pwc-archive/papers-with-abstracts huggingface dataset:
Learning Parametric Closed-Loop Policies for Markov Potential Games
Authors: Sergio Valcarcel Macua, Javier Zazo, Santiago Zazo
Date: 2018-02-03 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Reinforcement Learning, Reinforcement Learning

Multiagent systems where agents interact among themselves and with a
stochastic environment can be formalized as stochastic games. We study a
subclass named Markov potential games (MPGs) that appear often in economic and
engineering applications when the agents share a common resource. We consider
MPGs with continuous state-action variables, coupled constraints and nonconvex
rewards. Previous analysis followed a variational approach that is only valid
for very simple cases (convex rewards, invertible dynamics, and no coupled
constraints); or considered deterministic dynamics and provided open-loop (OL)
analysis, studying strategies that consist in predefined action sequences,
which are not optimal for stochastic environments. We present a closed-loop
(CL) analysis for MPGs and consider parametric policies that depend on the
current state. We provide easily verifiable, sufficient and necessary
conditions for a stochastic game to be an MPG, even for complex parametric
functions (e.g., deep neural networks); and show that a closed-loop Nash
equilibrium (NE) can be found (or at least approximated) by solving a related
optimal control problem (OCP). This is useful since solving an OCP--which is a
single-objective problem--is usually much simpler than solving the original set
of coupled OCPs that form the game--which is a multiobjective control problem.
This is a considerable improvement over the previously standard approach for
the CL analysis of MPGs, which gives no approximate solution if no NE belongs
to the chosen parametric family, and which is practical only for simple
parametric forms. We illustrate the theoretical contributions with an example
by applying our approach to a noncooperative communications engineering game.
We then solve the game with a deep reinforcement learning algorithm that learns
policies that closely approximates an exact variational NE of the game.
Improved Person Detection on Omnidirectional Images with Non-maxima Suppression
Authors: Roman Seidel, André Apitzsch, Gangolf Hirtz
Date: 2018-05-22 00:00:00
Tasks: Human Detection

We propose a person detector on omnidirectional images, an accurate method to
generate minimal enclosing rectangles of persons. The basic idea is to adapt
the qualitative detection performance of a convolutional neural network based
method, namely YOLOv2 to fish-eye images. The design of our approach picks up
the idea of a state-of-the-art object detector and highly overlapping areas of
images with their regions of interests. This overlap reduces the number of
false negatives. Based on the raw bounding boxes of the detector we fine-tuned
overlapping bounding boxes by three approaches: non-maximum suppression, soft
non-maximum suppression and soft non-maximum suppression with Gaussian
smoothing. The evaluation was done on the PIROPO database and an own annotated
Flat dataset, supplemented with bounding boxes on omnidirectional images. We
achieve an average precision of 64.4 % with YOLOv2 for the class person on
PIROPO and 77.6 % on Flat. For this purpose we fine-tuned the soft non-maximum
suppression with Gaussian smoothing.
Blind Predicting Similar Quality Map for Image Quality Assessment
Authors: Da Pan, Ping Shi, Ming Hou, Zefeng Ying, Sizhe Fu, Yuan Zhang
Date: 2018-05-22 00:00:00
Proceeding: CVPR 2018 6
Tasks: Blind Image Quality Assessment, Full reference image quality assessment, Full-Reference Image Quality Assessment, Image Quality Assessment

A key problem in blind image quality assessment (BIQA) is how to effectively
model the properties of human visual system in a data-driven manner. In this
paper, we propose a simple and efficient BIQA model based on a novel framework
which consists of a fully convolutional neural network (FCNN) and a pooling
network to solve this problem. In principle, FCNN is capable of predicting a
pixel-by-pixel similar quality map only from a distorted image by using the
intermediate similarity maps derived from conventional full-reference image
quality assessment methods. The predicted pixel-by-pixel quality maps have good
consistency with the distortion correlations between the reference and
distorted images. Finally, a deep pooling network regresses the quality map
into a score. Experiments have demonstrated that our predictions outperform
many state-of-the-art BIQA methods.
Policy Gradients for Contextual Recommendations
Authors: Feiyang Pan, Qingpeng Cai, Pingzhong Tang, Fuzhen Zhuang, Qing He
Date: 2018-02-12 00:00:00
Tasks: Decision Making, Multi-Armed Bandits, Policy Gradient Methods, Recommendation Systems

Decision making is a challenging task in online recommender systems. The
decision maker often needs to choose a contextual item at each step from a set
of candidates. Contextual bandit algorithms have been successfully deployed to
such applications, for the trade-off between exploration and exploitation and
the state-of-art performance on minimizing online costs. However, the
applicability of existing contextual bandit methods is limited by the
over-simplified assumptions of the problem, such as assuming a simple form of
the reward function or assuming a static environment where the states are not
affected by previous actions. In this work, we put forward Policy Gradients for
Contextual Recommendations (PGCR) to solve the problem without those
unrealistic assumptions. It optimizes over a restricted class of policies where
the marginal probability of choosing an item (in expectation of other items)
has a simple closed form, and the gradient of the expected return over the
policy in this class is in a succinct form. Moreover, PGCR leverages two useful
heuristic techniques called Time-Dependent Greed and Actor-Dropout. The former
ensures PGCR to be empirically greedy in the limit, and the latter addresses
the trade-off between exploration and exploitation by using the policy network
with Dropout as a Bayesian approximation. PGCR can solve the standard
contextual bandits as well as its Markov Decision Process generalization.
Therefore it can be applied to a wide range of realistic settings of
recommendations, such as personalized advertising. We evaluate PGCR on toy
datasets as well as a real-world dataset of personalized music recommendations.
Experiments show that PGCR enables fast convergence and low regret, and
outperforms both classic contextual-bandits and vanilla policy gradient
methods.
Knowledge-based Fully Convolutional Network and Its Application in Segmentation of Lung CT Images
Authors: Tao Yu, Yu Qiao, Huan Long
Date: 2018-05-22 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

A variety of deep neural networks have been applied in medical image
segmentation and achieve good performance. Unlike natural images, medical
images of the same imaging modality are characterized by the same pattern,
which indicates that same normal organs or tissues locate at similar positions
in the images. Thus, in this paper we try to incorporate the prior knowledge of
medical images into the structure of neural networks such that the prior
knowledge can be utilized for accurate segmentation. Based on this idea, we
propose a novel deep network called knowledge-based fully convolutional network
(KFCN) for medical image segmentation. The segmentation function and
corresponding error is analyzed. We show the existence of an asymptotically
stable region for KFCN which traditional FCN doesn't possess. Experiments
validate our knowledge assumption about the incorporation of prior knowledge
into the convolution kernels of KFCN and show that KFCN can achieve a
reasonable segmentation and a satisfactory accuracy.
General Bayesian Updating and the Loss-Likelihood Bootstrap
Authors: Simon Lyddon, Chris Holmes, Stephen Walker
Date: 2017-09-22 00:00:00

In this paper we revisit the weighted likelihood bootstrap, a method that
generates samples from an approximate Bayesian posterior of a parametric model.
We show that the same method can be derived, without approximation, under a
Bayesian nonparametric model with the parameter of interest defined as
minimising an expected negative log-likelihood under an unknown sampling
distribution. This interpretation enables us to extend the weighted likelihood
bootstrap to posterior sampling for parameters minimizing an expected loss. We
call this method the loss-likelihood bootstrap. We make a connection between
this and general Bayesian updating, which is a way of updating prior belief
distributions without needing to construct a global probability model, yet
requires the calibration of two forms of loss function. The loss-likelihood
bootstrap is used to calibrate the general Bayesian posterior by matching
asymptotic Fisher information. We demonstrate the methodology on a number of
examples.
Generative Code Modeling with Graphs
Authors: Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov
Date: 2018-05-22 00:00:00
Proceeding: ICLR 2019 5
Tasks: Structured Prediction

Generative models for source code are an interesting structured prediction
problem, requiring to reason about both hard syntactic and semantic constraints
as well as about natural, likely programs. We present a novel model for this
problem that uses a graph to represent the intermediate state of the generated
output. The generative procedure interleaves grammar-driven expansion steps
with graph augmentation and neural message passing steps. An experimental
evaluation shows that our new model can generate semantically meaningful
expressions, outperforming a range of strong baselines.
A Unified View of Piecewise Linear Neural Network Verification
Authors: Rudy Bunel, Ilker Turkaslan, Philip H. S. Torr, Pushmeet Kohli, M. Pawan Kumar
Date: 2017-11-01 00:00:00
Proceeding: NeurIPS 2018 12

The success of Deep Learning and its potential use in many safety-critical
applications has motivated research on formal verification of Neural Network
(NN) models. Despite the reputation of learned NN models to behave as black
boxes and the theoretical hardness of proving their properties, researchers
have been successful in verifying some classes of models by exploiting their
piecewise linear structure and taking insights from formal methods such as
Satisifiability Modulo Theory. These methods are however still far from scaling
to realistic neural networks. To facilitate progress on this crucial area, we
make two key contributions. First, we present a unified framework that
encompasses previous methods. This analysis results in the identification of
new methods that combine the strengths of multiple existing approaches,
accomplishing a speedup of two orders of magnitude compared to the previous
state of the art. Second, we propose a new data set of benchmarks which
includes a collection of previously released testcases. We use the benchmark to
provide the first experimental comparison of existing algorithms and identify
the factors impacting the hardness of verification problems.
Pose-Based Two-Stream Relational Networks for Action Recognition in Videos
Authors: Wei Wang, Jinjin Zhang, Chenyang Si, Liang Wang
Date: 2018-05-22 00:00:00
Tasks: Action Recognition, Action Recognition In Videos, Human-Object Interaction Detection, Temporal Action Localization, Vocal Bursts Valence Prediction

Recently, pose-based action recognition has gained more and more attention
due to the better performance compared with traditional appearance-based
methods. However, there still exist two problems to be further solved. First,
existing pose-based methods generally recognize human actions with captured 3D
human poses which are very difficult to obtain in real scenarios. Second, few
pose-based methods model the action-related objects in recognizing human-object
interaction actions in which objects play an important role. To solve the
problems above, we propose a pose-based two-stream relational network (PSRN)
for action recognition. In PSRN, one stream models the temporal dynamics of the
targeted 2D human pose sequences which are directly extracted from raw videos,
and the other stream models the action-related objects from a randomly sampled
video frame. Most importantly, instead of fusing two-streams in the class score
layer as before, we propose a pose-object relational network to model the
relationship between human poses and action-related objects. We evaluate the
proposed PSRN on two challenging benchmarks, i.e., Sub-JHMDB and PennAction.
Experimental results show that our PSRN obtains the state-the-of-art
performance on Sub-JHMDB (80.2%) and PennAction (98.1%). Our work opens a new
door to action recognition by combining 2D human pose extracted from raw video
and image appearance.
The Topology ToolKit
Authors: Julien Tierny, Guillaume Favelier, Joshua A. Levine, Charles Gueunet, Michael Michaux
Date: 2018-05-22 00:00:00
Tasks: Topological Data Analysis

This system paper presents the Topology ToolKit (TTK), a software platform designed for topological data analysis in scientific visualization. TTK provides a unified, generic, efficient, and robust implementation of key algorithms for the topological analysis of scalar data, including: critical points, integral lines, persistence diagrams, persistence curves, merge trees, contour trees, Morse-Smale complexes, fiber surfaces, continuous scatterplots, Jacobi sets, Reeb spaces, and more. TTK is easily accessible to end users due to a tight integration with ParaView. It is also easily accessible to developers through a variety of bindings (Python, VTK/C++) for fast prototyping or through direct, dependence-free, C++, to ease integration into pre-existing complex systems. While developing TTK, we faced several algorithmic and software engineering challenges, which we document in this paper. In particular, we present an algorithm for the construction of a discrete gradient that complies to the critical points extracted in the piecewise-linear setting. This algorithm guarantees a combinatorial consistency across the topological abstractions supported by TTK, and importantly, a unified implementation of topological data simplification for multi-scale exploration and analysis. We also present a cached triangulation data structure, that supports time efficient and generic traversals, which self-adjusts its memory usage on demand for input simplicial meshes and which implicitly emulates a triangulation for regular grids with no memory overhead. Finally, we describe an original software architecture, which guarantees memory efficient and direct accesses to TTK features, while still allowing for researchers powerful and easy bindings and extensions. TTK is open source (BSD license) and its code, online documentation and video tutorials are available on TTK's website.
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis
Authors: Rudy Bunel, Matthew Hausknecht, Jacob Devlin, Rishabh Singh, Pushmeet Kohli
Date: 2018-05-11 00:00:00
Proceeding: ICLR 2018 1
Tasks: Machine Translation, Program Synthesis, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Program synthesis is the task of automatically generating a program
consistent with a specification. Recent years have seen proposal of a number of
neural approaches for program synthesis, many of which adopt a sequence
generation paradigm similar to neural machine translation, in which
sequence-to-sequence models are trained to maximize the likelihood of known
reference programs. While achieving impressive results, this strategy has two
key limitations. First, it ignores Program Aliasing: the fact that many
different programs may satisfy a given specification (especially with
incomplete specifications such as a few input-output examples). By maximizing
the likelihood of only a single reference program, it penalizes many
semantically correct programs, which can adversely affect the synthesizer
performance. Second, this strategy overlooks the fact that programs have a
strict syntax that can be efficiently checked. To address the first limitation,
we perform reinforcement learning on top of a supervised model with an
objective that explicitly maximizes the likelihood of generating semantically
correct programs. For addressing the second limitation, we introduce a training
procedure that directly maximizes the probability of generating syntactically
correct programs that fulfill the specification. We show that our contributions
lead to improved accuracy of the models, especially in cases where the training
data is limited.
Memory-Efficient Deep Salient Object Segmentation Networks on Gridized Superpixels
Authors: Caglar Aytekin, Xingyang Ni, Francesco Cricri, Lixin Fan, Emre Aksu
Date: 2017-12-27 00:00:00
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection, Segmentation, Semantic Segmentation, Superpixels

Computer vision algorithms with pixel-wise labeling tasks, such as semantic
segmentation and salient object detection, have gone through a significant
accuracy increase with the incorporation of deep learning. Deep segmentation
methods slightly modify and fine-tune pre-trained networks that have hundreds
of millions of parameters. In this work, we question the need to have such
memory demanding networks for the specific task of salient object segmentation.
To this end, we propose a way to learn a memory-efficient network from scratch
by training it only on salient object detection datasets. Our method encodes
images to gridized superpixels that preserve both the object boundaries and the
connectivity rules of regular pixels. This representation allows us to use
convolutional neural networks that operate on regular grids. By using these
encoded images, we train a memory-efficient network using only 0.048\% of the
number of parameters that other deep salient object detection networks have.
Our method shows comparable accuracy with the state-of-the-art deep salient
object detection methods and provides a faster and a much more memory-efficient
alternative to them. Due to its easy deployment, such a network is preferable
for applications in memory limited devices such as mobile phones and IoT
devices.
On the importance of single directions for generalization
Authors: Ari S. Morcos, David G. T. Barrett, Neil C. Rabinowitz, Matthew Botvinick
Date: 2018-03-19 00:00:00
Proceeding: ICLR 2018 1

Despite their ability to memorize large datasets, deep neural networks often
achieve good generalization performance. However, the differences between the
learned solutions of networks which generalize and those which do not remain
unclear. Additionally, the tuning properties of single directions (defined as
the activation of a single unit or some linear combination of units in response
to some input) have been highlighted, but their importance has not been
evaluated. Here, we connect these lines of inquiry to demonstrate that a
network's reliance on single directions is a good predictor of its
generalization performance, across networks trained on datasets with different
fractions of corrupted labels, across ensembles of networks trained on datasets
with unmodified labels, across different hyperparameters, and over the course
of training. While dropout only regularizes this quantity up to a point, batch
normalization implicitly discourages single direction reliance, in part by
decreasing the class selectivity of individual units. Finally, we find that
class selectivity is a poor predictor of task importance, suggesting not only
that networks which generalize well minimize their dependence on individual
units by reducing their selectivity, but also that individually selective units
may not be necessary for strong network performance.
Gradient Energy Matching for Distributed Asynchronous Gradient Descent
Authors: Joeri Hermans, Gilles Louppe
Date: 2018-05-22 00:00:00

Distributed asynchronous SGD has become widely used for deep learning in
large-scale systems, but remains notorious for its instability when increasing
the number of workers. In this work, we study the dynamics of distributed
asynchronous SGD under the lens of Lagrangian mechanics. Using this
description, we introduce the concept of energy to describe the optimization
process and derive a sufficient condition ensuring its stability as long as the
collective energy induced by the active workers remains below the energy of a
target synchronous process. Making use of this criterion, we derive a stable
distributed asynchronous optimization procedure, GEM, that estimates and
maintains the energy of the asynchronous system below or equal to the energy of
sequential SGD with momentum. Experimental results highlight the stability and
speedup of GEM compared to existing schemes, even when scaling to one hundred
asynchronous workers. Results also indicate better generalization compared to
the targeted SGD with momentum.
MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
Authors: Pranav Rajpurkar, Jeremy Irvin, Aarti Bagul, Daisy Ding, Tony Duan, Hershel Mehta, Brandon Yang, Kaylie Zhu, Dillon Laird, Robyn L. Ball, Curtis Langlotz, Katie Shpanskaya, Matthew P. Lungren, Andrew Y. Ng
Date: 2017-12-11 00:00:00
Tasks: Anomaly Detection, Specificity

We introduce MURA, a large dataset of musculoskeletal radiographs containing
40,561 images from 14,863 studies, where each study is manually labeled by
radiologists as either normal or abnormal. To evaluate models robustly and to
get an estimate of radiologist performance, we collect additional labels from
six board-certified Stanford radiologists on the test set, consisting of 207
musculoskeletal studies. On this test set, the majority vote of a group of
three radiologists serves as gold standard. We train a 169-layer DenseNet
baseline model to detect and localize abnormalities. Our model achieves an
AUROC of 0.929, with an operating point of 0.815 sensitivity and 0.887
specificity. We compare our model and radiologists on the Cohen's kappa
statistic, which expresses the agreement of our model and of each radiologist
with the gold standard. Model performance is comparable to the best radiologist
performance in detecting abnormalities on finger and wrist studies. However,
model performance is lower than best radiologist performance in detecting
abnormalities on elbow, forearm, hand, humerus, and shoulder studies. We
believe that the task is a good challenge for future research. To encourage
advances, we have made our dataset freely available at
https://stanfordmlgroup.github.io/competitions/mura .
Rank Minimization on Tensor Ring: A New Paradigm in Scalable Tensor Decomposition and Completion
Authors: Longhao Yuan, Chao Li, Danilo Mandic, Jianting Cao, Qibin Zhao
Date: 2018-05-22 00:00:00
Tasks: Tensor Decomposition

In low-rank tensor completion tasks, due to the underlying multiple
large-scale singular value decomposition (SVD) operations and rank selection
problem of the traditional methods, they suffer from high computational cost
and high sensitivity of model complexity. In this paper, taking advantages of
high compressibility of the recently proposed tensor ring (TR) decomposition,
we propose a new model for tensor completion problem. This is achieved through
introducing convex surrogates of tensor low-rank assumption on latent tensor
ring factors, which makes it possible for the Schatten norm regularization
based models to be solved at much smaller scale. We propose two algorithms
which apply different structured Schatten norms on tensor ring factors
respectively. By the alternating direction method of multipliers (ADMM) scheme,
the tensor ring factors and the predicted tensor can be optimized
simultaneously. The experiments on synthetic data and real-world data show the
high performance and efficiency of the proposed approach.