pwc-archive/papers-with-abstracts huggingface dataset:
Towards a Seamless Integration of Word Senses into Downstream NLP Applications
Authors: Mohammad Taher Pilehvar, Jose Camacho-Collados, Roberto Navigli, Nigel Collier
Date: 2017-10-18 00:00:00
Proceeding: ACL 2017 7
Tasks: General Classification

Lexical ambiguity can impede NLP systems from accurate understanding of
semantics. Despite its potential benefits, the integration of sense-level
information into NLP systems has remained understudied. By incorporating a
novel disambiguation algorithm into a state-of-the-art classification model, we
create a pipeline to integrate sense-level information into downstream NLP
applications. We show that a simple disambiguation of the input text can lead
to consistent performance improvement on multiple topic categorization and
polarity detection datasets, particularly when the fine granularity of the
underlying sense inventory is reduced and the document is sufficiently large.
Our results also point to the need for sense representation research to focus
more on in vivo evaluations which target the performance in downstream NLP
applications rather than artificial benchmarks.
Generalized Concomitant Multi-Task Lasso for sparse multimodal regression
Authors: Mathurin Massias, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
Date: 2017-05-27 00:00:00
Tasks: EEG, Electroencephalogram (EEG), regression

In high dimension, it is customary to consider Lasso-type estimators to
enforce sparsity. For standard Lasso theory to hold, the regularization
parameter should be proportional to the noise level, yet the latter is
generally unknown in practice. A possible remedy is to consider estimators,
such as the Concomitant/Scaled Lasso, which jointly optimize over the
regression coefficients as well as over the noise level, making the choice of
the regularization independent of the noise level. However, when data from
different sources are pooled to increase sample size, or when dealing with
multimodal datasets, noise levels typically differ and new dedicated estimators
are needed. In this work we provide new statistical and computational solutions
to deal with such heteroscedastic regression models, with an emphasis on
functional brain imaging with combined magneto- and electroencephalographic
(M/EEG) signals. Adopting the formulation of Concomitant Lasso-type estimators,
we propose a jointly convex formulation to estimate both the regression
coefficients and the (square root of the) noise covariance. When our framework
is instantiated to de-correlated noise, it leads to an efficient algorithm
whose computational cost is not higher than for the Lasso and Concomitant
Lasso, while addressing more complex noise structures. Numerical experiments
demonstrate that our estimator yields improved prediction and support
identification while correctly estimating the noise (square root) covariance.
Results on multimodal neuroimaging problems with M/EEG data are also reported.
Cell Segmentation in 3D Confocal Images using Supervoxel Merge-Forests with CNN-based Hypothesis Selection
Authors: Johannes Stegmaier, Thiago V. Spina, Alexandre X. Falcão, Andreas Bartschat, Ralf Mikut, Elliot Meyerowitz, Alexandre Cunha
Date: 2017-10-18 00:00:00
Tasks: Cell Segmentation, Segmentation

Automated segmentation approaches are crucial to quantitatively analyze
large-scale 3D microscopy images. Particularly in deep tissue regions,
automatic methods still fail to provide error-free segmentations. To improve
the segmentation quality throughout imaged samples, we present a new
supervoxel-based 3D segmentation approach that outperforms current methods and
reduces the manual correction effort. The algorithm consists of gentle
preprocessing and a conservative super-voxel generation method followed by
supervoxel agglomeration based on local signal properties and a postprocessing
step to fix under-segmentation errors using a Convolutional Neural Network. We
validate the functionality of the algorithm on manually labeled 3D confocal
images of the plant Arabidopis thaliana and compare the results to a
state-of-the-art meristem segmentation algorithm.
Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications
Authors: Apostolia Tsirikoglou, Joel Kronander, Magnus Wrenninge, Jonas Unger
Date: 2017-10-17 00:00:00
Tasks: Autonomous Vehicles, Image Generation, Semantic Segmentation, Synthetic Data Generation

We present an overview and evaluation of a new, systematic approach for
generation of highly realistic, annotated synthetic data for training of deep
neural networks in computer vision tasks. The main contribution is a procedural
world modeling approach enabling high variability coupled with physically
accurate image synthesis, and is a departure from the hand-modeled virtual
worlds and approximate image synthesis methods used in real-time applications.
The benefits of our approach include flexible, physically accurate and scalable
image synthesis, implicit wide coverage of classes and features, and complete
data introspection for annotations, which all contribute to quality and cost
efficiency. To evaluate our approach and the efficacy of the resulting data, we
use semantic segmentation for autonomous vehicles and robotic navigation as the
main application, and we train multiple deep learning architectures using
synthetic data with and without fine tuning on organic (i.e. real-world) data.
The evaluation shows that our approach improves the neural network's
performance and that even modest implementation efforts produce
state-of-the-art results.
Toward the Starting Line: A Systems Engineering Approach to Strong AI
Authors: Tansu Alpcan, Sarah M. Erfani, Christopher Leckie
Date: 2017-07-28 00:00:00
Tasks: Position

Artificial General Intelligence (AGI) or Strong AI aims to create machines
with human-like or human-level intelligence, which is still a very ambitious
goal when compared to the existing computing and AI systems. After many hype
cycles and lessons from AI history, it is clear that a big conceptual leap is
needed for crossing the starting line to kick-start mainstream AGI research.
This position paper aims to make a small conceptual contribution toward
reaching that starting line. After a broad analysis of the AGI problem from
different perspectives, a system-theoretic and engineering-based research
approach is introduced, which builds upon the existing mainstream AI and
systems foundations. Several promising cross-fertilization opportunities
between systems disciplines and AI research are identified. Specific potential
research directions are discussed.
Drought Stress Classification using 3D Plant Models
Authors: Siddharth Srivastava, Swati Bhugra, Brejesh lall, Santanu Chaudhury
Date: 2017-09-21 00:00:00
Tasks: 3D Reconstruction, Classification, General Classification

Quantification of physiological changes in plants can capture different
drought mechanisms and assist in selection of tolerant varieties in a high
throughput manner. In this context, an accurate 3D model of plant canopy
provides a reliable representation for drought stress characterization in
contrast to using 2D images. In this paper, we propose a novel end-to-end
pipeline including 3D reconstruction, segmentation and feature extraction,
leveraging deep neural networks at various stages, for drought stress study. To
overcome the high degree of self-similarities and self-occlusions in plant
canopy, prior knowledge of leaf shape based on features from deep siamese
network are used to construct an accurate 3D model using structure from motion
on wheat plants. The drought stress is characterized with a deep network based
feature aggregation. We compare the proposed methodology on several
descriptors, and show that the network outperforms conventional methods.
Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learning
Authors: Ayaka Kume, Eiichi Matsumoto, Kuniyuki Takahashi, Wilson Ko, Jethro Tan
Date: 2017-10-17 00:00:00
Tasks: Bayesian Optimization, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In order for robots to perform mission-critical tasks, it is essential that
they are able to quickly adapt to changes in their environment as well as to
injuries and or other bodily changes. Deep reinforcement learning has been
shown to be successful in training robot control policies for operation in
complex environments. However, existing methods typically employ only a single
policy. This can limit the adaptability since a large environmental
modification might require a completely different behavior compared to the
learning environment. To solve this problem, we propose Map-based Multi-Policy
Reinforcement Learning (MMPRL), which aims to search and store multiple
policies that encode different behavioral features while maximizing the
expected reward in advance of the environment change. Thanks to these policies,
which are stored into a multi-dimensional discrete map according to its
behavioral feature, adaptation can be performed within reasonable time without
retraining the robot. An appropriate pre-trained policy from the map can be
recalled using Bayesian optimization. Our experiments show that MMPRL enables
robots to quickly adapt to large changes without requiring any prior knowledge
on the type of injuries that could occur. A highlight of the learned behaviors
can be found here: https://youtu.be/QwInbilXNOE .
Learning Social Image Embedding with Deep Multimodal Attention Networks
Authors: Feiran Huang, Xiao-Ming Zhang, Zhoujun Li, Tao Mei, Yueying He, Zhonghua Zhao
Date: 2017-10-18 00:00:00
Tasks: Classification, General Classification, Link Prediction, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Triplet

Learning social media data embedding by deep models has attracted extensive
research interest as well as boomed a lot of applications, such as link
prediction, classification, and cross-modal search. However, for social images
which contain both link information and multimodal contents (e.g., text
description, and visual content), simply employing the embedding learnt from
network structure or data content results in sub-optimal social image
representation. In this paper, we propose a novel social image embedding
approach called Deep Multimodal Attention Networks (DMAN), which employs a deep
model to jointly embed multimodal contents and link information. Specifically,
to effectively capture the correlations between multimodal contents, we propose
a multimodal attention network to encode the fine-granularity relation between
image regions and textual words. To leverage the network structure for
embedding learning, a novel Siamese-Triplet neural network is proposed to model
the links among images. With the joint deep model, the learnt embedding can
capture both the multimodal contents and the nonlinear network information.
Extensive experiments are conducted to investigate the effectiveness of our
approach in the applications of multi-label classification and cross-modal
search. Compared to state-of-the-art image embeddings, our proposed DMAN
achieves significant improvement in the tasks of multi-label classification and
cross-modal search.
On Using Linear Diophantine Equations to Tune the extent of Look Ahead while Hiding Decision Tree Rules
Authors: Georgios Feretzakis, Dimitris Kalles, Vassilios S. Verykios
Date: 2017-10-18 00:00:00
Tasks: General Classification

This paper focuses on preserving the privacy of sensitive pat-terns when
inducing decision trees. We adopt a record aug-mentation approach for hiding
sensitive classification rules in binary datasets. Such a hiding methodology is
preferred over other heuristic solutions like output perturbation or
crypto-graphic techniques - which restrict the usability of the data - since
the raw data itself is readily available for public use. In this paper, we
propose a look ahead approach using linear Diophantine equations in order to
add the appropriate number of instances while minimally disturbing the initial
entropy of the nodes.
DART: Noise Injection for Robust Imitation Learning
Authors: Michael Laskey, Jonathan Lee, Roy Fox, Anca Dragan, Ken Goldberg
Date: 2017-03-27 00:00:00
Tasks: Imitation Learning, MuJoCo

One approach to Imitation Learning is Behavior Cloning, in which a robot
observes a supervisor and infers a control policy. A known problem with this
"off-policy" approach is that the robot's errors compound when drifting away
from the supervisor's demonstrations. On-policy, techniques alleviate this by
iteratively collecting corrective actions for the current robot policy.
However, these techniques can be tedious for human supervisors, add significant
computation burden, and may visit dangerous states during training. We propose
an off-policy approach that injects noise into the supervisor's policy while
demonstrating. This forces the supervisor to demonstrate how to recover from
errors. We propose a new algorithm, DART (Disturbances for Augmenting Robot
Trajectories), that collects demonstrations with injected noise, and optimizes
the noise level to approximate the error of the robot's trained policy during
data collection. We compare DART with DAgger and Behavior Cloning in two
domains: in simulation with an algorithmic supervisor on the MuJoCo tasks
(Walker, Humanoid, Hopper, Half-Cheetah) and in physical experiments with human
supervisors training a Toyota HSR robot to perform grasping in clutter. For
high dimensional tasks like Humanoid, DART can be up to $3x$ faster in
computation time and only decreases the supervisor's cumulative reward by $5\%$
during training, whereas DAgger executes policies that have $80\%$ less
cumulative reward than the supervisor. On the grasping in clutter task, DART
obtains on average a $62\%$ performance increase over Behavior Cloning.
The Effects of Memory Replay in Reinforcement Learning
Authors: Ruishan Liu, James Zou
Date: 2017-10-18 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Experience replay is a key technique behind many recent advances in deep
reinforcement learning. Allowing the agent to learn from earlier memories can
speed up learning and break undesirable temporal correlations. Despite its
wide-spread application, very little is understood about the properties of
experience replay. How does the amount of memory kept affect learning dynamics?
Does it help to prioritize certain experiences? In this paper, we address these
questions by formulating a dynamical systems ODE model of Q-learning with
experience replay. We derive analytic solutions of the ODE for a simple
setting. We show that even in this very simple setting, the amount of memory
kept can substantially affect the agent's performance. Too much or too little
memory both slow down learning. Moreover, we characterize regimes where
prioritized replay harms the agent's learning. We show that our analytic
solutions have excellent agreement with experiments. Finally, we propose a
simple algorithm for adaptively changing the memory buffer size which achieves
consistently good empirical performance.
A Correspondence Between Random Neural Networks and Statistical Field Theory
Authors: Samuel S. Schoenholz, Jeffrey Pennington, Jascha Sohl-Dickstein
Date: 2017-10-18 00:00:00

A number of recent papers have provided evidence that practical design
questions about neural networks may be tackled theoretically by studying the
behavior of random networks. However, until now the tools available for
analyzing random neural networks have been relatively ad-hoc. In this work, we
show that the distribution of pre-activations in random neural networks can be
exactly mapped onto lattice models in statistical physics. We argue that
several previous investigations of stochastic networks actually studied a
particular factorial approximation to the full lattice model. For random linear
networks and random rectified linear networks we show that the corresponding
lattice models in the wide network limit may be systematically approximated by
a Gaussian distribution with covariance between the layers of the network. In
each case, the approximate distribution can be diagonalized by Fourier
transformation. We show that this approximation accurately describes the
results of numerical simulations of wide random neural networks. Finally, we
demonstrate that in each case the large scale behavior of the random networks
can be approximated by an effective field theory.
Revenue-based Attribution Modeling for Online Advertising
Authors: Kaifeng Zhao, Seyed Hanif Mahboobi, Saeed Bagheri
Date: 2017-10-18 00:00:00
Tasks: regression

This paper examines and proposes several attribution modeling methods that
quantify how revenue should be attributed to online advertising inputs. We
adopt and further develop relative importance method, which is based on
regression models that have been extensively studied and utilized to
investigate the relationship between advertising efforts and market reaction
(revenue). Relative importance method aims at decomposing and allocating
marginal contributions to the coefficient of determination (R^2) of regression
models as attribution values. In particular, we adopt two alternative
submethods to perform this decomposition: dominance analysis and relative
weight analysis. Moreover, we demonstrate an extension of the decomposition
methods from standard linear model to additive model. We claim that our new
approaches are more flexible and accurate in modeling the underlying
relationship and calculating the attribution values. We use simulation examples
to demonstrate the superior performance of our new approaches over traditional
methods. We further illustrate the value of our proposed approaches using a
real advertising campaign dataset.
A Two-Phase Safe Vehicle Routing and Scheduling Problem: Formulations and Solution Algorithms
Authors: Aschkan Omidvar, Eren Erman Ozguven, O. Arda Vanli, R. Tavakkoli-Moghaddam
Date: 2017-10-18 00:00:00
Tasks: ARC, Scheduling

We propose a two phase time dependent vehicle routing and scheduling
optimization model that identifies the safest routes, as a substitute for the
classical objectives given in the literature such as shortest distance or
travel time, through (1) avoiding recurring congestions, and (2) selecting
routes that have a lower probability of crash occurrences and non-recurring
congestion caused by those crashes. In the first phase, we solve a
mixed-integer programming model which takes the dynamic speed variations into
account on a graph of roadway networks according to the time of day, and
identify the routing of a fleet and sequence of nodes on the safest feasible
paths. Second phase considers each route as an independent transit path (fixed
route with fixed node sequences), and tries to avoid congestion by rescheduling
the departure times of each vehicle from each node, and by adjusting the
sub-optimal speed on each arc. A modified simulated annealing (SA) algorithm is
formulated to solve both complex models iteratively, which is found to be
capable of providing solutions in a considerably short amount of time.
Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification
Authors: Dangwei Li, Xiaotang Chen, Zhang Zhang, Kaiqi Huang
Date: 2017-10-18 00:00:00
Proceeding: CVPR 2017 7
Tasks: Person Identification, Person Re-Identification, Representation Learning

Person Re-identification (ReID) is to identify the same person across
different cameras. It is a challenging task due to the large variations in
person pose, occlusion, background clutter, etc How to extract powerful
features is a fundamental problem in ReID and is still an open problem today.
In this paper, we design a Multi-Scale Context-Aware Network (MSCAN) to learn
powerful features over full body and body parts, which can well capture the
local context knowledge by stacking multi-scale convolutions in each layer.
Moreover, instead of using predefined rigid parts, we propose to learn and
localize deformable pedestrian parts using Spatial Transformer Networks (STN)
with novel spatial constraints. The learned body parts can release some
difficulties, eg pose variations and background clutters, in part-based
representation. Finally, we integrate the representation learning processes of
full body and body parts into a unified framework for person ReID through
multi-class person identification tasks. Extensive evaluations on current
challenging large-scale person ReID datasets, including the image-based
Market1501, CUHK03 and sequence-based MARS datasets, show that the proposed
method achieves the state-of-the-art results.
Asymmetric Actor Critic for Image-Based Robot Learning
Authors: Lerrel Pinto, Marcin Andrychowicz, Peter Welinder, Wojciech Zaremba, Pieter Abbeel
Date: 2017-10-18 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Deep reinforcement learning (RL) has proven a powerful technique in many
sequential decision making domains. However, Robotics poses many challenges for
RL, most notably training on a physical system can be expensive and dangerous,
which has sparked significant interest in learning control policies using a
physics simulator. While several recent works have shown promising results in
transferring policies trained in simulation to the real world, they often do
not fully utilize the advantage of working with a simulator. In this work, we
exploit the full state observability in the simulator to train better policies
which take as input only partial observations (RGBD images). We do this by
employing an actor-critic training algorithm in which the critic is trained on
full states while the actor (or policy) gets rendered images as input. We show
experimentally on a range of simulated tasks that using these asymmetric inputs
significantly improves performance. Finally, we combine this method with domain
randomization and show real robot experiments for several tasks like picking,
pushing, and moving a block. We achieve this simulation to real world transfer
without training on any real world data.
Basic tasks of sentiment analysis
Authors: Iti Chaturvedi, Soujanya Poria, Erik Cambria
Date: 2017-10-18 00:00:00
Tasks: Aspect Extraction, Sentiment Analysis

Subjectivity detection is the task of identifying objective and subjective
sentences. Objective sentences are those which do not exhibit any sentiment.
So, it is desired for a sentiment analysis engine to find and separate the
objective sentences for further analysis, e.g., polarity detection. In
subjective sentences, opinions can often be expressed on one or multiple
topics. Aspect extraction is a subtask of sentiment analysis that consists in
identifying opinion targets in opinionated text, i.e., in detecting the
specific aspects of a product or service the opinion holder is either praising
or complaining about.