pwc-archive/papers-with-abstracts huggingface dataset:
Image Classification base on PCA of Multi-view Deep Representation
Authors: Yaoqi Sun, Liang Li, Liang Zheng, Ji Hu, Yatong Jiang, Chenggang Yan
Date: 2019-03-12 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

In the age of information explosion, image classification is the key
technology of dealing with and organizing a large number of image data.
Currently, the classical image classification algorithms are mostly based on
RGB images or grayscale images, and fail to make good use of the depth
information about objects or scenes. The depth information in the images has a
strong complementary effect, which can enhance the classification accuracy
significantly. In this paper, we propose an image classification technology
using principal component analysis based on multi-view depth characters. In
detail, firstly, the depth image of the original image is estimated; secondly,
depth characters are extracted from the RGB views and the depth view
separately, and then the reducing dimension operation through the PCA is
implemented. Eventually, the SVM is applied to image classification. The
experimental results show that the method has good performance.
Unsupervised motion saliency map estimation based on optical flow inpainting
Authors: L. Maczyta, P. Bouthemy, O. Le Meur
Date: 2019-03-12 00:00:00
Tasks: Optical Flow Estimation

The paper addresses the problem of motion saliency in videos, that is, identifying regions that undergo motion departing from its context. We propose a new unsupervised paradigm to compute motion saliency maps. The key ingredient is the flow inpainting stage. Candidate regions are determined from the optical flow boundaries. The residual flow in these regions is given by the difference between the optical flow and the flow inpainted from the surrounding areas. It provides the cue for motion saliency. The method is flexible and general by relying on motion information only. Experimental results on the DAVIS 2016 benchmark demonstrate that the method compares favourably with state-of-the-art video saliency methods.
Parallel Medical Imaging for Intelligent Medical Image Analysis: Concepts, Methods, and Applications
Authors: Chao Gou, Tianyu Shen, Wenbo Zheng, Huadan Xue, Hui Yu, Qiang Ji, Zhengyu Jin, Fei-Yue Wang
Date: 2019-03-12 00:00:00
Tasks: Diagnostic, Medical Image Analysis

There has been much progress in data-driven artificial intelligence technology for medical image analysis in the last decades. However, it still remains challenging due to its distinctive complexity of acquiring and annotating image data, extracting medical domain knowledge, and explaining the diagnostic decision for medical image analysis. In this paper, we propose a data-knowledge-driven framework termed as Parallel Medical Imaging (PMI) for intelligent medical image analysis based on the methodology of interactive ACP-based parallel intelligence. In the PMI framework, computational experiments with predictive learning in a data-driven way are conducted to extract medical knowledge for diagnostic decision support. Artificial imaging systems are introduced to select and prescriptively generate medical image data in a knowledge-driven way to utilize medical domain knowledge. Through the closed-loop optimization based on parallel execution, our proposed PMI framework can boost the generalization ability and alleviate the limitation of medical interpretation for diagnostic decisions. Furthermore, we illustrate the preliminary implementation of PMI method through the case studies of mammogram analysis and skin lesion image analysis. Experimental results on several public medical image datasets demonstrate the effectiveness of proposed PMI.
Fast Deep Stereo with 2D Convolutional Processing of Cost Signatures
Authors: Kyle Yee, Ayan Chakrabarti
Date: 2019-03-08 00:00:00
Tasks: Autonomous Vehicles, GPU

Modern neural network-based algorithms are able to produce highly accurate
depth estimates from stereo image pairs, nearly matching the reliability of
measurements from more expensive depth sensors. However, this accuracy comes
with a higher computational cost since these methods use network architectures
designed to compute and process matching scores across all candidate matches at
all locations, with floating point computations repeated across a match volume
with dimensions corresponding to both space and disparity. This leads to longer
running times to process each image pair, making them impractical for real-time
use in robots and autonomous vehicles. We propose a new stereo algorithm that
employs a significantly more efficient network architecture. Our method builds
an initial match cost volume using traditional matching costs that are fast to
compute, and trains a network to estimate disparity from this volume.
Crucially, our network only employs per-pixel and two-dimensional convolution
operations: to summarize the match information at each location as a
low-dimensional feature vector, and to spatially process these `cost-signature'
features to produce a dense disparity map. Experimental results on the KITTI
benchmark show that our method delivers competitive accuracy at significantly
higher speeds---running at 48 frames per second on a modern GPU.
Discriminative Principal Component Analysis: A REVERSE THINKING
Authors: Hanli Qiao
Date: 2019-03-12 00:00:00
Tasks: Face Recognition

In this paper, we propose a novel approach named by Discriminative Principal
Component Analysis which is abbreviated as Discriminative PCA in order to
enhance separability of PCA by Linear Discriminant Analysis (LDA). The proposed
method performs feature extraction by determining a linear projection that
captures the most scattered discriminative information. The most innovation of
Discriminative PCA is performing PCA on discriminative matrix rather than
original sample matrix. For calculating the required discriminative matrix
under low complexity, we exploit LDA on a converted matrix to obtain
within-class matrix and between-class matrix thereof. During the computation
process, we utilise direct linear discriminant analysis (DLDA) to solve the
encountered SSS problem. For evaluating the performances of Discriminative PCA
in face recognition, we analytically compare it with DLAD and PCA on four well
known facial databases, they are PIE, FERET, YALE and ORL respectively. Results
in accuracy and running time obtained by nearest neighbour classifier are
compared when different number of training images per person used. Not only the
superiority and outstanding performance of Discriminative PCA showed in
recognition rate, but also the comparable results of running time.
An End-to-End Network for Panoptic Segmentation
Authors: Huanyu Liu, Chao Peng, Changqian Yu, Jingbo Wang, Xu Liu, Gang Yu, Wei Jiang
Date: 2019-03-12 00:00:00
Proceeding: CVPR 2019 6
Tasks: Panoptic Segmentation, Segmentation

Panoptic segmentation, which needs to assign a category label to each pixel
and segment each object instance simultaneously, is a challenging topic.
Traditionally, the existing approaches utilize two independent models without
sharing features, which makes the pipeline inefficient to implement. In
addition, a heuristic method is usually employed to merge the results. However,
the overlapping relationship between object instances is difficult to determine
without sufficient context information during the merging process. To address
the problems, we propose a novel end-to-end network for panoptic segmentation,
which can efficiently and effectively predict both the instance and stuff
segmentation in a single network. Moreover, we introduce a novel spatial
ranking module to deal with the occlusion problem between the predicted
instances. Extensive experiments have been done to validate the performance of
our proposed method and promising results have been achieved on the COCO
Panoptic benchmark.
Placental Flattening via Volumetric Parameterization
Authors: S. Mazdak Abulnaga, Esra Abaci Turk, Mikhail Bessmeltsev, P. Ellen Grant, Justin Solomon, Polina Golland
Date: 2019-03-12 00:00:00
Tasks: Anatomy

We present a volumetric mesh-based algorithm for flattening the placenta to a canonical template to enable effective visualization of local anatomy and function. Monitoring placental function in vivo promises to support pregnancy assessment and to improve care outcomes. We aim to alleviate visualization and interpretation challenges presented by the shape of the placenta when it is attached to the curved uterine wall. To do so, we flatten the volumetric mesh that captures placental shape to resemble the well-studied ex vivo shape. We formulate our method as a map from the in vivo shape to a flattened template that minimizes the symmetric Dirichlet energy to control distortion throughout the volume. Local injectivity is enforced via constrained line search during gradient descent. We evaluate the proposed method on 28 placenta shapes extracted from MRI images in a clinical study of placental function. We achieve sub-voxel accuracy in mapping the boundary of the placenta to the template while successfully controlling distortion throughout the volume. We illustrate how the resulting mapping of the placenta enhances visualization of placental anatomy and function. Our code is freely available at https://github.com/mabulnaga/placenta-flattening .
Dense Classification and Implanting for Few-Shot Learning
Authors: Yann Lifchitz, Yannis Avrithis, Sylvaine Picard, Andrei Bursuc
Date: 2019-03-12 00:00:00
Proceeding: CVPR 2019 6
Tasks: Classification, Few-Shot Learning, General Classification, Transfer Learning

Training deep neural networks from few examples is a highly challenging and
key problem for many computer vision tasks. In this context, we are targeting
knowledge transfer from a set with abundant data to other sets with few
available examples. We propose two simple and effective solutions: (i) dense
classification over feature maps, which for the first time studies local
activations in the domain of few-shot learning, and (ii) implanting, that is,
attaching new neurons to a previously trained network to learn new,
task-specific features. On miniImageNet, we improve the prior state-of-the-art
on few-shot classification, i.e., we achieve 62.5%, 79.8% and 83.8% on 5-way
1-shot, 5-shot and 10-shot settings respectively.
A total variation based regularizer promoting piecewise-Lipschitz reconstructions
Authors: Martin Burger, Yury Korolev, Carola-Bibiane Schönlieb, Christiane Stollenwerk
Date: 2019-03-12 00:00:00

We introduce a new regularizer in the total variation family that promotes
reconstructions with a given Lipschitz constant (which can also vary
spatially). We prove regularizing properties of this functional and investigate
its connections to total variation and infimal convolution type regularizers
TVLp and, in particular, establish topological equivalence. Our numerical
experiments show that the proposed regularizer can achieve similar performance
as total generalized variation while having the advantage of a very intuitive
interpretation of its free parameter, which is just a local estimate of the
norm of the gradient. It also provides a natural approach to spatially adaptive
regularization.
Solving the Rubik's Cube with Approximate Policy Iteration
Authors: Stephen McAleer, Forest Agostinelli, Alexander Shmakov, Pierre Baldi
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Rubik's Cube

Recently, Approximate Policy Iteration (API) algorithms have achieved super-human proficiency in two-player zero-sum games such as Go, Chess, and Shogi without human data. These API algorithms iterate between two policies: a slow policy (tree search), and a fast policy (a neural network). In these two-player games, a reward is always received at the end of the game. However, the Rubik’s Cube has only a single solved state, and episodes are not guaranteed to terminate. This poses a major problem for these API algorithms since they rely on the reward received at the end of the game. We introduce Autodidactic Iteration: an API algorithm that overcomes the problem of sparse rewards by training on a distribution of states that allows the reward to propagate from the goal state to states farther away. Autodidactic Iteration is able to learn how to solve the Rubik’s Cube and the 15-puzzle without relying on human data. Our algorithm is able to solve 100% of randomly scrambled cubes while achieving a median solve length of 30 moves — less than or equal to solvers that employ human domain knowledge.
Redditors in Recovery: Text Mining Reddit to Investigate Transitions into Drug Addiction
Authors: John Lu, Sumati Sridhar, Ritika Pandey, Mohammad Al Hasan, George Mohler
Date: 2019-03-11 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Increasing rates of opioid drug abuse and heightened prevalence of online
support communities underscore the necessity of employing data mining
techniques to better understand drug addiction using these rapidly developing
online resources. In this work, we obtain data from Reddit, an online
collection of forums, to gather insight into drug use/misuse using text data
from users themselves. Specifically, using user posts, we trained 1) a binary
classifier which predicts transitions from casual drug discussion forums to
drug recovery forums and 2) a Cox regression model that outputs likelihoods of
such transitions. In doing so, we found that utterances of select drugs and
certain linguistic features contained in one's posts can help predict these
transitions. Using unfiltered drug-related posts, our research delineates drugs
that are associated with higher rates of transitions from recreational drug
discussion to support/recovery discussion, offers insight into modern drug
culture, and provides tools with potential applications in combating the opioid
crisis.
DeepTagRec: A Content-cum-User based Tag Recommendation Framework for Stack Overflow
Authors: Suman Kalyan Maity, Abhishek Panigrahi, Sayan Ghosh, Arundhati Banerjee, Pawan Goyal, Animesh Mukherjee
Date: 2019-03-10 00:00:00
Tasks: TAG

In this paper, we develop a content-cum-user based deep learning framework
DeepTagRec to recommend appropriate question tags on Stack Overflow. The
proposed system learns the content representation from question title and body.
Subsequently, the learnt representation from heterogeneous relationship between
user and tags is fused with the content representation for the final tag
prediction. On a very large-scale dataset comprising half a million question
posts, DeepTagRec beats all the baselines; in particular, it significantly
outperforms the best performing baseline T agCombine achieving an overall gain
of 60.8% and 36.8% in precision@3 and recall@10 respectively. DeepTagRec also
achieves 63% and 33.14% maximum improvement in exact-k accuracy and top-k
accuracy respectively over TagCombine
Finding Structure in Text, Genome and Other Symbolic Sequences
Authors: Ted Dunning
Date: 2012-07-08 00:00:00
Tasks: Information Retrieval, Language Identification, Retrieval

The statistical methods derived and described in this thesis provide new ways
to elucidate the structural properties of text and other symbolic sequences.
Generically, these methods allow detection of a difference in the frequency of
a single feature, the detection of a difference between the frequencies of an
ensemble of features and the attribution of the source of a text. These three
abstract tasks suffice to solve problems in a wide variety of settings.
Furthermore, the techniques described in this thesis can be extended to provide
a wide range of additional tests beyond the ones described here.
  A variety of applications for these methods are examined in detail. These
applications are drawn from the area of text analysis and genetic sequence
analysis. The textually oriented tasks include finding interesting collocations
and cooccurent phrases, language identification, and information retrieval. The
biologically oriented tasks include species identification and the discovery of
previously unreported long range structure in genes. In the applications
reported here where direct comparison is possible, the performance of these new
methods substantially exceeds the state of the art.
  Overall, the methods described here provide new and effective ways to analyse
text and other symbolic sequences. Their particular strength is that they deal
well with situations where relatively little data are available. Since these
methods are abstract in nature, they can be applied in novel situations with
relative ease.
Effective reinforcement learning based local search for the maximum k-plex problem
Authors: Yan Jin, John H. Drake, Una Benlic, Kun He
Date: 2019-03-13 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The maximum k-plex problem is a computationally complex problem, which
emerged from graph-theoretic social network studies. This paper presents an
effective hybrid local search for solving the maximum k-plex problem that
combines the recently proposed breakout local search algorithm with a
reinforcement learning strategy. The proposed approach includes distinguishing
features such as: a unified neighborhood search based on the swapping operator,
a distance-and-quality reward for actions and a new parameter control mechanism
based on reinforcement learning. Extensive experiments for the maximum k-plex
problem (k = 2, 3, 4, 5) on 80 benchmark instances from the second DIMACS
Challenge demonstrate that the proposed approach can match the best-known
results from the literature in all but four problem instances. In addition, the
proposed algorithm is able to find 32 new best solutions.
Richness of Deep Echo State Network Dynamics
Authors: Claudio Gallicchio, Alessio Micheli
Date: 2019-03-12 00:00:00

Reservoir Computing (RC) is a popular methodology for the efficient design of Recurrent Neural Networks (RNNs). Recently, the advantages of the RC approach have been extended to the context of multi-layered RNNs, with the introduction of the Deep Echo State Network (DeepESN) model. In this paper, we study the quality of state dynamics in progressively higher layers of DeepESNs, using tools from the areas of information theory and numerical analysis. Our experimental results on RC benchmark datasets reveal the fundamental role played by the strength of inter-reservoir connections to increasingly enrich the representations developed in higher layers. Our analysis also gives interesting insights into the possibility of effective exploitation of training algorithms based on stochastic gradient descent in the RC field.
Deep learning with asymmetric connections and Hebbian updates
Authors: Yali Amit
Date: 2018-11-19 00:00:00
Tasks: Deep Learning

We show that deep networks can be trained using Hebbian updates yielding
similar performance to ordinary back-propagation on challenging image datasets.
To overcome the unrealistic symmetry in connections between layers, implicit in
back-propagation, the feedback weights are separate from the feedforward
weights. The feedback weights are also updated with a local rule, the same as
the feedforward weights - a weight is updated solely based on the product of
activity of the units it connects. With fixed feedback weights as proposed in
Lillicrap et. al (2016) performance degrades quickly as the depth of the
network increases. If the feedforward and feedback weights are initialized with
the same values, as proposed in Zipser and Rumelhart (1990), they remain the
same throughout training thus precisely implementing back-propagation. We show
that even when the weights are initialized differently and at random, and the
algorithm is no longer performing back-propagation, performance is comparable
on challenging datasets. We also propose a cost function whose derivative can
be represented as a local Hebbian update on the last layer. Convolutional
layers are updated with tied weights across space, which is not biologically
plausible. We show that similar performance is achieved with untied layers,
also known as locally connected layers, corresponding to the connectivity
implied by the convolutional layers, but where weights are untied and updated
separately. In the linear case we show theoretically that the convergence of
the error to zero is accelerated by the update of the feedback weights.
Decay Replay Mining to Predict Next Process Events
Authors: Julian Theis, Houshang Darabi
Date: 2019-03-12 00:00:00
Tasks: Deep Learning

In complex processes, various events can happen in different sequences. The prediction of the next event given an a-priori process state is of importance in such processes. Recent methods have proposed deep learning techniques such as recurrent neural networks, developed on raw event logs, to predict the next event from a process state. However, such deep learning models by themselves lack a clear representation of the process states. At the same time, recent methods have neglected the time feature of event instances. In this paper, we take advantage of Petri nets as a powerful tool in modeling complex process behaviors considering time as an elemental variable. We propose an approach which starts from a Petri net process model constructed by a process mining algorithm. We enhance the Petri net model with time decay functions to create continuous process state samples. Finally, we use these samples in combination with discrete token movement counters and Petri net markings to train a deep learning model that predicts the next event. We demonstrate significant performance improvements and outperform the state-of-the-art methods on nine real-world benchmark event logs.
A Distributed Hierarchical SGD Algorithm with Sparse Global Reduction
Authors: Fan Zhou, Guojing Cong
Date: 2019-03-12 00:00:00
Tasks: Avg

Reducing communication in training large-scale machine learning applications on distributed platform is still a big challenge. To address this issue, we propose a distributed hierarchical averaging stochastic gradient descent (Hier-AVG) algorithm with infrequent global reduction by introducing local reduction. As a general type of parallel SGD, Hier-AVG can reproduce several popular synchronous parallel SGD variants by adjusting its parameters. We show that Hier-AVG with infrequent global reduction can still achieve standard convergence rate for non-convex optimization problems. In addition, we show that more frequent local averaging with more participants involved can lead to faster training convergence. By comparing Hier-AVG with another popular distributed training algorithm K-AVG, we show that through deploying local averaging with fewer number of global averaging, Hier-AVG can still achieve comparable training speed while frequently get better test accuracy. This indicates that local averaging can serve as an alternative remedy to effectively reduce communication overhead when the number of learners is large. Experimental results of Hier-AVG with several state-of-the-art deep neural nets on CIFAR-10 and IMAGENET-1K are presented to validate our analysis and show its superiority.