pwc-archive/papers-with-abstracts huggingface dataset:
How Curiosity can be modeled for a Clickbait Detector
Authors: Lasya Venneti, Aniket Alam
Date: 2018-06-11 00:00:00

The impact of continually evolving digital technologies and the proliferation
of communications and content has now been widely acknowledged to be central to
understanding our world. What is less acknowledged is that this is based on the
successful arousing of curiosity both at the collective and individual levels.
Advertisers, communication professionals and news editors are in constant
competition to capture attention of the digital population perennially shifty
and distracted. This paper, tries to understand how curiosity works in the
digital world by attempting the first ever work done on quantifying human
curiosity, basing itself on various theories drawn from humanities and social
sciences. Curious communication pushes people to spot, read and click the
message from their social feed or any other form of online presentation. Our
approach focuses on measuring the strength of the stimulus to generate reader
curiosity by using unsupervised and supervised machine learning algorithms, but
is also informed by philosophical, psychological, neural and cognitive studies
on this topic. Manually annotated news headlines - clickbaits - have been
selected for the study, which are known to have drawn huge reader response. A
binary classifier was developed based on human curiosity (unlike the work done
so far using words and other linguistic features). Our classifier shows an
accuracy of 97% . This work is part of the research in computational humanities
on digital politics quantifying the emotions of curiosity and outrage on
digital media.
Temporal Difference Variational Auto-Encoder
Authors: Karol Gregor, George Papamakarios, Frederic Besse, Lars Buesing, Theophane Weber
Date: 2018-06-08 00:00:00
Proceeding: ICLR 2019 5
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

To act and plan in complex environments, we posit that agents should have a
mental simulator of the world with three characteristics: (a) it should build
an abstract state representing the condition of the world; (b) it should form a
belief which represents uncertainty on the world; (c) it should go beyond
simple step-by-step simulation, and exhibit temporal abstraction. Motivated by
the absence of a model satisfying all these requirements, we propose TD-VAE, a
generative sequence model that learns representations containing explicit
beliefs about states several steps into the future, and that can be rolled out
directly without single-step transitions. TD-VAE is trained on pairs of
temporally separated time points, using an analogue of temporal difference
learning used in reinforcement learning.
In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking
Authors: Yuezun Li, Ming-Ching Chang, Siwei Lyu
Date: 2018-06-07 00:00:00
Tasks: Face Swapping

The new developments in deep generative networks have significantly improve
the quality and efficiency in generating realistically-looking fake face
videos. In this work, we describe a new method to expose fake face videos
generated with neural networks. Our method is based on detection of eye
blinking in the videos, which is a physiological signal that is not well
presented in the synthesized fake videos. Our method is tested over benchmarks
of eye-blinking detection datasets and also show promising performance on
detecting videos generated with DeepFake.
Swarming for Faster Convergence in Stochastic Optimization
Authors: Shi Pu, Alfredo Garcia
Date: 2018-06-11 00:00:00
Tasks: Stochastic Optimization

We study a distributed framework for stochastic optimization which is
inspired by models of collective motion found in nature (e.g., swarming) with
mild communication requirements. Specifically, we analyze a scheme in which
each one of $N > 1$ independent threads, implements in a distributed and
unsynchronized fashion, a stochastic gradient-descent algorithm which is
perturbed by a swarming potential. Assuming the overhead caused by
synchronization is not negligible, we show the swarming-based approach exhibits
better performance than a centralized algorithm (based upon the average of $N$
observations) in terms of (real-time) convergence speed. We also derive an
error bound that is monotone decreasing in network size and connectivity. We
characterize the scheme's finite-time performances for both convex and
non-convex objective functions.
A Note about: Local Explanation Methods for Deep Neural Networks lack Sensitivity to Parameter Values
Authors: Mukund Sundararajan, Ankur Taly
Date: 2018-06-11 00:00:00
Tasks: Attribute, Sensitivity

Local explanation methods, also known as attribution methods, attribute a
deep network's prediction to its input (cf. Baehrens et al. (2010)). We respond
to the claim from Adebayo et al. (2018) that local explanation methods lack
sensitivity, i.e., DNNs with randomly-initialized weights produce explanations
that are both visually and quantitatively similar to those produced by DNNs
with learned weights.
  Further investigation reveals that their findings are due to two choices in
their analysis: (a) ignoring the signs of the attributions; and (b) for
integrated gradients (IG), including pixels in their analysis that have zero
attributions by choice of the baseline (an auxiliary input relative to which
the attributions are computed). When both factors are accounted for, IG
attributions for a random network and the actual network are uncorrelated. Our
investigation also sheds light on how these issues affect visualizations,
although we note that more work is needed to understand how viewers interpret
the difference between the random and the actual attributions.
Calibrating Noise to Variance in Adaptive Data Analysis
Authors: Vitaly Feldman, Thomas Steinke
Date: 2017-12-19 00:00:00

Datasets are often used multiple times and each successive analysis may
depend on the outcome of previous analyses. Standard techniques for ensuring
generalization and statistical validity do not account for this adaptive
dependence. A recent line of work studies the challenges that arise from such
adaptive data reuse by considering the problem of answering a sequence of
"queries" about the data distribution where each query may depend arbitrarily
on answers to previous queries.
  The strongest results obtained for this problem rely on differential privacy
-- a strong notion of algorithmic stability with the important property that it
"composes" well when data is reused. However the notion is rather strict, as it
requires stability under replacement of an arbitrary data element. The simplest
algorithm is to add Gaussian (or Laplace) noise to distort the empirical
answers. However, analysing this technique using differential privacy yields
suboptimal accuracy guarantees when the queries have low variance. Here we
propose a relaxed notion of stability that also composes adaptively. We
demonstrate that a simple and natural algorithm based on adding noise scaled to
the standard deviation of the query provides our notion of stability. This
implies an algorithm that can answer statistical queries about the dataset with
substantially improved accuracy guarantees for low-variance queries. The only
previous approach that provides such accuracy guarantees is based on a more
involved differentially private median-of-means algorithm and its analysis
exploits stronger "group" stability of the algorithm.
Degree based Classification of Harmful Speech using Twitter Data
Authors: Sanjana Sharma, Saksham Agrawal, Manish Shrivastava
Date: 2018-06-11 00:00:00
Proceeding: COLING 2018 8
Tasks: Classification, General Classification

Harmful speech has various forms and it has been plaguing the social media in
different ways. If we need to crackdown different degrees of hate speech and
abusive behavior amongst it, the classification needs to be based on complex
ramifications which needs to be defined and hold accountable for, other than
racist, sexist or against some particular group and community. This paper
primarily describes how we created an ontological classification of harmful
speech based on degree of hateful intent, and used it to annotate twitter data
accordingly. The key contribution of this paper is the new dataset of tweets we
created based on ontological classes and degrees of harmful speech found in the
text. We also propose supervised classification system for recognizing these
respective harmful speech classes in the texts hence.
Enhancing human color vision by breaking binocular redundancy
Authors: Bradley S. Gundlach, Michel Frising, Alireza Shahsafi, Gregory Vershbow, Chenghao Wan, Jad Salman, Bas Rokers, Laurent Lessard, Mikhail A. Kats
Date: 2017-03-02 00:00:00
Tasks: Data Visualization

To see color, the human visual system combines the response of three types of
cone cells in the retina--a compressive process that discards a significant
amount of spectral information. Here, we present an approach to enhance human
color vision by breaking its inherent binocular redundancy, providing different
spectral content to each eye. We fabricated a set of optical filters that
"splits" the response of the short-wavelength cone between the two eyes in
individuals with typical trichromatic vision, simulating the presence of
approximately four distinct cone types ("tetrachromacy"). Such an increase in
the number of effective cone types can reduce the prevalence of metamers--pairs
of distinct spectra that resolve to the same tristimulus values. This technique
may result in an enhancement of spectral perception, with applications ranging
from camouflage detection and anti-counterfeiting to new types of artwork and
data visualization.
Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models
Authors: Minjia Zhang, Xiaodong Liu, Wenhan Wang, Jianfeng Gao, Yuxiong He
Date: 2018-06-11 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Decoder, Language Modeling, Language Modelling, Machine Translation, Translation

Neural language models (NLMs) have recently gained a renewed interest by
achieving state-of-the-art performance across many natural language processing
(NLP) tasks. However, NLMs are very computationally demanding largely due to
the computational cost of the softmax layer over a large vocabulary. We observe
that, in decoding of many NLP tasks, only the probabilities of the top-K
hypotheses need to be calculated preciously and K is often much smaller than
the vocabulary size. This paper proposes a novel softmax layer approximation
algorithm, called Fast Graph Decoder (FGD), which quickly identifies, for a
given context, a set of K words that are most likely to occur according to a
NLM. We demonstrate that FGD reduces the decoding time by an order of magnitude
while attaining close to the full softmax baseline accuracy on neural machine
translation and language modeling tasks. We also prove the theoretical
guarantee on the softmax approximation quality.
A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature
Authors: Benjamin Nye, Junyi Jessy Li, Roma Patel, Yinfei Yang, Iain J. Marshall, Ani Nenkova, Byron C. Wallace
Date: 2018-06-11 00:00:00
Proceeding: ACL 2018 7
Tasks: Articles, Participant Intervention Comparison Outcome Extraction, PICO

We present a corpus of 5,000 richly annotated abstracts of medical articles
describing clinical randomized controlled trials. Annotations include
demarcations of text spans that describe the Patient population enrolled, the
Interventions studied and to what they were Compared, and the Outcomes measured
(the `PICO' elements). These spans are further annotated at a more granular
level, e.g., individual interventions within them are marked and mapped onto a
structured medical vocabulary. We acquired annotations from a diverse set of
workers with varying levels of expertise and cost. We describe our data
collection process and the corpus itself in detail. We then outline a set of
challenging NLP tasks that would aid searching of the medical literature and
the practice of evidence-based medicine.
Synthetic Depth-of-Field with a Single-Camera Mobile Phone
Authors: Neal Wadhwa, Rahul Garg, David E. Jacobs, Bryan E. Feldman, Nori Kanazawa, Robert Carroll, Yair Movshovitz-Attias, Jonathan T. Barron, Yael Pritch, Marc Levoy
Date: 2018-06-11 00:00:00

Shallow depth-of-field is commonly used by photographers to isolate a subject
from a distracting background. However, standard cell phone cameras cannot
produce such images optically, as their short focal lengths and small apertures
capture nearly all-in-focus images. We present a system to computationally
synthesize shallow depth-of-field images with a single mobile camera and a
single button press. If the image is of a person, we use a person segmentation
network to separate the person and their accessories from the background. If
available, we also use dense dual-pixel auto-focus hardware, effectively a
2-sample light field with an approximately 1 millimeter baseline, to compute a
dense depth map. These two signals are combined and used to render a defocused
image. Our system can process a 5.4 megapixel image in 4 seconds on a mobile
phone, is fully automatic, and is robust enough to be used by non-experts. The
modular nature of our system allows it to degrade naturally in the absence of a
dual-pixel sensor or a human subject.
Defense Against the Dark Arts: An overview of adversarial example security research and future research directions
Authors: Ian Goodfellow
Date: 2018-06-11 00:00:00
Tasks: Deep Learning

This article presents a summary of a keynote lecture at the Deep Learning
Security workshop at IEEE Security and Privacy 2018. This lecture summarizes
the state of the art in defenses against adversarial examples and provides
recommendations for future research directions on this topic.
Straight to the Tree: Constituency Parsing with Neural Syntactic Distance
Authors: Yikang Shen, Zhouhan Lin, Athul Paul Jacob, Alessandro Sordoni, Aaron Courville, Yoshua Bengio
Date: 2018-06-11 00:00:00
Proceeding: ACL 2018 7
Tasks: Constituency Parsing, Position, Sentence

In this work, we propose a novel constituency parsing scheme. The model
predicts a vector of real-valued scalars, named syntactic distances, for each
split position in the input sentence. The syntactic distances specify the order
in which the split points will be selected, recursively partitioning the input,
in a top-down fashion. Compared to traditional shift-reduce parsing schemes,
our approach is free from the potential problem of compounding errors, while
being faster and easier to parallelize. Our model achieves competitive
performance amongst single model, discriminative parsers in the PTB dataset and
outperforms previous models in the CTB dataset.
Learning an Approximate Model Predictive Controller with Guarantees
Authors: Michael Hertneck, Johannes Köhler, Sebastian Trimpe, Frank Allgöwer
Date: 2018-06-11 00:00:00
Tasks: model

A supervised learning framework is proposed to approximate a model predictive
controller (MPC) with reduced computational complexity and guarantees on
stability and constraint satisfaction. The framework can be used for a wide
class of nonlinear systems. Any standard supervised learning technique (e.g.
neural networks) can be employed to approximate the MPC from samples. In order
to obtain closed-loop guarantees for the learned MPC, a robust MPC design is
combined with statistical learning bounds. The MPC design ensures robustness to
inaccurate inputs within given bounds, and Hoeffding's Inequality is used to
validate that the learned MPC satisfies these bounds with high confidence. The
result is a closed-loop statistical guarantee on stability and constraint
satisfaction for the learned MPC. The proposed learning-based MPC framework is
illustrated on a nonlinear benchmark problem, for which we learn a neural
network controller with guarantees.
Gesture-based Bootstrapping for Egocentric Hand Segmentation
Authors: Yubo Zhang, Vishnu Naresh Boddeti, Kris M. Kitani
Date: 2016-12-09 00:00:00
Tasks: Hand Segmentation

Accurately identifying hands in images is a key sub-task for human activity
understanding with wearable first-person point-of-view cameras. Traditional
hand segmentation approaches rely on a large corpus of manually labeled data to
generate robust hand detectors. However, these approaches still face challenges
as the appearance of the hand varies greatly across users, tasks, environments
or illumination conditions. A key observation in the case of many wearable
applications and interfaces is that, it is only necessary to accurately detect
the user's hands in a specific situational context. Based on this observation,
we introduce an interactive approach to learn a person-specific hand
segmentation model that does not require any manually labeled training data.
Our approach proceeds in two steps, an interactive bootstrapping step for
identifying moving hand regions, followed by learning a personalized user
specific hand appearance model. Concretely, our approach uses two convolutional
neural networks: (1) a gesture network that uses pre-defined motion information
to detect the hand region; and (2) an appearance network that learns a person
specific model of the hand region based on the output of the gesture network.
During training, to make the appearance network robust to errors in the gesture
network, the loss function of the former network incorporates the confidence of
the gesture network while learning. Experiments demonstrate the robustness of
our approach with an F1 score over 0.8 on all challenging datasets across a
wide range of illumination and hand appearance variations, improving over a
baseline approach by over 10%.
Learning to Decompose and Disentangle Representations for Video Prediction
Authors: Jun-Ting Hsieh, Bingbin Liu, De-An Huang, Li Fei-Fei, Juan Carlos Niebles
Date: 2018-06-11 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Disentanglement, Predict Future Video Frames, Video Prediction

Our goal is to predict future video frames given a sequence of input frames.
Despite large amounts of video data, this remains a challenging task because of
the high-dimensionality of video frames. We address this challenge by proposing
the Decompositional Disentangled Predictive Auto-Encoder (DDPAE), a framework
that combines structured probabilistic models and deep networks to
automatically (i) decompose the high-dimensional video that we aim to predict
into components, and (ii) disentangle each component to have low-dimensional
temporal dynamics that are easier to predict. Crucially, with an appropriately
specified generative model of video frames, our DDPAE is able to learn both the
latent decomposition and disentanglement without explicit supervision. For the
Moving MNIST dataset, we show that DDPAE is able to recover the underlying
components (individual digits) and disentanglement (appearance and location) as
we would intuitively do. We further demonstrate that DDPAE can be applied to
the Bouncing Balls dataset involving complex interactions between multiple
objects to predict the video frame directly from the pixels and recover
physical states without explicit supervision.
Roto-Translation Covariant Convolutional Networks for Medical Image Analysis
Authors: Erik J. Bekkers, Maxime W. Lafarge, Mitko Veta, Koen AJ Eppenhof, Josien PW Pluim, Remco Duits
Date: 2018-04-10 00:00:00
Tasks: Data Augmentation, Medical Image Analysis, Translation

We propose a framework for rotation and translation covariant deep learning
using $SE(2)$ group convolutions. The group product of the special Euclidean
motion group $SE(2)$ describes how a concatenation of two roto-translations
results in a net roto-translation. We encode this geometric structure into
convolutional neural networks (CNNs) via $SE(2)$ group convolutional layers,
which fit into the standard 2D CNN framework, and which allow to generically
deal with rotated input samples without the need for data augmentation.
  We introduce three layers: a lifting layer which lifts a 2D (vector valued)
image to an $SE(2)$-image, i.e., 3D (vector valued) data whose domain is
$SE(2)$; a group convolution layer from and to an $SE(2)$-image; and a
projection layer from an $SE(2)$-image to a 2D image. The lifting and group
convolution layers are $SE(2)$ covariant (the output roto-translates with the
input). The final projection layer, a maximum intensity projection over
rotations, makes the full CNN rotation invariant.
  We show with three different problems in histopathology, retinal imaging, and
electron microscopy that with the proposed group CNNs, state-of-the-art
performance can be achieved, without the need for data augmentation by rotation
and with increased performance compared to standard CNNs that do rely on
augmentation.
Finding Syntax in Human Encephalography with Beam Search
Authors: John Hale, Chris Dyer, Adhiguna Kuncoro, Jonathan R. Brennan
Date: 2018-06-11 00:00:00
Proceeding: ACL 2018 7
Tasks: Attribute, Language Modeling, Language Modelling

Recurrent neural network grammars (RNNGs) are generative models of
(tree,string) pairs that rely on neural networks to evaluate derivational
choices. Parsing with them using beam search yields a variety of incremental
complexity metrics such as word surprisal and parser action count. When used as
regressors against human electrophysiological responses to naturalistic text,
they derive two amplitude effects: an early peak and a P600-like later peak. By
contrast, a non-syntactic neural language model yields no reliable effects.
Model comparisons attribute the early peak to syntactic composition within the
RNNG. This pattern of results recommends the RNNG+beam search combination as a
mechanistic model of the syntactic processing that occurs during normal human
language comprehension.
Evaluating Robustness of Neural Networks with Mixed Integer Programming
Authors: Vincent Tjeng, Kai Xiao, Russ Tedrake
Date: 2017-11-20 00:00:00
Proceeding: ICLR 2019 5

Neural networks have demonstrated considerable success on a wide variety of
real-world problems. However, networks trained only to optimize for training
accuracy can often be fooled by adversarial examples - slightly perturbed
inputs that are misclassified with high confidence. Verification of networks
enables us to gauge their vulnerability to such adversarial examples. We
formulate verification of piecewise-linear neural networks as a mixed integer
program. On a representative task of finding minimum adversarial distortions,
our verifier is two to three orders of magnitude quicker than the
state-of-the-art. We achieve this computational speedup via tight formulations
for non-linearities, as well as a novel presolve algorithm that makes full use
of all information available. The computational speedup allows us to verify
properties on convolutional networks with an order of magnitude more ReLUs than
networks previously verified by any complete verifier. In particular, we
determine for the first time the exact adversarial accuracy of an MNIST
classifier to perturbations with bounded $l_\infty$ norm $\epsilon=0.1$: for
this classifier, we find an adversarial example for 4.38% of samples, and a
certificate of robustness (to perturbations with bounded norm) for the
remainder. Across all robust training procedures and network architectures
considered, we are able to certify more samples than the state-of-the-art and
find more adversarial examples than a strong first-order attack.