pwc-archive/papers-with-abstracts huggingface dataset:
Surfacing contextual hate speech words within social media
Authors: Jherez Taylor, Melvyn Peignon, Yi-Shin Chen
Date: 2017-11-28 00:00:00
Tasks: Community Detection, General Classification, Hate Speech Detection

Social media platforms have recently seen an increase in the occurrence of
hate speech discourse which has led to calls for improved detection methods.
Most of these rely on annotated data, keywords, and a classification technique.
While this approach provides good coverage, it can fall short when dealing with
new terms produced by online extremist communities which act as original
sources of words which have alternate hate speech meanings. These code words
(which can be both created and adopted words) are designed to evade automatic
detection and often have benign meanings in regular discourse. As an example,
"skypes", "googles", and "yahoos" are all instances of words which have an
alternate meaning that can be used for hate speech. This overlap introduces
additional challenges when relying on keywords for both the collection of data
that is specific to hate speech, and downstream classification. In this work,
we develop a community detection approach for finding extremist hate speech
communities and collecting data from their members. We also develop a word
embedding model that learns the alternate hate speech meaning of words and
demonstrate the candidacy of our code words with several annotation
experiments, designed to determine if it is possible to recognize a word as
being used for hate speech without knowing its alternate meaning. We report an
inter-annotator agreement rate of K=0.871, and K=0.676 for data drawn from our
extremist community and the keyword approach respectively, supporting our claim
that hate speech detection is a contextual task and does not depend on a fixed
list of keywords. Our goal is to advance the domain by providing a high quality
hate speech dataset in addition to learned code words that can be fed into
existing classification approaches, thus improving the accuracy of automated
detection.
Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes
Authors: Lei Wu, Zhanxing Zhu, Weinan E
Date: 2017-06-30 00:00:00
Tasks: Deep Learning

It is widely observed that deep learning models with learned parameters
generalize well, even with much more model parameters than the number of
training samples. We systematically investigate the underlying reasons why deep
neural networks often generalize well, and reveal the difference between the
minima (with the same training error) that generalize well and those they
don't. We show that it is the characteristics the landscape of the loss
function that explains the good generalization capability. For the landscape of
loss function for deep networks, the volume of basin of attraction of good
minima dominates over that of poor minima, which guarantees optimization
methods with random initialization to converge to good minima. We theoretically
justify our findings through analyzing 2-layer neural networks; and show that
the low-complexity solutions have a small norm of Hessian matrix with respect
to model parameters. For deeper networks, extensive numerical evidence helps to
support our arguments.
Actor-Critic Sequence Training for Image Captioning
Authors: Li Zhang, Flood Sung, Feng Liu, Tao Xiang, Shaogang Gong, Yongxin Yang, Timothy M. Hospedales
Date: 2017-06-29 00:00:00
Tasks: AI Agent, Image Captioning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Generating natural language descriptions of images is an important capability
for a robot or other visual-intelligence driven AI agent that may need to
communicate with human users about what it is seeing. Such image captioning
methods are typically trained by maximising the likelihood of ground-truth
annotated caption given the image. While simple and easy to implement, this
approach does not directly maximise the language quality metrics we care about
such as CIDEr. In this paper we investigate training image captioning methods
based on actor-critic reinforcement learning in order to directly optimise
non-differentiable quality metrics of interest. By formulating a per-token
advantage and value computation strategy in this novel reinforcement learning
based captioning model, we show that it is possible to achieve the state of the
art performance on the widely used MSCOCO benchmark.
Adversary Detection in Neural Networks via Persistent Homology
Authors: Thomas Gebhart, Paul Schrater
Date: 2017-11-28 00:00:00

We outline a detection method for adversarial inputs to deep neural networks.
By viewing neural network computations as graphs upon which information flows
from input space to out- put distribution, we compare the differences in graphs
induced by different inputs. Specifically, by applying persistent homology to
these induced graphs, we observe that the structure of the most persistent
subgraphs which generate the first homology group differ between adversarial
and unperturbed inputs. Based on this observation, we build a detection
algorithm that depends only on the topological information extracted during
training. We test our algorithm on MNIST and achieve 98% detection adversary
accuracy with F1-score 0.98.
Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery
Authors: Morteza Mardani, Hatef Monajemi, Vardan Papyan, Shreyas Vasanawala, David Donoho, John Pauly
Date: 2017-11-27 00:00:00
Tasks: compressed sensing, Denoising, Hallucination, MRI Reconstruction

Recovering images from undersampled linear measurements typically leads to an
ill-posed linear inverse problem, that asks for proper statistical priors.
Building effective priors is however challenged by the low train and test
overhead dictated by real-time tasks; and the need for retrieving visually
"plausible" and physically "feasible" images with minimal hallucination. To
cope with these challenges, we design a cascaded network architecture that
unrolls the proximal gradient iterations by permeating benefits from generative
residual networks (ResNet) to modeling the proximal operator. A mixture of
pixel-wise and perceptual costs is then deployed to train proximals. The
overall architecture resembles back-and-forth projection onto the intersection
of feasible and plausible images. Extensive computational experiments are
examined for a global task of reconstructing MR images of pediatric patients,
and a more local task of superresolving CelebA faces, that are insightful to
design efficient architectures. Our observations indicate that for MRI
reconstruction, a recurrent ResNet with a single residual block effectively
learns the proximal. This simple architecture appears to significantly
outperform the alternative deep ResNet architecture by 2dB SNR, and the
conventional compressed-sensing MRI by 4dB SNR with 100x faster inference. For
image superresolution, our preliminary results indicate that modeling the
denoising proximal demands deep ResNets.
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations
Authors: Tapio Schneider, Shiwei Lan, Andrew Stuart, João Teixeira
Date: 2017-08-31 00:00:00

Climate projections continue to be marred by large uncertainties, which
originate in processes that need to be parameterized, such as clouds,
convection, and ecosystems. But rapid progress is now within reach. New
computational tools and methods from data assimilation and machine learning
make it possible to integrate global observations and local high-resolution
simulations in an Earth system model (ESM) that systematically learns from
both. Here we propose a blueprint for such an ESM. We outline how
parameterization schemes can learn from global observations and targeted
high-resolution simulations, for example, of clouds and convection, through
matching low-order statistics between ESMs, observations, and high-resolution
simulations. We illustrate learning algorithms for ESMs with a simple dynamical
system that shares characteristics of the climate system; and we discuss the
opportunities the proposed framework presents and the challenges that remain to
realize it.
soc2seq: Social Embedding meets Conversation Model
Authors: Parminder Bhatia, Marsal Gavalda, Arash Einolghozati
Date: 2017-02-17 00:00:00
Tasks: model

While liking or upvoting a post on a mobile app is easy to do, replying with
a written note is much more difficult, due to both the cognitive load of coming
up with a meaningful response as well as the mechanics of entering the text.
Here we present a novel textual reply generation model that goes beyond the
current auto-reply and predictive text entry models by taking into account the
content preferences of the user, the idiosyncrasies of their conversational
style, and even the structure of their social graph. Specifically, we have
developed two types of models for personalized user interactions: a
content-based conversation model, which makes use of location together with
user information, and a social-graph-based conversation model, which combines
content-based conversation models with social graphs.
Variational Reasoning for Question Answering with Knowledge Graph
Authors: Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander J. Smola, Le Song
Date: 2017-09-12 00:00:00
Tasks: Knowledge Graphs, Question Answering, Translation

Knowledge graph (KG) is known to be helpful for the task of question
answering (QA), since it provides well-structured relational information
between entities, and allows one to further infer indirect facts. However, it
is challenging to build QA systems which can learn to reason over knowledge
graphs based on question-answer pairs alone. First, when people ask questions,
their expressions are noisy (for example, typos in texts, or variations in
pronunciations), which is non-trivial for the QA system to match those
mentioned entities to the knowledge graph. Second, many questions require
multi-hop logic reasoning over the knowledge graph to retrieve the answers. To
address these challenges, we propose a novel and unified deep learning
architecture, and an end-to-end variational learning algorithm which can handle
noise in questions, and learn multi-hop reasoning simultaneously. Our method
achieves state-of-the-art performance on a recent benchmark dataset in the
literature. We also derive a series of new benchmark datasets, including
questions for multi-hop reasoning, questions paraphrased by neural translation
model, and questions in human voice. Our method yields very promising results
on all these challenging datasets.
SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again
Authors: Wadim Kehl, Fabian Manhardt, Federico Tombari, Slobodan Ilic, Nassir Navab
Date: 2017-11-27 00:00:00
Proceeding: ICCV 2017 10
Tasks: 6D Pose Estimation, 6D Pose Estimation using RGB, Pose Estimation

We present a novel method for detecting 3D model instances and estimating
their 6D poses from RGB data in a single shot. To this end, we extend the
popular SSD paradigm to cover the full 6D pose space and train on synthetic
model data only. Our approach competes or surpasses current state-of-the-art
methods that leverage RGB-D data on multiple challenging datasets. Furthermore,
our method produces these results at around 10Hz, which is many times faster
than the related methods. For the sake of reproducibility, we make our trained
networks and detection code publicly available.
Deep Learning and Conditional Random Fields-based Depth Estimation and Topographical Reconstruction from Conventional Endoscopy
Authors: Faisal Mahmood, Nicholas J. Durr
Date: 2017-10-30 00:00:00
Tasks: Depth Estimation

Colorectal cancer is the fourth leading cause of cancer deaths worldwide and
the second leading cause in the United States. The risk of colorectal cancer
can be mitigated by the identification and removal of premalignant lesions
through optical colonoscopy. Unfortunately, conventional colonoscopy misses
more than 20% of the polyps that should be removed, due in part to poor
contrast of lesion topography. Imaging tissue topography during a colonoscopy
is difficult because of the size constraints of the endoscope and the deforming
mucosa. Most existing methods make geometric assumptions or incorporate a
priori information, which limits accuracy and sensitivity. In this paper, we
present a method that avoids these restrictions, using a joint deep
convolutional neural network-conditional random field (CNN-CRF) framework.
Estimated depth is used to reconstruct the topography of the surface of the
colon from a single image. We train the unary and pairwise potential functions
of a CRF in a CNN on synthetic data, generated by developing an endoscope
camera model and rendering over 100,000 images of an anatomically-realistic
colon. We validate our approach with real endoscopy images from a porcine
colon, transferred to a synthetic-like domain, with ground truth from
registered computed tomography measurements. The CNN-CRF approach estimates
depths with a relative error of 0.152 for synthetic endoscopy images and 0.242
for real endoscopy images. We show that the estimated depth maps can be used
for reconstructing the topography of the mucosa from conventional colonoscopy
images. This approach can easily be integrated into existing endoscopy systems
and provides a foundation for improving computer-aided detection algorithms for
detection, segmentation and classification of lesions.
Bootstrap Robust Prescriptive Analytics
Authors: Dimitris Bertsimas, Bart Van Parys
Date: 2017-11-27 00:00:00
Tasks: Decision Making

We address the problem of prescribing an optimal decision in a framework where the cost function depends on uncertain problem parameters that need to be learned from data. Earlier work proposed prescriptive formulations based on supervised machine learning methods. These prescriptive methods can factor in contextual information on a potentially large number of covariates to take context specific actions which are superior to any static decision. When working with noisy or corrupt data, however, such nominal prescriptive methods can be prone to adverse overfitting phenomena and fail to generalize on out-of-sample data. In this paper we combine ideas from robust optimization and the statistical bootstrap to propose novel prescriptive methods which safeguard against overfitting. We show indeed that a particular entropic robust counterpart to such nominal formulations guarantees good performance on synthetic bootstrap data. As bootstrap data is often a sensible proxy to actual out-of-sample data, our robust counterpart can be interpreted to directly encourage good out-of-sample performance. The associated robust prescriptive methods furthermore reduce to convenient tractable convex optimization problems in the context of local learning methods such as nearest neighbors and Nadaraya-Watson learning. We illustrate our data-driven decision-making framework and our novel robustness notion on a small newsvendor problem.
Collaborative Evolution of 3D Models
Authors: Juan C. Quiroz, Amit Banerjee, Sushil J. Louis, Sergiu M. Dascalu
Date: 2017-11-27 00:00:00

We present a computational model of creative design based on collaborative
interactive genetic algorithms. In our model, designers individually guide
interactive genetic algorithms (IGAs) to generate and explore potential design
solutions quickly. Collaboration is supported by allowing designers to share
solutions amongst each other while using IGAs, with the sharing of solutions
adding variables to the search space. We present experiments on 3D modeling as
a case study, with designers creating model transformations individually and
collaboratively. The transformations were evaluated by participants in surveys
and results show that individual and collaborative models were considered
equally creative. However, the use of our collaborative IGAs model materially
changes resulting designs compared to individual IGAs.
Training Convolutional Neural Networks with Limited Training Data for Ear Recognition in the Wild
Authors: Žiga Emeršič, Dejan Štepec, Vitomir Štruc, Peter Peer
Date: 2017-11-27 00:00:00
Tasks: Data Augmentation

Identity recognition from ear images is an active field of research within
the biometric community. The ability to capture ear images from a distance and
in a covert manner makes ear recognition technology an appealing choice for
surveillance and security applications as well as related application domains.
In contrast to other biometric modalities, where large datasets captured in
uncontrolled settings are readily available, datasets of ear images are still
limited in size and mostly of laboratory-like quality. As a consequence, ear
recognition technology has not benefited yet from advances in deep learning and
convolutional neural networks (CNNs) and is still lacking behind other
modalities that experienced significant performance gains owing to deep
recognition technology. In this paper we address this problem and aim at
building a CNNbased ear recognition model. We explore different strategies
towards model training with limited amounts of training data and show that by
selecting an appropriate model architecture, using aggressive data augmentation
and selective learning on existing (pre-trained) models, we are able to learn
an effective CNN-based model using a little more than 1300 training images. The
result of our work is the first CNN-based approach to ear recognition that is
also made publicly available to the research community. With our model we are
able to improve on the rank one recognition rate of the previous
state-of-the-art by more than 25% on a challenging dataset of ear images
captured from the web (a.k.a. in the wild).
AutoDIAL: Automatic DomaIn Alignment Layers
Authors: Fabio Maria Carlucci, Lorenzo Porzi, Barbara Caputo, Elisa Ricci, Samuel Rota Bulò
Date: 2017-04-26 00:00:00
Proceeding: ICCV 2017 10
Tasks: Domain Adaptation

Classifiers trained on given databases perform poorly when tested on data
acquired in different settings. This is explained in domain adaptation through
a shift among distributions of the source and target domains. Attempts to align
them have traditionally resulted in works reducing the domain shift by
introducing appropriate loss terms, measuring the discrepancies between source
and target distributions, in the objective function. Here we take a different
route, proposing to align the learned representations by embedding in any given
network specific Domain Alignment Layers, designed to match the source and
target feature distributions to a reference one. Opposite to previous works
which define a priori in which layers adaptation should be performed, our
method is able to automatically learn the degree of feature alignment required
at different levels of the deep network. Thorough experiments on different
public benchmarks, in the unsupervised setting, confirm the power of our
approach.
Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders
Authors: Hongyu Shen, Daniel George, E. A. Huerta, Zhizhen Zhao
Date: 2017-11-27 00:00:00
Tasks: Astronomy, Deep Learning, Denoising, Dictionary Learning

Gravitational wave astronomy is a rapidly growing field of modern
astrophysics, with observations being made frequently by the LIGO detectors.
Gravitational wave signals are often extremely weak and the data from the
detectors, such as LIGO, is contaminated with non-Gaussian and non-stationary
noise, often containing transient disturbances which can obscure real signals.
Traditional denoising methods, such as principal component analysis and
dictionary learning, are not optimal for dealing with this non-Gaussian noise,
especially for low signal-to-noise ratio gravitational wave signals.
Furthermore, these methods are computationally expensive on large datasets. To
overcome these issues, we apply state-of-the-art signal processing techniques,
based on recent groundbreaking advancements in deep learning, to denoise
gravitational wave signals embedded either in Gaussian noise or in real LIGO
noise. We introduce SMTDAE, a Staired Multi-Timestep Denoising Autoencoder,
based on sequence-to-sequence bi-directional Long-Short-Term-Memory recurrent
neural networks. We demonstrate the advantages of using our unsupervised deep
learning approach and show that, after training only using simulated Gaussian
noise, SMTDAE achieves superior recovery performance for gravitational wave
signals embedded in real non-Gaussian LIGO noise.
Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation
Authors: Jooyeon Kim, Behzad Tabibian, Alice Oh, Bernhard Schoelkopf, Manuel Gomez-Rodriguez
Date: 2017-11-27 00:00:00
Tasks: Fact Checking, Misinformation, Point Processes

Online social networking sites are experimenting with the following
crowd-powered procedure to reduce the spread of fake news and misinformation:
whenever a user is exposed to a story through her feed, she can flag the story
as misinformation and, if the story receives enough flags, it is sent to a
trusted third party for fact checking. If this party identifies the story as
misinformation, it is marked as disputed. However, given the uncertain number
of exposures, the high cost of fact checking, and the trade-off between flags
and exposures, the above mentioned procedure requires careful reasoning and
smart algorithms which, to the best of our knowledge, do not exist to date.
  In this paper, we first introduce a flexible representation of the above
procedure using the framework of marked temporal point processes. Then, we
develop a scalable online algorithm, Curb, to select which stories to send for
fact checking and when to do so to efficiently reduce the spread of
misinformation with provable guarantees. In doing so, we need to solve a novel
stochastic optimal control problem for stochastic differential equations with
jumps, which is of independent interest. Experiments on two real-world datasets
gathered from Twitter and Weibo show that our algorithm may be able to
effectively reduce the spread of fake news and misinformation.
Context-modulation of hippocampal dynamics and deep convolutional networks
Authors: James B. Aimone, William M. Severa
Date: 2017-11-27 00:00:00
Tasks: Hippocampus

Complex architectures of biological neural circuits, such as parallel
processing pathways, has been behaviorally implicated in many cognitive
studies. However, the theoretical consequences of circuit complexity on neural
computation have only been explored in limited cases. Here, we introduce a
mechanism by which direct and indirect pathways from cortex to the CA3 region
of the hippocampus can balance both contextual gating of memory formation and
driving network activity. We implement this concept in a deep artificial neural
network by enabling a context-sensitive bias. The motivation for this is to
improve performance of a size-constrained network. Using direct knowledge of
the superclass information in the CIFAR-100 and Fashion-MNIST datasets, we show
a dramatic increase in performance without an increase in network size.