pwc-archive/papers-with-abstracts huggingface dataset:
Using Textual Summaries to Describe a Set of Products
Authors: Kittipitch Kuptavanich
Date: 2018-07-16 00:00:00

When customers are faced with the task of making a purchase in an unfamiliar
product domain, it might be useful to provide them with an overview of the
product set to help them understand what they can expect. In this paper we
present and evaluate a method to summarise sets of products in natural
language, focusing on the price range, common product features across the set,
and product features that impact on price. In our study, participants reported
that they found our summaries useful, but we found no evidence that the
summaries influenced the selections made by participants.
Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation
Authors: Jungwook Lee, Sean Walsh, Ali Harakeh, Steven L. Waslander
Date: 2018-07-16 00:00:00
Tasks: 3D Object Detection, Autonomous Driving, Instance Segmentation, Object, object-detection, Object Detection, Point Cloud Segmentation, Semantic Segmentation

Training 3D object detectors for autonomous driving has been limited to small
datasets due to the effort required to generate annotations. Reducing both task
complexity and the amount of task switching done by annotators is key to
reducing the effort and time required to generate 3D bounding box annotations.
This paper introduces a novel ground truth generation method that combines
human supervision with pretrained neural networks to generate per-instance 3D
point cloud segmentation, 3D bounding boxes, and class annotations. The
annotators provide object anchor clicks which behave as a seed to generate
instance segmentation results in 3D. The points belonging to each instance are
then used to regress object centroids, bounding box dimensions, and object
orientation. Our proposed annotation scheme requires 30x lower human annotation
time. We use the KITTI 3D object detection dataset to evaluate the efficiency
and the quality of our annotation scheme. We also test the the proposed scheme
on previously unseen data from the Autonomoose self-driving vehicle to
demonstrate generalization capabilities of the network.
Automated Data Slicing for Model Validation:A Big data - AI Integration Approach
Authors: Yeounoh Chung, Tim Kraska, Neoklis Polyzotis, Ki Hyun Tae, Steven Euijong Whang
Date: 2018-07-16 00:00:00
Tasks: Clustering, Fairness, Fraud Detection

As machine learning systems become democratized, it becomes increasingly
important to help users easily debug their models. However, current data tools
are still primitive when it comes to helping users trace model performance
problems all the way to the data. We focus on the particular problem of slicing
data to identify subsets of the validation data where the model performs
poorly. This is an important problem in model validation because the overall
model performance can fail to reflect that of the smaller subsets, and slicing
allows users to analyze the model performance on a more granular-level. Unlike
general techniques (e.g., clustering) that can find arbitrary slices, our goal
is to find interpretable slices (which are easier to take action compared to
arbitrary subsets) that are problematic and large. We propose Slice Finder,
which is an interactive framework for identifying such slices using statistical
techniques. Applications include diagnosing model fairness and fraud detection,
where identifying slices that are interpretable to humans is crucial. This
research is part of a larger trend of Big data and Artificial Intelligence (AI)
integration and opens many opportunities for new research.
Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays
Authors: Chaochao Yan, Jiawen Yao, Ruoyu Li, Zheng Xu, Junzhou Huang
Date: 2018-07-16 00:00:00
Tasks: General Classification, Thoracic Disease Classification

Chest X-rays is one of the most commonly available and affordable
radiological examinations in clinical practice. While detecting thoracic
diseases on chest X-rays is still a challenging task for machine intelligence,
due to 1) the highly varied appearance of lesion areas on X-rays from patients
of different thoracic disease and 2) the shortage of accurate pixel-level
annotations by radiologists for model training. Existing machine learning
methods are unable to deal with the challenge that thoracic diseases usually
happen in localized disease-specific areas. In this article, we propose a
weakly supervised deep learning framework equipped with squeeze-and-excitation
blocks, multi-map transfer, and max-min pooling for classifying thoracic
diseases as well as localizing suspicious lesion regions. The comprehensive
experiments and discussions are performed on the ChestX-ray14 dataset. Both
numerical and visual results have demonstrated the effectiveness of the
proposed model and its better performance against the state-of-the-art
pipelines.
Online Robust Policy Learning in the Presence of Unknown Adversaries
Authors: Aaron J. Havens, Zhanhong Jiang, Soumik Sarkar
Date: 2018-07-16 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Deep Reinforcement Learning, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The growing prospect of deep reinforcement learning (DRL) being used in
cyber-physical systems has raised concerns around safety and robustness of
autonomous agents. Recent work on generating adversarial attacks have shown
that it is computationally feasible for a bad actor to fool a DRL policy into
behaving sub optimally. Although certain adversarial attacks with specific
attack models have been addressed, most studies are only interested in off-line
optimization in the data space (e.g., example fitting, distillation). This
paper introduces a Meta-Learned Advantage Hierarchy (MLAH) framework that is
attack model-agnostic and more suited to reinforcement learning, via handling
the attacks in the decision space (as opposed to data space) and directly
mitigating learned bias introduced by the adversary. In MLAH, we learn separate
sub-policies (nominal and adversarial) in an online manner, as guided by a
supervisory master agent that detects the presence of the adversary by
leveraging the advantage function for the sub-policies. We demonstrate that the
proposed algorithm enables policy learning with significantly lower bias as
compared to the state-of-the-art policy learning approaches even in the
presence of heavy state information attacks. We present algorithm analysis and
simulation results using popular OpenAI Gym environments.
Unlimited Road-scene Synthetic Annotation (URSA) Dataset
Authors: Matt Angus, Mohamed ElBalkini, Samin Khan, Ali Harakeh, Oles Andrienko, Cody Reading, Steven Waslander, Krzysztof Czarnecki
Date: 2018-07-16 00:00:00
Tasks: Semantic Segmentation

In training deep neural networks for semantic segmentation, the main limiting
factor is the low amount of ground truth annotation data that is available in
currently existing datasets. The limited availability of such data is due to
the time cost and human effort required to accurately and consistently label
real images on a pixel level. Modern sandbox video game engines provide open
world environments where traffic and pedestrians behave in a pseudo-realistic
manner. This caters well to the collection of a believable road-scene dataset.
Utilizing open-source tools and resources found in single-player modding
communities, we provide a method for persistent, ground truth, asset annotation
of a game world. By collecting a synthetic dataset containing upwards of
$1,000,000$ images, we demonstrate real-time, on-demand, ground truth data
annotation capability of our method. Supplementing this synthetic data to
Cityscapes dataset, we show that our data generation method provides
qualitative as well as quantitative improvements---for training networks---over
previous methods that use video games as surrogate.
On the Information Theoretic Distance Measures and Bidirectional Helmholtz Machines
Authors: Mahdi Azarafrooz, Xuan Zhao, Sepehr Akhavan-Masouleh
Date: 2018-07-16 00:00:00

By establishing a connection between bi-directional Helmholtz machines and
information theory, we propose a generalized Helmholtz machine. Theoretical and
experimental results show that given \textit{shallow} architectures, the
generalized model outperforms the previous ones substantially.
Zap: Making Predictions Based on Online User Behavior
Authors: Yuri Chervonyi, Dragos Harabor, Brian Zhang, Josh Sacks
Date: 2018-07-16 00:00:00
Tasks: BIG-bench Machine Learning

This paper introduces Zap, a generic machine learning pipeline for making
predictions based on online user behavior. Zap combines well known techniques
for processing sequential data with more obscure techniques such as Bloom
filters, bucketing, and model calibration into an end-to-end solution. The
pipeline creates website- and task-specific models without knowing anything
about the structure of the website. It is designed to minimize the amount of
website-specific code, which is realized by factoring all website-specific
logic into example generators. New example generators can typically be written
up in a few lines of code.
Pangloss: Fast Entity Linking in Noisy Text Environments
Authors: Michael Conover, Matthew Hayes, Scott Blackburn, Pete Skomoroch, Sam Shah
Date: 2018-07-16 00:00:00
Tasks: Articles, Entity Disambiguation, Entity Linking, graph construction, Question Answering, Semantic Similarity, Semantic Textual Similarity

Entity linking is the task of mapping potentially ambiguous terms in text to
their constituent entities in a knowledge base like Wikipedia. This is useful
for organizing content, extracting structured data from textual documents, and
in machine learning relevance applications like semantic search, knowledge
graph construction, and question answering. Traditionally, this work has
focused on text that has been well-formed, like news articles, but in common
real world datasets such as messaging, resumes, or short-form social media,
non-grammatical, loosely-structured text adds a new dimension to this problem.
  This paper presents Pangloss, a production system for entity disambiguation
on noisy text. Pangloss combines a probabilistic linear-time key phrase
identification algorithm with a semantic similarity engine based on
context-dependent document embeddings to achieve better than state-of-the-art
results (>5% in F1) compared to other research or commercially available
systems. In addition, Pangloss leverages a local embedded database with a
tiered architecture to house its statistics and metadata, which allows rapid
disambiguation in streaming contexts and on-device disambiguation in low-memory
environments such as mobile phones.
EC-Net: an Edge-aware Point set Consolidation Network
Authors: Lequan Yu, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng
Date: 2018-07-16 00:00:00
Proceeding: ECCV 2018 9
Tasks: Surface Reconstruction

Point clouds obtained from 3D scans are typically sparse, irregular, and
noisy, and required to be consolidated. In this paper, we present the first
deep learning based edge-aware technique to facilitate the consolidation of
point clouds. We design our network to process points grouped in local patches,
and train it to learn and help consolidate points, deliberately for edges. To
achieve this, we formulate a regression component to simultaneously recover 3D
point coordinates and point-to-edge distances from upsampled features, and an
edge-aware joint loss function to directly minimize distances from output
points to 3D meshes and to edges. Compared with previous neural network based
works, our consolidation is edge-aware. During the synthesis, our network can
attend to the detected sharp edges and enable more accurate 3D reconstructions.
Also, we trained our network on virtual scanned point clouds, demonstrated the
performance of our method on both synthetic and real point clouds, presented
various surface reconstruction results, and showed how our method outperforms
the state-of-the-arts.
ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems
Authors: Yinda Zhang, Sameh Khamis, Christoph Rhemann, Julien Valentin, Adarsh Kowdle, Vladimir Tankovich, Michael Schoenberg, Shahram Izadi, Thomas Funkhouser, Sean Fanello
Date: 2018-07-16 00:00:00
Proceeding: ECCV 2018 9
Tasks: Self-Supervised Learning, Stereo Disparity Estimation, Stereo Matching

In this paper we present ActiveStereoNet, the first deep learning solution
for active stereo systems. Due to the lack of ground truth, our method is fully
self-supervised, yet it produces precise depth with a subpixel precision of
$1/30th$ of a pixel; it does not suffer from the common over-smoothing issues;
it preserves the edges; and it explicitly handles occlusions. We introduce a
novel reconstruction loss that is more robust to noise and texture-less
patches, and is invariant to illumination changes. The proposed loss is
optimized using a window-based cost aggregation with an adaptive support weight
scheme. This cost aggregation is edge-preserving and smooths the loss function,
which is key to allow the network to reach compelling results. Finally we show
how the task of predicting invalid regions, such as occlusions, can be trained
end-to-end without ground-truth. This component is crucial to reduce blur and
particularly improves predictions along depth discontinuities. Extensive
quantitatively and qualitatively evaluations on real and synthetic data
demonstrate state of the art results in many challenging scenes.
Combining a Context Aware Neural Network with a Denoising Autoencoder for Measuring String Similarities
Authors: Mehdi Ben Lazreg, Morten Goodwin
Date: 2018-07-16 00:00:00
Tasks: Denoising, Information Retrieval, Retrieval

Measuring similarities between strings is central for many established and
fast growing research areas including information retrieval, biology, and
natural language processing. The traditional approach for string similarity
measurements is to define a metric over a word space that quantifies and sums
up the differences between characters in two strings. The state-of-the-art in
the area has, surprisingly, not evolved much during the last few decades. The
majority of the metrics are based on a simple comparison between character and
character distributions without consideration for the context of the words.
This paper proposes a string metric that encompasses similarities between
strings based on (1) the character similarities between the words including.
Non-Standard and standard spellings of the same words, and (2) the context of
the words. Our proposal is a neural network composed of a denoising autoencoder
and what we call a context encoder specifically designed to find similarities
between the words based on their context. The experimental results show that
the resulting metrics succeeds in 85.4\% of the cases in finding the correct
version of a non-standard spelling among the closest words, compared to 63.2\%
with the established Normalised-Levenshtein distance. Besides, we show that
words used in similar context are with our approach calculated to be similar
than words with different contexts, which is a desirable property missing in
established string metrics.
Iterative Joint Image Demosaicking and Denoising using a Residual Denoising Network
Authors: Filippos Kokkinos, Stamatios Lefkimmiatis
Date: 2018-07-16 00:00:00
Tasks: Demosaicking, Denoising

Modern digital cameras rely on the sequential execution of separate image
processing steps to produce realistic images. The first two steps are usually
related to denoising and demosaicking where the former aims to reduce noise
from the sensor and the latter converts a series of light intensity readings to
color images. Modern approaches try to jointly solve these problems, i.e. joint
denoising-demosaicking which is an inherently ill-posed problem given that
two-thirds of the intensity information is missing and the rest are perturbed
by noise. While there are several machine learning systems that have been
recently introduced to solve this problem, the majority of them relies on
generic network architectures which do not explicitly take into account the
physical image model. In this work we propose a novel algorithm which is
inspired by powerful classical image regularization methods, large-scale
optimization, and deep learning techniques. Consequently, our derived iterative
optimization algorithm, which involves a trainable denoising network, has a
transparent and clear interpretation compared to other black-box data driven
approaches. Our extensive experimentation line demonstrates that our proposed
method outperforms any previous approaches for both noisy and noise-free data
across many different datasets. This improvement in reconstruction quality is
attributed to the rigorous derivation of an iterative solution and the
principled way we design our denoising network architecture, which as a result
requires fewer trainable parameters than the current state-of-the-art solution
and furthermore can be efficiently trained by using a significantly smaller
number of training data than existing deep demosaicking networks. Code and
results can be found at https://github.com/cig-skoltech/deep_demosaick
Motion Invariance in Visual Environments
Authors: Alessandro Betti, Marco Gori, Stefano Melacci
Date: 2018-07-14 00:00:00

The puzzle of computer vision might find new challenging solutions when we
realize that most successful methods are working at image level, which is
remarkably more difficult than processing directly visual streams, just as
happens in nature. In this paper, we claim that their processing naturally
leads to formulate the motion invariance principle, which enables the
construction of a new theory of visual learning based on convolutional
features. The theory addresses a number of intriguing questions that arise in
natural vision, and offers a well-posed computational scheme for the discovery
of convolutional filters over the retina. They are driven by the Euler-Lagrange
differential equations derived from the principle of least cognitive action,
that parallels laws of mechanics. Unlike traditional convolutional networks,
which need massive supervision, the proposed theory offers a truly new scenario
in which feature learning takes place by unsupervised processing of video
signals. An experimental report of the theory is presented where we show that
features extracted under motion invariance yield an improvement that can be
assessed by measuring information-based indexes.
Bridging the Gap Between Layout Pattern Sampling and Hotspot Detection via Batch Active Sampling
Authors: Haoyu Yang, Shuhe Li, Cyrus Tabery, Bingqing Lin, Bei Yu
Date: 2018-07-13 00:00:00
Tasks: Active Learning

Layout hotpot detection is one of the main steps in modern VLSI design. A
typical hotspot detection flow is extremely time consuming due to the
computationally expensive mask optimization and lithographic simulation. Recent
researches try to facilitate the procedure with a reduced flow including
feature extraction, training set generation and hotspot detection, where
feature extraction methods and hotspot detection engines are deeply studied.
However, the performance of hotspot detectors relies highly on the quality of
reference layout libraries which are costly to obtain and usually predetermined
or randomly sampled in previous works. In this paper, we propose an active
learning-based layout pattern sampling and hotspot detection flow, which
simultaneously optimizes the machine learning model and the training set that
aims to achieve similar or better hotspot detection performance with much
smaller number of training instances. Experimental results show that our
proposed method can significantly reduce lithography simulation overhead while
attaining satisfactory detection accuracy on designs under both DUV and EUV
lithography technologies.
On Ternary Coding and Three-Valued Logic
Authors: Subhash Kak
Date: 2018-07-13 00:00:00
Tasks: General Classification

Mathematically, ternary coding is more efficient than binary coding. It is
little used in computation because technology for binary processing is already
established and the implementation of ternary coding is more complicated, but
remains relevant in algorithms that use decision trees and in communications.
In this paper we present a new comparison of binary and ternary coding and
their relative efficiencies are computed both for number representation and
decision trees. The implications of our inability to use optimal representation
through mathematics or logic are examined. Apart from considerations of
representation efficiency, ternary coding appears preferable to binary coding
in classification of many real-world problems of artificial intelligence (AI)
and medicine. We examine the problem of identifying appropriate three classes
for domain-specific applications.
A Model for Evaluating Algorithmic Systems Accountability
Authors: Yiannis Kanellopoulos
Date: 2018-07-12 00:00:00
Tasks: General Classification, model

Algorithmic systems make decisions that have a great impact in our lives. As
our dependency on them is growing so does the need for transparency and holding
them accountable. This paper presents a model for evaluating how transparent
these systems are by focusing on their algorithmic part as well as the maturity
of the organizations that utilize them. We applied this model on a
classification algorithm created and utilized by a large financial institution.
The results of our analysis indicated that the organization was only partially
in control of their algorithm and they lacked the necessary benchmark to
interpret the deducted results and assess the validity of its inferencing.
A Comparison of Adaptation Techniques and Recurrent Neural Network Architectures
Authors: Jan Vanek, Josef Michalek, Jan Zelinka, Josef Psutka
Date: 2018-07-12 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Recently, recurrent neural networks have become state-of-the-art in acoustic
modeling for automatic speech recognition. The long short-term memory (LSTM)
units are the most popular ones. However, alternative units like gated
recurrent unit (GRU) and its modifications outperformed LSTM in some
publications. In this paper, we compared five neural network (NN) architectures
with various adaptation and feature normalization techniques. We have evaluated
feature-space maximum likelihood linear regression, five variants of i-vector
adaptation and two variants of cepstral mean normalization. The most adaptation
and normalization techniques were developed for feed-forward NNs and, according
to results in this paper, not all of them worked also with RNNs. For
experiments, we have chosen a well known and available TIMIT phone recognition
task. The phone recognition is much more sensitive to the quality of AM than
large vocabulary task with a complex language model. Also, we published the
open-source scripts to easily replicate the results and to help continue the
development.
Gnirut: The Trouble With Being Born Human In An Autonomous World
Authors: Luca Viganó, Diego Sempreboni
Date: 2018-07-11 00:00:00

What if we delegated so much to autonomous AI and intelligent machines that
They passed a law that forbids humans to carry out a number of professions? We
conceive the plot of a new episode of Black Mirror to reflect on what might
await us and how we can deal with such a future.