pwc-archive/papers-with-abstracts huggingface dataset:
Zeno++: Robust Fully Asynchronous SGD
Authors: Cong Xie, Sanmi Koyejo, Indranil Gupta
Date: 2019-03-17 00:00:00
Proceeding: ICML 2020 1

We propose Zeno++, a new robust asynchronous Stochastic Gradient Descent~(SGD) procedure which tolerates Byzantine failures of the workers. In contrast to previous work, Zeno++ removes some unrealistic restrictions on worker-server communications, allowing for fully asynchronous updates from anonymous workers, arbitrarily stale worker updates, and the possibility of an unbounded number of Byzantine workers. The key idea is to estimate the descent of the loss value after the candidate gradient is applied, where large descent values indicate that the update results in optimization progress. We prove the convergence of Zeno++ for non-convex problems under Byzantine failures. Experimental results show that Zeno++ outperforms existing approaches.
Patch Clustering for Representation of Histopathology Images
Authors: Wafa Chenni, Habib Herbi, Morteza Babaie, H. R. Tizhoosh
Date: 2019-03-17 00:00:00
Tasks: Clustering, Retrieval

Whole Slide Imaging (WSI) has become an important topic during the last
decade. Even though significant progress in both medical image processing and
computational resources has been achieved, there are still problems in WSI that
need to be solved. A major challenge is the scan size. The dimensions of
digitized tissue samples may exceed 100,000 by 100,000 pixels causing memory
and efficiency obstacles for real-time processing. The main contribution of
this work is representing a WSI by selecting a small number of patches for
algorithmic processing (e.g., indexing and search). As a result, we reduced the
search time and storage by various factors between ($50\% - 90\%$), while
losing only a few percentages in the patch retrieval accuracy. A
self-organizing map (SOM) has been applied on local binary patterns (LBP) and
deep features of the KimiaPath24 dataset in order to cluster patches that share
the same characteristics. We used a Gaussian mixture model (GMM) to represent
each class with a rather small ($10\%-50\%$) portion of patches. The results
showed that LBP features can outperform deep features. By selecting only $50\%$
of all patches after SOM clustering and GMM patch selection, we received $65\%$
accuracy for retrieval of the best match, while the maximum accuracy (using all
patches) was $69\%$.
Deep Features for Tissue-Fold Detection in Histopathology Images
Authors: Morteza Babaie, H. R. Tizhoosh
Date: 2019-03-17 00:00:00

Whole slide imaging (WSI) refers to the digitization of a tissue specimen
which enables pathologists to explore high-resolution images on a monitor
rather than through a microscope. The formation of tissue folds occur during
tissue processing. Their presence may not only cause out-of-focus digitization
but can also negatively affect the diagnosis in some cases. In this paper, we
have compared five pre-trained convolutional neural networks (CNNs) of
different depths as feature extractors to characterize tissue folds. We have
also explored common classifiers to discriminate folded tissue against the
normal tissue in hematoxylin and eosin (H\&E) stained biopsy samples. In our
experiments, we manually select the folded area in roughly 2.5mm $\times$ 2.5mm
patches at $20$x magnification level as the training data. The ``DenseNet''
with 201 layers alongside an SVM classifier outperformed all other
configurations. Based on the leave-one-out validation strategy, we achieved
$96.3\%$ accuracy, whereas with augmentation the accuracy increased to
$97.2\%$. We have tested the generalization of our method with five unseen WSIs
from the NIH (National Cancer Institute) dataset. The accuracy for patch-wise
detection was $81\%$. One folded patch within an image suffices to flag the
entire specimen for visual inspection.
Leveling the Playing Field -- Fairness in AI Versus Human Game Benchmarks
Authors: Rodrigo Canaan, Christoph Salge, Julian Togelius, Andy Nealen
Date: 2019-03-17 00:00:00
Tasks: Fairness

From the beginning if the history of AI, there has been interest in games as a platform of research. As the field developed, human-level competence in complex games became a target researchers worked to reach. Only relatively recently has this target been finally met for traditional tabletop games such as Backgammon, Chess and Go. Current research focus has shifted to electronic games, which provide unique challenges. As is often the case with AI research, these results are liable to be exaggerated or misrepresented by either authors or third parties. The extent to which these games benchmark consist of fair competition between human and AI is also a matter of debate. In this work, we review the statements made by authors and third parties in the general media and academic circle about these game benchmark results and discuss factors that can impact the perception of fairness in the contest between humans and machines
Task Decomposition for Iterative Learning Model Predictive Control
Authors: Charlott Vallon, Francesco Borrelli
Date: 2019-03-16 00:00:00
Tasks: Autonomous Racing, model, Model Predictive Control, Transfer Learning

A task decomposition method for iterative learning model predictive control is presented. We consider a constrained nonlinear dynamical system and assume the availability of state-input pair datasets which solve a task T1. Our objective is to find a feasible model predictive control policy for a second task, T2, using stored data from T1. Our approach applies to tasks T2 which are composed of subtasks contained in T1. In this paper we propose a formal definition of subtasks and the task decomposition problem, and provide proofs of feasibility and iteration cost improvement over simple initializations. We demonstrate the effectiveness of the proposed method on autonomous racing and robotic manipulation experiments.
GFD-SSD: Gated Fusion Double SSD for Multispectral Pedestrian Detection
Authors: Yang Zheng, Izzat H. Izzat, Shahrzad Ziaee
Date: 2019-03-16 00:00:00
Tasks: Autonomous Driving, Pedestrian Detection

Pedestrian detection is an essential task in autonomous driving research. In
addition to typical color images, thermal images benefit the detection in dark
environments. Hence, it is worthwhile to explore an integrated approach to take
advantage of both color and thermal images simultaneously. In this paper, we
propose a novel approach to fuse color and thermal sensors using deep neural
networks (DNN). Current state-of-the-art DNN object detectors vary from
two-stage to one-stage mechanisms. Two-stage detectors, like Faster-RCNN,
achieve higher accuracy, while one-stage detectors such as Single Shot Detector
(SSD) demonstrate faster performance. To balance the trade-off, especially in
the consideration of autonomous driving applications, we investigate a fusion
strategy to combine two SSDs on color and thermal inputs. Traditional fusion
methods stack selected features from each channel and adjust their weights. In
this paper, we propose two variations of novel Gated Fusion Units (GFU), that
learn the combination of feature maps generated by the two SSD middle layers.
Leveraging GFUs for the entire feature pyramid structure, we propose several
mixed versions of both stack fusion and gated fusion. Experiments are conducted
on the KAIST multispectral pedestrian detection dataset. Our Gated Fusion
Double SSD (GFD-SSD) outperforms the stacked fusion and achieves the lowest
miss rate in the benchmark, at an inference speed that is two times faster than
Faster-RCNN based fusion networks.
SLSGD: Secure and Efficient Distributed On-device Machine Learning
Authors: Cong Xie, Sanmi Koyejo, Indranil Gupta
Date: 2019-03-16 00:00:00
Tasks: BIG-bench Machine Learning, Data Poisoning, Distributed Optimization

We consider distributed on-device learning with limited communication and security requirements. We propose a new robust distributed optimization algorithm with efficient communication and attack tolerance. The proposed algorithm has provable convergence and robustness under non-IID settings. Empirical results show that the proposed algorithm stabilizes the convergence and tolerates data poisoning on a small number of workers.
Visual Query Answering by Entity-Attribute Graph Matching and Reasoning
Authors: Peixi Xiong, Huayi Zhan, Xin Wang, Baivab Sinha, Ying Wu
Date: 2019-03-16 00:00:00
Proceeding: CVPR 2019 6
Tasks: Attribute, Graph Matching, Visual Question Answering (VQA)

Visual Query Answering (VQA) is of great significance in offering people
convenience: one can raise a question for details of objects, or high-level
understanding about the scene, over an image. This paper proposes a novel
method to address the VQA problem. In contrast to prior works, our method that
targets single scene VQA, replies on graph-based techniques and involves
reasoning. In a nutshell, our approach is centered on three graphs. The first
graph, referred to as inference graph GI , is constructed via learning over
labeled data. The other two graphs, referred to as query graph Q and
entity-attribute graph GEA, are generated from natural language query Qnl and
image Img, that are issued from users, respectively. As GEA often does not take
sufficient information to answer Q, we develop techniques to infer missing
information of GEA with GI . Based on GEA and Q, we provide techniques to find
matches of Q in GEA, as the answer of Qnl in Img. Unlike commonly used VQA
methods that are based on end-to-end neural networks, our graph-based method
shows well-designed reasoning capability, and thus is highly interpretable. We
also create a dataset on soccer match (Soccer-VQA) with rich annotations. The
experimental results show that our approach outperforms the state-of-the-art
method and has high potential for future investigation.
Domain adaptation for holistic skin detection
Authors: Aloisio Dourado, Frederico Guth, Teofilo Emidio de Campos, Li Weigang
Date: 2019-03-16 00:00:00
Tasks: Domain Adaptation, Transfer Learning, Unsupervised Domain Adaptation

Human skin detection in images is a widely studied topic of Computer Vision for which it is commonly accepted that analysis of pixel color or local patches may suffice. This is because skin regions appear to be relatively uniform and many argue that there is a small chromatic variation among different samples. However, we found that there are strong biases in the datasets commonly used to train or tune skin detection methods. Furthermore, the lack of contextual information may hinder the performance of local approaches. In this paper we present a comprehensive evaluation of holistic and local Convolutional Neural Network (CNN) approaches on in-domain and cross-domain experiments and compare with state-of-the-art pixel-based approaches. We also propose a combination of inductive transfer learning and unsupervised domain adaptation methods, which are evaluated on different domains under several amounts of labelled data availability. We show a clear superiority of CNN over pixel-based approaches even without labelled training samples on the target domain. Furthermore, we provide experimental support for the counter-intuitive superiority of holistic over local approaches for human skin detection.
Imbalanced multi-label classification using multi-task learning with extractive summarization
Authors: John Brandt
Date: 2019-03-16 00:00:00
Tasks: Classification, Extractive Summarization, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Task Learning, Sentence

Extractive summarization and imbalanced multi-label classification often
require vast amounts of training data to avoid overfitting. In situations where
training data is expensive to generate, leveraging information between tasks is
an attractive approach to increasing the amount of available information. This
paper employs multi-task training of an extractive summarizer and an RNN-based
classifier to improve summarization and classification accuracy by 50% and 75%,
respectively, relative to RNN baselines. We hypothesize that concatenating
sentence encodings based on document and class context increases
generalizability for highly variable corpuses.
Hand range of motion evaluation for Rheumatoid Arthritis patients
Authors: Luciano Walenty Xavier Cejnog, Roberto Marcondes Cesar Jr., Teofilo Emidio de Campos, Valeria Meirelles Carril Elui
Date: 2019-03-16 00:00:00
Tasks: Hand Pose Estimation, Pose Estimation

We introduce a framework for dynamic evaluation of the fingers movements:
flexion, extension, abduction and adduction. This framework estimates angle
measurements from joints computed by a hand pose estimation algorithm using a
depth sensor (Realsense SR300). Given depth maps as input, our framework uses
Pose-REN, which is a state-of-art hand pose estimation method that estimates 3D
hand joint positions using a deep convolutional neural network. The pose
estimation algorithm runs in real-time, allowing users to visualise 3D skeleton
tracking results at the same time as the depth images are acquired. Once 3D
joint poses are obtained, our framework estimates a plane containing the wrist
and MCP joints and measures flexion/extension and abduction/aduction angles by
applying computational geometry operations with respect to this plane. We
analysed flexion and abduction movement patterns using real data, extracting
the movement trajectories. Our preliminary results show that this method allows
an automatic discrimination of hands with Rheumatoid Arthritis (RA) and healthy
patients. The angle between joints can be used as an indicative of current
movement capabilities and function. Although the measurements can be noisy and
less accurate than those obtained statically through goniometry, the
acquisition is much easier, non-invasive and patient-friendly, which shows the
potential of our approach. The system can be used with and without orthosis.
Our framework allows the acquisition of measurements with minimal intervention
and significantly reduces the evaluation time.
Improving Lemmatization of Non-Standard Languages with Joint Learning
Authors: Enrique Manjavacas, Ákos Kádár, Mike Kestemont
Date: 2019-03-16 00:00:00
Proceeding: NAACL 2019 6
Tasks: Decoder, Language Modeling, Language Modelling, LEMMA, Lemmatization, POS, Sentence

Lemmatization of standard languages is concerned with (i) abstracting over
morphological differences and (ii) resolving token-lemma ambiguities of
inflected words in order to map them to a dictionary headword. In the present
paper we aim to improve lemmatization performance on a set of non-standard
historical languages in which the difficulty is increased by an additional
aspect (iii): spelling variation due to lacking orthographic standards. We
approach lemmatization as a string-transduction task with an encoder-decoder
architecture which we enrich with sentence context information using a
hierarchical sentence encoder. We show significant improvements over the
state-of-the-art when training the sentence encoder jointly for lemmatization
and language modeling. Crucially, our architecture does not require POS or
morphological annotations, which are not always available for historical
corpora. Additionally, we also test the proposed model on a set of
typologically diverse standard languages showing results on par or better than
a model without enhanced sentence representations and previous state-of-the-art
systems. Finally, to encourage future work on processing of non-standard
varieties, we release the dataset of non-standard languages underlying the
present study, based on openly accessible sources.
swCaffe: a Parallel Framework for Accelerating Deep Learning Applications on Sunway TaihuLight
Authors: Jiarui Fang, Liandeng Li, Haohuan Fu, Jinlei Jiang, Wenlai Zhao, Conghui He, Xin You, Guangwen Yang
Date: 2019-03-16 00:00:00
Tasks: CPU, GPU

This paper reports our efforts on swCaffe, a highly efficient parallel
framework for accelerating deep neural networks (DNNs) training on Sunway
TaihuLight, the current fastest supercomputer in the world that adopts a unique
many-core heterogeneous architecture, with 40,960 SW26010 processors connected
through a customized communication network. First, we point out some insightful
principles to fully exploit the performance of the innovative many-core
architecture. Second, we propose a set of optimization strategies for
redesigning a variety of neural network layers based on Caffe. Third, we put
forward a topology-aware parameter synchronization scheme to scale the
synchronous Stochastic Gradient Descent (SGD) method to multiple processors
efficiently. We evaluate our framework by training a variety of widely used
neural networks with the ImageNet dataset. On a single node, swCaffe can
achieve 23\%\~{}119\% overall performance compared with Caffe running on K40m
GPU. As compared with the Caffe on CPU, swCaffe runs 3.04\~{}7.84x faster on
all the networks. Finally, we present the scalability of swCaffe for the
training of ResNet-50 and AlexNet on the scale of 1024 nodes.
Active and Passive Portfolio Management with Latent Factors
Authors: Ali Al-Aradi, Sebastian Jaimungal
Date: 2019-03-16 00:00:00
Tasks: Management

We address a portfolio selection problem that combines active
(outperformance) and passive (tracking) objectives using techniques from convex
analysis. We assume a general semimartingale market model where the assets'
growth rate processes are driven by a latent factor. Using techniques from
convex analysis we obtain a closed-form solution for the optimal portfolio and
provide a theorem establishing its uniqueness. The motivation for incorporating
latent factors is to achieve improved growth rate estimation, an otherwise
notoriously difficult task. To this end, we focus on a model where growth rates
are driven by an unobservable Markov chain. The solution in this case requires
a filtering step to obtain posterior probabilities for the state of the Markov
chain from asset price information, which are subsequently used to find the
optimal allocation. We show the optimal strategy is the posterior average of
the optimal strategies the investor would have held in each state assuming the
Markov chain remains in that state. Finally, we implement a number of
historical backtests to demonstrate the performance of the optimal portfolio.
Spatiotemporal Feature Learning for Event-Based Vision
Authors: Rohan Ghosh, Anupam Gupta, Siyi Tang, Alcimar Soares, Nitish Thakor
Date: 2019-03-16 00:00:00
Tasks: Event-based vision, Object Recognition, Point Tracking, Representation Learning, Translation

Unlike conventional frame-based sensors, event-based visual sensors output
information through spikes at a high temporal resolution. By only encoding
changes in pixel intensity, they showcase a low-power consuming, low-latency
approach to visual information sensing. To use this information for higher
sensory tasks like object recognition and tracking, an essential simplification
step is the extraction and learning of features. An ideal feature descriptor
must be robust to changes involving (i) local transformations and (ii)
re-appearances of a local event pattern. To that end, we propose a novel
spatiotemporal feature representation learning algorithm based on slow feature
analysis (SFA). Using SFA, smoothly changing linear projections are learnt
which are robust to local visual transformations. In order to determine if the
features can learn to be invariant to various visual transformations, feature
point tracking tasks are used for evaluation. Extensive experiments across two
datasets demonstrate the adaptability of the spatiotemporal feature learner to
translation, scaling and rotational transformations of the feature points. More
importantly, we find that the obtained feature representations are able to
exploit the high temporal resolution of such event-based cameras in generating
better feature tracks.
Real time backbone for semantic segmentation
Authors: Zhengeng Yang, Hongshan Yu, Qiang Fu, Wei Sun, Wenyan Jia, Mingui Sun, Zhi-Hong Mao
Date: 2019-03-16 00:00:00
Tasks: Autonomous Driving, Model Compression, Scene Understanding, Segmentation, Semantic Segmentation

The rapid development of autonomous driving in recent years presents lots of
challenges for scene understanding. As an essential step towards scene
understanding, semantic segmentation thus received lots of attention in past
few years. Although deep learning based state-of-the-arts have achieved great
success in improving the segmentation accuracy, most of them suffer from an
inefficiency problem and can hardly applied to practical applications. In this
paper, we systematically analyze the computation cost of Convolutional Neural
Network(CNN) and found that the inefficiency of CNN is mainly caused by its
wide structure rather than the deep structure. In addition, the success of
pruning based model compression methods proved that there are many redundant
channels in CNN. Thus, we designed a very narrow while deep backbone network to
improve the efficiency of semantic segmentation. By casting our network to
FCN32 segmentation architecture, the basic structure of most segmentation
methods, we achieved 60.6\% mIoU on Cityscape val dataset with 54 frame per
seconds(FPS) on $1024\times2048$ inputs, which already outperforms one of the
earliest real time deep learning based segmentation methods: ENet.
Robust Super-Resolution GAN, with Manifold-based and Perception Loss
Authors: Uddeshya Upadhyay, Suyash P. Awate
Date: 2019-03-16 00:00:00
Tasks: Super-Resolution

Super-resolution using deep neural networks typically relies on highly
curated training sets that are often unavailable in clinical deployment
scenarios. Using loss functions that assume Gaussian-distributed residuals
makes the learning sensitive to corruptions in clinical training sets. We
propose novel loss functions that are robust to corruptions in training sets by
modeling heavy-tailed non-Gaussian distributions on the residuals. We propose a
loss based on an autoencoder-based manifold-distance between the super-resolved
and high-resolution images, to reproduce realistic textural content in
super-resolved images. We propose to learn to super-resolve images to match
human perceptions of structure, luminance, and contrast. Results on a large
clinical dataset shows the advantages of each of our contributions, where our
framework improves over the state of the art.
Classification of dry age-related macular degeneration and diabetic macular edema from optical coherence tomography images using dictionary learning
Authors: Elahe Mousavi, Rahele Kafieh, Hossein Rabbani
Date: 2019-03-16 00:00:00
Tasks: Dictionary Learning, General Classification

Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are
the major causes of vision loss in developed countries. Alteration of retinal
layer structure and appearance of exudate are the most significant signs of
these diseases. With the aim of automatic classification of DME, AMD and normal
subjects from Optical Coherence Tomography (OCT) images, we proposed a
classification algorithm. The two important issues intended in this approach
are, not utilizing retinal layer segmentation which by itself is a challenging
task and attempting to identify diseases in their early stages, where the signs
of diseases appear in a small fraction of B-Scans. We used a histogram of
oriented gradients (HOG) feature descriptor to well characterize the
distribution of local intensity gradients and edge directions. In order to
capture the structure of extracted features, we employed different dictionary
learning-based classifiers. Our dataset consists of 45 subjects: 15 patients
with AMD, 15 patients with DME and 15 normal subjects. The proposed classifier
leads to an accuracy of 95.13%, 100.00%, and 100.00% for DME, AMD, and normal
OCT images, respectively, only by considering the 4% of all B-Scans of a volume
which outperforms the state of the art methods.