pwc-archive/papers-with-abstracts huggingface dataset:
Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding
Authors: Chenxu Luo, Zhenheng Yang, Peng Wang, Yang Wang, Wei Xu, Ram Nevatia, Alan Yuille
Date: 2018-10-14 00:00:00
Tasks: 3D geometry, Depth Estimation, Optical Flow Estimation, Scene Flow Estimation, Semantic Segmentation

Learning to estimate 3D geometry in a single frame and optical flow from consecutive frames by watching unlabeled videos via deep convolutional network has made significant progress recently. Current state-of-the-art (SoTA) methods treat the two tasks independently. One typical assumption of the existing depth estimation methods is that the scenes contain no independent moving objects. while object moving could be easily modeled using optical flow. In this paper, we propose to address the two tasks as a whole, i.e. to jointly understand per-pixel 3D geometry and motion. This eliminates the need of static scene assumption and enforces the inherent geometrical consistency during the learning process, yielding significantly improved results for both tasks. We call our method as "Every Pixel Counts++" or "EPC++". Specifically, during training, given two consecutive frames from a video, we adopt three parallel networks to predict the camera motion (MotionNet), dense depth map (DepthNet), and per-pixel optical flow between two frames (OptFlowNet) respectively. The three types of information are fed into a holistic 3D motion parser (HMP), and per-pixel 3D motion of both rigid background and moving objects are disentangled and recovered. Comprehensive experiments were conducted on datasets with different scenes, including driving scenario (KITTI 2012 and KITTI 2015 datasets), mixed outdoor/indoor scenes (Make3D) and synthetic animation (MPI Sintel dataset). Performance on the five tasks of depth estimation, optical flow estimation, odometry, moving object segmentation and scene flow estimation shows that our approach outperforms other SoTA methods. Code will be available at: https://github.com/chenxuluo/EPC.
Variational Neural Networks: Every Layer and Neuron Can Be Unique
Authors: Yiwei Li, Enzhi Li
Date: 2018-10-14 00:00:00

The choice of activation function can significantly influence the performance
of neural networks. The lack of guiding principles for the selection of
activation function is lamentable. We try to address this issue by introducing
our variational neural networks, where the activation function is represented
as a linear combination of possible candidate functions, and an optimal
activation is obtained via minimization of a loss function using gradient
descent method. The gradient formulae for the loss function with respect to
these expansion coefficients are central for the implementation of gradient
descent algorithm, and here we derive these gradient formulae.
Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks
Authors: Max Schwarzer, Bryce Rogan, Yadong Ruan, Zhengming Song, Diana Y. Lee, Allon G. Percus, Viet T. Chau, Bryan A. Moore, Esteban Rougier, Hari S. Viswanathan, Gowri Srinivasan
Date: 2018-10-14 00:00:00
Tasks: Data Augmentation

We propose a machine learning approach to address a key challenge in
materials science: predicting how fractures propagate in brittle materials
under stress, and how these materials ultimately fail. Our methods use deep
learning and train on simulation data from high-fidelity models, emulating the
results of these models while avoiding the overwhelming computational demands
associated with running a statistically significant sample of simulations. We
employ a graph convolutional network that recognizes features of the fracturing
material and a recurrent neural network that models the evolution of these
features, along with a novel form of data augmentation that compensates for the
modest size of our training data. We simultaneously generate predictions for
qualitatively distinct material properties. Results on fracture damage and
length are within 3% of their simulated values, and results on time to material
failure, which is notoriously difficult to predict even with high-fidelity
models, are within approximately 15% of simulated values. Once trained, our
neural networks generate predictions within seconds, rather than the hours
needed to run a single simulation.
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
Authors: Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Marco Domenico Santambrogio, Markus Weimer, Matteo Interlandi
Date: 2018-10-14 00:00:00
Tasks: BIG-bench Machine Learning, Prediction

Machine Learning models are often composed of pipelines of transformations.
While this design allows to efficiently execute single model components at
training time, prediction serving has different requirements such as low
latency, high throughput and graceful performance degradation under heavy load.
Current prediction serving systems consider models as black boxes, whereby
prediction-time-specific optimizations are ignored in favor of ease of
deployment. In this paper, we present PRETZEL, a prediction serving system
introducing a novel white box architecture enabling both end-to-end and
multi-model optimizations. Using production-like model pipelines, our
experiments show that PRETZEL is able to introduce performance improvements
over different dimensions; compared to state-of-the-art approaches PRETZEL is
on average able to reduce 99th percentile latency by 5.5x while reducing memory
footprint by 25x, and increasing throughput by 4.7x.
Asymptotics for Sketching in Least Squares Regression
Authors: Edgar Dobriban, Sifan Liu
Date: 2018-10-14 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Dimensionality Reduction, regression

We consider a least squares regression problem where the data has been generated from a linear model, and we are interested to learn the unknown regression parameters. We consider "sketch-and-solve" methods that randomly project the data first, and do regression after. Previous works have analyzed the statistical and computational performance of such methods. However, the existing analysis is not fine-grained enough to show the fundamental differences between various methods, such as the Subsampled Randomized Hadamard Transform (SRHT) and Gaussian projections. In this paper, we make progress on this problem, working in an asymptotic framework where the number of datapoints and dimension of features goes to infinity. We find the limits of the accuracy loss (for estimation and test error) incurred by popular sketching methods. We show separation between different methods, so that SRHT is better than Gaussian projections. Our theoretical results are verified on both real and synthetic data. The analysis of SRHT relies on novel methods from random matrix theory that may be of independent interest.
Assessing the Potential of Classical Q-learning in General Game Playing
Authors: Hui Wang, Michael Emmerich, Aske Plaat
Date: 2018-10-14 00:00:00
Tasks: Board Games, Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

After the recent groundbreaking results of AlphaGo and AlphaZero, we have
seen strong interests in deep reinforcement learning and artificial general
intelligence (AGI) in game playing. However, deep learning is
resource-intensive and the theory is not yet well developed. For small games,
simple classical table-based Q-learning might still be the algorithm of choice.
General Game Playing (GGP) provides a good testbed for reinforcement learning
to research AGI. Q-learning is one of the canonical reinforcement learning
methods, and has been used by (Banerjee $\&$ Stone, IJCAI 2007) in GGP. In this
paper we implement Q-learning in GGP for three small-board games (Tic-Tac-Toe,
Connect Four, Hex)\footnote{source code: https://github.com/wh1992v/ggp-rl}, to
allow comparison to Banerjee et al.. We find that Q-learning converges to a
high win rate in GGP. For the $\epsilon$-greedy strategy, we propose a first
enhancement, the dynamic $\epsilon$ algorithm. In addition, inspired by (Gelly
$\&$ Silver, ICML 2007) we combine online search (Monte Carlo Search) to
enhance offline learning, and propose QM-learning for GGP. Both enhancements
improve the performance of classical Q-learning. In this work, GGP allows us to
show, if augmented by appropriate enhancements, that classical table-based
Q-learning can perform well in small games.
A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation
Authors: Ismail Irmakci, Sarfaraz Hussein, Aydogan Savran, Rita R. Kalyani, David Reiter, Chee W. Chia, Kenneth W. Fishbein, Richard G. Spencer, Luigi Ferrucci, Ulas Bagci
Date: 2018-10-14 00:00:00
Tasks: Clustering, Image Segmentation, Segmentation, Semantic Segmentation

Magnetic resonance imaging (MRI) is the non-invasive modality of choice for
body tissue composition analysis due to its excellent soft tissue contrast and
lack of ionizing radiation. However, quantification of body composition
requires an accurate segmentation of fat, muscle and other tissues from MR
images, which remains a challenging goal due to the intensity overlap between
them. In this study, we propose a fully automated, data-driven image
segmentation platform that addresses multiple difficulties in segmenting MR
images such as varying inhomogeneity, non-standardness, and noise, while
producing high-quality definition of different tissues. In contrast to most
approaches in the literature, we perform segmentation operation by combining
three different MRI contrasts and a novel segmentation tool which takes into
account variability in the data. The proposed system, based on a novel affinity
definition within the fuzzy connectivity (FC) image segmentation family,
prevents the need for user intervention and reparametrization of the
segmentation algorithms. In order to make the whole system fully automated, we
adapt an affinity propagation clustering algorithm to roughly identify tissue
regions and image background. We perform a thorough evaluation of the proposed
algorithm's individual steps as well as comparison with several approaches from
the literature for the main application of muscle/fat separation. Furthermore,
whole-body tissue composition and brain tissue delineation were conducted to
show the generalization ability of the proposed system. This new automated
platform outperforms other state-of-the-art segmentation approaches both in
accuracy and efficiency.
Quantification of Trabeculae Inside the Heart from MRI Using Fractal Analysis
Authors: Md. Kamrul Hasan, Fakrul Islam Tushar
Date: 2018-09-30 00:00:00

Left ventricular non-compaction (LVNC) is a rare cardiomyopathy (CMP) that
should be considered as a possible diagnosis because of its potential
complications which are heart failure, ventricular arrhythmias, and embolic
events. For analysis cardiac functionality, extracting information from the
Left ventricular (LV) is already a broad field of Medical Imaging. Different
algorithms and strategies ranging that is semiautomated or automated has
already been developed to get useful information from such a critical structure
of heart. Trabeculae in the heart undergoes difference changes like solid from
spongy. Due to failure of this process left ventricle non-compaction occurred.
In this project, we will demonstrate the fractal dimension (FD) and manual
segmentation of the Magnetic Resonance Imaging (MRI) of the heart that quantify
amount of trabeculae inside the heart. The greater the value of fractal
dimension inside the heart indicates the greater complex pattern of the
trabeculae in the heart.
Distributed learning of deep neural network over multiple agents
Authors: Otkrist Gupta, Ramesh Raskar
Date: 2018-10-14 00:00:00

In domains such as health care and finance, shortage of labeled data and
computational resources is a critical issue while developing machine learning
algorithms. To address the issue of labeled data scarcity in training and
deployment of neural network-based systems, we propose a new technique to train
deep neural networks over several data sources. Our method allows for deep
neural networks to be trained using data from multiple entities in a
distributed fashion. We evaluate our algorithm on existing datasets and show
that it obtains performance which is similar to a regular neural network
trained on a single machine. We further extend it to incorporate
semi-supervised learning when training with few labeled samples, and analyze
any security concerns that may arise. Our algorithm paves the way for
distributed training of deep neural networks in data sensitive applications
when raw data may not be shared directly.
Fine-Grained Classification of Cervical Cells Using Morphological and Appearance Based Convolutional Neural Networks
Authors: Haoming Lin, Yuyang Hu, Siping Chen, Jianhua Yao, Ling Zhang
Date: 2018-10-14 00:00:00
Tasks: Classification, General Classification

Fine-grained classification of cervical cells into different abnormality
levels is of great clinical importance but remains very challenging. Contrary
to traditional classification methods that rely on hand-crafted or engineered
features, convolution neural network (CNN) can classify cervical cells based on
automatically learned deep features. However, CNN in previous studies do not
involve cell morphological information, and it is unknown whether morphological
features can be directly modeled by CNN to classify cervical cells. This paper
presents a CNN-based method that combines cell image appearance with cell
morphology for classification of cervical cells in Pap smear. The training
cervical cell dataset consists of adaptively re-sampled image patches coarsely
centered on the nuclei. Several CNN models (AlexNet, GoogleNet, ResNet and
DenseNet) pre-trained on ImageNet dataset were fine-tuned on the cervical
dataset for comparison. The proposed method is evaluated on the Herlev cervical
dataset by five-fold cross-validation at patient level splitting. Results show
that by adding cytoplasm and nucleus masks as raw morphological information
into appearance-based CNN learning, higher classification accuracies can be
achieved in general. Among the four CNN models, GoogleNet fed with both
morphological and appearance information obtains the highest classification
accuracies of 94.5% for 2-class classification task and 64.5% for 7-class
classification task. Our method demonstrates that combining cervical cell
morphology with appearance information can provide improved classification
performance, which is clinically important for early diagnosis of cervical
dysplastic changes.
A Simple Change Comparison Method for Image Sequences Based on Uncertainty Coefficient
Authors: Ruzhang Zhao, Yajun Fang, Berthold K. P. Horn
Date: 2018-10-14 00:00:00
Tasks: Change Detection, object-detection, Object Detection

For identification of change information in image sequences, most studies
focus on change detection in one image sequence, while few studies have
considered the change level comparison between two different image sequences.
Moreover, most studies require the detection of image information in details,
for example, object detection. Based on Uncertainty Coefficient(UC), this paper
proposes an innovative method CCUC for change comparison between two image
sequences. The proposed method is computationally efficient and simple to
implement. The change comparison stems from video monitoring system. The
limited number of provided screens and a large number of monitoring cameras
require the videos or image sequences ordered by change level. We demonstrate
this new method by applying it on two publicly available image sequences. The
results are able to show the method can distinguish the different change level
for sequences.
An ETF view of Dropout regularization
Authors: Dor Bank, Raja Giryes
Date: 2018-10-14 00:00:00

Dropout is a popular regularization technique in deep learning. Yet, the reason for its success is still not fully understood. This paper provides a new interpretation of Dropout from a frame theory perspective. By drawing a connection to recent developments in analog channel coding, we suggest that for a certain family of autoencoders with a linear encoder, optimizing the encoder with dropout regularization leads to an equiangular tight frame (ETF). Since this optimization is non-convex, we add another regularization that promotes such structures by minimizing the cross-correlation between filters in the network. We demonstrate its applicability in convolutional and fully connected layers in both feed-forward and recurrent networks. All these results suggest that there is indeed a relationship between dropout and ETF structure of the regularized linear operations.
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost
Authors: Henry Zhu, Abhishek Gupta, Aravind Rajeswaran, Sergey Levine, Vikash Kumar
Date: 2018-10-14 00:00:00
Tasks: Contact-rich Manipulation, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Dexterous multi-fingered robotic hands can perform a wide range of
manipulation skills, making them an appealing component for general-purpose
robotic manipulators. However, such hands pose a major challenge for autonomous
control, due to the high dimensionality of their configuration space and
complex intermittent contact interactions. In this work, we propose deep
reinforcement learning (deep RL) as a scalable solution for learning complex,
contact rich behaviors with multi-fingered hands. Deep RL provides an
end-to-end approach to directly map sensor readings to actions, without the
need for task specific models or policy classes. We show that contact-rich
manipulation behavior with multi-fingered hands can be learned by directly
training with model-free deep RL algorithms in the real world, with minimal
additional assumption and without the aid of simulation. We learn a variety of
complex behaviors on two different low-cost hardware platforms. We show that
each task can be learned entirely from scratch, and further study how the
learning process can be further accelerated by using a small number of human
demonstrations to bootstrap learning. Our experiments demonstrate that complex
multi-fingered manipulation skills can be learned in the real world in about
4-7 hours for most tasks, and that demonstrations can decrease this to 2-3
hours, indicating that direct deep RL training in the real world is a viable
and practical alternative to simulation and model-based control.
\url{https://sites.google.com/view/deeprl-handmanipulation}
Modeling relation paths for knowledge base completion via joint adversarial training
Authors: Chen Li, Xutan Peng, Shanghang Zhang, Hao Peng, Philip S. Yu, Min He, Linfeng Du, Lihong Wang
Date: 2018-10-14 00:00:00
Tasks: Knowledge Base Completion, Relation

Knowledge Base Completion (KBC), which aims at determining the missing relations between entity pairs, has received increasing attention in recent years. Most existing KBC methods focus on either embedding the Knowledge Base (KB) into a specific semantic space or leveraging the joint probability of Random Walks (RWs) on multi-hop paths. Only a few unified models take both semantic and path-related features into consideration with adequacy. In this paper, we propose a novel method to explore the intrinsic relationship between the single relation (i.e. 1-hop path) and multi-hop paths between paired entities. We use Hierarchical Attention Networks (HANs) to select important relations in multi-hop paths and encode them into low-dimensional vectors. By treating relations and multi-hop paths as two different input sources, we use a feature extractor, which is shared by two downstream components (i.e. relation classifier and source discriminator), to capture shared/similar information between them. By joint adversarial training, we encourage our model to extract features from the multi-hop paths which are representative for relation completion. We apply the trained model (except for the source discriminator) to several large-scale KBs for relation completion. Experimental results show that our method outperforms existing path information-based approaches. Since each sub-module of our model can be well interpreted, our model can be applied to a large number of relation learning tasks.
Adaptive Low-Nonnegative-Rank Approximation for State Aggregation of Markov Chains
Authors: Yaqi Duan, Mengdi Wang, Zaiwen Wen, Yaxiang Yuan
Date: 2018-10-14 00:00:00

This paper develops a low-nonnegative-rank approximation method to identify
the state aggregation structure of a finite-state Markov chain under an
assumption that the state space can be mapped into a handful of meta-states.
The number of meta-states is characterized by the nonnegative rank of the
Markov transition matrix. Motivated by the success of the nuclear norm
relaxation in low rank minimization problems, we propose an atomic regularizer
as a convex surrogate for the nonnegative rank and formulate a convex
optimization problem. Because the atomic regularizer itself is not
computationally tractable, we instead solve a sequence of problems involving a
nonnegative factorization of the Markov transition matrices by using the
proximal alternating linearized minimization method. Two methods for adjusting
the rank of factorization are developed so that local minima are escaped. One
is to append an additional column to the factorized matrices, which can be
interpreted as an approximation of a negative subgradient step. The other is to
reduce redundant dimensions by means of linear combinations. Overall, the
proposed algorithm very likely converges to the global solution. The efficiency
and statistical properties of our approach are illustrated on synthetic data.
We also apply our state aggregation algorithm on a Manhattan transportation
data set and make extensive comparisons with an existing method.
DPASF: A Flink Library for Streaming Data preprocessing
Authors: Alejandro Alcalde-Barros, Diego García-Gil, Salvador García, Francisco Herrera
Date: 2018-10-14 00:00:00
Tasks: feature selection

Data preprocessing techniques are devoted to correct or alleviate errors in
data. Discretization and feature selection are two of the most extended data
preprocessing techniques. Although we can find many proposals for static Big
Data preprocessing, there is little research devoted to the continuous Big Data
problem. Apache Flink is a recent and novel Big Data framework, following the
MapReduce paradigm, focused on distributed stream and batch data processing. In
this paper we propose a data stream library for Big Data preprocessing, named
DPASF, under Apache Flink. We have implemented six of the most popular data
preprocessing algorithms, three for discretization and the rest for feature
selection. The algorithms have been tested using two Big Data datasets.
Experimental results show that preprocessing can not only reduce the size of
the data, but to maintain or even improve the original accuracy in a short
time. DPASF contains useful algorithms when dealing with Big Data data streams.
The preprocessing algorithms included in the library are able to tackle Big
Datasets efficiently and to correct imperfections in the data.
AAAI FSS-18: Artificial Intelligence in Government and Public Sector Proceedings
Authors: Frank Stein, Alun Preece, Mihai Boicu
Date: 2018-10-14 00:00:00

Proceedings of the AAAI Fall Symposium on Artificial Intelligence in
Government and Public Sector, Arlington, Virginia, USA, October 18-20, 2018