pwc-archive/papers-with-abstracts huggingface dataset:
ImageGCN: Multi-Relational Image Graph Convolutional Networks for Disease Identification with Chest X-rays
Authors: Chengsheng Mao, Liang Yao, Yuan Luo
Date: 2019-03-31 00:00:00
Tasks: object-detection, Object Detection

Image representation is a fundamental task in computer vision. However, most of the existing approaches for image representation ignore the relations between images and consider each input image independently. Intuitively, relations between images can help to understand the images and maintain model consistency over related images, leading to better explainability. In this paper, we consider modeling the image-level relations to generate more informative image representations, and propose ImageGCN, an end-to-end graph convolutional network framework for inductive multi-relational image modeling. We apply ImageGCN to chest X-ray images where rich relational information is available for disease identification. Unlike previous image representation models, ImageGCN learns the representation of an image using both its original pixel features and its relationship with other images. Besides learning informative representations for images, ImageGCN can also be used for object detection in a weakly supervised manner. The experimental results on 3 open-source x-ray datasets, ChestX-ray14, CheXpert and MIMIC-CXR demonstrate that ImageGCN can outperform respective baselines in both disease identification and localization tasks and can achieve comparable and often better results than the state-of-the-art methods.
MedGCN: Medication recommendation and lab test imputation via graph convolutional networks
Authors: Chengsheng Mao, Liang Yao, Yuan Luo
Date: 2019-03-31 00:00:00
Tasks: Graph Neural Network, Imputation

Laboratory testing and medication prescription are two of the most important routines in daily clinical practice. Developing an artificial intelligence system that can automatically make lab test imputations and medication recommendations can save costs on potentially redundant lab tests and inform physicians of a more effective prescription. We present an intelligent medical system (named MedGCN) that can automatically recommend the patients' medications based on their incomplete lab tests, and can even accurately estimate the lab values that have not been taken. In our system, we integrate the complex relations between multiple types of medical entities with their inherent features in a heterogeneous graph. Then we model the graph to learn a distributed representation for each entity in the graph based on graph convolutional networks (GCN). By the propagation of graph convolutional networks, the entity representations can incorporate multiple types of medical information that can benefit multiple medical tasks. Moreover, we introduce a cross regularization strategy to reduce overfitting for multi-task training by the interaction between the multiple tasks. In this study, we construct a graph to associate 4 types of medical entities, i.e., patients, encounters, lab tests, and medications, and applied a graph neural network to learn node embeddings for medication recommendation and lab test imputation. we validate our MedGCN model on two real-world datasets: NMEDW and MIMIC-III. The experimental results on both datasets demonstrate that our model can outperform the state-of-the-art in both tasks. We believe that our innovative system can provide a promising and reliable way to assist physicians to make medication prescriptions and to save costs on potentially redundant lab tests.
Risk Averse Robust Adversarial Reinforcement Learning
Authors: Xinlei Pan, Daniel Seita, Yang Gao, John Canny
Date: 2019-03-31 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning has recently made significant progress in solving
computer games and robotic control tasks. A known problem, though, is that
policies overfit to the training environment and may not avoid rare,
catastrophic events such as automotive accidents. A classical technique for
improving the robustness of reinforcement learning algorithms is to train on a
set of randomized environments, but this approach only guards against common
situations. Recently, robust adversarial reinforcement learning (RARL) was
developed, which allows efficient applications of random and systematic
perturbations by a trained adversary. A limitation of RARL is that only the
expected control objective is optimized; there is no explicit modeling or
optimization of risk. Thus the agents do not consider the probability of
catastrophic events (i.e., those inducing abnormally large negative reward),
except through their effect on the expected objective. In this paper we
introduce risk-averse robust adversarial reinforcement learning (RARARL), using
a risk-averse protagonist and a risk-seeking adversary. We test our approach on
a self-driving vehicle controller. We use an ensemble of policy networks to
model risk as the variance of value functions. We show through experiments that
a risk-averse agent is better equipped to handle a risk-seeking adversary, and
experiences substantially fewer crashes compared to agents trained without an
adversary.
Distributed Power Control for Large Energy Harvesting Networks: A Multi-Agent Deep Reinforcement Learning Approach
Authors: Mohit K. Sharma, Alessio Zappone, Mohamad Assaad, Merouane Debbah, Spyridon Vassilaras
Date: 2019-04-01 00:00:00
Tasks: Deep Reinforcement Learning, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we develop a multi-agent reinforcement learning (MARL) framework to obtain online power control policies for a large energy harvesting (EH) multiple access channel, when only causal information about the EH process and wireless channel is available. In the proposed framework, we model the online power control problem as a discrete-time mean-field game (MFG), and analytically show that the MFG has a unique stationary solution. Next, we leverage the fictitious play property of the mean-field games, and the deep reinforcement learning technique to learn the stationary solution of the game, in a completely distributed fashion. We analytically show that the proposed procedure converges to the unique stationary solution of the MFG. This, in turn, ensures that the optimal policies can be learned in a completely distributed fashion. In order to benchmark the performance of the distributed policies, we also develop a deep neural network (DNN) based centralized as well as distributed online power control schemes. Our simulation results show the efficacy of the proposed power control policies. In particular, the DNN based centralized power control policies provide a very good performance for large EH networks for which the design of optimal policies is intractable using the conventional methods such as Markov decision processes. Further, performance of both the distributed policies is close to the throughput achieved by the centralized policies.
Towards Ranking Geometric Automated Theorem Provers
Authors: Nuno Baeta, Pedro Quaresma
Date: 2019-04-01 00:00:00

The field of geometric automated theorem provers has a long and rich history,
from the early AI approaches of the 1960s, synthetic provers, to today
algebraic and synthetic provers.
  The geometry automated deduction area differs from other areas by the strong
connection between the axiomatic theories and its standard models. In many
cases the geometric constructions are used to establish the theorems'
statements, geometric constructions are, in some provers, used to conduct the
proof, used as counter-examples to close some branches of the automatic proof.
Synthetic geometry proofs are done using geometric properties, proofs that can
have a visual counterpart in the supporting geometric construction.
  With the growing use of geometry automatic deduction tools as applications in
other areas, e.g. in education, the need to evaluate them, using different
criteria, is felt. Establishing a ranking among geometric automated theorem
provers will be useful for the improvement of the current
methods/implementations. Improvements could concern wider scope, better
efficiency, proof readability and proof reliability.
  To achieve the goal of being able to compare geometric automated theorem
provers a common test bench is needed: a common language to describe the
geometric problems; a comprehensive repository of geometric problems and a set
of quality measures.
RILOD: Near Real-Time Incremental Learning for Object Detection at the Edge
Authors: Dawei Li, Serafettin Tasci, Shalini Ghosh, Jingwen Zhu, Junting Zhang, Larry Heck
Date: 2019-03-26 00:00:00
Tasks: Incremental Learning, Object, object-detection, Object Detection

Object detection models shipped with camera-equipped edge devices cannot cover the objects of interest for every user. Therefore, the incremental learning capability is a critical feature for a robust and personalized object detection system that many applications would rely on. In this paper, we present an efficient yet practical system, RILOD, to incrementally train an existing object detection model such that it can detect new object classes without losing its capability to detect old classes. The key component of RILOD is a novel incremental learning algorithm that trains end-to-end for one-stage deep object detection models only using training data of new object classes. Specifically to avoid catastrophic forgetting, the algorithm distills three types of knowledge from the old model to mimic the old model's behavior on object classification, bounding box regression and feature extraction. In addition, since the training data for the new classes may not be available, a real-time dataset construction pipeline is designed to collect training images on-the-fly and automatically label the images with both category and bounding box annotations. We have implemented RILOD under both edge-cloud and edge-only setups. Experiment results show that the proposed system can learn to detect a new object class in just a few minutes, including both dataset construction and model training. In comparison, traditional fine-tuning based method may take a few hours for training, and in most cases would also need a tedious and costly manual dataset labeling step.
Learning More with Less: GAN-based Medical Image Augmentation
Authors: Changhee Han, Kohei Murao, Shin'ichi Satoh, Hideki Nakayama
Date: 2019-03-29 00:00:00
Tasks: Data Augmentation, Image Augmentation, object-detection, Object Detection

Convolutional Neural Network (CNN)-based accurate prediction typically requires large-scale annotated training data. In Medical Imaging, however, both obtaining medical data and annotating them by expert physicians are challenging; to overcome this lack of data, Data Augmentation (DA) using Generative Adversarial Networks (GANs) is essential, since they can synthesize additional annotated training data to handle small and fragmented medical images from various scanners--those generated images, realistic but completely novel, can further fill the real image distribution uncovered by the original dataset. As a tutorial, this paper introduces GAN-based Medical Image Augmentation, along with tricks to boost classification/object detection/segmentation performance using them, based on our experience and related work. Moreover, we show our first GAN-based DA work using automatic bounding box annotation, for robust CNN-based brain metastases detection on 256 x 256 MR images; GAN-based DA can boost 10% sensitivity in diagnosis with a clinically acceptable number of additional False Positives, even with highly-rough and inconsistent bounding boxes.
Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images
Authors: David Dov, Shahar Kovalsky, Jonathan Cohen, Danielle Range, Ricardo Henao, Lawrence Carin
Date: 2019-03-29 00:00:00
Tasks: Diagnostic, Multiple Instance Learning, Prediction

We consider preoperative prediction of thyroid cancer based on
ultra-high-resolution whole-slide cytopathology images. Inspired by how human
experts perform diagnosis, our approach first identifies and classifies
diagnostic image regions containing informative thyroid cells, which only
comprise a tiny fraction of the entire image. These local estimates are then
aggregated into a single prediction of thyroid malignancy. Several unique
characteristics of thyroid cytopathology guide our deep-learning-based
approach. While our method is closely related to multiple-instance learning, it
deviates from these methods by using a supervised procedure to extract
diagnostically relevant regions. Moreover, we propose to simultaneously predict
thyroid malignancy, as well as a diagnostic score assigned by a human expert,
which further allows us to devise an improved training strategy. Experimental
results show that the proposed algorithm achieves performance comparable to
human experts, and demonstrate the potential of using the algorithm for
screening and as an assistive tool for the improved diagnosis of indeterminate
cases.
Deep, spatially coherent Inverse Sensor Models with Uncertainty Incorporation using the evidential Framework
Authors: Daniel Bauer, Lars Kuhnert, Lutz Eckstein
Date: 2019-03-29 00:00:00

To perform high speed tasks, sensors of autonomous cars have to provide as
much information in as few time steps as possible. However, radars, one of the
sensor modalities autonomous cars heavily rely on, often only provide sparse,
noisy detections. These have to be accumulated over time to reach a high enough
confidence about the static parts of the environment. For radars, the state is
typically estimated by accumulating inverse detection models (IDMs). We employ
the recently proposed evidential convolutional neural networks which, in
contrast to IDMs, compute dense, spatially coherent inference of the
environment state. Moreover, these networks are able to incorporate sensor
noise in a principled way which we further extend to also incorporate model
uncertainty. We present experimental results that show This makes it possible
to obtain a denser environment perception in fewer time steps.
Significance-aware Information Bottleneck for Domain Adaptive Semantic Segmentation
Authors: Yawei Luo, Ping Liu, Tao Guan, Junqing Yu, Yi Yang
Date: 2019-04-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Domain Adaptation, image-classification, Image Classification, Segmentation, Semantic Segmentation, Unsupervised Domain Adaptation

For unsupervised domain adaptation problems, the strategy of aligning the two
domains in latent feature space through adversarial learning has achieved much
progress in image classification, but usually fails in semantic segmentation
tasks in which the latent representations are overcomplex. In this work, we
equip the adversarial network with a "significance-aware information bottleneck
(SIB)", to address the above problem. The new network structure, called SIBAN,
enables a significance-aware feature purification before the adversarial
adaptation, which eases the feature alignment and stabilizes the adversarial
training course. In two domain adaptation tasks, i.e., GTA5 -> Cityscapes and
SYNTHIA -> Cityscapes, we validate that the proposed method can yield leading
results compared with other feature-space alternatives. Moreover, SIBAN can
even match the state-of-the-art output-space methods in segmentation accuracy,
while the latter are often considered to be better choices for domain adaptive
segmentation task.
Guided Meta-Policy Search
Authors: Russell Mendonca, Abhishek Gupta, Rosen Kralev, Pieter Abbeel, Sergey Levine, Chelsea Finn
Date: 2019-04-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: continuous-control, Continuous Control, Imitation Learning, Meta-Learning, Meta Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) algorithms have demonstrated promising results on complex tasks, yet often require impractical numbers of samples since they learn from scratch. Meta-RL aims to address this challenge by leveraging experience from previous tasks so as to more quickly solve new tasks. However, in practice, these algorithms generally also require large amounts of on-policy experience during the meta-training process, making them impractical for use in many problems. To this end, we propose to learn a reinforcement learning procedure in a federated way, where individual off-policy learners can solve the individual meta-training tasks, and then consolidate these solutions into a single meta-learner. Since the central meta-learner learns by imitating the solutions to the individual tasks, it can accommodate either the standard meta-RL problem setting or a hybrid setting where some or all tasks are provided with example demonstrations. The former results in an approach that can leverage policies learned for previous tasks without significant amounts of on-policy data during meta-training, whereas the latter is particularly useful in cases where demonstrations are easy for a person to provide. Across a number of continuous control meta-RL problems, we demonstrate significant improvements in meta-RL sample efficiency in comparison to prior work as well as the ability to scale to domains with visual observations.
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
Authors: Yang You, Jing Li, Sashank Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, Cho-Jui Hsieh
Date: 2019-04-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Question Answering, Stochastic Optimization

Training large deep neural networks on massive datasets is computationally very challenging. There has been recent surge in interest in using large batch stochastic optimization methods to tackle this issue. The most prominent algorithm in this line of research is LARS, which by employing layerwise adaptive learning rates trains ResNet on ImageNet in a few minutes. However, LARS performs poorly for attention models like BERT, indicating that its performance gains are not consistent across tasks. In this paper, we first study a principled layerwise adaptation strategy to accelerate training of deep neural networks using large mini-batches. Using this strategy, we develop a new layerwise adaptive large batch optimization technique called LAMB; we then provide convergence analysis of LAMB as well as LARS, showing convergence to a stationary point in general nonconvex settings. Our empirical results demonstrate the superior performance of LAMB across various tasks such as BERT and ResNet-50 training with very little hyperparameter tuning. In particular, for BERT training, our optimizer enables use of very large batch sizes of 32868 without any degradation of performance. By increasing the batch size to the memory limit of a TPUv3 Pod, BERT training time can be reduced from 3 days to just 76 minutes (Table 1). The LAMB implementation is available at https://github.com/tensorflow/addons/blob/master/tensorflow_addons/optimizers/lamb.py
SE2Net: Siamese Edge-Enhancement Network for Salient Object Detection
Authors: Sanping Zhou, Jimuyang Zhang, Jinjun Wang, Fei Wang, Dong Huang
Date: 2019-03-29 00:00:00
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

Deep convolutional neural network significantly boosted the capability of salient object detection in handling large variations of scenes and object appearances. However, convolution operations seek to generate strong responses on individual pixels, while lack the ability to maintain the spatial structure of objects. Moreover, the down-sampling operations, such as pooling and striding, lose spatial details of the salient objects. In this paper, we propose a simple yet effective Siamese Edge-Enhancement Network (SE2Net) to preserve the edge structure for salient object detection. Specifically, a novel multi-stage siamese network is built to aggregate the low-level and high-level features, and parallelly estimate the salient maps of edges and regions. As a result, the predicted regions become more accurate by enhancing the responses at edges, and the predicted edges become more semantic by suppressing the false positives in background. After the refined salient maps of edges and regions are produced by the SE2Net, an edge-guided inference algorithm is designed to further improve the resulting salient masks along the predicted edges. Extensive experiments on several benchmark datasets have been conducted, which show that our method is superior than the state-of-the-art approaches.
Brain Tissue Segmentation Using NeuroNet With Different Pre-processing Techniques
Authors: Fakrul Islam Tushar, Basel Alyafi, Md. Kamrul Hasan, Lavsen Dahal
Date: 2019-03-29 00:00:00
Tasks: 3D Semantic Segmentation, Decoder

Automatic segmentation of brain Magnetic Resonance Imaging (MRI) images is
one of the vital steps for quantitative analysis of brain for further
inspection. In this paper, NeuroNet has been adopted to segment the brain
tissues (white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF))
which uses Residual Network (ResNet) in encoder and Fully Convolution Network
(FCN) in the decoder. To achieve the best performance, various hyper-parameters
have been tuned, while, network parameters (kernel and bias) were initialized
using the NeuroNet pre-trained model. Different pre-processing pipelines have
also been introduced to get a robust trained model. The model has been trained
and tested on IBSR18 data-set. To validate the research outcome, performance
was measured quantitatively using Dice Similarity Coefficient (DSC) and is
reported on average as 0.84 for CSF, 0.94 for GM, and 0.94 for WM. The outcome
of the research indicates that for the IBSR18 data-set, pre-processing and
proper tuning of hyper-parameters for NeuroNet model have improvement in DSC
for the brain tissue segmentation.
Unpaired Point Cloud Completion on Real Scans using Adversarial Training
Authors: Xuelin Chen, Baoquan Chen, Niloy J. Mitra
Date: 2019-03-29 00:00:00
Proceeding: ICLR 2020 1
Tasks: Point Cloud Completion

As 3D scanning solutions become increasingly popular, several deep learning setups have been developed geared towards that task of scan completion, i.e., plausibly filling in regions there were missed in the raw scans. These methods, however, largely rely on supervision in the form of paired training data, i.e., partial scans with corresponding desired completed scans. While these methods have been successfully demonstrated on synthetic data, the approaches cannot be directly used on real scans in absence of suitable paired training data. We develop a first approach that works directly on input point clouds, does not require paired training data, and hence can directly be applied to real scans for scan completion. We evaluate the approach qualitatively on several real-world datasets (ScanNet, Matterport, KITTI), quantitatively on 3D-EPN shape completion benchmark dataset, and demonstrate realistic completions under varying levels of incompleteness.
3D Organ Shape Reconstruction from Topogram Images
Authors: Elena Balashova, Jiangping Wang, Vivek Singh, Bogdan Georgescu, Brian Teixeira, Ankur Kapoor
Date: 2019-03-29 00:00:00
Tasks: Computed Tomography (CT)

Automatic delineation and measurement of main organs such as liver is one of
the critical steps for assessment of hepatic diseases, planning and
postoperative or treatment follow-up. However, addressing this problem
typically requires performing computed tomography (CT) scanning and complicated
postprocessing of the resulting scans using slice-by-slice techniques. In this
paper, we show that 3D organ shape can be automatically predicted directly from
topogram images, which are easier to acquire and have limited exposure to
radiation during acquisition, compared to CT scans. We evaluate our approach on
the challenging task of predicting liver shape using a generative model. We
also demonstrate that our method can be combined with user annotations, such as
a 2D mask, for improved prediction accuracy. We show compelling results on 3D
liver shape reconstruction and volume estimation on 2129 CT scans.