pwc-archive/papers-with-abstracts huggingface dataset:
Computerized Multiparametric MR image Analysis for Prostate Cancer Aggressiveness-Assessment
Authors: Imon Banerjee, Lewis Hahn, Geoffrey Sonn, Richard Fan, Daniel L. Rubin
Date: 2016-12-01 00:00:00

We propose an automated method for detecting aggressive prostate cancer(CaP)
(Gleason score >=7) based on a comprehensive analysis of the lesion and the
surrounding normal prostate tissue which has been simultaneously captured in
T2-weighted MR images, diffusion-weighted images (DWI) and apparent diffusion
coefficient maps (ADC). The proposed methodology was tested on a dataset of 79
patients (40 aggressive, 39 non-aggressive). We evaluated the performance of a
wide range of popular quantitative imaging features on the characterization of
aggressive versus non-aggressive CaP. We found that a group of 44
discriminative predictors among 1464 quantitative imaging features can be used
to produce an area under the ROC curve of 0.73.
Encoding Temporal Markov Dynamics in Graph for Visualizing and Mining Time Series
Authors: Lu Liu, Zhiguang Wang
Date: 2016-10-24 00:00:00
Tasks: General Classification, Time Series, Time Series Analysis

Time series and signals are attracting more attention across statistics,
machine learning and pattern recognition as it appears widely in the industry
especially in sensor and IoT related research and applications, but few
advances has been achieved in effective time series visual analytics and
interaction due to its temporal dimensionality and complex dynamics. Inspired
by recent effort on using network metrics to characterize time series for
classification, we present an approach to visualize time series as complex
networks based on the first order Markov process in its temporal ordering. In
contrast to the classical bar charts, line plots and other statistics based
graph, our approach delivers more intuitive visualization that better preserves
both the temporal dependency and frequency structures. It provides a natural
inverse operation to map the graph back to raw signals, making it possible to
use graph statistics to characterize time series for better visual exploration
and statistical analysis. Our experimental results suggest the effectiveness on
various tasks such as pattern discovery and classification on both synthetic
and the real time series and sensor data.
Definition Modeling: Learning to define word embeddings in natural language
Authors: Thanapon Noraset, Chen Liang, Larry Birnbaum, Doug Downey
Date: 2016-12-01 00:00:00
Tasks: Word Embeddings, Word Similarity

Distributed representations of words have been shown to capture lexical
semantics, as demonstrated by their effectiveness in word similarity and
analogical relation tasks. But, these tasks only evaluate lexical semantics
indirectly. In this paper, we study whether it is possible to utilize
distributed representations to generate dictionary definitions of words, as a
more direct and transparent representation of the embeddings' semantics. We
introduce definition modeling, the task of generating a definition for a given
word and its embedding. We present several definition model architectures based
on recurrent neural networks, and experiment with the models over multiple data
sets. Our results show that a model that controls dependencies between the word
being defined and the definition words performs significantly better, and that
a character-level convolution layer designed to leverage morphology can
complement word-level embeddings. Finally, an error analysis suggests that the
errors made by a definition model may provide insight into the shortcomings of
word embeddings.
Hypervolume-based Multi-objective Bayesian Optimization with Student-t Processes
Authors: Joachim van der Herten, Ivo Couckuyt, Tom Dhaene
Date: 2016-12-01 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes

Student-$t$ processes have recently been proposed as an appealing alternative
non-parameteric function prior. They feature enhanced flexibility and
predictive variance. In this work the use of Student-$t$ processes are explored
for multi-objective Bayesian optimization. In particular, an analytical
expression for the hypervolume-based probability of improvement is developed
for independent Student-$t$ process priors of the objectives. Its effectiveness
is shown on a multi-objective optimization problem which is known to be
difficult with traditional Gaussian processes.
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable
Authors: Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
Date: 2016-10-29 00:00:00
Proceeding: ICML 2017 8
Tasks: Efficient Exploration, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper studies systematic exploration for reinforcement learning with
rich observations and function approximation. We introduce a new model called
contextual decision processes, that unifies and generalizes most prior
settings. Our first contribution is a complexity measure, the Bellman rank,
that we show enables tractable learning of near-optimal behavior in these
processes and is naturally small for many well-studied reinforcement learning
settings. Our second contribution is a new reinforcement learning algorithm
that engages in systematic exploration to learn contextual decision processes
with low Bellman rank. Our algorithm provably learns near-optimal behavior with
a number of samples that is polynomial in all relevant parameters but
independent of the number of unique observations. The approach uses Bellman
error minimization with optimistic exploration and provides new insights into
efficient exploration for reinforcement learning with function approximation.
Diet2Vec: Multi-scale analysis of massive dietary data
Authors: Wesley Tansey, Edward W. Lowe Jr., James G. Scott
Date: 2016-12-01 00:00:00
Tasks: Nutrition

Smart phone apps that enable users to easily track their diets have become
widespread in the last decade. This has created an opportunity to discover new
insights into obesity and weight loss by analyzing the eating habits of the
users of such apps. In this paper, we present diet2vec: an approach to modeling
latent structure in a massive database of electronic diet journals. Through an
iterative contract-and-expand process, our model learns real-valued embeddings
of users' diets, as well as embeddings for individual foods and meals. We
demonstrate the effectiveness of our approach on a real dataset of 55K users of
the popular diet-tracking app LoseIt\footnote{http://www.loseit.com/}. To the
best of our knowledge, this is the largest fine-grained diet tracking study in
the history of nutrition and obesity research. Our results suggest that
diet2vec finds interpretable results at all levels, discovering intuitive
representations of foods, meals, and diets.
Tuning the Scheduling of Distributed Stochastic Gradient Descent with Bayesian Optimization
Authors: Valentin Dalibard, Michael Schaarschmidt, Eiko Yoneki
Date: 2016-12-01 00:00:00
Tasks: Bayesian Optimization, Scheduling

We present an optimizer which uses Bayesian optimization to tune the system
parameters of distributed stochastic gradient descent (SGD). Given a specific
context, our goal is to quickly find efficient configurations which
appropriately balance the load between the available machines to minimize the
average SGD iteration time. Our experiments consider setups with over thirty
parameters. Traditional Bayesian optimization, which uses a Gaussian process as
its model, is not well suited to such high dimensional domains. To reduce
convergence time, we exploit the available structure. We design a probabilistic
model which simulates the behavior of distributed SGD and use it within
Bayesian optimization. Our model can exploit many runtime measurements for
inference per evaluation of the objective function. Our experiments show that
our resulting optimizer converges to efficient configurations within ten
iterations, the optimized configurations outperform those found by generic
optimizer in thirty iterations by up to 2X.
Playing Doom with SLAM-Augmented Deep Reinforcement Learning
Authors: Shehroze Bhatti, Alban Desmaison, Ondrej Miksik, Nantas Nardelli, N. Siddharth, Philip H. S. Torr
Date: 2016-12-01 00:00:00
Tasks: Deep Reinforcement Learning, object-detection, Object Detection, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A number of recent approaches to policy learning in 2D game domains have been
successful going directly from raw input images to actions. However when
employed in complex 3D environments, they typically suffer from challenges
related to partial observability, combinatorial exploration spaces, path
planning, and a scarcity of rewarding scenarios. Inspired from prior work in
human cognition that indicates how humans employ a variety of semantic concepts
and abstractions (object categories, localisation, etc.) to reason about the
world, we build an agent-model that incorporates such abstractions into its
policy-learning framework. We augment the raw image input to a Deep Q-Learning
Network (DQN), by adding details of objects and structural elements
encountered, along with the agent's localisation. The different components are
automatically extracted and composed into a topological representation using
on-the-fly object detection and 3D-scene reconstruction.We evaluate the
efficacy of our approach in Doom, a 3D first-person combat game that exhibits a
number of challenges discussed, and show that our augmented framework
consistently learns better, more effective policies.
Active Discriminative Text Representation Learning
Authors: Ye Zhang, Matthew Lease, Byron C. Wallace
Date: 2016-06-14 00:00:00
Tasks: Active Learning, Classification, Document Classification, General Classification, Representation Learning, Sentence, Sentence Classification, text-classification, Text Classification, Word Embeddings

We propose a new active learning (AL) method for text classification with
convolutional neural networks (CNNs). In AL, one selects the instances to be
manually labeled with the aim of maximizing model performance with minimal
effort. Neural models capitalize on word embeddings as representations
(features), tuning these to the task at hand. We argue that AL strategies for
multi-layered neural models should focus on selecting instances that most
affect the embedding space (i.e., induce discriminative word representations).
This is in contrast to traditional AL approaches (e.g., entropy-based
uncertainty sampling), which specify higher level objectives. We propose a
simple approach for sentence classification that selects instances containing
words whose embeddings are likely to be updated with the greatest magnitude,
thereby rapidly learning discriminative, task-specific embeddings. We extend
this approach to document classification by jointly considering: (1) the
expected changes to the constituent word representations; and (2) the model's
current overall uncertainty regarding the instance. The relative emphasis
placed on these criteria is governed by a stochastic process that favors
selecting instances likely to improve representations at the outset of
learning, and then shifts toward general uncertainty sampling as AL progresses.
Empirical results show that our method outperforms baseline AL approaches on
both sentence and document classification tasks. We also show that, as
expected, the method quickly learns discriminative word embeddings. To the best
of our knowledge, this is the first work on AL addressing neural models for
text classification.
Structured Prediction Theory Based on Factor Graph Complexity
Authors: Corinna Cortes, Mehryar Mohri, Vitaly Kuznetsov, Scott Yang
Date: 2016-05-20 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Prediction, Structured Prediction

We present a general theoretical analysis of structured prediction with a
series of new results. We give new data-dependent margin guarantees for
structured prediction for a very wide family of loss functions and a general
family of hypotheses, with an arbitrary factor graph decomposition. These are
the tightest margin bounds known for both standard multi-class and general
structured prediction problems. Our guarantees are expressed in terms of a
data-dependent complexity measure, factor graph complexity, which we show can
be estimated from data and bounded in terms of familiar quantities. We further
extend our theory by leveraging the principle of Voted Risk Minimization (VRM)
and show that learning is possible even with complex factor graphs. We present
new learning bounds for this advanced setting, which we use to design two new
algorithms, Voted Conditional Random Field (VCRF) and Voted Structured Boosting
(StructBoost). These algorithms can make use of complex features and factor
graphs and yet benefit from favorable learning guarantees. We also report the
results of experiments with VCRF on several datasets to validate our theory.
Bootstrapping incremental dialogue systems: using linguistic knowledge to learn from minimal data
Authors: Dimitrios Kalatzis, Arash Eshghi, Oliver Lemon
Date: 2016-12-01 00:00:00
Tasks: Dialogue Management, Management, Reinforcement Learning, Reinforcement Learning (RL), Text Generation

We present a method for inducing new dialogue systems from very small amounts
of unannotated dialogue data, showing how word-level exploration using
Reinforcement Learning (RL), combined with an incremental and semantic grammar
- Dynamic Syntax (DS) - allows systems to discover, generate, and understand
many new dialogue variants. The method avoids the use of expensive and
time-consuming dialogue act annotations, and supports more natural
(incremental) dialogues than turn-based systems. Here, language generation and
dialogue management are treated as a joint decision/optimisation problem, and
the MDP model for RL is constructed automatically. With an implemented system,
we show that this method enables a wide range of dialogue variations to be
automatically captured, even when the system is trained from only a single
dialogue. The variants include question-answer pairs, over- and
under-answering, self- and other-corrections, clarification interaction,
split-utterances, and ellipsis. This generalisation property results from the
structural knowledge and constraints present within the DS grammar, and
highlights some limitations of recent systems built using machine learning
techniques only.
Multilingual Multiword Expressions
Authors: Lahari Poddar
Date: 2016-12-01 00:00:00
Tasks: POS

The project aims to provide a semi-supervised approach to identify Multiword
Expressions in a multilingual context consisting of English and most of the
major Indian languages. Multiword expressions are a group of words which refers
to some conventional or regional way of saying things. If they are literally
translated from one language to another the expression will lose its inherent
meaning.
  To automatically extract multiword expressions from a corpus, an extraction
pipeline have been constructed which consist of a combination of rule based and
statistical approaches. There are several types of multiword expressions which
differ from each other widely by construction. We employ different methods to
detect different types of multiword expressions. Given a POS tagged corpus in
English or any Indian language the system initially applies some regular
expression filters to narrow down the search space to certain patterns (like,
reduplication, partial reduplication, compound nouns, compound verbs, conjunct
verbs etc.). The word sequences matching the required pattern are subjected to
a series of linguistic tests which include verb filtering, named entity
filtering and hyphenation filtering test to exclude false positives. The
candidates are then checked for semantic relationships among themselves (using
Wordnet). In order to detect partial reduplication we make use of Wordnet as a
lexical database as well as a tool for lemmatising. We detect complex
predicates by investigating the features of the constituent words. Statistical
methods are applied to detect collocations. Finally, lexicographers examine the
list of automatically extracted candidates to validate whether they are true
multiword expressions or not and add them to the multiword dictionary
accordingly.
An Evaluation of Models for Runtime Approximation in Link Discovery
Authors: Kleanthi Georgala, Micheal Hoffmann, Axel-Cyrille Ngonga Ngomo
Date: 2016-12-01 00:00:00

Time-efficient link discovery is of central importance to implement the
vision of the Semantic Web. Some of the most rapid Link Discovery approaches
rely internally on planning to execute link specifications. In newer works,
linear models have been used to estimate the runtime the fastest planners.
However, no other category of models has been studied for this purpose so far.
In this paper, we study non-linear runtime estimation functions for runtime
estimation. In particular, we study exponential and mixed models for the
estimation of the runtimes of planners. To this end, we evaluate three
different models for runtime on six datasets using 400 link specifications. We
show that exponential and mixed models achieve better fits when trained but are
only to be preferred in some cases. Our evaluation also shows that the use of
better runtime approximation models has a positive impact on the overall
execution of link specifications.
Video Captioning with Multi-Faceted Attention
Authors: Xiang Long, Chuang Gan, Gerard de Melo
Date: 2016-12-01 00:00:00
Proceeding: TACL 2018 1
Tasks: Information Retrieval, Retrieval, Sentence, Video Captioning

Recently, video captioning has been attracting an increasing amount of
interest, due to its potential for improving accessibility and information
retrieval. While existing methods rely on different kinds of visual features
and model structures, they do not fully exploit relevant semantic information.
We present an extensible approach to jointly leverage several sorts of visual
features and semantic attributes. Our novel architecture builds on LSTMs for
sentence generation, with several attention layers and two multimodal layers.
The attention mechanism learns to automatically select the most salient visual
features or semantic attributes, and the multimodal layer yields overall
representations for the input and outputs of the sentence generation component.
Experimental results on the challenging MSVD and MSR-VTT datasets show that our
framework outperforms the state-of-the-art approaches, while ground truth based
semantic attributes are able to further elevate the output quality to a
near-human level.
On Coreferring Text-extracted Event Descriptions with the aid of Ontological Reasoning
Authors: Stefano Borgo, Loris Bozzato, Alessio Palmero Aprosio, Marco Rospocher, Luciano Serafini
Date: 2016-12-01 00:00:00

Systems for automatic extraction of semantic information about events from
large textual resources are now available: these tools are capable to generate
RDF datasets about text extracted events and this knowledge can be used to
reason over the recognized events. On the other hand, text based tasks for
event recognition, as for example event coreference (i.e. recognizing whether
two textual descriptions refer to the same event), do not take into account
ontological information of the extracted events in their process. In this
paper, we propose a method to derive event coreference on text extracted event
data using semantic based rule reasoning. We demonstrate our method considering
a limited (yet representative) set of event types: we introduce a formal
analysis on their ontological properties and, on the base of this, we define a
set of coreference criteria. We then implement these criteria as RDF-based
reasoning rules to be applied on text extracted event data. We evaluate the
effectiveness of our approach over a standard coreference benchmark dataset.
Interaction Networks for Learning about Objects, Relations and Physics
Authors: Peter W. Battaglia, Razvan Pascanu, Matthew Lai, Danilo Rezende, Koray Kavukcuoglu
Date: 2016-12-01 00:00:00
Proceeding: NeurIPS 2016 12

Reasoning about objects, relations, and physics is central to human
intelligence, and a key goal of artificial intelligence. Here we introduce the
interaction network, a model which can reason about how objects in complex
systems interact, supporting dynamical predictions, as well as inferences about
the abstract properties of the system. Our model takes graphs as input,
performs object- and relation-centric reasoning in a way that is analogous to a
simulation, and is implemented using deep neural networks. We evaluate its
ability to reason about several challenging physical domains: n-body problems,
rigid-body collision, and non-rigid dynamics. Our results show it can be
trained to accurately simulate the physical trajectories of dozens of objects
over thousands of time steps, estimate abstract quantities such as energy, and
generalize automatically to systems with different numbers and configurations
of objects and relations. Our interaction network implementation is the first
general-purpose, learnable physics engine, and a powerful general framework for
reasoning about object and relations in a wide variety of complex real-world
domains.
The Coconut Model with Heterogeneous Strategies and Learning
Authors: Sven Banisch, Eckehard Olbrich
Date: 2016-12-01 00:00:00
Tasks: model

In this paper, we develop an agent-based version of the Diamond search
equilibrium model - also called Coconut Model. In this model, agents are faced
with production decisions that have to be evaluated based on their expectations
about the future utility of the produced entity which in turn depends on the
global production level via a trading mechanism. While the original dynamical
systems formulation assumes an infinite number of homogeneously adapting agents
obeying strong rationality conditions, the agent-based setting allows to
discuss the effects of heterogeneous and adaptive expectations and enables the
analysis of non-equilibrium trajectories. Starting from a baseline
implementation that matches the asymptotic behavior of the original model, we
show how agent heterogeneity can be accounted for in the aggregate dynamical
equations. We then show that when agents adapt their strategies by a simple
temporal difference learning scheme, the system converges to one of the fixed
points of the original system. Systematic simulations reveal that this is the
only stable equilibrium solution.
Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts
Authors: Levent Karacan, Zeynep Akata, Aykut Erdem, Erkut Erdem
Date: 2016-12-01 00:00:00
Tasks: Generative Adversarial Network, Image Generation, Object

Automatic image synthesis research has been rapidly growing with deep
networks getting more and more expressive. In the last couple of years, we have
observed images of digits, indoor scenes, birds, chairs, etc. being
automatically generated. The expressive power of image generators have also
been enhanced by introducing several forms of conditioning variables such as
object names, sentences, bounding box and key-point locations. In this work, we
propose a novel deep conditional generative adversarial network architecture
that takes its strength from the semantic layout and scene attributes
integrated as conditioning variables. We show that our architecture is able to
generate realistic outdoor scene images under different conditions, e.g.
day-night, sunny-foggy, with clear object boundaries.