pwc-archive/papers-with-abstracts huggingface dataset:
SFCN-OPI: Detection and Fine-grained Classification of Nuclei Using Sibling FCN with Objectness Prior Interaction
Authors: Yanning Zhou, Qi Dou, Hao Chen, Jing Qin, Pheng-Ann Heng
Date: 2017-12-22 00:00:00
Tasks: General Classification

Cell nuclei detection and fine-grained classification have been fundamental
yet challenging problems in histopathology image analysis. Due to the nuclei
tiny size, significant inter-/intra-class variances, as well as the inferior
image quality, previous automated methods would easily suffer from limited
accuracy and robustness. In the meanwhile, existing approaches usually deal
with these two tasks independently, which would neglect the close relatedness
of them. In this paper, we present a novel method of sibling fully
convolutional network with prior objectness interaction (called SFCN-OPI) to
tackle the two tasks simultaneously and interactively using a unified
end-to-end framework. Specifically, the sibling FCN branches share features in
earlier layers while holding respective higher layers for specific tasks. More
importantly, the detection branch outputs the objectness prior which
dynamically interacts with the fine-grained classification sibling branch
during the training and testing processes. With this mechanism, the
fine-grained classification successfully focuses on regions with high
confidence of nuclei existence and outputs the conditional probability, which
in turn benefits the detection through back propagation. Extensive experiments
on colon cancer histology images have validated the effectiveness of our
proposed SFCN-OPI and our method has outperformed the state-of-the-art methods
by a large margin.
TFW, DamnGina, Juvie, and Hotsie-Totsie: On the Linguistic and Social Aspects of Internet Slang
Authors: Vivek Kulkarni, William Yang Wang
Date: 2017-12-22 00:00:00
Tasks: Question Answering

Slang is ubiquitous on the Internet. The emergence of new social contexts
like micro-blogs, question-answering forums, and social networks has enabled
slang and non-standard expressions to abound on the web. Despite this, slang
has been traditionally viewed as a form of non-standard language -- a form of
language that is not the focus of linguistic analysis and has largely been
neglected. In this work, we use UrbanDictionary to conduct the first
large-scale linguistic analysis of slang and its social aspects on the Internet
to yield insights into this variety of language that is increasingly used all
over the world online.
  We begin by computationally analyzing the phonological, morphological and
syntactic properties of slang. We then study linguistic patterns in four
specific categories of slang namely alphabetisms, blends, clippings, and
reduplicatives. Our analysis reveals that slang demonstrates extra-grammatical
rules of phonological and morphological formation that markedly distinguish it
from the standard form shedding insight into its generative patterns. Next, we
analyze the social aspects of slang by studying subject restriction and
stereotyping in slang usage. Analyzing tens of thousands of such slang words
reveals that the majority of slang on the Internet belongs to two major
categories: sex and drugs. We also noted that not only is slang usage not
immune to prevalent social biases and prejudices but also reflects such biases
and stereotypes more intensely than the standard variety.
Sentence Ordering and Coherence Modeling using Recurrent Neural Networks
Authors: Lajanugen Logeswaran, Honglak Lee, Dragomir Radev
Date: 2016-11-08 00:00:00
Tasks: Articles, Sentence, Sentence Ordering, Sentence Similarity

Modeling the structure of coherent texts is a key NLP problem. The task of
coherently organizing a given set of sentences has been commonly used to build
and evaluate models that understand such structure. We propose an end-to-end
unsupervised deep learning approach based on the set-to-sequence framework to
address this problem. Our model strongly outperforms prior methods in the order
discrimination task and a novel task of ordering abstracts from scientific
articles. Furthermore, our work shows that useful text representations can be
obtained by learning to order sentences. Visualizing the learned sentence
representations shows that the model captures high-level logical structure in
paragraphs. Our representations perform comparably to state-of-the-art
pre-training methods on sentence similarity and paraphrase detection tasks.
A Bidirectional Adaptive Bandwidth Mean Shift Strategy for Clustering
Authors: Fanyang Meng, Hong Liu, Yongsheng Liang, Wei Liu, Jihong Pei
Date: 2017-12-22 00:00:00
Tasks: Clustering

The bandwidth of a kernel function is a crucial parameter in the mean shift
algorithm. This paper proposes a novel adaptive bandwidth strategy which
contains three main contributions. (1) The differences among different adaptive
bandwidth are analyzed. (2) A new mean shift vector based on bidirectional
adaptive bandwidth is defined, which combines the advantages of different
adaptive bandwidth strategies. (3) A bidirectional adaptive bandwidth mean
shift (BAMS) strategy is proposed to improve the ability to escape from the
local maximum density. Compared with contemporary adaptive bandwidth mean shift
strategies, experiments demonstrate the effectiveness of the proposed strategy.
Slim Embedding Layers for Recurrent Neural Language Models
Authors: Zhongliang Li, Raymond Kulhanek, Shaojun Wang, Yunxin Zhao, Shuang Wu
Date: 2017-11-27 00:00:00
Tasks: Language Modeling, Language Modelling

Recurrent neural language models are the state-of-the-art models for language
modeling. When the vocabulary size is large, the space taken to store the model
parameters becomes the bottleneck for the use of recurrent neural language
models. In this paper, we introduce a simple space compression method that
randomly shares the structured parameters at both the input and output
embedding layers of the recurrent neural language models to significantly
reduce the size of model parameters, but still compactly represent the original
input and output embedding layers. The method is easy to implement and tune.
Experiments on several data sets show that the new method can get similar
perplexity and BLEU score results while only using a very tiny fraction of
parameters.
Recurrent Pixel Embedding for Instance Grouping
Authors: Shu Kong, Charless Fowlkes
Date: 2017-12-22 00:00:00
Proceeding: CVPR 2018 6
Tasks: Boundary Detection, Clustering, Instance Segmentation, Object Proposal Generation, Segmentation, Semantic Segmentation

We introduce a differentiable, end-to-end trainable framework for solving
pixel-level grouping problems such as instance segmentation consisting of two
novel components. First, we regress pixels into a hyper-spherical embedding
space so that pixels from the same group have high cosine similarity while
those from different groups have similarity below a specified margin. We
analyze the choice of embedding dimension and margin, relating them to
theoretical results on the problem of distributing points uniformly on the
sphere. Second, to group instances, we utilize a variant of mean-shift
clustering, implemented as a recurrent neural network parameterized by kernel
bandwidth. This recurrent grouping module is differentiable, enjoys convergent
dynamics and probabilistic interpretability. Backpropagating the group-weighted
loss through this module allows learning to focus on only correcting embedding
errors that won't be resolved during subsequent clustering. Our framework,
while conceptually simple and theoretically abundant, is also practically
effective and computationally efficient. We demonstrate substantial
improvements over state-of-the-art instance segmentation for object proposal
generation, as well as demonstrating the benefits of grouping loss for
classification tasks such as boundary detection and semantic segmentation.
Achieving the time of $1$-NN, but the accuracy of $k$-NN
Authors: Lirong Xue, Samory Kpotufe
Date: 2017-12-06 00:00:00
Tasks: Computational Efficiency, Distributed Computing, Prediction

We propose a simple approach which, given distributed computing resources,
can nearly achieve the accuracy of $k$-NN prediction, while matching (or
improving) the faster prediction time of $1$-NN. The approach consists of
aggregating denoised $1$-NN predictors over a small number of distributed
subsamples. We show, both theoretically and experimentally, that small
subsample sizes suffice to attain similar performance as $k$-NN, without
sacrificing the computational efficiency of $1$-NN.
Federated Control with Hierarchical Multi-Agent Deep Reinforcement Learning
Authors: Saurabh Kumar, Pararth Shah, Dilek Hakkani-Tur, Larry Heck
Date: 2017-12-22 00:00:00
Tasks: Deep Reinforcement Learning, Efficient Exploration, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

We present a framework combining hierarchical and multi-agent deep
reinforcement learning approaches to solve coordination problems among a
multitude of agents using a semi-decentralized model. The framework extends the
multi-agent learning setup by introducing a meta-controller that guides the
communication between agent pairs, enabling agents to focus on communicating
with only one other agent at any step. This hierarchical decomposition of the
task allows for efficient exploration to learn policies that identify globally
optimal solutions even as the number of collaborating agents increases. We show
promising initial experimental results on a simulated distributed scheduling
problem.
Using LIP to Gloss Over Faces in Single-Stage Face Detection Networks
Authors: Siqi Yang, Arnold Wiliem, Shaokang Chen, Brian C. Lovell
Date: 2017-12-22 00:00:00
Proceeding: ECCV 2018 9
Tasks: Face Detection

This work shows that it is possible to fool/attack recent state-of-the-art
face detectors which are based on the single-stage networks. Successfully
attacking face detectors could be a serious malware vulnerability when
deploying a smart surveillance system utilizing face detectors. We show that
existing adversarial perturbation methods are not effective to perform such an
attack, especially when there are multiple faces in the input image. This is
because the adversarial perturbation specifically generated for one face may
disrupt the adversarial perturbation for another face. In this paper, we call
this problem the Instance Perturbation Interference (IPI) problem. This IPI
problem is addressed by studying the relationship between the deep neural
network receptive field and the adversarial perturbation. As such, we propose
the Localized Instance Perturbation (LIP) that uses adversarial perturbation
constrained to the Effective Receptive Field (ERF) of a target to perform the
attack. Experiment results show the LIP method massively outperforms existing
adversarial perturbation generation methods -- often by a factor of 2 to 10.
Collision Selective Visual Neural Network Inspired by LGMD2 Neurons in Juvenile Locusts
Authors: Qinbing Fu, Cheng Hu, Shigang Yue
Date: 2017-12-22 00:00:00

For autonomous robots in dynamic environments mixed with human, it is vital
to detect impending collision quickly and robustly. The biological visual
systems evolved over millions of years may provide us efficient solutions for
collision detection in complex environments. In the cockpit of locusts, two
Lobula Giant Movement Detectors, i.e. LGMD1 and LGMD2, have been identified
which respond to looming objects rigorously with high firing rates. Compared to
LGMD1, LGMD2 matures early in the juvenile locusts with specific selectivity to
dark moving objects against bright background in depth while not responding to
light objects embedded in dark background - a similar situation which ground
vehicles and robots are facing with. However, little work has been done on
modeling LGMD2, let alone its potential in robotics and other vision-based
applications. In this article, we propose a novel way of modeling LGMD2 neuron,
with biased ON and OFF pathways splitting visual streams into parallel channels
encoding brightness increments and decrements separately to fulfill its
selectivity. Moreover, we apply a biophysical mechanism of spike frequency
adaptation to shape the looming selectivity in such a collision-detecting
neuron model. The proposed visual neural network has been tested with
systematic experiments, challenged against synthetic and real physical stimuli,
as well as image streams from the sensor of a miniature robot. The results
demonstrated this framework is able to detect looming dark objects embedded in
bright backgrounds selectively, which make it ideal for ground mobile
platforms. The robotic experiments also showed its robustness in collision
detection - it performed well for near range navigation in an arena with many
obstacles. Its enhanced collision selectivity to dark approaching objects
versus receding and translating ones has also been verified via systematic
experiments.
Linear centralization classifier
Authors: Mohammad Reza Bonyadi, Viktor Vegh, David C. Reutens
Date: 2017-12-22 00:00:00
Tasks: Classification, General Classification

A classification algorithm, called the Linear Centralization Classifier
(LCC), is introduced. The algorithm seeks to find a transformation that best
maps instances from the feature space to a space where they concentrate towards
the center of their own classes, while maximimizing the distance between class
centers. We formulate the classifier as a quadratic program with quadratic
constraints. We then simplify this formulation to a linear program that can be
solved effectively using a linear programming solver (e.g., simplex-dual). We
extend the formulation for LCC to enable the use of kernel functions for
non-linear classification applications. We compare our method with two standard
classification methods (support vector machine and linear discriminant
analysis) and four state-of-the-art classification methods when they are
applied to eight standard classification datasets. Our experimental results
show that LCC is able to classify instances more accurately (based on the area
under the receiver operating characteristic) in comparison to other tested
methods on the chosen datasets. We also report the results for LCC with a
particular kernel to solve for synthetic non-linear classification problems.
Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models
Authors: Jesse Engel, Matthew Hoffman, Adam Roberts
Date: 2017-11-15 00:00:00
Proceeding: ICLR 2018 1
Tasks: Attribute

Deep generative neural networks have proven effective at both conditional and
unconditional modeling of complex data distributions. Conditional generation
enables interactive control, but creating new controls often requires expensive
retraining. In this paper, we develop a method to condition generation without
retraining the model. By post-hoc learning latent constraints, value functions
that identify regions in latent space that generate outputs with desired
attributes, we can conditionally sample from these regions with gradient-based
optimization or amortized actor functions. Combining attribute constraints with
a universal "realism" constraint, which enforces similarity to the data
distribution, we generate realistic conditional images from an unconditional
variational autoencoder. Further, using gradient-based optimization, we
demonstrate identity-preserving transformations that make the minimal
adjustment in latent space to modify the attributes of an image. Finally, with
discrete sequences of musical notes, we demonstrate zero-shot conditional
generation, learning latent constraints in the absence of labeled data or a
differentiable reward function. Code with dedicated cloud instance has been
made publicly available (https://goo.gl/STGMGx).
ReabsNet: Detecting and Revising Adversarial Examples
Authors: Jiefeng Chen, Zihang Meng, Changtian Sun, Wei Tang, Yinglun Zhu
Date: 2017-12-21 00:00:00
Tasks: General Classification

Though deep neural network has hit a huge success in recent studies and
applica- tions, it still remains vulnerable to adversarial perturbations which
are imperceptible to humans. To address this problem, we propose a novel
network called ReabsNet to achieve high classification accuracy in the face of
various attacks. The approach is to augment an existing classification network
with a guardian network to detect if a sample is natural or has been
adversarially perturbed. Critically, instead of simply rejecting adversarial
examples, we revise them to get their true labels. We exploit the observation
that a sample containing adversarial perturbations has a possibility of
returning to its true class after revision. We demonstrate that our ReabsNet
outperforms the state-of-the-art defense method under various adversarial
attacks.
CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
Authors: Michela Paganini, Luke de Oliveira, Benjamin Nachman
Date: 2017-12-21 00:00:00
Tasks: CPU, GPU

The precise modeling of subatomic particle interactions and propagation
through matter is paramount for the advancement of nuclear and particle physics
searches and precision measurements. The most computationally expensive step in
the simulation pipeline of a typical experiment at the Large Hadron Collider
(LHC) is the detailed modeling of the full complexity of physics processes that
govern the motion and evolution of particle showers inside calorimeters. We
introduce \textsc{CaloGAN}, a new fast simulation technique based on generative
adversarial networks (GANs). We apply these neural networks to the modeling of
electromagnetic showers in a longitudinally segmented calorimeter, and achieve
speedup factors comparable to or better than existing full simulation
techniques on CPU ($100\times$-$1000\times$) and even faster on GPU (up to
$\sim10^5\times$). There are still challenges for achieving precision across
the entire phase space, but our solution can reproduce a variety of geometric
shower shape properties of photons, positrons and charged pions. This
represents a significant stepping stone toward a full neural network-based
detector simulation that could save significant computing time and enable many
analyses now and in the future.
Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters
Authors: Michela Paganini, Luke de Oliveira, Benjamin Nachman
Date: 2017-05-05 00:00:00

Physicists at the Large Hadron Collider (LHC) rely on detailed simulations of
particle collisions to build expectations of what experimental data may look
like under different theory modeling assumptions. Petabytes of simulated data
are needed to develop analysis techniques, though they are expensive to
generate using existing algorithms and computing resources. The modeling of
detectors and the precise description of particle cascades as they interact
with the material in the calorimeter are the most computationally demanding
steps in the simulation pipeline. We therefore introduce a deep neural
network-based generative model to enable high-fidelity, fast, electromagnetic
calorimeter simulation. There are still challenges for achieving precision
across the entire phase space, but our current solution can reproduce a variety
of particle shower properties while achieving speed-up factors of up to
100,000$\times$. This opens the door to a new era of fast simulation that could
save significant computing time and disk space, while extending the reach of
physics searches and precision measurements at the LHC and beyond.
Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment
Authors: Chelsea Barabas, Karthik Dinakar, Joichi Ito, Madars Virza, Jonathan Zittrain
Date: 2017-12-21 00:00:00
Tasks: BIG-bench Machine Learning, Causal Inference, Fairness

Actuarial risk assessments might be unduly perceived as a neutral way to
counteract implicit bias and increase the fairness of decisions made at almost
every juncture of the criminal justice system, from pretrial release to
sentencing, parole and probation. In recent times these assessments have come
under increased scrutiny, as critics claim that the statistical techniques
underlying them might reproduce existing patterns of discrimination and
historical biases that are reflected in the data. Much of this debate is
centered around competing notions of fairness and predictive accuracy, resting
on the contested use of variables that act as "proxies" for characteristics
legally protected against discrimination, such as race and gender. We argue
that a core ethical debate surrounding the use of regression in risk
assessments is not simply one of bias or accuracy. Rather, it's one of purpose.
If machine learning is operationalized merely in the service of predicting
individual future crime, then it becomes difficult to break cycles of
criminalization that are driven by the iatrogenic effects of the criminal
justice system itself. We posit that machine learning should not be used for
prediction, but rather to surface covariates that are fed into a causal model
for understanding the social, structural and psychological drivers of crime. We
propose an alternative application of machine learning and causal inference
away from predicting risk scores to risk mitigation.
Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection
Authors: Dimitrios Marmanis, Konrad Schindler, Jan Dirk Wegner, Silvano Galliani, Mihai Datcu, Uwe Stilla
Date: 2016-12-05 00:00:00
Tasks: Boundary Detection, Decoder, Edge Detection, General Classification, Image Segmentation, Segmentation, Semantic Segmentation

We present an end-to-end trainable deep convolutional neural network (DCNN)
for semantic segmentation with built-in awareness of semantically meaningful
boundaries. Semantic segmentation is a fundamental remote sensing task, and
most state-of-the-art methods rely on DCNNs as their workhorse. A major reason
for their success is that deep networks learn to accumulate contextual
information over very large windows (receptive fields). However, this success
comes at a cost, since the associated loss of effecive spatial resolution
washes out high-frequency details and leads to blurry object boundaries. Here,
we propose to counter this effect by combining semantic segmentation with
semantically informed edge detection, thus making class-boundaries explicit in
the model, First, we construct a comparatively simple, memory-efficient model
by adding boundary detection to the Segnet encoder-decoder architecture.
Second, we also include boundary detection in FCN-type models and set up a
high-end classifier ensemble. We show that boundary detection significantly
improves semantic segmentation with CNNs. Our high-end ensemble achieves > 90%
overall accuracy on the ISPRS Vaihingen benchmark.
Robust Detection of Covariate-Treatment Interactions in Clinical Trials
Authors: Baptiste Goujaud, Eric W. Tramel, Pierre Courtiol, Mikhail Zaslavskiy, Gilles Wainrib
Date: 2017-12-21 00:00:00

Detection of interactions between treatment effects and patient descriptors
in clinical trials is critical for optimizing the drug development process. The
increasing volume of data accumulated in clinical trials provides a unique
opportunity to discover new biomarkers and further the goal of personalized
medicine, but it also requires innovative robust biomarker detection methods
capable of detecting non-linear, and sometimes weak, signals. We propose a set
of novel univariate statistical tests, based on the theory of random walks,
which are able to capture non-linear and non-monotonic covariate-treatment
interactions. We also propose a novel combined test, which leverages the power
of all of our proposed univariate tests into a single general-case tool. We
present results for both synthetic trials as well as real-world clinical
trials, where we compare our method with state-of-the-art techniques and
demonstrate the utility and robustness of our approach.