pwc-archive/papers-with-abstracts huggingface dataset:
PolyNet: A Pursuit of Structural Diversity in Very Deep Networks
Authors: Xingcheng Zhang, Zhizhong Li, Chen Change Loy, Dahua Lin
Date: 2016-11-17 00:00:00
Proceeding: CVPR 2017 7
Tasks: Diversity, Image Classification

A number of studies have shown that increasing the depth or width of
convolutional networks is a rewarding approach to improve the performance of
image recognition. In our study, however, we observed difficulties along both
directions. On one hand, the pursuit for very deep networks is met with a
diminishing return and increased training difficulty; on the other hand,
widening a network would result in a quadratic growth in both computational
cost and memory demand. These difficulties motivate us to explore structural
diversity in designing deep networks, a new dimension beyond just depth and
width. Specifically, we present a new family of modules, namely the
PolyInception, which can be flexibly inserted in isolation or in a composition
as replacements of different parts of a network. Choosing PolyInception modules
with the guidance of architectural efficiency can improve the expressive power
while preserving comparable computational cost. The Very Deep PolyNet, designed
following this direction, demonstrates substantial improvements over the
state-of-the-art on the ILSVRC 2012 benchmark. Compared to Inception-ResNet-v2,
it reduces the top-5 validation error on single crops from 4.9% to 4.25%, and
that on multi-crops from 3.7% to 3.45%.
graph2vec: Learning Distributed Representations of Graphs
Authors: Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu, Shantanu Jaiswal
Date: 2017-07-17 00:00:00
Tasks: Clustering, General Classification, Graph Classification, Graph Embedding, Graph Matching, Representation Learning

Recent works on representation learning for graph structured data
predominantly focus on learning distributed representations of graph
substructures such as nodes and subgraphs. However, many graph analytics tasks
such as graph classification and clustering require representing entire graphs
as fixed length feature vectors. While the aforementioned approaches are
naturally unequipped to learn such representations, graph kernels remain as the
most effective way of obtaining them. However, these graph kernels use
handcrafted features (e.g., shortest paths, graphlets, etc.) and hence are
hampered by problems such as poor generalization. To address this limitation,
in this work, we propose a neural embedding framework named graph2vec to learn
data-driven distributed representations of arbitrary sized graphs. graph2vec's
embeddings are learnt in an unsupervised manner and are task agnostic. Hence,
they could be used for any downstream task such as graph classification,
clustering and even seeding supervised representation learning approaches. Our
experiments on several benchmark and large real-world datasets show that
graph2vec achieves significant improvements in classification and clustering
accuracies over substructure representation learning approaches and are
competitive with state-of-the-art graph kernels.
Coalition formation for Multi-agent Pursuit based on Neural Network and AGRMF Model
Authors: Zhaoyi Pei, Songhao Piao, Mohammed Ei Souidi
Date: 2017-07-17 00:00:00

An approach for coalition formation of multi-agent pursuit based on neural
network and AGRMF model is proposed.This paper constructs a novel neural work
called AGRMF-ANN which consists of feature extraction part and group generation
part. On one hand,The convolutional layers of feature extraction part can
abstract the features of agent group role membership function(AGRMF) for all of
the groups,on the other hand,those features will be fed to the group generation
part based on self-organizing map(SOM) layer which is used to group the
pursuers with similar features in the same group. Besides, we also come up the
group attractiveness function(GAF) to evaluate the quality of groups and the
pursuers contribution in order to adjust the main ability indicators of AGRMF
and other weight of all neural network. The simulation experiment showed that
this proposal can improve the effectiveness of coalition formation for
multi-agent pursuit and ability to adopt pursuit-evasion problem with the scale
of pursuer team growing.
In-Order Transition-based Constituent Parsing
Authors: Jiangming Liu, Yue Zhang
Date: 2017-07-17 00:00:00
Proceeding: TACL 2017 1
Tasks: Reranking

Both bottom-up and top-down strategies have been used for neural
transition-based constituent parsing. The parsing strategies differ in terms of
the order in which they recognize productions in the derivation tree, where
bottom-up strategies and top-down strategies take post-order and pre-order
traversal over trees, respectively. Bottom-up parsers benefit from rich
features from readily built partial parses, but lack lookahead guidance in the
parsing process; top-down parsers benefit from non-local guidance for local
decisions, but rely on a strong encoder over the input to predict a constituent
hierarchy before its construction.To mitigate both issues, we propose a novel
parsing system based on in-order traversal over syntactic trees, designing a
set of transition actions to find a compromise between bottom-up constituent
information and top-down lookahead information. Based on stack-LSTM, our
psycholinguistically motivated constituent parsing system achieves 91.8 F1 on
WSJ benchmark. Furthermore, the system achieves 93.6 F1 with supervised
reranking and 94.2 F1 with semi-supervised reranking, which are the best
results on the WSJ benchmark.
Tracking as Online Decision-Making: Learning a Policy from Streaming Videos with Reinforcement Learning
Authors: James Steven Supancic III, Deva Ramanan
Date: 2017-07-17 00:00:00
Proceeding: ICCV 2017 10
Tasks: Decision Making, Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

We formulate tracking as an online decision-making process, where a tracking
agent must follow an object despite ambiguous image frames and a limited
computational budget. Crucially, the agent must decide where to look in the
upcoming frames, when to reinitialize because it believes the target has been
lost, and when to update its appearance model for the tracked object. Such
decisions are typically made heuristically. Instead, we propose to learn an
optimal decision-making policy by formulating tracking as a partially
observable decision-making process (POMDP). We learn policies with deep
reinforcement learning algorithms that need supervision (a reward signal) only
when the track has gone awry. We demonstrate that sparse rewards allow us to
quickly train on massive datasets, several orders of magnitude more than past
work. Interestingly, by treating the data source of Internet videos as
unlimited streams, we both learn and evaluate our trackers in a single, unified
computational stream.
Online Multi-Armed Bandit
Authors: Uma Roy, Ashwath Thirmulai, Joe Zurier
Date: 2017-07-17 00:00:00

We introduce a novel variant of the multi-armed bandit problem, in which
bandits are streamed one at a time to the player, and at each point, the player
can either choose to pull the current bandit or move on to the next bandit.
Once a player has moved on from a bandit, they may never visit it again, which
is a crucial difference between our problem and classic multi-armed bandit
problems. In this online context, we study Bernoulli bandits (bandits with
payout Ber($p_i$) for some underlying mean $p_i$) with underlying means drawn
i.i.d. from various distributions, including the uniform distribution, and in
general, all distributions that have a CDF satisfying certain differentiability
conditions near zero. In all cases, we suggest several strategies and
investigate their expected performance. Furthermore, we bound the performance
of any optimal strategy and show that the strategies we have suggested are
indeed optimal up to a constant factor. We also investigate the case where the
distribution from which the underlying means are drawn is not known ahead of
time. We again, are able to suggest algorithms that are optimal up to a
constant factor for this case, given certain mild conditions on the universe of
distributions.
Query-Focused Video Summarization: Dataset, Evaluation, and A Memory Network Based Approach
Authors: Aidean Sharghi, Jacob S. Laurel, Boqing Gong
Date: 2017-07-16 00:00:00
Proceeding: CVPR 2017 7
Tasks: Query focused video summarization, Video Summarization

Recent years have witnessed a resurgence of interest in video summarization.
However, one of the main obstacles to the research on video summarization is
the user subjectivity - users have various preferences over the summaries. The
subjectiveness causes at least two problems. First, no single video summarizer
fits all users unless it interacts with and adapts to the individual users.
Second, it is very challenging to evaluate the performance of a video
summarizer.
  To tackle the first problem, we explore the recently proposed query-focused
video summarization which introduces user preferences in the form of text
queries about the video into the summarization process. We propose a memory
network parameterized sequential determinantal point process in order to attend
the user query onto different video frames and shots. To address the second
challenge, we contend that a good evaluation metric for video summarization
should focus on the semantic information that humans can perceive rather than
the visual features or temporal overlaps. To this end, we collect dense
per-video-shot concept annotations, compile a new dataset, and suggest an
efficient evaluation method defined upon the concept annotations. We conduct
extensive experiments contrasting our video summarizer to existing ones and
present detailed analyses about the dataset and the new evaluation method.
An Ensemble Boosting Model for Predicting Transfer to the Pediatric Intensive Care Unit
Authors: Jonathan Rubin, Cristhian Potes, Minnan Xu-Wilson, Junzi Dong, Asif Rahman, Hiep Nguyen, David Moromisato
Date: 2017-07-16 00:00:00
Tasks: Specificity

Our work focuses on the problem of predicting the transfer of pediatric
patients from the general ward of a hospital to the pediatric intensive care
unit. Using data collected over 5.5 years from the electronic health records of
two medical facilities, we develop classifiers based on adaptive boosting and
gradient tree boosting. We further combine these learned classifiers into an
ensemble model and compare its performance to a modified pediatric early
warning score (PEWS) baseline that relies on expert defined guidelines. To
gauge model generalizability, we perform an inter-facility evaluation where we
train our algorithm on data from one facility and perform evaluation on a
hidden test dataset from a separate facility. We show that improvements are
witnessed over the PEWS baseline in accuracy (0.77 vs. 0.69), sensitivity (0.80
vs. 0.68), specificity (0.74 vs. 0.70) and AUROC (0.85 vs. 0.73).
Improving Adherence to Heart Failure Management Guidelines via Abductive Reasoning
Authors: Zhuo Chen, Elmer Salazar, Kyle Marple, Gopal Gupta, Lakshman Tamil, Sandeep Das, Alpesh Amin
Date: 2017-07-16 00:00:00
Tasks: Management

Management of chronic diseases such as heart failure (HF) is a major public
health problem. A standard approach to managing chronic diseases by medical
community is to have a committee of experts develop guidelines that all
physicians should follow. Due to their complexity, these guidelines are
difficult to implement and are adopted slowly by the medical community at
large. We have developed a physician advisory system that codes the entire set
of clinical practice guidelines for managing HF using answer set
programming(ASP). In this paper we show how abductive reasoning can be deployed
to find missing symptoms and conditions that the patient must exhibit in order
for a treatment prescribed by a physician to work effectively. Thus, if a
physician does not make an appropriate recommendation or makes a non-adherent
recommendation, our system will advise the physician about symptoms and
conditions that must be in effect for that recommendation to apply. It is under
consideration for acceptance in TPLP.
Learning Simpler Language Models with the Differential State Framework
Authors: Alexander G. Ororbia II, Tomas Mikolov, David Reitter
Date: 2017-03-26 00:00:00
Tasks: Language Modeling, Language Modelling

Learning useful information across long time lags is a critical and difficult
problem for temporal neural models in tasks such as language modeling. Existing
architectures that address the issue are often complex and costly to train. The
Differential State Framework (DSF) is a simple and high-performing design that
unifies previously introduced gated neural models. DSF models maintain
longer-term memory by learning to interpolate between a fast-changing
data-driven representation and a slowly changing, implicitly stable state. This
requires hardly any more parameters than a classical, simple recurrent network.
Within the DSF framework, a new architecture is presented, the Delta-RNN. In
language modeling at the word and character levels, the Delta-RNN outperforms
popular complex architectures, such as the Long Short Term Memory (LSTM) and
the Gated Recurrent Unit (GRU), and, when regularized, performs comparably to
several state-of-the-art baselines. At the subword level, the Delta-RNN's
performance is comparable to that of complex gated architectures.
Relevance-based Word Embedding
Authors: Hamed Zamani, W. Bruce Croft
Date: 2017-05-09 00:00:00
Tasks: General Classification, Information Retrieval, Retrieval, Semantic Similarity, Semantic Textual Similarity

Learning a high-dimensional dense representation for vocabulary terms, also
known as a word embedding, has recently attracted much attention in natural
language processing and information retrieval tasks. The embedding vectors are
typically learned based on term proximity in a large corpus. This means that
the objective in well-known word embedding algorithms, e.g., word2vec, is to
accurately predict adjacent word(s) for a given word or context. However, this
objective is not necessarily equivalent to the goal of many information
retrieval (IR) tasks. The primary objective in various IR tasks is to capture
relevance instead of term proximity, syntactic, or even semantic similarity.
This is the motivation for developing unsupervised relevance-based word
embedding models that learn word representations based on query-document
relevance information. In this paper, we propose two learning models with
different objective functions; one learns a relevance distribution over the
vocabulary set for each query, and the other classifies each term as belonging
to the relevant or non-relevant class for each query. To train our models, we
used over six million unique queries and the top ranked documents retrieved in
response to each query, which are assumed to be relevant to the query. We
extrinsically evaluate our learned word representation models using two IR
tasks: query expansion and query classification. Both query expansion
experiments on four TREC collections and query classification experiments on
the KDD Cup 2005 dataset suggest that the relevance-based word embedding models
significantly outperform state-of-the-art proximity-based embedding models,
such as word2vec and GloVe.
Comparative Performance Analysis of Neural Networks Architectures on H2O Platform for Various Activation Functions
Authors: Yuriy Kochura, Sergii Stirenko, Yuri Gordienko
Date: 2017-07-16 00:00:00
Tasks: BIG-bench Machine Learning

Deep learning (deep structured learning, hierarchi- cal learning or deep
machine learning) is a branch of machine learning based on a set of algorithms
that attempt to model high- level abstractions in data by using multiple
processing layers with complex structures or otherwise composed of multiple
non-linear transformations. In this paper, we present the results of testing
neural networks architectures on H2O platform for various activation functions,
stopping metrics, and other parameters of machine learning algorithm. It was
demonstrated for the use case of MNIST database of handwritten digits in
single-threaded mode that blind selection of these parameters can hugely
increase (by 2-3 orders) the runtime without the significant increase of
precision. This result can have crucial influence for opitmization of available
and new machine learning methods, especially for image recognition problems.
Automatized Generation of Alphabets of Symbols
Authors: Serhii Hamotskyi, Anis Rojbi, Sergii Stirenko, Yuri Gordienko
Date: 2017-07-16 00:00:00
Tasks: multimodal interaction

In this paper, we discuss the generation of symbols (and alphabets) based on
specific user requirements (medium, priorities, type of information that needs
to be conveyed). A framework for the generation of alphabets is proposed, and
its use for the generation of a shorthand writing system is explored. We
discuss the possible use of machine learning and genetic algorithms to gather
inputs for generation of such alphabets and for optimization of already
generated ones. The alphabets generated using such methods may be used in very
different fields, from the creation of synthetic languages and constructed
scripts to the creation of sensible commands for multimodal interaction through
Human-Computer Interfaces, such as mouse gestures, touchpads, body gestures,
eye-tracking cameras, and brain-computing Interfaces, especially in
applications for elderly care and people with disabilities.
Pathological OCT Retinal Layer Segmentation using Branch Residual U-shape Networks
Authors: Stefanos Apostolopoulos, Sandro De Zanet, Carlos Ciller, Sebastian Wolf, Raphael Sznitman
Date: 2017-07-16 00:00:00

The automatic segmentation of retinal layer structures enables
clinically-relevant quantification and monitoring of eye disorders over time in
OCT imaging. Eyes with late-stage diseases are particularly challenging to
segment, as their shape is highly warped due to pathological biomarkers. In
this context, we propose a novel fully Convolutional Neural Network (CNN)
architecture which combines dilated residual blocks in an asymmetric U-shape
configuration, and can segment multiple layers of highly pathological eyes in
one shot. We validate our approach on a dataset of late-stage AMD patients and
demonstrate lower computational costs and higher performance compared to other
state-of-the-art methods.
Theoretical insights into the optimization landscape of over-parameterized shallow neural networks
Authors: Mahdi Soltanolkotabi, Adel Javanmard, Jason D. Lee
Date: 2017-07-16 00:00:00

In this paper we study the problem of learning a shallow artificial neural network that best fits a training data set. We study this problem in the over-parameterized regime where the number of observations are fewer than the number of parameters in the model. We show that with quadratic activations the optimization landscape of training such shallow neural networks has certain favorable characteristics that allow globally optimal models to be found efficiently using a variety of local search heuristics. This result holds for an arbitrary training data of input/output pairs. For differentiable activation functions we also show that gradient descent, when suitably initialized, converges at a linear rate to a globally optimal model. This result focuses on a realizable model where the inputs are chosen i.i.d. from a Gaussian distribution and the labels are generated according to planted weight coefficients.
End-to-End Information Extraction without Token-Level Supervision
Authors: Rasmus Berg Palm, Dirk Hovy, Florian Laws, Ole Winther
Date: 2017-07-16 00:00:00
Proceeding: WS 2017 9

Most state-of-the-art information extraction approaches rely on token-level
labels to find the areas of interest in text. Unfortunately, these labels are
time-consuming and costly to create, and consequently, not available for many
real-life IE tasks. To make matters worse, token-level labels are usually not
the desired output, but just an intermediary step. End-to-end (E2E) models,
which take raw text as input and produce the desired output directly, need not
depend on token-level labels. We propose an E2E model based on pointer
networks, which can be trained directly on pairs of raw input and output text.
We evaluate our model on the ATIS data set, MIT restaurant corpus and the MIT
movie corpus and compare to neural baselines that do use token-level labels. We
achieve competitive results, within a few percentage points of the baselines,
showing the feasibility of E2E information extraction without the need for
token-level labels. This opens up new possibilities, as for many tasks
currently addressed by human extractors, raw input and output data are
available, but not token-level labels.
RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems
Authors: Chongyang Tao, Lili Mou, Dongyan Zhao, Rui Yan
Date: 2017-01-11 00:00:00
Tasks: Dialogue Evaluation, Open-Domain Dialog, Retrieval

Open-domain human-computer conversation has been attracting increasing
attention over the past few years. However, there does not exist a standard
automatic evaluation metric for open-domain dialog systems; researchers usually
resort to human annotation for model evaluation, which is time- and
labor-intensive. In this paper, we propose RUBER, a Referenced metric and
Unreferenced metric Blended Evaluation Routine, which evaluates a reply by
taking into consideration both a groundtruth reply and a query (previous
user-issued utterance). Our metric is learnable, but its training does not
require labels of human satisfaction. Hence, RUBER is flexible and extensible
to different datasets and languages. Experiments on both retrieval and
generative dialog systems show that RUBER has a high correlation with human
annotation.
Friendships, Rivalries, and Trysts: Characterizing Relations between Ideas in Texts
Authors: Chenhao Tan, Dallas Card, Noah A. Smith
Date: 2017-04-25 00:00:00
Proceeding: ACL 2017 7
Tasks: Articles

Understanding how ideas relate to each other is a fundamental question in
many domains, ranging from intellectual history to public communication.
Because ideas are naturally embedded in texts, we propose the first framework
to systematically characterize the relations between ideas based on their
occurrence in a corpus of documents, independent of how these ideas are
represented. Combining two statistics --- cooccurrence within documents and
prevalence correlation over time --- our approach reveals a number of different
ways in which ideas can cooperate and compete. For instance, two ideas can
closely track each other's prevalence over time, and yet rarely cooccur, almost
like a "cold war" scenario. We observe that pairwise cooccurrence and
prevalence correlation exhibit different distributions. We further demonstrate
that our approach is able to uncover intriguing relations between ideas through
in-depth case studies on news articles and research papers.
Expected exponential loss for gaze-based video and volume ground truth annotation
Authors: Laurent Lejeune, Mario Christoudias, Raphael Sznitman
Date: 2017-07-16 00:00:00
Tasks: Object, Semantic Segmentation

Many recent machine learning approaches used in medical imaging are highly
reliant on large amounts of image and ground truth data. In the context of
object segmentation, pixel-wise annotations are extremely expensive to collect,
especially in video and 3D volumes. To reduce this annotation burden, we
propose a novel framework to allow annotators to simply observe the object to
segment and record where they have looked at with a \$200 eye gaze tracker. Our
method then estimates pixel-wise probabilities for the presence of the object
throughout the sequence from which we train a classifier in semi-supervised
setting using a novel Expected Exponential loss function. We show that our
framework provides superior performances on a wide range of medical image
settings compared to existing strategies and that our method can be combined
with current crowd-sourcing paradigms as well.