pwc-archive/papers-with-abstracts huggingface dataset:
Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory
Authors: Jianyi Zhang, Ruiyi Zhang, Lawrence Carin, Changyou Chen
Date: 2018-09-05 00:00:00
Tasks: POS

Particle-optimization-based sampling (POS) is a recently developed effective sampling technique that interactively updates a set of particles. A representative algorithm is the Stein variational gradient descent (SVGD). We prove, under certain conditions, SVGD experiences a theoretical pitfall, {\it i.e.}, particles tend to collapse. As a remedy, we generalize POS to a stochastic setting by injecting random noise into particle updates, thus yielding particle-optimization sampling (SPOS). Notably, for the first time, we develop {\em non-asymptotic convergence theory} for the SPOS framework (related to SVGD), characterizing algorithm convergence in terms of the 1-Wasserstein distance w.r.t.\! the numbers of particles and iterations. Somewhat surprisingly, with the same number of updates (not too large) for each particle, our theory suggests adopting more particles does not necessarily lead to a better approximation of a target distribution, due to limited computational budget and numerical errors. This phenomenon is also observed in SVGD and verified via an experiment on synthetic data. Extensive experimental results verify our theory and demonstrate the effectiveness of our proposed framework.
Effective Exploration for Deep Reinforcement Learning via Bootstrapped Q-Ensembles under Tsallis Entropy Regularization
Authors: Gang Chen, Yiming Peng, Mengjie Zhang
Date: 2018-09-02 00:00:00
Tasks: Deep Reinforcement Learning, Diversity, Reinforcement Learning, Reinforcement Learning (RL)

Recently deep reinforcement learning (DRL) has achieved outstanding success
on solving many difficult and large-scale RL problems. However the high sample
cost required for effective learning often makes DRL unaffordable in
resource-limited applications. With the aim of improving sample efficiency and
learning performance, we will develop a new DRL algorithm in this paper that
seamless integrates entropy-induced and bootstrap-induced techniques for
efficient and deep exploration of the learning environment. Specifically, a
general form of Tsallis entropy regularizer will be utilized to drive
entropy-induced exploration based on efficient approximation of optimal
action-selection policies. Different from many existing works that rely on
action dithering strategies for exploration, our algorithm is efficient in
exploring actions with clear exploration value. Meanwhile, by employing an
ensemble of Q-networks under varied Tsallis entropy regularization, the
diversity of the ensemble can be further enhanced to enable effective
bootstrap-induced exploration. Experiments on Atari game playing tasks clearly
demonstrate that our new algorithm can achieve more efficient and effective
exploration for DRL, in comparison to recently proposed exploration methods
including Bootstrapped Deep Q-Network and UCB Q-Ensemble.
Learning Concept Abstractness Using Weak Supervision
Authors: Ella Rabinovich, Benjamin Sznajder, Artem Spector, Ilya Shnayderman, Ranit Aharonov, David Konopnicki, Noam Slonim
Date: 2018-09-05 00:00:00
Proceeding: EMNLP 2018 10

We introduce a weakly supervised approach for inferring the property of
abstractness of words and expressions in the complete absence of labeled data.
Exploiting only minimal linguistic clues and the contextual usage of a concept
as manifested in textual data, we train sufficiently powerful classifiers,
obtaining high correlation with human labels. The results imply the
applicability of this approach to additional properties of concepts, additional
languages, and resource-scarce scenarios.
BOLD5000: A public fMRI dataset of 5000 images
Authors: Nadine Chang, John A. Pyles, Abhinav Gupta, Michael J. Tarr, Elissa M. Aminoff
Date: 2018-09-05 00:00:00
Tasks: Diversity, Scene Understanding

Vision science, particularly machine vision, has been revolutionized by
introducing large-scale image datasets and statistical learning approaches.
Yet, human neuroimaging studies of visual perception still rely on small
numbers of images (around 100) due to time-constrained experimental procedures.
To apply statistical learning approaches that integrate neuroscience, the
number of images used in neuroimaging must be significantly increased. We
present BOLD5000, a human functional MRI (fMRI) study that includes almost
5,000 distinct images depicting real-world scenes. Beyond dramatically
increasing image dataset size relative to prior fMRI studies, BOLD5000 also
accounts for image diversity, overlapping with standard computer vision
datasets by incorporating images from the Scene UNderstanding (SUN), Common
Objects in Context (COCO), and ImageNet datasets. The scale and diversity of
these image datasets, combined with a slow event-related fMRI design, enable
fine-grained exploration into the neural representation of a wide range of
visual features, categories, and semantics. Concurrently, BOLD5000 brings us
closer to realizing Marr's dream of a singular vision science - the intertwined
study of biological and computer vision.
Unsupervised Statistical Machine Translation
Authors: Mikel Artetxe, Gorka Labaka, Eneko Agirre
Date: 2018-09-04 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Language Modeling, Language Modelling, Machine Translation, NMT, Translation, Unsupervised Machine Translation

While modern machine translation has relied on large parallel corpora, a
recent line of work has managed to train Neural Machine Translation (NMT)
systems from monolingual corpora only (Artetxe et al., 2018c; Lample et al.,
2018). Despite the potential of this approach for low-resource settings,
existing systems are far behind their supervised counterparts, limiting their
practical interest. In this paper, we propose an alternative approach based on
phrase-based Statistical Machine Translation (SMT) that significantly closes
the gap with supervised systems. Our method profits from the modular
architecture of SMT: we first induce a phrase table from monolingual corpora
through cross-lingual embedding mappings, combine it with an n-gram language
model, and fine-tune hyperparameters through an unsupervised MERT variant. In
addition, iterative backtranslation improves results further, yielding, for
instance, 14.08 and 26.22 BLEU points in WMT 2014 English-German and
English-French, respectively, an improvement of more than 7-10 BLEU points over
previous unsupervised systems, and closing the gap with supervised SMT (Moses
trained on Europarl) down to 2-5 BLEU points. Our implementation is available
at https://github.com/artetxem/monoses
Developing a Purely Visual Based Obstacle Detection using Inverse Perspective Mapping
Authors: Julian Nubert, Niklas Funk, Fabio Meier, Fabrice Oehler
Date: 2018-09-04 00:00:00
Tasks: Autonomous Driving, Management

Our solution is implemented in and for the frame of Duckietown. The goal of
Duckietown is to provide a relatively simple platform to explore, tackle and
solve many problems linked to autonomous driving. "Duckietown" is simple in the
basics, but an infinitely expandable environment. From controlling single
driving Duckiebots until complete fleet management, every scenario is possible
and can be put into practice. So far, none of the existing modules was capable
of reliably detecting obstacles and reacting to them in real time. We faced the
general problem of detecting obstacles given images from a monocular RGB camera
mounted at the front of our Duckiebot and reacting to them properly without
crashing or erroneously stopping the Duckiebot. Both, the detection as well as
the reaction have to be implemented and have to run on a Raspberry Pi in real
time. Due to the strong hardware limitations, we decided to not use any
learning algorithms for the obstacle detection part. As it later transpired, a
working "hard coded" software needs thorough analysis and understanding of the
given problem. In layman's terms, we simply seek to make Duckietown a safer
place.
DeepHunter: Hunting Deep Neural Network Defects via Coverage-Guided Fuzzing
Authors: Xiaofei Xie, Lei Ma, Felix Juefei-Xu, Hongxu Chen, Minhui Xue, Bo Li, Yang Liu, Jianjun Zhao, Jianxiong Yin, Simon See
Date: 2018-09-04 00:00:00
Tasks: Autonomous Driving, Quantization

In company with the data explosion over the past decade, deep neural network
(DNN) based software has experienced unprecedented leap and is becoming the key
driving force of many novel industrial applications, including many
safety-critical scenarios such as autonomous driving. Despite great success
achieved in various human intelligence tasks, similar to traditional software,
DNNs could also exhibit incorrect behaviors caused by hidden defects causing
severe accidents and losses. In this paper, we propose DeepHunter, an automated
fuzz testing framework for hunting potential defects of general-purpose DNNs.
DeepHunter performs metamorphic mutation to generate new semantically preserved
tests, and leverages multiple plugable coverage criteria as feedback to guide
the test generation from different perspectives. To be scalable towards
practical-sized DNNs, DeepHunter maintains multiple tests in a batch, and
prioritizes the tests selection based on active feedback. The effectiveness of
DeepHunter is extensively investigated on 3 popular datasets (MNIST, CIFAR-10,
ImageNet) and 7 DNNs with diverse complexities, under a large set of 6 coverage
criteria as feedback. The large-scale experiments demonstrate that DeepHunter
can (1) significantly boost the coverage with guidance; (2) generate useful
tests to detect erroneous behaviors and facilitate the DNN model quality
evaluation; (3) accurately capture potential defects during DNN quantization
for platform migration.
An Efficient Approach for Polyps Detection in Endoscopic Videos Based on Faster R-CNN
Authors: Xi Mo, Ke Tao, Quan Wang, Guanghui Wang
Date: 2018-09-04 00:00:00

Polyp has long been considered as one of the major etiologies to colorectal
cancer which is a fatal disease around the world, thus early detection and
recognition of polyps plays a crucial role in clinical routines. Accurate
diagnoses of polyps through endoscopes operated by physicians becomes a
challenging task not only due to the varying expertise of physicians, but also
the inherent nature of endoscopic inspections. To facilitate this process,
computer-aid techniques that emphasize fully-conventional image processing and
novel machine learning enhanced approaches have been dedicatedly designed for
polyp detection in endoscopic videos or images. Among all proposed algorithms,
deep learning based methods take the lead in terms of multiple metrics in
evolutions for algorithmic performance. In this work, a highly effective model,
namely the faster region-based convolutional neural network (Faster R-CNN) is
implemented for polyp detection. In comparison with the reported results of the
state-of-the-art approaches on polyps detection, extensive experiments
demonstrate that the Faster R-CNN achieves very competing results, and it is an
efficient approach for clinical practice.
Deep Priority Hashing
Authors: Zhangjie Cao, Ziping Sun, Mingsheng Long, Jian-Min Wang, Philip S. Yu
Date: 2018-09-04 00:00:00
Tasks: Deep Hashing, Image Retrieval, Quantization, Retrieval

Deep hashing enables image retrieval by end-to-end learning of deep
representations and hash codes from training data with pairwise similarity
information. Subject to the distribution skewness underlying the similarity
information, most existing deep hashing methods may underperform for imbalanced
data due to misspecified loss functions. This paper presents Deep Priority
Hashing (DPH), an end-to-end architecture that generates compact and balanced
hash codes in a Bayesian learning framework. The main idea is to reshape the
standard cross-entropy loss for similarity-preserving learning such that it
down-weighs the loss associated to highly-confident pairs. This idea leads to a
novel priority cross-entropy loss, which prioritizes the training on uncertain
pairs over confident pairs. Also, we propose another priority quantization
loss, which prioritizes hard-to-quantize examples for generation of nearly
lossless hash codes. Extensive experiments demonstrate that DPH can generate
high-quality hash codes and yield state-of-the-art image retrieval results on
three datasets, ImageNet, NUS-WIDE, and MS-COCO.
t-Exponential Memory Networks for Question-Answering Machines
Authors: Kyriakos Tolias, Sotirios Chatzis
Date: 2018-09-04 00:00:00
Tasks: Language Modeling, Language Modelling, Question Answering, Variational Inference

Recent advances in deep learning have brought to the fore models that can
make multiple computational steps in the service of completing a task; these
are capable of describ- ing long-term dependencies in sequential data. Novel
recurrent attention models over possibly large external memory modules
constitute the core mechanisms that enable these capabilities. Our work
addresses learning subtler and more complex underlying temporal dynamics in
language modeling tasks that deal with sparse sequential data. To this end, we
improve upon these recent advances, by adopting concepts from the field of
Bayesian statistics, namely variational inference. Our proposed approach
consists in treating the network parameters as latent variables with a prior
distribution imposed over them. Our statistical assumptions go beyond the
standard practice of postulating Gaussian priors. Indeed, to allow for handling
outliers, which are prevalent in long observed sequences of multivariate data,
multivariate t-exponential distributions are imposed. On this basis, we proceed
to infer corresponding posteriors; these can be used for inference and
prediction at test time, in a way that accounts for the uncertainty in the
available sparse training data. Specifically, to allow for our approach to best
exploit the merits of the t-exponential family, our method considers a new
t-divergence measure, which generalizes the concept of the Kullback-Leibler
divergence. We perform an extensive experimental evaluation of our approach,
using challenging language modeling benchmarks, and illustrate its superiority
over existing state-of-the-art techniques.
Compositional Stochastic Average Gradient for Machine Learning and Related Applications
Authors: Tsung-Yu Hsieh, Yasser EL-Manzalawy, Yiwei Sun, Vasant Honavar
Date: 2018-09-04 00:00:00
Tasks: BIG-bench Machine Learning, Portfolio Optimization

Many machine learning, statistical inference, and portfolio optimization
problems require minimization of a composition of expected value functions
(CEVF). Of particular interest is the finite-sum versions of such compositional
optimization problems (FS-CEVF). Compositional stochastic variance reduced
gradient (C-SVRG) methods that combine stochastic compositional gradient
descent (SCGD) and stochastic variance reduced gradient descent (SVRG) methods
are the state-of-the-art methods for FS-CEVF problems. We introduce
compositional stochastic average gradient descent (C-SAG) a novel extension of
the stochastic average gradient method (SAG) to minimize composition of
finite-sum functions. C-SAG, like SAG, estimates gradient by incorporating
memory of previous gradient information. We present theoretical analyses of
C-SAG which show that C-SAG, like SAG, and C-SVRG, achieves a linear
convergence rate when the objective function is strongly convex; However, C-CAG
achieves lower oracle query complexity per iteration than C-SVRG. Finally, we
present results of experiments showing that C-SAG converges substantially
faster than full gradient (FG), as well as C-SVRG.
Vulcan: A Monte Carlo Algorithm for Large Chance Constrained MDPs with Risk Bounding Functions
Authors: Benjamin J. Ayton, Brian C. Williams
Date: 2018-09-04 00:00:00

Chance Constrained Markov Decision Processes maximize reward subject to a
bounded probability of failure, and have been frequently applied for planning
with potentially dangerous outcomes or unknown environments. Solution
algorithms have required strong heuristics or have been limited to relatively
small problems with up to millions of states, because the optimal action to
take from a given state depends on the probability of failure in the rest of
the policy, leading to a coupled problem that is difficult to solve. In this
paper we examine a generalization of a CCMDP that trades off probability of
failure against reward through a functional relationship. We derive a
constraint that can be applied to each state history in a policy individually,
and which guarantees that the chance constraint will be satisfied. The approach
decouples states in the CCMDP, so that large problems can be solved
efficiently. We then introduce Vulcan, which uses our constraint in order to
apply Monte Carlo Tree Search to CCMDPs. Vulcan can be applied to problems
where it is unfeasible to generate the entire state space, and policies must be
returned in an anytime manner. We show that Vulcan and its variants run tens to
hundreds of times faster than linear programming methods, and over ten times
faster than heuristic based methods, all without the need for a heuristic, and
returning solutions with a mean suboptimality on the order of a few percent.
Finally, we use Vulcan to solve for a chance constrained policy in a CCMDP with
over $10^{13}$ states in 3 minutes.
Graph-based Deep-Tree Recursive Neural Network (DTRNN) for Text Classification
Authors: Fenxiao Chen, Bin Wang, C. -C. Jay Kuo
Date: 2018-09-04 00:00:00
Tasks: Benchmarking, General Classification, text-classification, Text Classification

A novel graph-to-tree conversion mechanism called the deep-tree generation
(DTG) algorithm is first proposed to predict text data represented by graphs.
The DTG method can generate a richer and more accurate representation for nodes
(or vertices) in graphs. It adds flexibility in exploring the vertex
neighborhood information to better reflect the second order proximity and
homophily equivalence in a graph. Then, a Deep-Tree Recursive Neural Network
(DTRNN) method is presented and used to classify vertices that contains text
data in graphs. To demonstrate the effectiveness of the DTRNN method, we apply
it to three real-world graph datasets and show that the DTRNN method
outperforms several state-of-the-art benchmarking methods.
Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints
Authors: Ashutosh Baheti, Alan Ritter, Jiwei Li, Bill Dolan
Date: 2018-09-04 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Semantic Similarity, Semantic Textual Similarity

Neural conversation models tend to generate safe, generic responses for most
inputs. This is due to the limitations of likelihood-based decoding objectives
in generation tasks with diverse outputs, such as conversation. To address this
challenge, we propose a simple yet effective approach for incorporating side
information in the form of distributional constraints over the generated
responses. We propose two constraints that help generate more content rich
responses that are based on a model of syntax and topics (Griffiths et al.,
2005) and semantic similarity (Arora et al., 2016). We evaluate our approach
against a variety of competitive baselines, using both automatic metrics and
human judgments, showing that our proposed approach generates responses that
are much less generic without sacrificing plausibility. A working demo of our
code can be found at https://github.com/abaheti95/DC-NeuralConversation.
Causal Explanation Analysis on Social Media
Authors: Youngseo Son, Nipun Bayas, H. Andrew Schwartz
Date: 2018-09-04 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Discourse Parsing

Understanding causal explanations - reasons given for happenings in one's
life - has been found to be an important psychological factor linked to
physical and mental health. Causal explanations are often studied through
manual identification of phrases over limited samples of personal writing.
Automatic identification of causal explanations in social media, while
challenging in relying on contextual and sequential cues, offers a larger-scale
alternative to expensive manual ratings and opens the door for new applications
(e.g. studying prevailing beliefs about causes, such as climate change). Here,
we explore automating causal explanation analysis, building on discourse
parsing, and presenting two novel subtasks: causality detection (determining
whether a causal explanation exists at all) and causal explanation
identification (identifying the specific phrase that is the explanation). We
achieve strong accuracies for both tasks but find different approaches best: an
SVM for causality prediction (F1 = 0.791) and a hierarchy of Bidirectional
LSTMs for causal explanation identification (F1 = 0.853). Finally, we explore
applications of our complete pipeline (F1 = 0.868), showing demographic
differences in mentions of causal explanation and that the association between
a word and sentiment can change when it is used within a causal explanation.
Random Language Model
Authors: E. DeGiuli
Date: 2018-09-04 00:00:00
Tasks: Language Modeling, Language Modelling, model

Many complex generative systems use languages to create structured objects.
We consider a model of random languages, defined by weighted context-free
grammars. As the distribution of grammar weights broadens, a transition is
found from a random phase, in which sentences are indistinguishable from noise,
to an organized phase in which nontrivial information is carried. This marks
the emergence of deep structure in the language, and can be understood by a
competition between energy and entropy.
DeepPINK: reproducible feature selection in deep neural networks
Authors: Yang Young Lu, Yingying Fan, Jinchi Lv, William Stafford Noble
Date: 2018-09-04 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: feature selection

Deep learning has become increasingly popular in both supervised and
unsupervised machine learning thanks to its outstanding empirical performance.
However, because of their intrinsic complexity, most deep learning methods are
largely treated as black box tools with little interpretability. Even though
recent attempts have been made to facilitate the interpretability of deep
neural networks (DNNs), existing methods are susceptible to noise and lack of
robustness.
  Therefore, scientists are justifiably cautious about the reproducibility of
the discoveries, which is often related to the interpretability of the
underlying statistical models. In this paper, we describe a method to increase
the interpretability and reproducibility of DNNs by incorporating the idea of
feature selection with controlled error rate. By designing a new DNN
architecture and integrating it with the recently proposed knockoffs framework,
we perform feature selection with a controlled error rate, while maintaining
high power. This new method, DeepPINK (Deep feature selection using
Paired-Input Nonlinear Knockoffs), is applied to both simulated and real data
sets to demonstrate its empirical utility.