pwc-archive/papers-with-abstracts huggingface dataset:
AccUDNN: A GPU Memory Efficient Accelerator for Training Ultra-deep Neural Networks
Authors: Jinrong Guo, Wantao Liu, Wang Wang, Qu Lu, Songlin Hu, Jizhong Han, Ruixuan Li
Date: 2019-01-21 00:00:00
Tasks: GPU

Typically, Ultra-deep neural network(UDNN) tends to yield high-quality model, but its training process is usually resource intensive and time-consuming. Modern GPU's scarce DRAM capacity is the primary bottleneck that hinders the trainability and the training efficiency of UDNN. In this paper, we present "AccUDNN", an accelerator that aims to make the utmost use of finite GPU memory resources to speed up the training process of UDNN. AccUDNN mainly includes two modules: memory optimizer and hyperparameter tuner. Memory optimizer develops a performance-model guided dynamic swap out/in strategy, by offloading appropriate data to host memory, GPU memory footprint can be significantly slashed to overcome the restriction of trainability of UDNN. After applying the memory optimization strategy, hyperparameter tuner is designed to explore the efficiency-optimal minibatch size and the matched learning rate. Evaluations demonstrate that AccUDNN cuts down the GPU memory requirement of ResNet-152 from more than 24GB to 8GB. In turn, given 12GB GPU memory budget, the efficiency-optimal minibatch size can reach 4.2x larger than original Caffe. Benefiting from better utilization of single GPU's computing resources and fewer parameter synchronization of large minibatch size, 7.7x speed-up is achieved by 8 GPUs' cluster without any communication optimization and no accuracy losses.
LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators
Authors: Jianan Li, Jimei Yang, Aaron Hertzmann, Jianming Zhang, Tingfa Xu
Date: 2019-01-21 00:00:00
Tasks: Generative Adversarial Network, Layout Generation, Scene Generation

Layout is important for graphic design and scene generation. We propose a
novel Generative Adversarial Network, called LayoutGAN, that synthesizes
layouts by modeling geometric relations of different types of 2D elements. The
generator of LayoutGAN takes as input a set of randomly-placed 2D graphic
elements and uses self-attention modules to refine their labels and geometric
parameters jointly to produce a realistic layout. Accurate alignment is
critical for good layouts. We thus propose a novel differentiable wireframe
rendering layer that maps the generated layout to a wireframe image, upon which
a CNN-based discriminator is used to optimize the layouts in image space. We
validate the effectiveness of LayoutGAN in various experiments including MNIST
digit generation, document layout generation, clipart abstract scene generation
and tangram graphic design.
Real-time 3D Face-Eye Performance Capture of a Person Wearing VR Headset
Authors: Guoxian Song, Jianfei Cai, Tat-Jen Cham, Jianmin Zheng, Juyong Zhang, Henry Fuchs
Date: 2019-01-21 00:00:00

Teleconference or telepresence based on virtual reality (VR) headmount
display (HMD) device is a very interesting and promising application since HMD
can provide immersive feelings for users. However, in order to facilitate
face-to-face communications for HMD users, real-time 3D facial performance
capture of a person wearing HMD is needed, which is a very challenging task due
to the large occlusion caused by HMD. The existing limited solutions are very
complex either in setting or in approach as well as lacking the performance
capture of 3D eye gaze movement. In this paper, we propose a convolutional
neural network (CNN) based solution for real-time 3D face-eye performance
capture of HMD users without complex modification to devices. To address the
issue of lacking training data, we generate massive pairs of HMD face-label
dataset by data synthesis as well as collecting VR-IR eye dataset from multiple
subjects. Then, we train a dense-fitting network for facial region and an eye
gaze network to regress 3D eye model parameters. Extensive experimental results
demonstrate that our system can efficiently and effectively produce in real
time a vivid personalized 3D avatar with the correct identity, pose, expression
and eye motion corresponding to the HMD user.
Iterative Refinement for $\ell_p$-norm Regression
Authors: Deeksha Adil, Rasmus Kyng, Richard Peng, Sushant Sachdeva
Date: 2019-01-21 00:00:00
Tasks: regression

We give improved algorithms for the $\ell_{p}$-regression problem, $\min_{x}
\|x\|_{p}$ such that $A x=b,$ for all $p \in (1,2) \cup (2,\infty).$ Our
algorithms obtain a high accuracy solution in $\tilde{O}_{p}(m^{\frac{|p-2|}{2p
+ |p-2|}}) \le \tilde{O}_{p}(m^{\frac{1}{3}})$ iterations, where each iteration
requires solving an $m \times m$ linear system, $m$ being the dimension of the
ambient space.
  By maintaining an approximate inverse of the linear systems that we solve in
each iteration, we give algorithms for solving $\ell_{p}$-regression to $1 /
\text{poly}(n)$ accuracy that run in time $\tilde{O}_p(m^{\max\{\omega,
7/3\}}),$ where $\omega$ is the matrix multiplication constant. For the current
best value of $\omega > 2.37$, we can thus solve $\ell_{p}$ regression as fast
as $\ell_{2}$ regression, for all constant $p$ bounded away from $1.$
  Our algorithms can be combined with fast graph Laplacian linear equation
solvers to give minimum $\ell_{p}$-norm flow / voltage solutions to $1 /
\text{poly}(n)$ accuracy on an undirected graph with $m$ edges in
$\tilde{O}_{p}(m^{1 + \frac{|p-2|}{2p + |p-2|}}) \le
\tilde{O}_{p}(m^{\frac{4}{3}})$ time.
  For sparse graphs and for matrices with similar dimensions, our iteration
counts and running times improve on the $p$-norm regression algorithm by
[Bubeck-Cohen-Lee-Li STOC`18] and general-purpose convex optimization
algorithms. At the core of our algorithms is an iterative refinement scheme for
$\ell_{p}$-norms, using the smoothed $\ell_{p}$-norms introduced in the work of
Bubeck et al. Given an initial solution, we construct a problem that seeks to
minimize a quadratically-smoothed $\ell_{p}$ norm over a subspace, such that a
crude solution to this problem allows us to improve the initial solution by a
constant factor, leading to algorithms with fast convergence.
Pattern Generation Strategies for Improving Recognition of Handwritten Mathematical Expressions
Authors: Anh Duc Le, Bipin Indurkhya, Masaki Nakagawa
Date: 2019-01-21 00:00:00
Tasks: Decoder

Recognition of Handwritten Mathematical Expressions (HMEs) is a challenging
problem because of the ambiguity and complexity of two-dimensional handwriting.
Moreover, the lack of large training data is a serious issue, especially for
academic recognition systems. In this paper, we propose pattern generation
strategies that generate shape and structural variations to improve the
performance of recognition systems based on a small training set. For data
generation, we employ the public databases: CROHME 2014 and 2016 of online
HMEs. The first strategy employs local and global distortions to generate shape
variations. The second strategy decomposes an online HME into sub-online HMEs
to get more structural variations. The hybrid strategy combines both these
strategies to maximize shape and structural variations. The generated online
HMEs are converted to images for offline HME recognition. We tested our
strategies in an end-to-end recognition system constructed from a recent deep
learning model: Convolutional Neural Network and attention-based
encoder-decoder. The results of experiments on the CROHME 2014 and 2016
databases demonstrate the superiority and effectiveness of our strategies: our
hybrid strategy achieved classification rates of 48.78% and 45.60%,
respectively, on these databases. These results are competitive compared to
others reported in recent literature. Our generated datasets are openly
available for research community and constitute a useful resource for the HME
recognition research in future.
A deep learning approach to real-time parking occupancy prediction in spatio-temporal networks incorporating multiple spatio-temporal data sources
Authors: Shuguan Yang, Wei Ma, Xidong Pi, Sean Qian
Date: 2019-01-21 00:00:00

A deep learning model is applied for predicting block-level parking occupancy in real time. The model leverages Graph-Convolutional Neural Networks (GCNN) to extract the spatial relations of traffic flow in large-scale networks, and utilizes Recurrent Neural Networks (RNN) with Long-Short Term Memory (LSTM) to capture the temporal features. In addition, the model is capable of taking multiple heterogeneously structured traffic data sources as input, such as parking meter transactions, traffic speed, and weather conditions. The model performance is evaluated through a case study in Pittsburgh downtown area. The proposed model outperforms other baseline methods including multi-layer LSTM and Lasso with an average testing MAPE of 10.6\% when predicting block-level parking occupancies 30 minutes in advance. The case study also shows that, in generally, the prediction model works better for business areas than for recreational locations. We found that incorporating traffic speed and weather information can significantly improve the prediction performance. Weather data is particularly useful for improving predicting accuracy in recreational areas.
Predicting wind pressures around circular cylinders using machine learning techniques
Authors: Gang Hu, K. C. S. Kwok
Date: 2019-01-21 00:00:00
Tasks: BIG-bench Machine Learning

Numerous studies have been carried out to measure wind pressures around
circular cylinders since the early 20th century due to its engineering
significance. Consequently, a large amount of wind pressure data sets have
accumulated, which presents an excellent opportunity for using machine learning
(ML) techniques to train models to predict wind pressures around circular
cylinders. Wind pressures around smooth circular cylinders are a function of
mainly the Reynolds number (Re), turbulence intensity (Ti) of the incident
wind, and circumferential angle of the cylinder. Considering these three
parameters as the inputs, this study trained two ML models to predict mean and
fluctuating pressures respectively. Three machine learning algorithms including
decision tree regressor, random forest, and gradient boosting regression trees
(GBRT) were tested. The GBRT models exhibited the best performance for
predicting both mean and fluctuating pressures, and they are capable of making
accurate predictions for Re ranging from 10^4 to 10^6 and Ti ranging from 0% to
15%. It is believed that the GBRT models provide very efficient and economical
alternative to traditional wind tunnel tests and computational fluid dynamic
simulations for determining wind pressures around smooth circular cylinders
within the studied Re and Ti range.
Fitting 3D Shapes from Partial and Noisy Point Clouds with Evolutionary Computing
Authors: Jean F. Liénard
Date: 2019-01-20 00:00:00
Tasks: Diversity

Point clouds obtained from photogrammetry are noisy and incomplete models of
reality. We propose an evolutionary optimization methodology that is able to
approximate the underlying object geometry on such point clouds. This approach
assumes a priori knowledge on the 3D structure modeled and enables the
identification of a collection of primitive shapes approximating the scene.
Built-in mechanisms that enforce high shape diversity and adaptive population
size make this method suitable to modeling both simple and complex scenes. We
focus here on the case of cylinder approximations and we describe, test, and
compare a set of mutation operators designed for optimal exploration of their
search space. We assess the robustness and limitations of this algorithm
through a series of synthetic examples, and we finally demonstrate its general
applicability on two real-life cases in vegetation and industrial settings.
Fitting A Mixture Distribution to Data: Tutorial
Authors: Benyamin Ghojogh, Aydin Ghojogh, Mark Crowley, Fakhri Karray
Date: 2019-01-20 00:00:00
Tasks: Bayesian Inference, Bayesian Optimisation, Clustering

This paper is a step-by-step tutorial for fitting a mixture distribution to data. It merely assumes the reader has the background of calculus and linear algebra. Other required background is briefly reviewed before explaining the main algorithm. In explaining the main algorithm, first, fitting a mixture of two distributions is detailed and examples of fitting two Gaussians and Poissons, respectively for continuous and discrete cases, are introduced. Thereafter, fitting several distributions in general case is explained and examples of several Gaussians (Gaussian Mixture Model) and Poissons are again provided. Model-based clustering, as one of the applications of mixture distributions, is also introduced. Numerical simulations are also provided for both Gaussian and Poisson examples for the sake of better clarification.
Visual Entailment: A Novel Task for Fine-Grained Image Understanding
Authors: Ning Xie, Farley Lai, Derek Doran, Asim Kadav
Date: 2019-01-20 00:00:00
Tasks: Natural Language Inference, Question Answering, Sentence, Visual Entailment, Visual Question Answering, Visual Question Answering (VQA), Visual Reasoning

Existing visual reasoning datasets such as Visual Question Answering (VQA),
often suffer from biases conditioned on the question, image or answer
distributions. The recently proposed CLEVR dataset addresses these limitations
and requires fine-grained reasoning but the dataset is synthetic and consists
of similar objects and sentence structures across the dataset.
  In this paper, we introduce a new inference task, Visual Entailment (VE) -
consisting of image-sentence pairs whereby a premise is defined by an image,
rather than a natural language sentence as in traditional Textual Entailment
tasks. The goal of a trained VE model is to predict whether the image
semantically entails the text. To realize this task, we build a dataset SNLI-VE
based on the Stanford Natural Language Inference corpus and Flickr30k dataset.
We evaluate various existing VQA baselines and build a model called Explainable
Visual Entailment (EVE) system to address the VE task. EVE achieves up to 71%
accuracy and outperforms several other state-of-the-art VQA based models.
Finally, we demonstrate the explainability of EVE through cross-modal attention
visualizations. The SNLI-VE dataset is publicly available at
https://github.com/ necla-ml/SNLI-VE.
Localizing dexterous surgical tools in X-ray for image-based navigation
Authors: Cong Gao, Mathias Unberath, Russell Taylor, Mehran Armand
Date: 2019-01-20 00:00:00
Tasks: Retrieval

X-ray image based surgical tool navigation is fast and supplies accurate images of deep seated structures. Typically, recovering the 6 DOF rigid pose and deformation of tools with respect to the X-ray camera can be accurately achieved through intensity-based 2D/3D registration of 3D images or models to 2D X-rays. However, the capture range of image-based 2D/3D registration is inconveniently small suggesting that automatic and robust initialization strategies are of critical importance. This manuscript describes a first step towards leveraging semantic information of the imaged object to initialize 2D/3D registration within the capture range of image-based registration by performing concurrent segmentation and localization of dexterous surgical tools in X-ray images. We presented a learning-based strategy to simultaneously localize and segment dexterous surgical tools in X-ray images and demonstrate promising performance on synthetic and ex vivo data. We currently investigate methods to use semantic information extracted by the proposed network to reliably and robustly initialize image-based 2D/3D registration. While image-based 2D/3D registration has been an obvious focus of the CAI community, robust initialization thereof (albeit critical) has largely been neglected. This manuscript discusses learning-based retrieval of semantic information on imaged-objects as a stepping stone for such initialization and may therefore be of interest to the IPCAI community. Since results are still preliminary and only focus on localization, we target the Long Abstract category.
Training Neural Networks with Local Error Signals
Authors: Arild Nøkland, Lars Hiller Eidnes
Date: 2019-01-20 00:00:00
Tasks: Image Classification

Supervised training of neural networks for classification is typically performed with a global loss function. The loss function provides a gradient for the output layer, and this gradient is back-propagated to hidden layers to dictate an update direction for the weights. An alternative approach is to train the network with layer-wise loss functions. In this paper we demonstrate, for the first time, that layer-wise training can approach the state-of-the-art on a variety of image datasets. We use single-layer sub-networks and two different supervised loss functions to generate local error signals for the hidden layers, and we show that the combination of these losses help with optimization in the context of local learning. Using local errors could be a step towards more biologically plausible deep learning because the global error does not have to be transported back to hidden layers. A completely backprop free variant outperforms previously reported results among methods aiming for higher biological plausibility. Code is available https://github.com/anokland/local-loss
Removal of Batch Effects using Generative Adversarial Networks
Authors: Uddeshya Upadhyay, Arjun Jain
Date: 2019-01-20 00:00:00

Many biological data analysis processes like Cytometry or Next Generation Sequencing (NGS) produce massive amounts of data which needs to be processed in batches for down-stream analysis. Such datasets are prone to technical variations due to difference in handling the batches possibly at different times, by different experimenters or under other different conditions. This adds variation to the batches coming from the same source sample. These variations are known as Batch Effects. It is possible that these variations and natural variations due to biology confound but such situations can be avoided by performing experiments in a carefully planned manner. Batch effects can hamper downstream analysis and may also cause results to be inconclusive. Thus, it is essential to correct for these effects. This can be solved using a novel Generative Adversarial Networks (GANs) based framework that is proposed here, advantage of using this framework over other prior approaches is that here it is not required to choose a reproducing kernel and define its parameters. Results of the framework on a mass cytometry dataset are reported.
Improved Selective Refinement Network for Face Detection
Authors: Shifeng Zhang, Rui Zhu, Xiaobo Wang, Hailin Shi, Tianyu Fu, Shuo Wang, Tao Mei, Stan Z. Li
Date: 2019-01-20 00:00:00
Tasks: Data Augmentation, Face Detection, General Classification

As a long-standing problem in computer vision, face detection has attracted
much attention in recent decades for its practical applications. With the
availability of face detection benchmark WIDER FACE dataset, much of the
progresses have been made by various algorithms in recent years. Among them,
the Selective Refinement Network (SRN) face detector introduces the two-step
classification and regression operations selectively into an anchor-based face
detector to reduce false positives and improve location accuracy
simultaneously. Moreover, it designs a receptive field enhancement block to
provide more diverse receptive field. In this report, to further improve the
performance of SRN, we exploit some existing techniques via extensive
experiments, including new data augmentation strategy, improved backbone
network, MS COCO pretraining, decoupled classification module, segmentation
branch and Squeeze-and-Excitation block. Some of these techniques bring
performance improvements, while few of them do not well adapt to our baseline.
As a consequence, we present an improved SRN face detector by combining these
useful techniques together and obtain the best performance on widely used face
detection benchmark WIDER FACE dataset.
CommunityGAN: Community Detection with Generative Adversarial Nets
Authors: Yuting Jia, Qinqin Zhang, Wei-Nan Zhang, Xinbing Wang
Date: 2019-01-20 00:00:00
Tasks: Clustering, Community Detection, Graph Representation Learning, Representation Learning

Community detection refers to the task of discovering groups of vertices sharing similar properties or functions so as to understand the network data. With the recent development of deep learning, graph representation learning techniques are also utilized for community detection. However, the communities can only be inferred by applying clustering algorithms based on learned vertex embeddings. These general cluster algorithms like K-means and Gaussian Mixture Model cannot output much overlapped communities, which have been proved to be very common in many real-world networks. In this paper, we propose CommunityGAN, a novel community detection framework that jointly solves overlapping community detection and graph representation learning. First, unlike the embedding of conventional graph representation learning algorithms where the vector entry values have no specific meanings, the embedding of CommunityGAN indicates the membership strength of vertices to communities. Second, a specifically designed Generative Adversarial Net (GAN) is adopted to optimize such embedding. Through the minimax competition between the motif-level generator and discriminator, both of them can alternatively and iteratively boost their performance and finally output a better community structure. Extensive experiments on synthetic data and real-world tasks demonstrate that CommunityGAN achieves substantial community detection performance gains over the state-of-the-art methods.
Mixed Formal Learning: A Path to Transparent Machine Learning
Authors: Sandra Carrico
Date: 2019-01-20 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

This paper presents Mixed Formal Learning, a new architecture that learns
models based on formal mathematical representations of the domain of interest
and exposes latent variables. The second element in the architecture learns a
particular skill, typically by using traditional prediction or classification
mechanisms. Our key findings include that this architecture: (1) Facilitates
transparency by exposing key latent variables based on a learned mathematical
model; (2) Enables Low Shot and Zero Shot training of machine learning without
sacrificing accuracy or recall.
Dialogue Design and Management for Multi-Session Casual Conversation with Older Adults
Authors: S. Zahra Razavi, Lenhart K. Schubert, Benjamin Kane, Mohammad Rafayet Ali, Kimberly Van Orden, Tianyi Ma
Date: 2019-01-20 00:00:00
Tasks: Management

We address the problem of designing a conversational avatar capable of a sequence of casual conversations with older adults. Users at risk of loneliness, social anxiety or a sense of ennui may benefit from practicing such conversations in private, at their convenience. We describe an automatic spoken dialogue manager for LISSA, an on-screen virtual agent that can keep older users involved in conversations over several sessions, each lasting 10-20 minutes. The idea behind LISSA is to improve users' communication skills by providing feedback on their non-verbal behavior at certain points in the course of the conversations. In this paper, we analyze the dialogues collected from the first session between LISSA and each of 8 participants. We examine the quality of the conversations by comparing the transcripts with those collected in a WOZ setting. LISSA's contributions to the conversations were judged by research assistants who rated the extent to which the contributions were "natural", "on track", "encouraging", "understanding", "relevant", and "polite". The results show that the automatic dialogue manager was able to handle conversation with the users smoothly and naturally.