pwc-archive/papers-with-abstracts huggingface dataset:
ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification
Authors: Subhrajit Roy, Isabell Kiral-Kornek, Stefan Harrer
Date: 2018-01-30 00:00:00
Tasks: EEG, Electroencephalogram (EEG), image-classification, Image Classification, Time Series Analysis

Brain-related disorders such as epilepsy can be diagnosed by analyzing
electroencephalograms (EEG). However, manual analysis of EEG data requires
highly trained clinicians, and is a procedure that is known to have relatively
low inter-rater agreement (IRA). Moreover, the volume of the data and the rate
at which new data becomes available make manual interpretation a
time-consuming, resource-hungry, and expensive process. In contrast, automated
analysis of EEG data offers the potential to improve the quality of patient
care by shortening the time to diagnosis and reducing manual error. In this
paper, we focus on one of the first steps in interpreting an EEG session -
identifying whether the brain activity is abnormal or normal. To solve this
task, we propose a novel recurrent neural network (RNN) architecture termed
ChronoNet which is inspired by recent developments from the field of image
classification and designed to work efficiently with EEG data. ChronoNet is
formed by stacking multiple 1D convolution layers followed by deep gated
recurrent unit (GRU) layers where each 1D convolution layer uses multiple
filters of exponentially varying lengths and the stacked GRU layers are densely
connected in a feed-forward manner. We used the recently released TUH Abnormal
EEG Corpus dataset for evaluating the performance of ChronoNet. Unlike previous
studies using this dataset, ChronoNet directly takes time-series EEG as input
and learns meaningful representations of brain activity patterns. ChronoNet
outperforms the previously reported best results by 7.79% thereby setting a new
benchmark for this dataset. Furthermore, we demonstrate the domain-independent
nature of ChronoNet by successfully applying it to classify speech commands.
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models
Authors: Pouya Samangouei, Maya Kabkab, Rama Chellappa
Date: 2018-05-17 00:00:00
Proceeding: ICLR 2018 1
Tasks: Adversarial Defense, Adversarial Defense against FGSM Attack, General Classification

In recent years, deep neural network approaches have been widely adopted for
machine learning tasks, including classification. However, they were shown to
be vulnerable to adversarial perturbations: carefully crafted small
perturbations can cause misclassification of legitimate images. We propose
Defense-GAN, a new framework leveraging the expressive capability of generative
models to defend deep neural networks against such attacks. Defense-GAN is
trained to model the distribution of unperturbed images. At inference time, it
finds a close output to a given image which does not contain the adversarial
changes. This output is then fed to the classifier. Our proposed method can be
used with any classification model and does not modify the classifier structure
or training procedure. It can also be used as a defense against any attack as
it does not assume knowledge of the process for generating the adversarial
examples. We empirically show that Defense-GAN is consistently effective
against different attack methods and improves on existing defense strategies.
Our code has been made publicly available at
https://github.com/kabkabm/defensegan
Practical Algorithms for STV and Ranked Pairs with Parallel Universes Tiebreaking
Authors: Jun Wang, Sujoy Sikdar, Tyler Shepherd, Zhibing Zhao, Chunheng Jiang, Lirong Xia
Date: 2018-05-17 00:00:00
Tasks: Decision Making

STV and ranked pairs (RP) are two well-studied voting rules for group
decision-making. They proceed in multiple rounds, and are affected by how ties
are broken in each round. However, the literature is surprisingly vague about
how ties should be broken. We propose the first algorithms for computing the
set of alternatives that are winners under some tiebreaking mechanism under STV
and RP, which is also known as parallel-universes tiebreaking (PUT).
Unfortunately, PUT-winners are NP-complete to compute under STV and RP, and
standard search algorithms from AI do not apply. We propose multiple DFS-based
algorithms along with pruning strategies and heuristics to prioritize search
direction to significantly improve the performance using machine learning. We
also propose novel ILP formulations for PUT-winners under STV and RP,
respectively. Experiments on synthetic and real-world data show that our
algorithms are overall significantly faster than ILP, while there are a few
cases where ILP is significantly faster for RP.
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
Authors: Nicolas Papernot, Fartash Faghri, Nicholas Carlini, Ian Goodfellow, Reuben Feinman, Alexey Kurakin, Cihang Xie, Yash Sharma, Tom Brown, Aurko Roy, Alexander Matyasko, Vahid Behzadan, Karen Hambardzumyan, Zhishuai Zhang, Yi-Lin Juang, Zhi Li, Ryan Sheatsley, Abhibhav Garg, Jonathan Uesato, Willi Gierke, Yinpeng Dong, David Berthelot, Paul Hendricks, Jonas Rauber, Rujun Long, Patrick McDaniel
Date: 2016-10-03 00:00:00
Tasks: Adversarial Attack, Adversarial Defense, Benchmarking

An adversarial example library for constructing attacks, building defenses, and benchmarking both
Terabyte-scale Deep Multiple Instance Learning for Classification and Localization in Pathology
Authors: Gabriele Campanella, Vitor Werneck Krauss Silva, Thomas J. Fuchs
Date: 2018-05-17 00:00:00
Tasks: General Classification, Multiple Instance Learning

In the field of computational pathology, the use of decision support systems
powered by state-of-the-art deep learning solutions has been hampered by the
lack of large labeled datasets. Until recently, studies relied on datasets in
the order of few hundreds of slides which are not enough to train a model that
can work at scale in the clinic. Here, we have gathered a dataset consisting of
12,160 slides, two orders of magnitude larger than previous datasets in
pathology and equivalent to 25 times the pixel count of the entire ImageNet
dataset. Given the size of our dataset it is possible for us to train a deep
learning model under the Multiple Instance Learning (MIL) assumption where only
the overall slide diagnosis is necessary for training, avoiding all the
expensive pixel-wise annotations that are usually part of supervised learning
approaches. We test our framework on a complex task, that of prostate cancer
diagnosis on needle biopsies. We performed a thorough evaluation of the
performance of our MIL pipeline under several conditions achieving an AUC of
0.98 on a held-out test set of 1,824 slides. These results open the way for
training accurate diagnosis prediction models at scale, laying the foundation
for decision support system deployment in the clinic.
Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation
Authors: Gregory Kahn, Adam Villaflor, Bosen Ding, Pieter Abbeel, Sergey Levine
Date: 2017-09-29 00:00:00
Tasks: Deep Reinforcement Learning, Navigate, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Robot Navigation

Enabling robots to autonomously navigate complex environments is essential
for real-world deployment. Prior methods approach this problem by having the
robot maintain an internal map of the world, and then use a localization and
planning method to navigate through the internal map. However, these approaches
often include a variety of assumptions, are computationally intensive, and do
not learn from failures. In contrast, learning-based methods improve as the
robot acts in the environment, but are difficult to deploy in the real-world
due to their high sample complexity. To address the need to learn complex
policies with few samples, we propose a generalized computation graph that
subsumes value-based model-free methods and model-based methods, with specific
instantiations interpolating between model-free and model-based. We then
instantiate this graph to form a navigation model that learns from raw images
and is sample efficient. Our simulated car experiments explore the design
decisions of our navigation model, and show our approach outperforms
single-step and $N$-step double Q-learning. We also evaluate our approach on a
real-world RC car and show it can learn to navigate through a complex indoor
environment with a few hours of fully autonomous, self-supervised training.
Videos of the experiments and code can be found at github.com/gkahn13/gcg
How Developers Iterate on Machine Learning Workflows -- A Survey of the Applied Machine Learning Literature
Authors: Doris Xin, Litian Ma, Shuchen Song, Aditya Parameswaran
Date: 2018-03-27 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning workflow development is anecdotally regarded to be an
iterative process of trial-and-error with humans-in-the-loop. However, we are
not aware of quantitative evidence corroborating this popular belief. A
quantitative characterization of iteration can serve as a benchmark for machine
learning workflow development in practice, and can aid the development of
human-in-the-loop machine learning systems. To this end, we conduct a
small-scale survey of the applied machine learning literature from five
distinct application domains. We collect and distill statistics on the role of
iteration within machine learning workflow development, and report preliminary
trends and insights from our investigation, as a starting point towards this
benchmark. Based on our findings, we finally describe desiderata for effective
and versatile human-in-the-loop machine learning systems that can cater to
users in diverse domains.
Tracking State Changes in Procedural Text: A Challenge Dataset and Models for Process Paragraph Comprehension
Authors: Bhavana Dalvi Mishra, Lifu Huang, Niket Tandon, Wen-tau Yih, Peter Clark
Date: 2018-05-17 00:00:00
Proceeding: NAACL 2018 6
Tasks: Procedural Text Understanding

We present a new dataset and models for comprehending paragraphs about
processes (e.g., photosynthesis), an important genre of text describing a
dynamic world. The new dataset, ProPara, is the first to contain natural
(rather than machine-generated) text about a changing world along with a full
annotation of entity states (location and existence) during those changes (81k
datapoints). The end-task, tracking the location and existence of entities
through the text, is challenging because the causal effects of actions are
often implicit and need to be inferred. We find that previous models that have
worked well on synthetic data achieve only mediocre performance on ProPara, and
introduce two new neural models that exploit alternative mechanisms for state
prediction, in particular using LSTM input encoding and span prediction. The
new models improve accuracy by up to 19%. The dataset and models are available
to the community at http://data.allenai.org/propara.
Using Statistical and Semantic Models for Multi-Document Summarization
Authors: Divyanshu Daiya, Anukarsh Singh, Mukesh Jadon
Date: 2018-05-11 00:00:00
Proceeding: ROCLINGIJCLCLP 2018 10
Tasks: Document Summarization, Extractive Text Summarization, Multi-Document Summarization, Text Summarization

We report a series of experiments with different semantic models on top of
various statistical models for extractive text summarization. Though
statistical models may better capture word co-occurrences and distribution
around the text, they fail to detect the context and the sense of sentences
/words as a whole. Semantic models help us gain better insight into the context
of sentences. We show that how tuning weights between different models can help
us achieve significant results on various benchmarks. Learning pre-trained
vectors used in semantic models further, on given corpus, can give addition
spike in performance. Using weighing techniques in between different
statistical models too further refines our result. For Statistical models, we
have used TF/IDF, TextRAnk, Jaccard/Cosine Similarities. For Semantic Models,
we have used WordNet-based Model and proposed two models based on Glove Vectors
and Facebook's InferSent. We tested our approach on DUC 2004 dataset,
generating 100-word summaries. We have discussed the system, algorithms,
analysis and also proposed and tested possible improvements. ROUGE scores were
used to compare to other summarizers.
Neural User Simulation for Corpus-based Policy Optimisation for Spoken Dialogue Systems
Authors: Florian Kreyssig, Inigo Casanueva, Pawel Budzianowski, Milica Gasic
Date: 2018-05-17 00:00:00
Tasks: Diversity, Reinforcement Learning, Spoken Dialogue Systems, Task-Oriented Dialogue Systems, User Simulation

User Simulators are one of the major tools that enable offline training of
task-oriented dialogue systems. For this task the Agenda-Based User Simulator
(ABUS) is often used. The ABUS is based on hand-crafted rules and its output is
in semantic form. Issues arise from both properties such as limited diversity
and the inability to interface a text-level belief tracker. This paper
introduces the Neural User Simulator (NUS) whose behaviour is learned from a
corpus and which generates natural language, hence needing a less labelled
dataset than simulators generating a semantic output. In comparison to much of
the past work on this topic, which evaluates user simulators on corpus-based
metrics, we use the NUS to train the policy of a reinforcement learning based
Spoken Dialogue System. The NUS is compared to the ABUS by evaluating the
policies that were trained using the simulators. Cross-model evaluation is
performed i.e. training on one simulator and testing on the other. Furthermore,
the trained policies are tested on real users. In both evaluation tasks the NUS
outperformed the ABUS.
Counterexample-Guided Data Augmentation
Authors: Tommaso Dreossi, Shromona Ghosh, Xiangyu Yue, Kurt Keutzer, Alberto Sangiovanni-Vincentelli, Sanjit A. Seshia
Date: 2018-05-17 00:00:00
Tasks: Autonomous Driving, Data Augmentation, object-detection, Object Detection

We present a novel framework for augmenting data sets for machine learning
based on counterexamples. Counterexamples are misclassified examples that have
important properties for retraining and improving the model. Key components of
our framework include a counterexample generator, which produces data items
that are misclassified by the model and error tables, a novel data structure
that stores information pertaining to misclassifications. Error tables can be
used to explain the model's vulnerabilities and are used to efficiently
generate counterexamples for augmentation. We show the efficacy of the proposed
framework by comparing it to classical augmentation techniques on a case study
of object detection in autonomous driving based on deep neural networks.
Generalizing multistain immunohistochemistry tissue segmentation using one-shot color deconvolution deep neural networks
Authors: Amal Lahiani, Jacob Gildenblat, Irina Klaman, Nassir Navab, Eldad Klaiman
Date: 2018-05-17 00:00:00
Tasks: whole slide images

A key challenge in cancer immunotherapy biomarker research is quantification
of pattern changes in microscopic whole slide images of tumor biopsies.
Different cell types tend to migrate into various tissue compartments and form
variable distribution patterns. Drug development requires correlative analysis
of various biomarkers in and between the tissue compartments. To enable that,
tissue slides are manually annotated by expert pathologists. Manual annotation
of tissue slides is a labor intensive, tedious and error-prone task. Automation
of this annotation process can improve accuracy and consistency while reducing
workload and cost in a way that will positively influence drug development
efforts. In this paper we present a novel one-shot color deconvolution deep
learning method to automatically segment and annotate digitized slide images
with multiple stainings into compartments of tumor, healthy tissue, and
necrosis. We address the task in the context of drug development where multiple
stains, tissue and tumor types exist and look into solutions for
generalizations over these image populations.
A Forest Mixture Bound for Block-Free Parallel Inference
Authors: Neal Lawton, Aram Galstyan, Greg Ver Steeg
Date: 2018-05-17 00:00:00
Tasks: Variational Inference

Coordinate ascent variational inference is an important algorithm for
inference in probabilistic models, but it is slow because it updates only a
single variable at a time. Block coordinate methods perform inference faster by
updating blocks of variables in parallel. However, the speed and stability of
these algorithms depends on how the variables are partitioned into blocks. In
this paper, we give a stable parallel algorithm for inference in deep
exponential families that doesn't require the variables to be partitioned into
blocks. We achieve this by lower bounding the ELBO by a new objective we call
the forest mixture bound (FM bound) that separates the inference problem for
variables within a hidden layer. We apply this to the simple case when all
random variables are Gaussian and show empirically that the algorithm converges
faster for models that are inherently more forest-like.
Who Let The Dogs Out? Modeling Dog Behavior From Visual Data
Authors: Kiana Ehsani, Hessam Bagherinezhad, Joseph Redmon, Roozbeh Mottaghi, Ali Farhadi
Date: 2018-03-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: image-classification, Representation Learning

We introduce the task of directly modeling a visually intelligent agent.
Computer vision typically focuses on solving various subtasks related to visual
intelligence. We depart from this standard approach to computer vision; instead
we directly model a visually intelligent agent. Our model takes visual
information as input and directly predicts the actions of the agent. Toward
this end we introduce DECADE, a large-scale dataset of ego-centric videos from
a dog's perspective as well as her corresponding movements. Using this data we
model how the dog acts and how the dog plans her movements. We show under a
variety of metrics that given just visual input we can successfully model this
intelligent agent in many situations. Moreover, the representation learned by
our model encodes distinct information compared to representations trained on
image classification, and our learned representation can generalize to other
domains. In particular, we show strong results on the task of walkable surface
estimation by using this dog modeling task as representation learning.
Learning to detect chest radiographs containing lung nodules using visual attention networks
Authors: Emanuele Pesce, Petros-Pavlos Ypsilantis, Samuel Withey, Robert Bakewell, Vicky Goh, Giovanni Montana
Date: 2017-12-04 00:00:00
Tasks: General Classification, Reinforcement Learning

Machine learning approaches hold great potential for the automated detection
of lung nodules in chest radiographs, but training the algorithms requires vary
large amounts of manually annotated images, which are difficult to obtain. Weak
labels indicating whether a radiograph is likely to contain pulmonary nodules
are typically easier to obtain at scale by parsing historical free-text
radiological reports associated to the radiographs. Using a repositotory of
over 700,000 chest radiographs, in this study we demonstrate that promising
nodule detection performance can be achieved using weak labels through
convolutional neural networks for radiograph classification. We propose two
network architectures for the classification of images likely to contain
pulmonary nodules using both weak labels and manually-delineated bounding
boxes, when these are available. Annotated nodules are used at training time to
deliver a visual attention mechanism informing the model about its localisation
performance. The first architecture extracts saliency maps from high-level
convolutional layers and compares the estimated position of a nodule against
the ground truth, when this is available. A corresponding localisation error is
then back-propagated along with the softmax classification error. The second
approach consists of a recurrent attention model that learns to observe a short
sequence of smaller image portions through reinforcement learning. When a
nodule annotation is available at training time, the reward function is
modified accordingly so that exploring portions of the radiographs away from a
nodule incurs a larger penalty. Our empirical results demonstrate the potential
advantages of these architectures in comparison to competing methodologies.
Accurate Real Time Localization Tracking in A Clinical Environment using Bluetooth Low Energy and Deep Learning
Authors: Zohaib Iqbal, Da Luo, Peter Henry, Samaneh Kazemifar, Timothy Rozario, Yulong Yan, Kenneth Westover, Weiguo Lu, Dan Nguyen, Troy Long, Jing Wang, Hak Choy, Steve Jiang
Date: 2017-11-22 00:00:00
Tasks: TAG

Deep learning has started to revolutionize several different industries, and
the applications of these methods in medicine are now becoming more
commonplace. This study focuses on investigating the feasibility of tracking
patients and clinical staff wearing Bluetooth Low Energy (BLE) tags in a
radiation oncology clinic using artificial neural networks (ANNs) and
convolutional neural networks (CNNs). The performance of these networks was
compared to relative received signal strength indicator (RSSI) thresholding and
triangulation. By utilizing temporal information, a combined CNN+ANN network
was capable of correctly identifying the location of the BLE tag with an
accuracy of 99.9%. It outperformed a CNN model (accuracy = 94%), a thresholding
model employing majority voting (accuracy = 95%), and a triangulation
classifier utilizing majority voting (accuracy = 95%). Future studies will seek
to deploy this affordable real time location system in hospitals to improve
clinical workflow, efficiency, and patient safety.
Event2Mind: Commonsense Inference on Events, Intents, and Reactions
Authors: Hannah Rashkin, Maarten Sap, Emily Allaway, Noah A. Smith, Yejin Choi
Date: 2018-05-17 00:00:00
Proceeding: ACL 2018 7
Tasks: Common Sense Reasoning, Decoder

We investigate a new commonsense inference task: given an event described in a short free-form text ("X drinks coffee in the morning"), a system reasons about the likely intents ("X wants to stay awake") and reactions ("X feels alert") of the event's participants. To support this study, we construct a new crowdsourced corpus of 25,000 event phrases covering a diverse range of everyday events and situations. We report baseline performance on this task, demonstrating that neural encoder-decoder models can successfully compose embedding representations of previously unseen events and reason about the likely intents and reactions of the event participants. In addition, we demonstrate how commonsense inference on people's intents and reactions can help unveil the implicit gender inequality prevalent in modern movie scripts.
Evaluating Compositionality in Sentence Embeddings
Authors: Ishita Dasgupta, Demi Guo, Andreas Stuhlmüller, Samuel J. Gershman, Noah D. Goodman
Date: 2018-02-12 00:00:00
Tasks: Natural Language Inference, Sentence, Sentence Embeddings, valid

An important challenge for human-like AI is compositional semantics. Recent
research has attempted to address this by using deep neural networks to learn
vector space embeddings of sentences, which then serve as input to other tasks.
We present a new dataset for one such task, `natural language inference' (NLI),
that cannot be solved using only word-level knowledge and requires some
compositionality. We find that the performance of state of the art sentence
embeddings (InferSent; Conneau et al., 2017) on our new dataset is poor. We
analyze the decision rules learned by InferSent and find that they are
consistent with simple heuristics that are ecologically valid in its training
dataset. Further, we find that augmenting training with our dataset improves
test performance on our dataset without loss of performance on the original
training dataset. This highlights the importance of structured datasets in
better understanding and improving AI systems.