly/deeplifticmlslides, ICML talk: https://vimeo.com/238275076, code: http://goo.gl/RM8jvH.
All You Need is Beyond a Good Init: Exploring Better Solution for Training Extremely Deep Convolutional Neural Networks with Orthonormality and Modulation
Authors: Di Xie, Jiang Xiong, ShiLiang Pu
Date: 2017-03-06 00:00:00
Proceeding: CVPR 2017 7
Tasks: All

Deep neural network is difficult to train and this predicament becomes worse
as the depth increases. The essence of this problem exists in the magnitude of
backpropagated errors that will result in gradient vanishing or exploding
phenomenon. We show that a variant of regularizer which utilizes orthonormality
among different filter banks can alleviate this problem. Moreover, we design a
backward error modulation mechanism based on the quasi-isometry assumption
between two consecutive parametric layers. Equipped with these two ingredients,
we propose several novel optimization solutions that can be utilized for
training a specific-structured (repetitively triple modules of Conv-BNReLU)
extremely deep convolutional neural network (CNN) WITHOUT any shortcuts/
identity mappings from scratch. Experiments show that our proposed solutions
can achieve distinct improvements for a 44-layer and a 110-layer plain networks
on both the CIFAR-10 and ImageNet datasets. Moreover, we can successfully train
plain CNNs to match the performance of the residual counterparts.
  Besides, we propose new principles for designing network structure from the
insights evoked by orthonormality. Combined with residual structure, we achieve
comparative performance on the ImageNet dataset.
Learning From Noisy Large-Scale Datasets With Minimal Supervision
Authors: Andreas Veit, Neil Alldrin, Gal Chechik, Ivan Krasin, Abhinav Gupta, Serge Belongie
Date: 2017-01-06 00:00:00
Proceeding: CVPR 2017 7

We present an approach to effectively use millions of images with noisy
annotations in conjunction with a small subset of cleanly-annotated images to
learn powerful image representations. One common approach to combine clean and
noisy data is to first pre-train a network using the large noisy dataset and
then fine-tune with the clean dataset. We show this approach does not fully
leverage the information contained in the clean set. Thus, we demonstrate how
to use the clean annotations to reduce the noise in the large dataset before
fine-tuning the network using both the clean set and the full set with reduced
noise. The approach comprises a multi-task network that jointly learns to clean
noisy annotations and to accurately classify images. We evaluate our approach
on the recently released Open Images dataset, containing ~9 million images,
multiple annotations per image and over 6000 unique classes. For the small
clean set of annotations we use a quarter of the validation set with ~40k
images. Our results demonstrate that the proposed approach clearly outperforms
direct fine-tuning across all major categories of classes in the Open Image
dataset. Further, our approach is particularly effective for a large number of
classes with wide range of noise in annotations (20-80% false positive
annotations).