pwc-archive/papers-with-abstracts huggingface dataset:
Trajectory Normalized Gradients for Distributed Optimization
Authors: Jianqiao Wangni, Ke Li, Jianbo Shi, Jitendra Malik
Date: 2019-01-24 00:00:00
Tasks: Benchmarking, Distributed Optimization

Recently, researchers proposed various low-precision gradient compression,
for efficient communication in large-scale distributed optimization. Based on
these work, we try to reduce the communication complexity from a new direction.
We pursue an ideal bijective mapping between two spaces of gradient
distribution, so that the mapped gradient carries greater information entropy
after the compression. In our setting, all servers should share a reference
gradient in advance, and they communicate via the normalized gradients, which
are the subtraction or quotient, between current gradients and the reference.
To obtain a reference vector that yields a stronger signal-to-noise ratio,
dynamically in each iteration, we extract and fuse information from the past
trajectory in hindsight, and search for an optimal reference for compression.
We name this to be the trajectory-based normalized gradients (TNG). It bridges
the research from different societies, like coding, optimization, systems, and
learning. It is easy to implement and can universally combine with existing
algorithms. Our experiments on benchmarking hard non-convex functions, convex
problems like logistic regression demonstrate that TNG is more
compression-efficient for communication of distributed optimization of general
functions.
Object Detection based on Region Decomposition and Assembly
Authors: Seung-Hwan Bae
Date: 2019-01-24 00:00:00
Tasks: Object, object-detection, Object Detection, Region Proposal

Region-based object detection infers object regions for one or more categories in an image. Due to the recent advances in deep learning and region proposal methods, object detectors based on convolutional neural networks (CNNs) have been flourishing and provided the promising detection results. However, the detection accuracy is degraded often because of the low discriminability of object CNN features caused by occlusions and inaccurate region proposals. In this paper, we therefore propose a region decomposition and assembly detector (R-DAD) for more accurate object detection. In the proposed R-DAD, we first decompose an object region into multiple small regions. To capture an entire appearance and part details of the object jointly, we extract CNN features within the whole object region and decomposed regions. We then learn the semantic relations between the object and its parts by combining the multi-region features stage by stage with region assembly blocks, and use the combined and high-level semantic features for the object classification and localization. In addition, for more accurate region proposals, we propose a multi-scale proposal layer that can generate object proposals of various scales. We integrate the R-DAD into several feature extractors, and prove the distinct performance improvement on PASCAL07/12 and MSCOCO18 compared to the recent convolutional detectors.
When is it right and good for an intelligent autonomous vehicle to take over control (and hand it back)?
Authors: Ajit Narayanan
Date: 2019-01-24 00:00:00
Tasks: Autonomous Vehicles, Ethics

There is much debate in machine ethics about the most appropriate way to
introduce ethical reasoning capabilities into intelligent autonomous machines.
Recent incidents involving autonomous vehicles in which humans have been killed
or injured have raised questions about how we ensure that such vehicles have an
ethical dimension to their behaviour and are therefore trustworthy. The main
problem is that hardwiring such machines with rules not to cause harm or damage
is not consistent with the notion of autonomy and intelligence. Also, such
ethical hardwiring does not leave intelligent autonomous machines with any
course of action if they encounter situations or dilemmas for which they are
not programmed or where some harm is caused no matter what course of action is
taken. Teaching machines so that they learn ethics may also be problematic
given recent findings in machine learning that machines pick up the prejudices
and biases embedded in their learning algorithms or data. This paper describes
a fuzzy reasoning approach to machine ethics. The paper shows how it is
possible for an ethics architecture to reason when taking over from a human
driver is morally justified. The design behind such an ethical reasoner is also
applied to an ethical dilemma resolution case. One major advantage of the
approach is that the ethical reasoner can generate its own data for learning
moral rules (hence, autometric) and thereby reduce the possibility of picking
up human biases and prejudices. The results show that a new type of
metric-based ethics appropriate for autonomous intelligent machines is feasible
and that our current concept of ethical reasoning being largely qualitative in
nature may need revising if want to construct future autonomous machines that
have an ethical dimension to their reasoning so that they become moral
machines.
Greedy Strategy Works for $k$-Center Clustering with Outliers and Coreset Construction
Authors: Hu Ding, Haikuo Yu, Zixiu Wang
Date: 2019-01-24 00:00:00
Tasks: Clustering

We study the problem of $k$-center clustering with outliers in arbitrary
metrics and Euclidean space. Though a number of methods have been developed in
the past decades, it is still quite challenging to design quality guaranteed
algorithm with low complexity for this problem. Our idea is inspired by the
greedy method, Gonzalez's algorithm, for solving the problem of ordinary
$k$-center clustering. Based on some novel observations, we show that this
greedy strategy actually can handle $k$-center clustering with outliers
efficiently, in terms of clustering quality and time complexity. We further
show that the greedy approach yields small coreset for the problem in doubling
metrics, so as to reduce the time complexity significantly. Our algorithms are
easy to implement in practice. We test our method on both synthetic and real
datasets. The experimental results suggest that our algorithms can achieve near
optimal solutions and yield lower running times comparing with existing
methods.
MREAK : Morphological Retina Keypoint Descriptor
Authors: Himanshu Vaghela, Manan Oza, Sudhir Bagul
Date: 2019-01-24 00:00:00

A variety of computer vision applications depend on the efficiency of image
matching algorithms used. Various descriptors are designed to detect and match
features in images. Deployment of this algorithms in mobile applications
creates a need for low computation time. Binary descriptors requires less
computation time than float-point based descriptors because of the intensity
comparison between pairs of sample points and comparing after creating a binary
string. In order to decrease time complexity, quality of keypoints matched is
often compromised. We propose a keypoint descriptor named Morphological Retina
Keypoint Descriptor (MREAK) inspired by the function of human pupil which
dilates and constricts responding to the amount of light. By using
morphological operators of opening and closing and modifying the retinal
sampling pattern accordingly, an increase in the number of accurately matched
keypoints is observed. Our results show that matched keypoints are more
efficient than FREAK descriptor and requires low computation time than various
descriptors like SIFT, BRISK and SURF.
Semi-Supervised Image-to-Image Translation
Authors: Manan Oza, Himanshu Vaghela, Sudhir Bagul
Date: 2019-01-24 00:00:00
Tasks: Generative Adversarial Network, Image Segmentation, Image-to-Image Translation, Multimodal Unsupervised Image-To-Image Translation, Segmentation, Semantic Segmentation, Translation, Unsupervised Image-To-Image Translation

Image-to-image translation is a long-established and a difficult problem in
computer vision. In this paper we propose an adversarial based model for
image-to-image translation. The regular deep neural-network based methods
perform the task of image-to-image translation by comparing gram matrices and
using image segmentation which requires human intervention. Our generative
adversarial network based model works on a conditional probability approach.
This approach makes the image translation independent of any local, global and
content or style features. In our approach we use a bidirectional
reconstruction model appended with the affine transform factor that helps in
conserving the content and photorealism as compared to other models. The
advantage of using such an approach is that the image-to-image translation is
semi-supervised, independant of image segmentation and inherits the properties
of generative adversarial networks tending to produce realistic. This method
has proven to produce better results than Multimodal Unsupervised
Image-to-image translation.
Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation
Authors: Cheng Chen, Qi Dou, Hao Chen, Jing Qin, Pheng-Ann Heng
Date: 2019-01-24 00:00:00
Tasks: Domain Adaptation, Image Segmentation, Medical Image Segmentation, Semantic Segmentation, Unsupervised Domain Adaptation

This paper presents a novel unsupervised domain adaptation framework, called Synergistic Image and Feature Adaptation (SIFA), to effectively tackle the problem of domain shift. Domain adaptation has become an important and hot topic in recent studies on deep learning, aiming to recover performance degradation when applying the neural networks to new testing domains. Our proposed SIFA is an elegant learning diagram which presents synergistic fusion of adaptations from both image and feature perspectives. In particular, we simultaneously transform the appearance of images across domains and enhance domain-invariance of the extracted features towards the segmentation task. The feature encoder layers are shared by both perspectives to grasp their mutual benefits during the end-to-end learning procedure. Without using any annotation from the target domain, the learning of our unified model is guided by adversarial losses, with multiple discriminators employed from various aspects. We have extensively validated our method with a challenging application of cross-modality medical image segmentation of cardiac structures. Experimental results demonstrate that our SIFA model recovers the degraded performance from 17.2% to 73.0%, and outperforms the state-of-the-art methods by a significant margin.
A PCB Dataset for Defects Detection and Classification
Authors: Weibo Huang, Peng Wei
Date: 2019-01-24 00:00:00
Tasks: Classification, General Classification

To coupe with the difficulties in the process of inspection and
classification of defects in Printed Circuit Board (PCB), other researchers
have proposed many methods. However, few of them published their dataset
before, which hindered the introduction and comparison of new methods. In this
paper, we published a synthesized PCB dataset containing 1386 images with 6
kinds of defects for the use of detection, classification and registration
tasks. Besides, we proposed a reference based method to inspect and trained an
end-to-end convolutional neural network to classify the defects. Unlike
conventional approaches that require pixel-by-pixel processing, our method
firstly locate the defects and then classify them by neural networks, which
shows superior performance on our dataset.
Sequential Skip Prediction with Few-shot in Streamed Music Contents
Authors: Sungkyun Chang, Seungjin Lee, Kyogu Lee
Date: 2019-01-24 00:00:00
Tasks: Few-Shot Learning, Meta-Learning, Metric Learning, Sequential skip prediction

This paper provides an outline of the algorithms submitted for the WSDM Cup 2019 Spotify Sequential Skip Prediction Challenge (team name: mimbres). In the challenge, complete information including acoustic features and user interaction logs for the first half of a listening session is provided. Our goal is to predict whether the individual tracks in the second half of the session will be skipped or not, only given acoustic features. We proposed two different kinds of algorithms that were based on metric learning and sequence learning. The experimental results showed that the sequence learning approach performed significantly better than the metric learning approach. Moreover, we conducted additional experiments to find that significant performance gain can be achieved using complete user log information.
ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU
Authors: William Caicedo-Torres, Jairo Gutierrez
Date: 2019-01-24 00:00:00
Tasks: Deep Learning, Mortality Prediction

To improve the performance of Intensive Care Units (ICUs), the field of
bio-statistics has developed scores which try to predict the likelihood of
negative outcomes. These help evaluate the effectiveness of treatments and
clinical practice, and also help to identify patients with unexpected outcomes.
However, they have been shown by several studies to offer sub-optimal
performance. Alternatively, Deep Learning offers state of the art capabilities
in certain prediction tasks and research suggests deep neural networks are able
to outperform traditional techniques. Nevertheless, a main impediment for the
adoption of Deep Learning in healthcare is its reduced interpretability, for in
this field it is crucial to gain insight on the why of predictions, to assure
that models are actually learning relevant features instead of spurious
correlations. To address this, we propose a deep multi-scale convolutional
architecture trained on the Medical Information Mart for Intensive Care III
(MIMIC-III) for mortality prediction, and the use of concepts from coalitional
game theory to construct visual explanations aimed to show how important these
inputs are deemed by the network. Our results show our model attains state of
the art performance while remaining interpretable. Supporting code can be found
at https://github.com/williamcaicedo/ISeeU.
Correcting rural building annotations in OpenStreetMap using convolutional neural networks
Authors: John E. Vargas-Muñoz, Sylvain Lobry, Alexandre X. Falcão, Devis Tuia
Date: 2019-01-24 00:00:00

Rural building mapping is paramount to support demographic studies and plan
actions in response to crisis that affect those areas. Rural building
annotations exist in OpenStreetMap (OSM), but their quality and quantity are
not sufficient for training models that can create accurate rural building
maps. The problems with these annotations essentially fall into three
categories: (i) most commonly, many annotations are geometrically misaligned
with the updated imagery; (ii) some annotations do not correspond to buildings
in the images (they are misannotations or the buildings have been destroyed);
and (iii) some annotations are missing for buildings in the images (the
buildings were never annotated or were built between subsequent image
acquisitions). First, we propose a method based on Markov Random Field (MRF) to
align the buildings with their annotations. The method maximizes the
correlation between annotations and a building probability map while enforcing
that nearby buildings have similar alignment vectors. Second, the annotations
with no evidence in the building probability map are removed. Third, we present
a method to detect non-annotated buildings with predefined shapes and add their
annotation. The proposed methodology shows considerable improvement in accuracy
of the OSM annotations for two regions of Tanzania and Zimbabwe, being more
accurate than state-of-the-art baselines.
Generating and Aligning from Data Geometries with Generative Adversarial Networks
Authors: Matthew Amodio, Smita Krishnaswamy
Date: 2019-01-24 00:00:00
Tasks: Generative Adversarial Network

Unsupervised domain mapping has attracted substantial attention in recent
years due to the success of models based on the cycle-consistency assumption.
These models map between two domains by fooling a probabilistic discriminator,
thereby matching the probability distributions of the real and generated data.
Instead of this probabilistic approach, we cast the problem in terms of
aligning the geometry of the manifolds of the two domains. We introduce the
Manifold Geometry Matching Generative Adversarial Network (MGM GAN), which adds
two novel mechanisms to facilitate GANs sampling from the geometry of the
manifold rather than the density and then aligning two manifold geometries: (1)
an importance sampling technique that reweights points based on their density
on the manifold, making the discriminator only able to discern geometry and (2)
a penalty adapted from traditional manifold alignment literature that
explicitly enforces the geometry to be preserved. The MGM GAN leverages the
manifolds arising from a pre-trained autoencoder to bridge the gap between
formal manifold alignment literature and existing GAN work, and demonstrate the
advantages of modeling the manifold geometry over its density.
A Fully Stochastic Primal-Dual Algorithm
Authors: Pascal Bianchi, Walid Hachem, Adil Salim
Date: 2019-01-23 00:00:00

A new stochastic primal--dual algorithm for solving a composite optimization problem is proposed. It is assumed that all the functions/operators that enter the optimization problem are given as statistical expectations. These expectations are unknown but revealed across time through i.i.d. realizations. The proposed algorithm is proven to converge to a saddle point of the Lagrangian function. In the framework of the monotone operator theory, the convergence proof relies on recent results on the stochastic Forward Backward algorithm involving random monotone operators. An example of convex optimization under stochastic linear constraints is considered.
Loss Landscapes of Regularized Linear Autoencoders
Authors: Daniel Kunin, Jonathan M. Bloom, Aleksandrina Goeva, Cotton Seed
Date: 2019-01-23 00:00:00
Tasks: Decoder, Representation Learning

Autoencoders are a deep learning model for representation learning. When trained to minimize the distance between the data and its reconstruction, linear autoencoders (LAEs) learn the subspace spanned by the top principal directions but cannot learn the principal directions themselves. In this paper, we prove that $L_2$-regularized LAEs are symmetric at all critical points and learn the principal directions as the left singular vectors of the decoder. We smoothly parameterize the critical manifold and relate the minima to the MAP estimate of probabilistic PCA. We illustrate these results empirically and consider implications for PCA algorithms, computational neuroscience, and the algebraic topology of learning.
Decoupled Greedy Learning of CNNs
Authors: Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon
Date: 2019-01-23 00:00:00
Proceeding: ICML 2020 1
Tasks: image-classification, Image Classification

A commonly cited inefficiency of neural network training by back-propagation is the update locking problem: each layer must wait for the signal to propagate through the full network before updating. Several alternatives that can alleviate this issue have been proposed. In this context, we consider a simpler, but more effective, substitute that uses minimal feedback, which we call Decoupled Greedy Learning (DGL). It is based on a greedy relaxation of the joint training objective, recently shown to be effective in the context of Convolutional Neural Networks (CNNs) on large-scale image classification. We consider an optimization of this objective that permits us to decouple the layer training, allowing for layers or modules in networks to be trained with a potentially linear parallelization in layers. With the use of a replay buffer we show this approach can be extended to asynchronous settings, where modules can operate with possibly large communication delays. We show theoretically and empirically that this approach converges. Then, we empirically find that it can lead to better generalization than sequential greedy optimization. We demonstrate the effectiveness of DGL against alternative approaches on the CIFAR-10 dataset and on the large-scale ImageNet dataset.
Causal Reasoning from Meta-reinforcement Learning
Authors: Ishita Dasgupta, Jane Wang, Silvia Chiappa, Jovana Mitrovic, Pedro Ortega, David Raposo, Edward Hughes, Peter Battaglia, Matthew Botvinick, Zeb Kurth-Nelson
Date: 2019-01-23 00:00:00
Proceeding: ICLR 2019 5
Tasks: counterfactual, Meta Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Discovering and exploiting the causal structure in the environment is a
crucial challenge for intelligent agents. Here we explore whether causal
reasoning can emerge via meta-reinforcement learning. We train a recurrent
network with model-free reinforcement learning to solve a range of problems
that each contain causal structure. We find that the trained agent can perform
causal reasoning in novel situations in order to obtain rewards. The agent can
select informative interventions, draw causal inferences from observational
data, and make counterfactual predictions. Although established formal causal
reasoning algorithms also exist, in this paper we show that such reasoning can
arise from model-free reinforcement learning, and suggest that causal reasoning
in complex settings may benefit from the more end-to-end learning-based
approaches presented here. This work also offers new strategies for structured
exploration in reinforcement learning, by providing agents with the ability to
perform -- and interpret -- experiments.
Meta-Learning for Contextual Bandit Exploration
Authors: Amr Sharaf, Hal Daumé III
Date: 2019-01-23 00:00:00
Proceeding: ICLR 2019 5
Tasks: Imitation Learning, Meta-Learning

We describe MELEE, a meta-learning algorithm for learning a good exploration
policy in the interactive contextual bandit setting. Here, an algorithm must
take actions based on contexts, and learn based only on a reward signal from
the action taken, thereby generating an exploration/exploitation trade-off.
MELEE addresses this trade-off by learning a good exploration strategy for
offline tasks based on synthetic data, on which it can simulate the contextual
bandit setting. Based on these simulations, MELEE uses an imitation learning
strategy to learn a good exploration policy that can then be applied to true
contextual bandit tasks at test time. We compare MELEE to seven strong baseline
contextual bandit algorithms on a set of three hundred real-world datasets, on
which it outperforms alternatives in most settings, especially when differences
in rewards are large. Finally, we demonstrate the importance of having a rich
feature representation for learning how to explore.
A Tool for Spatio-Temporal Analysis of Social Anxiety with Twitter Data
Authors: Joohong Lee, Dongyoung Son, Yong Suk Choi
Date: 2019-01-23 00:00:00

In this paper, we present a tool for analyzing spatio-temporal distribution
of social anxiety. Twitter, one of the most popular social network services,
has been chosen as data source for analysis of social anxiety. Tweets (posted
on the Twitter) contain various emotions and thus these individual emotions
reflect social atmosphere and public opinion, which are often dependent on
spatial and temporal factors. The reason why we choose anxiety among various
emotions is that anxiety is very important emotion that is useful for observing
and understanding social events of communities. We develop a machine learning
based tool to analyze the changes of social atmosphere spatially and
temporally. Our tool classifies whether each Tweet contains anxious content or
not, and also estimates degree of Tweet anxiety. Furthermore, it also
visualizes spatio-temporal distribution of anxiety as a form of web
application, which is incorporated with physical map, word cloud, search engine
and chart viewer. Our tool is applied to a big tweet data in South Korea to
illustrate its usefulness for exploring social atmosphere and public opinion
spatio-temporally.