pwc-archive/papers-with-abstracts huggingface dataset:
A Neural Markovian Multiresolution Image Labeling Algorithm
Authors: John Mashford, Brad Lane, Vic Ciesielski, Felix Lipkin
Date: 2018-03-20 00:00:00
Tasks: image-classification, Image Classification, Image Segmentation, Semantic Segmentation

This paper describes the results of formally evaluating the MCV (Markov concurrent vision) image labeling algorithm which is a (semi-) hierarchical algorithm commencing with a partition made up of single pixel regions and merging regions or subsets of regions using a Markov random field (MRF) image model. It is an example of a general approach to computer vision called concurrent vision in which the operations of image segmentation and image classification are carried out concurrently. While many image labeling algorithms output a single partition, or segmentation, the MCV algorithm outputs a sequence of partitions and this more elaborate structure may provide information that is valuable for higher level vision systems. With certain types of MRF the component of the system for image evaluation can be implemented as a hardwired feed forward neural network. While being applicable to images (i.e. 2D signals), the algorithm is equally applicable to 1D signals (e.g. speech) or 3D signals (e.g. video sequences) (though its performance in such domains remains to be tested). The algorithm is assessed using subjective and objective criteria with very good results.
Learning Dynamic Memory Networks for Object Tracking
Authors: Tianyu Yang, Antoni B. Chan
Date: 2018-03-20 00:00:00
Proceeding: ECCV 2018 9
Tasks: Object, Object Tracking, Template Matching, Visual Tracking

Template-matching methods for visual tracking have gained popularity recently
due to their comparable performance and fast speed. However, they lack
effective ways to adapt to changes in the target object's appearance, making
their tracking accuracy still far from state-of-the-art. In this paper, we
propose a dynamic memory network to adapt the template to the target's
appearance variations during tracking. An LSTM is used as a memory controller,
where the input is the search feature map and the outputs are the control
signals for the reading and writing process of the memory block. As the
location of the target is at first unknown in the search feature map, an
attention mechanism is applied to concentrate the LSTM input on the potential
target. To prevent aggressive model adaptivity, we apply gated residual
template learning to control the amount of retrieved memory that is used to
combine with the initial template. Unlike tracking-by-detection methods where
the object's information is maintained by the weight parameters of neural
networks, which requires expensive online fine-tuning to be adaptable, our
tracker runs completely feed-forward and adapts to the target's appearance
changes by updating the external memory. Moreover, unlike other tracking
methods where the model capacity is fixed after offline training --- the
capacity of our tracker can be easily enlarged as the memory requirements of a
task increase, which is favorable for memorizing long-term object information.
Extensive experiments on OTB and VOT demonstrates that our tracker MemTrack
performs favorably against state-of-the-art tracking methods while retaining
real-time speed of 50 fps.
DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars
Authors: Yuchi Tian, Kexin Pei, Suman Jana, Baishakhi Ray
Date: 2017-08-28 00:00:00
Tasks: Autonomous Vehicles

Recent advances in Deep Neural Networks (DNNs) have led to the development of
DNN-driven autonomous cars that, using sensors like camera, LiDAR, etc., can
drive without any human intervention. Most major manufacturers including Tesla,
GM, Ford, BMW, and Waymo/Google are working on building and testing different
types of autonomous vehicles. The lawmakers of several US states including
California, Texas, and New York have passed new legislation to fast-track the
process of testing and deployment of autonomous vehicles on their roads.
  However, despite their spectacular progress, DNNs, just like traditional
software, often demonstrate incorrect or unexpected corner case behaviors that
can lead to potentially fatal collisions. Several such real-world accidents
involving autonomous cars have already happened including one which resulted in
a fatality. Most existing testing techniques for DNN-driven vehicles are
heavily dependent on the manual collection of test data under different driving
conditions which become prohibitively expensive as the number of test
conditions increases.
  In this paper, we design, implement and evaluate DeepTest, a systematic
testing tool for automatically detecting erroneous behaviors of DNN-driven
vehicles that can potentially lead to fatal crashes. First, our tool is
designed to automatically generated test cases leveraging real-world changes in
driving conditions like rain, fog, lighting conditions, etc. DeepTest
systematically explores different parts of the DNN logic by generating test
inputs that maximize the numbers of activated neurons. DeepTest found thousands
of erroneous behaviors under different realistic driving conditions (e.g.,
blurring, rain, fog, etc.) many of which lead to potentially fatal crashes in
three top performing DNNs in the Udacity self-driving car challenge.
An application of cascaded 3D fully convolutional networks for medical image segmentation
Authors: Holger R. Roth, Hirohisa ODA, Xiangrong Zhou, Natsuki Shimizu, Ying Yang, Yuichiro Hayashi, Masahiro Oda, Michitaka Fujiwara, Kazunari Misawa, Kensaku MORI
Date: 2018-03-14 00:00:00
Tasks: 3D Medical Imaging Segmentation, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Recent advances in 3D fully convolutional networks (FCN) have made it
feasible to produce dense voxel-wise predictions of volumetric images. In this
work, we show that a multi-class 3D FCN trained on manually labeled CT scans of
several anatomical structures (ranging from the large organs to thin vessels)
can achieve competitive segmentation results, while avoiding the need for
handcrafting features or training class-specific models.
  To this end, we propose a two-stage, coarse-to-fine approach that will first
use a 3D FCN to roughly define a candidate region, which will then be used as
input to a second 3D FCN. This reduces the number of voxels the second FCN has
to classify to ~10% and allows it to focus on more detailed segmentation of the
organs and vessels.
  We utilize training and validation sets consisting of 331 clinical CT images
and test our models on a completely unseen data collection acquired at a
different hospital that includes 150 CT scans, targeting three anatomical
organs (liver, spleen, and pancreas). In challenging organs such as the
pancreas, our cascaded approach improves the mean Dice score from 68.5 to
82.2%, achieving the highest reported average score on this dataset. We compare
with a 2D FCN method on a separate dataset of 240 CT scans with 18 classes and
achieve a significantly higher performance in small organs and vessels.
Furthermore, we explore fine-tuning our models to different datasets.
  Our experiments illustrate the promise and robustness of current 3D FCN based
semantic segmentation of medical images, achieving state-of-the-art results.
Our code and trained models are available for download:
https://github.com/holgerroth/3Dunet_abdomen_cascade.
Towards Generalization and Simplicity in Continuous Control
Authors: Aravind Rajeswaran, Kendall Lowrey, Emanuel Todorov, Sham Kakade
Date: 2017-03-08 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: continuous-control, Continuous Control, OpenAI Gym

This work shows that policies with simple linear and RBF parameterizations
can be trained to solve a variety of continuous control tasks, including the
OpenAI gym benchmarks. The performance of these trained policies are
competitive with state of the art results, obtained with more elaborate
parameterizations such as fully connected neural networks. Furthermore,
existing training and testing scenarios are shown to be very limited and prone
to over-fitting, thus giving rise to only trajectory-centric policies. Training
with a diverse initial state distribution is shown to produce more global
policies with better generalization. This allows for interactive control
scenarios where the system recovers from large on-line perturbations; as shown
in the supplementary video.
Transferring Rich Deep Features for Facial Beauty Prediction
Authors: Lu Xu, Jinhai Xiang, Xiaohui Yuan
Date: 2018-03-20 00:00:00
Tasks: Face Verification, Facial Beauty Prediction, Prediction, regression

Feature extraction plays a significant part in computer vision tasks. In this
paper, we propose a method which transfers rich deep features from a pretrained
model on face verification task and feeds the features into Bayesian ridge
regression algorithm for facial beauty prediction. We leverage the deep neural
networks that extracts more abstract features from stacked layers. Through
simple but effective feature fusion strategy, our method achieves improved or
comparable performance on SCUT-FBP dataset and ECCV HotOrNot dataset. Our
experiments demonstrate the effectiveness of the proposed method and clarify
the inner interpretability of facial beauty perception.
3D Point Cloud Denoising using Graph Laplacian Regularization of a Low Dimensional Manifold Model
Authors: Jin Zeng, Gene Cheung, Michael Ng, Jiahao Pang, Cheng Yang
Date: 2018-03-20 00:00:00
Tasks: Denoising, graph construction, Stereo Matching, Stereo Matching Hand

3D point cloud - a new signal representation of volumetric objects - is a
discrete collection of triples marking exterior object surface locations in 3D
space. Conventional imperfect acquisition processes of 3D point cloud - e.g.,
stereo-matching from multiple viewpoint images or depth data acquired directly
from active light sensors - imply non-negligible noise in the data. In this
paper, we adopt a previously proposed low-dimensional manifold model for the
surface patches in the point cloud and seek self-similar patches to denoise
them simultaneously using the patch manifold prior. Due to discrete
observations of the patches on the manifold, we approximate the manifold
dimension computation defined in the continuous domain with a patch-based graph
Laplacian regularizer and propose a new discrete patch distance measure to
quantify the similarity between two same-sized surface patches for graph
construction that is robust to noise. We show that our graph Laplacian
regularizer has a natural graph spectral interpretation, and has desirable
numerical stability properties via eigenanalysis. Extensive simulation results
show that our proposed denoising scheme can outperform state-of-the-art methods
in objective metrics and can better preserve visually salient structural
features like edges.
Sparse Reduced Rank Regression With Nonconvex Regularization
Authors: Ziping Zhao, Daniel P. Palomar
Date: 2018-03-20 00:00:00
Tasks: Dimensionality Reduction, Econometrics, regression, Variable Selection

In this paper, the estimation problem for sparse reduced rank regression
(SRRR) model is considered. The SRRR model is widely used for dimension
reduction and variable selection with applications in signal processing,
econometrics, etc. The problem is formulated to minimize the least squares loss
with a sparsity-inducing penalty considering an orthogonality constraint.
Convex sparsity-inducing functions have been used for SRRR in literature. In
this work, a nonconvex function is proposed for better sparsity inducing. An
efficient algorithm is developed based on the alternating minimization (or
projection) method to solve the nonconvex optimization problem. Numerical
simulations show that the proposed algorithm is much more efficient compared to
the benchmark methods and the nonconvex function can result in a better
estimation accuracy.
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines
Authors: Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M. Bayen, Sham Kakade, Igor Mordatch, Pieter Abbeel
Date: 2018-03-20 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Reinforcement Learning, Policy Gradient Methods, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Policy gradient methods have enjoyed great success in deep reinforcement
learning but suffer from high variance of gradient estimates. The high variance
problem is particularly exasperated in problems with long horizons or
high-dimensional action spaces. To mitigate this issue, we derive a bias-free
action-dependent baseline for variance reduction which fully exploits the
structural form of the stochastic policy itself and does not make any
additional assumptions about the MDP. We demonstrate and quantify the benefit
of the action-dependent baseline through both theoretical analysis as well as
numerical results, including an analysis of the suboptimality of the optimal
state-dependent baseline. The result is a computationally efficient policy
gradient algorithm, which scales to high-dimensional control problems, as
demonstrated by a synthetic 2000-dimensional target matching task. Our
experimental results indicate that action-dependent baselines allow for faster
learning on standard reinforcement learning benchmarks and high-dimensional
hand manipulation and synthetic tasks. Finally, we show that the general idea
of including additional information in baselines for improved variance
reduction can be extended to partially observed and multi-agent tasks.
Controlling Decoding for More Abstractive Summaries with Copy-Based Networks
Authors: Noah Weber, Leena Shekhar, Niranjan Balasubramanian, Kyunghyun Cho
Date: 2018-03-19 00:00:00
Tasks: Abstractive Text Summarization, Extractive Summarization

Attention-based neural abstractive summarization systems equipped with copy
mechanisms have shown promising results. Despite this success, it has been
noticed that such a system generates a summary by mostly, if not entirely,
copying over phrases, sentences, and sometimes multiple consecutive sentences
from an input paragraph, effectively performing extractive summarization. In
this paper, we verify this behavior using the latest neural abstractive
summarization system - a pointer-generator network. We propose a simple
baseline method that allows us to control the amount of copying without
retraining. Experiments indicate that the method provides a strong baseline for
abstractive systems looking to obtain high ROUGE scores while minimizing
overlap with the source article, substantially reducing the n-gram overlap with
the original article while keeping within 2 points of the original model's
ROUGE score.
SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep Neural Networks
Authors: Teng Zhang, Johanna Carvajal, Daniel F. Smith, Kun Zhao, Arnold Wiliem, Peter Hobson, Anthony Jennings, Brian C. Lovell
Date: 2018-03-20 00:00:00

This work tackles the automatic fine-grained slide quality assessment problem
for digitized direct smears test using the Gram staining protocol. Automatic
quality assessment can provide useful information for the pathologists and the
whole digital pathology workflow. For instance, if the system found a slide to
have a low staining quality, it could send a request to the automatic slide
preparation system to remake the slide. If the system detects severe damage in
the slides, it could notify the experts that manual microscope reading may be
required. In order to address the quality assessment problem, we propose a deep
neural network based framework to automatically assess the slide quality in a
semantic way. Specifically, the first step of our framework is to perform dense
fine-grained region classification on the whole slide and calculate the region
distribution histogram. Next, our framework will generate assessments of the
slide quality from various perspectives: staining quality, information density,
damage level and which regions are more valuable for subsequent
high-magnification analysis. To make the information more accessible, we
present our results in the form of a heat map and text summaries. Additionally,
in order to stimulate research in this direction, we propose a novel dataset
for slide quality assessment. Experiments show that the proposed framework
outperforms recent related works.
On the role of synaptic stochasticity in training low-precision neural networks
Authors: Carlo Baldassi, Federica Gerace, Hilbert J. Kappen, Carlo Lucibello, Luca Saglietti, Enzo Tartaglione, Riccardo Zecchina
Date: 2017-10-26 00:00:00

Stochasticity and limited precision of synaptic weights in neural network
models are key aspects of both biological and hardware modeling of learning
processes. Here we show that a neural network model with stochastic binary
weights naturally gives prominence to exponentially rare dense regions of
solutions with a number of desirable properties such as robustness and good
generalization performance, while typical solutions are isolated and hard to
find. Binary solutions of the standard perceptron problem are obtained from a
simple gradient descent procedure on a set of real values parametrizing a
probability distribution over the binary synapses. Both analytical and
numerical results are presented. An algorithmic extension aimed at training
discrete deep neural networks is also investigated.
Closing the AI Knowledge Gap
Authors: Ziv Epstein, Blakeley H. Payne, Judy Hanwen Shen, Abhimanyu Dubey, Bjarke Felbo, Matthew Groh, Nick Obradovich, Manuel Cebrian, Iyad Rahwan
Date: 2018-03-20 00:00:00
Tasks: Fairness, Two-sample testing

AI researchers employ not only the scientific method, but also methodology
from mathematics and engineering. However, the use of the scientific method -
specifically hypothesis testing - in AI is typically conducted in service of
engineering objectives. Growing interest in topics such as fairness and
algorithmic bias show that engineering-focused questions only comprise a subset
of the important questions about AI systems. This results in the AI Knowledge
Gap: the number of unique AI systems grows faster than the number of studies
that characterize these systems' behavior. To close this gap, we argue that the
study of AI could benefit from the greater inclusion of researchers who are
well positioned to formulate and test hypotheses about the behavior of AI
systems. We examine the barriers preventing social and behavioral scientists
from conducting such studies. Our diagnosis suggests that accelerating the
scientific study of AI systems requires new incentives for academia and
industry, mediated by new tools and institutions. To address these needs, we
propose a two-sided marketplace called TuringBox. On one side, AI contributors
upload existing and novel algorithms to be studied scientifically by others. On
the other side, AI examiners develop and post machine intelligence tasks
designed to evaluate and characterize algorithmic behavior. We discuss this
market's potential to democratize the scientific study of AI behavior, and thus
narrow the AI Knowledge Gap.
Learning the Hierarchical Parts of Objects by Deep Non-Smooth Nonnegative Matrix Factorization
Authors: Jinshi Yu, Guoxu Zhou, Andrzej Cichocki, Shengli Xie
Date: 2018-03-20 00:00:00
Tasks: Clustering

Nonsmooth Nonnegative Matrix Factorization (nsNMF) is capable of producing
more localized, less overlapped feature representations than other variants of
NMF while keeping satisfactory fit to data. However, nsNMF as well as other
existing NMF methods is incompetent to learn hierarchical features of complex
data due to its shallow structure. To fill this gap, we propose a deep nsNMF
method coined by the fact that it possesses a deeper architecture compared with
standard nsNMF. The deep nsNMF not only gives parts-based features due to the
nonnegativity constraints, but also creates higher-level, more abstract
features by combing lower-level ones. The in-depth description of how deep
architecture can help to efficiently discover abstract features in dnsNMF is
presented. And we also show that the deep nsNMF has close relationship with the
deep autoencoder, suggesting that the proposed model inherits the major
advantages from both deep learning and NMF. Extensive experiments demonstrate
the standout performance of the proposed method in clustering analysis.
Monte Carlo Information Geometry: The dually flat case
Authors: Frank Nielsen, Gaëtan Hadjeres
Date: 2018-03-20 00:00:00
Tasks: Clustering

Exponential families and mixture families are parametric probability models
that can be geometrically studied as smooth statistical manifolds with respect
to any statistical divergence like the Kullback-Leibler (KL) divergence or the
Hellinger divergence. When equipping a statistical manifold with the KL
divergence, the induced manifold structure is dually flat, and the KL
divergence between distributions amounts to an equivalent Bregman divergence on
their corresponding parameters. In practice, the corresponding Bregman
generators of mixture/exponential families require to perform definite integral
calculus that can either be too time-consuming (for exponentially large
discrete support case) or even do not admit closed-form formula (for continuous
support case). In these cases, the dually flat construction remains theoretical
and cannot be used by information-geometric algorithms. To bypass this problem,
we consider performing stochastic Monte Carlo (MC) estimation of those
integral-based mixture/exponential family Bregman generators. We show that,
under natural assumptions, these MC generators are almost surely Bregman
generators. We define a series of dually flat information geometries, termed
Monte Carlo Information Geometries, that increasingly-finely approximate the
untractable geometry. The advantage of this MCIG is that it allows a practical
use of the Bregman algorithmic toolbox on a wide range of probability
distribution families. We demonstrate our approach with a clustering task on a
mixture family manifold.
Temporal Gaussian Mixture Layer for Videos
Authors: AJ Piergiovanni, Michael S. Ryoo
Date: 2018-03-16 00:00:00
Proceeding: ICLR 2019 5
Tasks: Action Detection, Activity Detection

We introduce a new convolutional layer named the Temporal Gaussian Mixture (TGM) layer and present how it can be used to efficiently capture longer-term temporal information in continuous activity videos. The TGM layer is a temporal convolutional layer governed by a much smaller set of parameters (e.g., location/variance of Gaussians) that are fully differentiable. We present our fully convolutional video models with multiple TGM layers for activity detection. The extensive experiments on multiple datasets, including Charades and MultiTHUMOS, confirm the effectiveness of TGM layers, significantly outperforming the state-of-the-arts.