pwc-archive/papers-with-abstracts huggingface dataset:
Generated Loss, Augmented Training, and Multiscale VAE
Authors: Jason Chou, Gautam Hathi
Date: 2019-04-23 00:00:00

The variational autoencoder (VAE) framework remains a popular option for
training unsupervised generative models, especially for discrete data where
generative adversarial networks (GANs) require workaround to create gradient
for the generator. In our work modeling US postal addresses, we show that our
discrete VAE with tree recursive architecture demonstrates limited capability
of capturing field correlations within structured data, even after overcoming
the challenge of posterior collapse with scheduled sampling and tuning of the
KL-divergence weight $\beta$. Worse, VAE seems to have difficulty mapping its
generated samples to the latent space, as their VAE loss lags behind or even
increases during the training process. Motivated by this observation, we show
that augmenting training data with generated variants (augmented training) and
training a VAE with multiple values of $\beta$ simultaneously (multiscale VAE)
both improve the generation quality of VAE. Despite their differences in
motivation and emphasis, we show that augmented training and multiscale VAE are
actually connected and have similar effects on the model.
DenseNet Models for Tiny ImageNet Classification
Authors: Zoheb Abai, Nishad Rajmalwar
Date: 2019-04-23 00:00:00
Tasks: Classification, General Classification, Image Augmentation, image-classification, Image Classification

In this paper, we present two image classification models on the Tiny ImageNet dataset. We built two very different networks from scratch based on the idea of Densely Connected Convolution Networks. The architecture of the networks is designed based on the image resolution of this specific dataset and by calculating the Receptive Field of the convolution layers. We also used some non-conventional techniques related to image augmentation and Cyclical Learning Rate to improve the accuracy of our models. The networks are trained under high constraints and low computation resources. We aimed to achieve top-1 validation accuracy of 60%; the results and error analysis are also presented.
Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting
Authors: Shengcai Liao, Ling Shao
Date: 2019-04-23 00:00:00
Proceeding: ECCV 2020 8
Tasks: Domain Generalization, Generalizable Person Re-identification, Metric Learning, Person Re-Identification, Unsupervised Domain Adaptation

For person re-identification, existing deep networks often focus on representation learning. However, without transfer learning, the learned model is fixed as is, which is not adaptable for handling various unseen scenarios. In this paper, beyond representation learning, we consider how to formulate person image matching directly in deep feature maps. We treat image matching as finding local correspondences in feature maps, and construct query-adaptive convolution kernels on the fly to achieve local matching. In this way, the matching process and results are interpretable, and this explicit matching is more generalizable than representation features to unseen scenarios, such as unknown misalignments, pose or viewpoint changes. To facilitate end-to-end training of this architecture, we further build a class memory module to cache feature maps of the most recent samples of each class, so as to compute image matching losses for metric learning. Through direct cross-dataset evaluation, the proposed Query-Adaptive Convolution (QAConv) method gains large improvements over popular learning methods (about 10%+ mAP), and achieves comparable results to many transfer learning methods. Besides, a model-free temporal cooccurrence based score weighting method called TLift is proposed, which improves the performance to a further extent, achieving state-of-the-art results in cross-dataset person re-identification. Code is available at https://github.com/ShengcaiLiao/QAConv.
GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection
Authors: Yue Yu, YIlun Zhu, Yang Liu, Yan Liu, Siyao Peng, Mackenzie Gong, Amir Zeldes
Date: 2019-04-23 00:00:00
Proceeding: WS 2019 6
Tasks: Connective Detection, Discourse Segmentation, Segmentation, Sentence

In this paper we present GumDrop, Georgetown University's entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompasses three trainable component stacks: one for sentence splitting, one for discourse unit segmentation and one for connective detection. The flexibility of each ensemble allows the system to generalize well to datasets of different sizes and with varying levels of homogeneity.
Regression-Enhanced Random Forests
Authors: Haozhe Zhang, Dan Nettleton, Zhengyuan Zhu
Date: 2019-04-23 00:00:00
Tasks: BIG-bench Machine Learning, regression

Random forest (RF) methodology is one of the most popular machine learning
techniques for prediction problems. In this article, we discuss some cases
where random forests may suffer and propose a novel generalized RF method,
namely regression-enhanced random forests (RERFs), that can improve on RFs by
borrowing the strength of penalized parametric regression. The algorithm for
constructing RERFs and selecting its tuning parameters is described. Both
simulation study and real data examples show that RERFs have better predictive
performance than RFs in important situations often encountered in practice.
Moreover, RERFs may incorporate known relationships between the response and
the predictors, and may give reliable predictions in extrapolation problems
where predictions are required at points out of the domain of the training
dataset. Strategies analogous to those described here can be used to improve
other machine learning methods via combination with penalized parametric
regression techniques.
BIT: Biologically Inspired Tracker
Authors: Bolun Cai, Xiangmin Xu, Xiaofen Xing, Kui Jia, Jie Miao, DaCheng Tao
Date: 2019-04-23 00:00:00
Tasks: Visual Tracking

Visual tracking is challenging due to image variations caused by various
factors, such as object deformation, scale change, illumination change and
occlusion. Given the superior tracking performance of human visual system
(HVS), an ideal design of biologically inspired model is expected to improve
computer visual tracking. This is however a difficult task due to the
incomplete understanding of neurons' working mechanism in HVS. This paper aims
to address this challenge based on the analysis of visual cognitive mechanism
of the ventral stream in the visual cortex, which simulates shallow neurons (S1
units and C1 units) to extract low-level biologically inspired features for the
target appearance and imitates an advanced learning mechanism (S2 units and C2
units) to combine generative and discriminative models for target location. In
addition, fast Gabor approximation (FGA) and fast Fourier transform (FFT) are
adopted for real-time learning and detection in this framework. Extensive
experiments on large-scale benchmark datasets show that the proposed
biologically inspired tracker performs favorably against state-of-the-art
methods in terms of efficiency, accuracy, and robustness. The acceleration
technique in particular ensures that BIT maintains a speed of approximately 45
frames per second.
Big Math and the One-Brain Barrier A Position Paper and Architecture Proposal
Authors: Jacques Carette, William M. Farmer, Michael Kohlhase, Florian Rabe
Date: 2019-04-23 00:00:00
Tasks: Math, Position

Over the last decades, a class of important mathematical results have required an ever increasing amount of human effort to carry out. For some, the help of computers is now indispensable. We analyze the implications of this trend towards "big mathematics", its relation to human cognition, and how machine support for big math can be organized. The central contribution of this position paper is an information model for "doing mathematics", which posits that humans very efficiently integrate four aspects: inference, computation, tabulation, and narration around a well-organized core of mathematical knowledge. The challenge for mathematical software systems is that these four aspects need to be integrated as well. We briefly survey the state of the art.
Optimizing Search API Queries for Twitter Topic Classifiers Using a Maximum Set Coverage Approach
Authors: Kasra Safari, Scott Sanner
Date: 2019-04-23 00:00:00

Twitter has grown to become an important platform to access immediate information about major events and dynamic topics. As one example, recent work has shown that classifiers trained to detect topical content on Twitter can generalize well beyond the training data. Since access to Twitter data is hidden behind a limited search API, it is impossible (for most users) to apply these classifiers directly to the Twitter unfiltered data streams ("firehose"). Rather, applications must first decide what content to retrieve through the search API before filtering that content with topical classifiers. Thus, it is critically important to query the Twitter API relative to the intended topical classifier in a way that minimizes the amount of negatively classified data retrieved. In this paper, we propose a sequence of query optimization methods that generalize notions of the maximum coverage problem to find the subset of query terms within the API limits that cover most of the topically relevant tweets without sacrificing precision. We evaluate the proposed methods on a large dataset of Twitter data collected during 2013 and 2014 labeled using manually curated hashtags for eight topics. Among many insights, our analysis shows that the best of the proposed methods can significantly outperform the firehose on precision and F1-score while achieving high recall within strict API limitations.
Minimizing Perceived Image Quality Loss Through Adversarial Attack Scoping
Authors: Kostiantyn Khabarlak, Larysa Koriashkina
Date: 2019-04-23 00:00:00
Tasks: Adversarial Attack, Autonomous Vehicles, Face Recognition, SSIM

Neural networks are now actively being used for computer vision tasks in
security critical areas such as robotics, face recognition, autonomous vehicles
yet their safety is under question after the discovery of adversarial attacks.
In this paper we develop simplified adversarial attack algorithms based on a
scoping idea, which enables execution of fast adversarial attacks that minimize
structural image quality (SSIM) loss, allows performing efficient transfer
attacks with low target inference network call count and opens a possibility of
an attack using pen-only drawings on a paper for the MNIST handwritten digit
dataset. The presented adversarial attack analysis and the idea of attack
scoping can be easily expanded to different datasets, thus making the paper's
results applicable to a wide range of practical tasks.
Learning relevant features for statistical inference
Authors: Cédric Bény
Date: 2019-04-23 00:00:00

Given two views of data, we consider the problem of finding the features of one view which can be most faithfully inferred from the other. We find that these are also the most correlated variables in the sense of deep canonical correlation analysis (DCCA). Moreover, we show that these variables can be used to construct a non-parametric representation of the implied joint probability distribution, which can be thought of as a classical version of the Schmidt decomposition of quantum states. This representation can be used to compute the expectations of functions over one view of data conditioned on the other, such as Bayesian estimators and their standard deviations. We test the approach using inference on occluded MNIST images, and show that our representation contains multiple modes. Surprisingly, when applied to supervised learning (one dataset consists of labels), this approach automatically provides regularization and faster convergence compared to the cross-entropy objective. We also explore using this approach to discover salient independent variables of a single dataset.
Risk Structures: Towards Engineering Risk-aware Autonomous Systems
Authors: Mario Gleirscher
Date: 2019-04-23 00:00:00
Tasks: Decision Making

Inspired by widely-used techniques of causal modelling in risk, failure, and
accident analysis, this work discusses a compositional framework for risk
modelling. Risk models capture fragments of the space of risky events likely to
occur when operating a machine in a given environment. Moreover, one can build
such models into machines such as autonomous robots, to equip them with the
ability of risk-aware perception, monitoring, decision making, and control.
With the notion of a risk factor as the modelling primitive, the framework
provides several means to construct and shape risk models. Relational and
algebraic properties are investigated and proofs support the validity and
consistency of these properties over the corresponding models. Several examples
throughout the discussion illustrate the applicability of the concepts.
Overall, this work focuses on the qualitative treatment of risk with the
outlook of transferring these results to probabilistic refinements of the
discussed framework.
Multi-modal 3D Shape Reconstruction Under Calibration Uncertainty using Parametric Level Set Methods
Authors: Moshe Eliasof, Andrei Sharf, Eran Treister
Date: 2019-04-23 00:00:00
Tasks: 3D Shape Reconstruction

We consider the problem of 3D shape reconstruction from multi-modal data, given uncertain calibration parameters. Typically, 3D data modalities can be in diverse forms such as sparse point sets, volumetric slices, 2D photos and so on. To jointly process these data modalities, we exploit a parametric level set method that utilizes ellipsoidal radial basis functions. This method not only allows us to analytically and compactly represent the object, it also confers on us the ability to overcome calibration related noise that originates from inaccurate acquisition parameters. This essentially implicit regularization leads to a highly robust and scalable reconstruction, surpassing other traditional methods. In our results we first demonstrate the ability of the method to compactly represent complex objects. We then show that our reconstruction method is robust both to a small number of measurements and to noise in the acquisition parameters. Finally, we demonstrate our reconstruction abilities from diverse modalities such as volume slices obtained from liquid displacement (similar to CTscans and XRays), and visual measurements obtained from shape silhouettes.
A survey on Big Data and Machine Learning for Chemistry
Authors: Jose F. Rodrigues Jr, Larisa Florea, Maria C F de Oliveira, Dermot Diamond, Osvaldo N. Oliveira Jr
Date: 2019-04-23 00:00:00
Tasks: BIG-bench Machine Learning

Herein we review aspects of leading-edge research and innovation in chemistry
which exploits big data and machine learning (ML), two computer science fields
that combine to yield machine intelligence. ML can accelerate the solution of
intricate chemical problems and even solve problems that otherwise would not be
tractable. But the potential benefits of ML come at the cost of big data
production; that is, the algorithms, in order to learn, demand large volumes of
data of various natures and from different sources, from materials properties
to sensor data. In the survey, we propose a roadmap for future developments,
with emphasis on materials discovery and chemical sensing, and within the
context of the Internet of Things (IoT), both prominent research fields for ML
in the context of big data. In addition to providing an overview of recent
advances, we elaborate upon the conceptual and practical limitations of big
data and ML applied to chemistry, outlining processes, discussing pitfalls, and
reviewing cases of success and failure.
Identifying cross country skiing techniques using power meters in ski poles
Authors: Moa Johansson, Marie Korneliusson, Nickey Lizbat Lawrence
Date: 2019-04-23 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis

Power meters are becoming a widely used tool for measuring training and racing effort in cycling, and are now spreading also to other sports. This means that increasing volumes of data can be collected from athletes, with the aim of helping coaches and athletes analyse and understanding training load, racing efforts, technique etc. In this project, we have collaborated with Skisens AB, a company producing handles for cross country ski poles equipped with power meters. We have conducted a pilot study in the use of machine learning techniques on data from Skisens poles to identify which "gear" a skier is using (double poling or gears 2-4 in skating), based only on the sensor data from the ski poles. The dataset for this pilot study contained labelled time-series data from three individual skiers using four different gears recorded in varied locations and varied terrain. We systematically evaluated a number of machine learning techniques based on neural networks with best results obtained by a LSTM network (accuracy of 95% correctly classified strokes), when a subset of data from all three skiers was used for training. As expected, accuracy dropped to 78% when the model was trained on data from only two skiers and tested on the third. To achieve better generalisation to individuals not appearing in the training set more data is required, which is ongoing work.
HAUAR: Home Automation Using Action Recognition
Authors: Shashank Kotyan, Nishant Kumar, Pankaj Kumar Sahu, Venkanna Udutalapally
Date: 2019-04-23 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Today, many of the home automation systems deployed are mostly controlled by
humans. This control by humans restricts the automation of home appliances to
an extent. Also, most of the deployed home automation systems use the Internet
of Things technology to control the appliances. In this paper, we propose a
system developed using action recognition to fully automate the home
appliances. We recognize the three actions of a person (sitting, standing and
lying) along with the recognition of an empty room. The accuracy of the system
was 90% in the real-life test experiments. With this system, we remove the
human intervention in home automation systems for controlling the home
appliances and at the same time we ensure the data privacy and reduce the
energy consumption by efficiently and optimally using home appliances.
Read classification using semi-supervised deep learning
Authors: Tomislav Šebrek, Jan Tomljanović, Josip Krapac, Mile Šikić
Date: 2019-04-23 00:00:00
Tasks: Classification, Deep Learning, General Classification

In this paper, we propose a semi-supervised deep learning method for
detecting the specific types of reads that impede the de novo genome assembly
process. Instead of dealing directly with sequenced reads, we analyze their
coverage graphs converted to 1D-signals. We noticed that specific signal
patterns occur in each relevant class of reads. Semi-supervised approach is
chosen because manually labelling the data is a very slow and tedious process,
so our goal was to facilitate the assembly process with as little labeled data
as possible. We tested two models to learn patterns in the coverage graphs:
M1+M2 and semi-GAN. We evaluated the performance of each model based on a
manually labeled dataset that comprises various reads from multiple reference
genomes with respect to the number of labeled examples that were used during
the training process. In addition, we embedded our detection in the assembly
process which improved the quality of assemblies.
Drishtikon: An advanced navigational aid system for visually impaired people
Authors: Shashank Kotyan, Nishant Kumar, Pankaj Kumar Sahu, Venkanna Udutalapally
Date: 2019-04-23 00:00:00
Tasks: Navigate, Object, object-detection, Object Detection

Today, many of the aid systems deployed for visually impaired people are
mostly made for a single purpose. Be it navigation, object detection, or
distance perceiving. Also, most of the deployed aid systems use indoor
navigation which requires a pre-knowledge of the environment. These aid systems
often fail to help visually impaired people in the unfamiliar scenario. In this
paper, we propose an aid system developed using object detection and depth
perceivement to navigate a person without dashing into an object. The prototype
developed detects 90 different types of objects and compute their distances
from the user. We also, implemented a navigation feature to get input from the
user about the target destination and hence, navigate the impaired person to
his/her destination using Google Directions API. With this system, we built a
multi-feature, high accuracy navigational aid system which can be deployed in
the wild and help the visually impaired people in their daily life by
navigating them effortlessly to their desired destination.
Monte-Carlo Tree Search for Efficient Visually Guided Rearrangement Planning
Authors: Yann Labbé, Sergey Zagoruyko, Igor Kalevatykh, Ivan Laptev, Justin Carpentier, Mathieu Aubry, Josef Sivic
Date: 2019-04-23 00:00:00
Tasks: State Estimation

We address the problem of visually guided rearrangement planning with many movable objects, i.e., finding a sequence of actions to move a set of objects from an initial arrangement to a desired one, while relying on visual inputs coming from an RGB camera. To do so, we introduce a complete pipeline relying on two key contributions. First, we introduce an efficient and scalable rearrangement planning method, based on a Monte-Carlo Tree Search exploration strategy. We demonstrate that because of its good trade-off between exploration and exploitation our method (i) scales well with the number of objects while (ii) finding solutions which require a smaller number of moves compared to the other state-of-the-art approaches. Note that on the contrary to many approaches, we do not require any buffer space to be available. Second, to precisely localize movable objects in the scene, we develop an integrated approach for robust multi-object workspace state estimation from a single uncalibrated RGB camera using a deep neural network trained only with synthetic data. We validate our multi-object visually guided manipulation pipeline with several experiments on a real UR-5 robotic arm by solving various rearrangement planning instances, requiring only 60 ms to compute the plan to rearrange 25 objects. In addition, we show that our system is insensitive to camera movements and can successfully recover from external perturbations. Supplementary video, source code and pre-trained models are available at https://ylabbe.github.io/rearrangement-planning.