pwc-archive/papers-with-abstracts huggingface dataset:
A Deeper Look at Power Normalizations
Authors: Piotr Koniusz, Hongguang Zhang, Fatih Porikli
Date: 2018-06-24 00:00:00
Proceeding: CVPR 2018 6
Tasks: Material Classification, Scene Recognition

Power Normalizations (PN) are very useful non-linear operators in the context
of Bag-of-Words data representations as they tackle problems such as feature
imbalance. In this paper, we reconsider these operators in the deep learning
setup by introducing a novel layer that implements PN for non-linear pooling of
feature maps. Specifically, by using a kernel formulation, our layer combines
the feature vectors and their respective spatial locations in the feature maps
produced by the last convolutional layer of CNN. Linearization of such a kernel
results in a positive definite matrix capturing the second-order statistics of
the feature vectors, to which PN operators are applied. We study two types of
PN functions, namely (i) MaxExp and (ii) Gamma, addressing their role and
meaning in the context of nonlinear pooling. We also provide a probabilistic
interpretation of these operators and derive their surrogates with well-behaved
gradients for end-to-end CNN learning. We apply our theory to practice by
implementing the PN layer on a ResNet-50 model and showcase experiments on four
benchmarks for fine-grained recognition, scene recognition, and material
classification. Our results demonstrate state-of-the-art performance across all
these tasks.
Towards A Unified Analysis of Random Fourier Features
Authors: Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic
Date: 2018-06-24 00:00:00

Random Fourier features is a widely used, simple, and effective technique for scaling up kernel methods. The existing theoretical analysis of the approach, however, remains focused on specific learning tasks and typically gives pessimistic bounds which are at odds with the empirical results. We tackle these problems and provide the first unified risk analysis of learning with random Fourier features using the squared error and Lipschitz continuous loss functions. In our bounds, the trade-off between the computational cost and the expected risk convergence rate is problem specific and expressed in terms of the regularization parameter and the \emph{number of effective degrees of freedom}. We study both the standard random Fourier features method for which we improve the existing bounds on the number of features required to guarantee the corresponding minimax risk convergence rate of kernel ridge regression, as well as a data-dependent modification which samples features proportional to \emph{ridge leverage scores} and further reduces the required number of features. As ridge leverage scores are expensive to compute, we devise a simple approximation scheme which provably reduces the computational cost without loss of statistical efficiency.
Dilated Temporal Fully-Convolutional Network for Semantic Segmentation of Motion Capture Data
Authors: Noshaba Cheema, Somayeh Hosseini, Janis Sprenger, Erik Herrmann, Han Du, Klaus Fischer, Philipp Slusallek
Date: 2018-06-24 00:00:00
Tasks: Action Segmentation, Motion Synthesis, Segmentation, Semantic Segmentation

Semantic segmentation of motion capture sequences plays a key part in many
data-driven motion synthesis frameworks. It is a preprocessing step in which
long recordings of motion capture sequences are partitioned into smaller
segments. Afterwards, additional methods like statistical modeling can be
applied to each group of structurally-similar segments to learn an abstract
motion manifold. The segmentation task however often remains a manual task,
which increases the effort and cost of generating large-scale motion databases.
We therefore propose an automatic framework for semantic segmentation of motion
capture data using a dilated temporal fully-convolutional network. Our model
outperforms a state-of-the-art model in action segmentation, as well as three
networks for sequence modeling. We further show our model is robust against
high noisy training labels.
Fusion of complex networks and randomized neural networks for texture analysis
Authors: Lucas C. Ribas, Jarbas J. M. Sa Junior, Leonardo F. S. Scabini, Odemir M. Bruno
Date: 2018-06-24 00:00:00
Tasks: Texture Classification

This paper presents a high discriminative texture analysis method based on the fusion of complex networks and randomized neural networks. In this approach, the input image is modeled as a complex networks and its topological properties as well as the image pixels are used to train randomized neural networks in order to create a signature that represents the deep characteristics of the texture. The results obtained surpassed the accuracies of many methods available in the literature. This performance demonstrates that our proposed approach opens a promising source of research, which consists of exploring the synergy of neural networks and complex networks in the texture analysis field.
Inferring Routing Preferences of Bicyclists from Sparse Sets of Trajectories
Authors: J. Oehrlein, A. Förster, D. Schunck, Y. Dehbi, R. Roscher, J. -H. Haunert
Date: 2018-06-24 00:00:00

Understanding the criteria that bicyclists apply when they choose their
routes is crucial for planning new bicycle paths or recommending routes to
bicyclists. This is becoming more and more important as city councils are
becoming increasingly aware of limitations of the transport infrastructure and
problems related to automobile traffic. Since different groups of cyclists have
different preferences, however, searching for a single set of criteria is prone
to failure. Therefore, in this paper, we present a new approach to classify
trajectories recorded and shared by bicyclists into different groups and, for
each group, to identify favored and unfavored road types. Based on these
results we show how to assign weights to the edges of a graph representing the
road network such that minimum-weight paths in the graph, which can be computed
with standard shortest-path algorithms, correspond to adequate routes. Our
method combines known algorithms for machine learning and the analysis of
trajectories in an innovative way and, thereby, constitutes a new comprehensive
solution for the problem of deriving routing preferences from initially
unclassified trajectories. An important property of our method is that it
yields reasonable results even if the given set of trajectories is sparse in
the sense that it does not cover all segments of the cycle network.
SSIMLayer: Towards Robust Deep Representation Learning via Nonlinear Structural Similarity
Authors: Ahmed Abobakr, Mohammed Hossny, Saeid Nahavandi
Date: 2018-06-24 00:00:00
Tasks: Representation Learning, SSIM

Deeper convolutional neural networks provide more capacity to approximate
complex mapping functions. However, increasing network depth imposes
difficulties on training and increases model complexity. This paper presents a
new nonlinear computational layer of considerably high capacity to the deep
convolutional neural network architectures. This layer performs a set of
comprehensive convolution operations that mimics the overall function of the
human visual system (HVS) via focusing on learning structural information in
its input. The core of its computations is evaluating the components of the
structural similarity metric (SSIM) in a setting that allows the kernels to
learn to match structural information. The proposed SSIMLayer is inherently
nonlinear and hence, it does not require subsequent nonlinear transformations.
Experiments conducted on CIFAR-10 benchmark demonstrates that the SSIMLayer
provides better convergence than the traditional convolutional layer, bypasses
the need for nonlinear transformations and shows more robustness against noise
perturbations and adversarial attacks.
Constructing Deep Neural Networks by Bayesian Network Structure Learning
Authors: Raanan Y. Rohekar, Shami Nisimov, Yaniv Gurwicz, Guy Koren, Gal Novik
Date: 2018-06-24 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: CPU, General Classification, image-classification, Image Classification

We introduce a principled approach for unsupervised structure learning of
deep neural networks. We propose a new interpretation for depth and inter-layer
connectivity where conditional independencies in the input distribution are
encoded hierarchically in the network structure. Thus, the depth of the network
is determined inherently. The proposed method casts the problem of neural
network structure learning as a problem of Bayesian network structure learning.
Then, instead of directly learning the discriminative structure, it learns a
generative graph, constructs its stochastic inverse, and then constructs a
discriminative graph. We prove that conditional-dependency relations among the
latent variables in the generative graph are preserved in the class-conditional
discriminative graph. We demonstrate on image classification benchmarks that
the deepest layers (convolutional and dense) of common networks can be replaced
by significantly smaller learned structures, while maintaining classification
accuracy---state-of-the-art on tested benchmarks. Our structure learning
algorithm requires a small computational cost and runs efficiently on a
standard desktop CPU.
One-shot Learning for Question-Answering in Gaokao History Challenge
Authors: Zhuosheng Zhang, Hai Zhao
Date: 2018-06-24 00:00:00
Proceeding: COLING 2018 8
Tasks: One-Shot Learning, Question Answering

Answering questions from university admission exams (Gaokao in Chinese) is a
challenging AI task since it requires effective representation to capture
complicated semantic relations between questions and answers. In this work, we
propose a hybrid neural model for deep question-answering task from history
examinations. Our model employs a cooperative gated neural network to retrieve
answers with the assistance of extra labels given by a neural turing machine
labeler. Empirical study shows that the labeler works well with only a small
training dataset and the gated mechanism is good at fetching the semantic
representation of lengthy answers. Experiments on question answering
demonstrate the proposed model obtains substantial performance gains over
various neural model baselines in terms of multiple evaluation metrics.
Subword-augmented Embedding for Cloze Reading Comprehension
Authors: Zhuosheng Zhang, Yafang Huang, Hai Zhao
Date: 2018-06-24 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Reading Comprehension, Reading Comprehension, Representation Learning

Representation learning is the foundation of machine reading comprehension.
In state-of-the-art models, deep learning methods broadly use word and
character level representations. However, character is not naturally the
minimal linguistic unit. In addition, with a simple concatenation of character
and word embedding, previous models actually give suboptimal solution. In this
paper, we propose to use subword rather than character for word embedding
enhancement. We also empirically explore different augmentation strategies on
subword-augmented embedding to enhance the cloze-style reading comprehension
model reader. In detail, we present a reader that uses subword-level
representation to augment word embedding with a short list to handle rare words
effectively. A thorough examination is conducted to evaluate the comprehensive
performance and generalization ability of the proposed reader. Experimental
results show that the proposed approach helps the reader significantly
outperform the state-of-the-art baselines on various public datasets.
Modeling Multi-turn Conversation with Deep Utterance Aggregation
Authors: Zhuosheng Zhang, Jiangtong Li, Pengfei Zhu, Hai Zhao, Gongshen Liu
Date: 2018-06-24 00:00:00
Proceeding: COLING 2018 8
Tasks: Conversational Response Selection, Retrieval

Multi-turn conversation understanding is a major challenge for building
intelligent dialogue systems. This work focuses on retrieval-based response
matching for multi-turn conversation whose related work simply concatenates the
conversation utterances, ignoring the interactions among previous utterances
for context modeling. In this paper, we formulate previous utterances into
context using a proposed deep utterance aggregation model to form a
fine-grained context representation. In detail, a self-matching attention is
first introduced to route the vital information in each utterance. Then the
model matches a response with each refined utterance and the final matching
score is obtained after attentive turns aggregation. Experimental results show
our model outperforms the state-of-the-art methods on three multi-turn
conversation benchmarks, including a newly introduced e-commerce dialogue
corpus.
Analysis of Cellular Feature Differences of Astrocytomas with Distinct Mutational Profiles Using Digitized Histopathology Images
Authors: Mousumi Roy, Fusheng Wang, George Teodoro, Jose Velazqeuz Vega, Daniel Brat, Jun Kong
Date: 2018-06-24 00:00:00
Tasks: Clustering, Dimensionality Reduction

Cellular phenotypic features derived from histopathology images are the basis
of pathologic diagnosis and are thought to be related to underlying molecular
profiles. Due to overwhelming cell numbers and population heterogeneity, it
remains challenging to quantitatively compute and compare features of cells
with distinct molecular signatures. In this study, we propose a self-reliant
and efficient analysis framework that supports quantitative analysis of
cellular phenotypic difference across distinct molecular groups. To demonstrate
efficacy, we quantitatively analyze astrocytomas that are molecularly
characterized as either Isocitrate Dehydrogenase (IDH) mutant (MUT) or wildtype
(WT) using imaging data from The Cancer Genome Atlas database. Representative
cell instances that are phenotypically different between these two groups are
retrieved after segmentation, feature computation, data pruning, dimensionality
reduction, and unsupervised clustering. Our analysis is generic and can be
applied to a wide set of cell-based biomedical research.
Segmentation of Overlapped Steatosis in Whole-Slide Liver Histopathology Microscopy Images
Authors: Mousumi Roy, Fusheng Wang, George Teodoro, Miriam B Vos, Alton Brad Farris, Jun Kong
Date: 2018-06-24 00:00:00
Tasks: Quantization

An accurate steatosis quantification with pathology tissue samples is of high
clinical importance. However, such pathology measurement is manually made in
most clinical practices, subject to severe reader variability due to large
sampling bias and poor reproducibility. Although some computerized automated
methods are developed to quantify the steatosis regions, they present limited
analysis capacity for high resolution whole-slide microscopy images and
accurate overlapped steatosis division. In this paper, we propose a method that
extracts an individual whole tissue piece at high resolution with minimum
background area by estimating tissue bounding box and rotation angle. This is
followed by the segmentation and segregation of steatosis regions with high
curvature point detection and an ellipse fitting quality assessment method. We
validate our method with isolated and overlapped steatosis regions in liver
tissue images of 11 patients. The experimental results suggest that our method
is promising for enhanced support of steatosis quantization during the
pathology review for liver disease treatment.
Character-Level Feature Extraction with Densely Connected Networks
Authors: Chanhee Lee, Young-Bum Kim, Dongyub Lee, Heuiseok Lim
Date: 2018-06-24 00:00:00
Proceeding: COLING 2018 8
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Part-Of-Speech Tagging, POS, POS Tagging

Generating character-level features is an important step for achieving good
results in various natural language processing tasks. To alleviate the need for
human labor in generating hand-crafted features, methods that utilize neural
architectures such as Convolutional Neural Network (CNN) or Recurrent Neural
Network (RNN) to automatically extract such features have been proposed and
have shown great results. However, CNN generates position-independent features,
and RNN is slow since it needs to process the characters sequentially. In this
paper, we propose a novel method of using a densely connected network to
automatically extract character-level features. The proposed method does not
require any language or task specific assumptions, and shows robustness and
effectiveness while being faster than CNN- or RNN-based methods. Evaluating
this method on three sequence labeling tasks - slot tagging, Part-of-Speech
(POS) tagging, and Named-Entity Recognition (NER) - we obtain state-of-the-art
performance with a 96.62 F1-score and 97.73% accuracy on slot tagging and POS
tagging, respectively, and comparable performance to the state-of-the-art 91.13
F1-score on NER.
Artwork Identification from Wearable Camera Images for Enhancing Experience of Museum Audiences
Authors: Rui Zhang, Yusuf Tas, Piotr Koniusz
Date: 2018-06-24 00:00:00
Tasks: Recommendation Systems

Recommendation systems based on image recognition could prove a vital tool in
enhancing the experience of museum audiences. However, for practical systems
utilizing wearable cameras, a number of challenges exist which affect the
quality of image recognition. In this pilot study, we focus on recognition of
museum collections by using a wearable camera in three different museum spaces.
We discuss the application of wearable cameras, and the practical and technical
challenges in devising a robust system that can recognize artworks viewed by
the visitors to create a detailed record of their visit. Specifically, to
illustrate the impact of different kinds of museum spaces on image recognition,
we collect three training datasets of museum exhibits containing variety of
paintings, clocks, and sculptures. Subsequently, we equip selected visitors
with wearable cameras to capture artworks viewed by them as they stroll along
exhibitions. We use Convolutional Neural Networks (CNN) which are pre-trained
on the ImageNet dataset and fine-tuned on each of the training sets for the
purpose of artwork identification. In the testing stage, we use CNNs to
identify artworks captured by the visitors with a wearable camera. We analyze
the accuracy of their recognition and provide an insight into the applicability
of such a system to further engage audiences with museum exhibitions.
CNN-based Action Recognition and Supervised Domain Adaptation on 3D Body Skeletons via Kernel Feature Maps
Authors: Yusuf Tas, Piotr Koniusz
Date: 2018-06-24 00:00:00
Tasks: Action Recognition, Domain Adaptation, Optical Flow Estimation, Temporal Action Localization

Deep learning is ubiquitous across many areas areas of computer vision. It
often requires large scale datasets for training before being fine-tuned on
small-to-medium scale problems. Activity, or, in other words, action
recognition, is one of many application areas of deep learning. While there
exist many Convolutional Neural Network architectures that work with the RGB
and optical flow frames, training on the time sequences of 3D body skeleton
joints is often performed via recurrent networks such as LSTM.
  In this paper, we propose a new representation which encodes sequences of 3D
body skeleton joints in texture-like representations derived from
mathematically rigorous kernel methods. Such a representation becomes the first
layer in a standard CNN network e.g., ResNet-50, which is then used in the
supervised domain adaptation pipeline to transfer information from the source
to target dataset. This lets us leverage the available Kinect-based data beyond
training on a single dataset and outperform simple fine-tuning on any two
datasets combined in a naive manner. More specifically, in this paper we
utilize the overlapping classes between datasets. We associate datapoints of
the same class via so-called commonality, known from the supervised domain
adaptation. We demonstrate state-of-the-art results on three publicly available
benchmarks.
Beyond Backprop: Online Alternating Minimization with Auxiliary Variables
Authors: Anna Choromanska, Benjamin Cowen, Sadhana Kumaravel, Ronny Luss, Mattia Rigotti, Irina Rish, Brian Kingsbury, Paolo DiAchille, Viatcheslav Gurev, Ravi Tejwani, Djallel Bouneffouf
Date: 2018-06-24 00:00:00
Tasks: Reinforcement Learning

Despite significant recent advances in deep neural networks, training them remains a challenge due to the highly non-convex nature of the objective function. State-of-the-art methods rely on error backpropagation, which suffers from several well-known issues, such as vanishing and exploding gradients, inability to handle non-differentiable nonlinearities and to parallelize weight-updates across layers, and biological implausibility. These limitations continue to motivate exploration of alternative training algorithms, including several recently proposed auxiliary-variable methods which break the complex nested objective function into local subproblems. However, those techniques are mainly offline (batch), which limits their applicability to extremely large datasets, as well as to online, continual or reinforcement learning. The main contribution of our work is a novel online (stochastic/mini-batch) alternating minimization (AM) approach for training deep neural networks, together with the first theoretical convergence guarantees for AM in stochastic settings and promising empirical results on a variety of architectures and datasets.
CT-image Super Resolution Using 3D Convolutional Neural Network
Authors: Yu-Kai Wang, Qizhi Teng, Xiaohai He, Junxi Feng, Tingrong Zhang
Date: 2018-06-24 00:00:00
Tasks: Computed Tomography (CT), Image Super-Resolution, Medical Diagnosis, SSIM, Super-Resolution

Computed Tomography (CT) imaging technique is widely used in geological
exploration, medical diagnosis and other fields. In practice, however, the
resolution of CT image is usually limited by scanning devices and great
expense. Super resolution (SR) methods based on deep learning have achieved
surprising performance in two-dimensional (2D) images. Unfortunately, there are
few effective SR algorithms for three-dimensional (3D) images. In this paper,
we proposed a novel network named as three-dimensional super resolution
convolutional neural network (3DSRCNN) to realize voxel super resolution for CT
images. To solve the practical problems in training process such as slow
convergence of network training, insufficient memory, etc., we utilized
adjustable learning rate, residual-learning, gradient clipping, momentum
stochastic gradient descent (SGD) strategies to optimize training procedure. In
addition, we have explored the empirical guidelines to set appropriate number
of layers of network and how to use residual learning strategy. Additionally,
previous learning-based algorithms need to separately train for different scale
factors for reconstruction, yet our single model can complete the multi-scale
SR. At last, our method has better performance in terms of PSNR, SSIM and
efficiency compared with conventional methods.
Generative Models for Pose Transfer
Authors: Patrick Chao, Alexander Li, Gokul Swamy
Date: 2018-06-24 00:00:00
Tasks: Face Detection, Pose Transfer

We investigate nearest neighbor and generative models for transferring pose
between persons. We take in a video of one person performing a sequence of
actions and attempt to generate a video of another person performing the same
actions. Our generative model (pix2pix) outperforms k-NN at both generating
corresponding frames and generalizing outside the demonstrated action set. Our
most salient contribution is determining a pipeline (pose detection, face
detection, k-NN based pairing) that is effective at perform-ing the desired
task. We also detail several iterative improvements and failure modes.