pwc-archive/papers-with-abstracts huggingface dataset:
An agent-based evaluation of impacts of transport developments on the modal shift in Tehran, Iran
Authors: A. Shirzadi Babakan, A. Alimohammadi, M. Taleai
Date: 2018-03-13 00:00:00
Tasks: Decision Making

Changes in travel modes used by people, particularly reduction of the private
car use, is an important determinant of effectiveness of transportation plans.
Because of dependencies between the choices of residential location and travel
mode, integrated modelling of these choices has been proposed by some
researchers. In this paper, an agent-based microsimulation model has been
developed to evaluate impacts of different transport development plans on
choices of residential location and commuting mode of tenant households in
Tehran, the capital of Iran. In the proposed model, households are considered
as agents who select their desired residential location using a constrained
NSGA-II algorithm and in a competition with other households. In addition, they
choose their commuting mode by applying a multi-criteria decision making
method. Afterwards, effects of development of a new highway, subway and bus
rapid transit (BRT) line on their residential location and commuting mode
choices are evaluated. Results show that despite the residential self-selection
effects, these plans result in considerable changes in the commuting mode of
different socioeconomic categories of households. Development of the new subway
line shows promising results by reducing the private car use among the all
socio-economic categories of households. But the new highway development
unsatisfactorily results in increase in the private car use. In addition,
development of the new BRT line does not show significant effects on the
commuting mode change, particularly on decrease in the private car use.
Small Moving Window Calibration Models for Soft Sensing Processes with Limited History
Authors: Casey Kneale, Steven D. Brown
Date: 2017-10-31 00:00:00
Tasks: regression

Five simple soft sensor methodologies with two update conditions were
compared on two experimentally-obtained datasets and one simulated dataset. The
soft sensors investigated were moving window partial least squares regression
(and a recursive variant), moving window random forest regression, the mean
moving window of $y$, and a novel random forest partial least squares
regression ensemble (RF-PLS), all of which can be used with small sample sizes
so that they can be rapidly placed online. It was found that, on two of the
datasets studied, small window sizes led to the lowest prediction errors for
all of the moving window methods studied. On the majority of datasets studied,
the RF-PLS calibration method offered the lowest one-step-ahead prediction
errors compared to those of the other methods, and it demonstrated greater
predictive stability at larger time delays than moving window PLS alone. It was
found that both the random forest and RF-PLS methods most adequately modeled
the datasets that did not feature purely monotonic increases in property
values, but that both methods performed more poorly than moving window PLS
models on one dataset with purely monotonic property values. Other data
dependent findings are presented and discussed.
Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy Search
Authors: Rémi Pautrat, Konstantinos Chatzilygeroudis, Jean-Baptiste Mouret
Date: 2017-09-20 00:00:00
Tasks: Bayesian Optimization, Transfer Learning

One of the most interesting features of Bayesian optimization for direct
policy search is that it can leverage priors (e.g., from simulation or from
previous tasks) to accelerate learning on a robot. In this paper, we are
interested in situations for which several priors exist but we do not know in
advance which one fits best the current situation. We tackle this problem by
introducing a novel acquisition function, called Most Likely Expected
Improvement (MLEI), that combines the likelihood of the priors and the expected
improvement. We evaluate this new acquisition function on a transfer learning
task for a 5-DOF planar arm and on a possibly damaged, 6-legged robot that has
to learn to walk on flat ground and on stairs, with priors corresponding to
different stairs and different kinds of damages. Our results show that MLEI
effectively identifies and exploits the priors, even when there is no obvious
match between the current situations and the priors.
Impacts of transport development on residence choice of renter households: An agent-based evaluation
Authors: A. S. Babakan, M. Taleai
Date: 2018-03-13 00:00:00

Because of improving accessibility, transport developments play an important
role in residence choice of renter households. In this paper, an agent-based
model is developed to investigate impacts of different transport developments
on residence choice of renter households in Tehran, the capital of Iran. In the
proposed model, renter households are considered as agents who make a
multi-objective decision and compete with each other to rent a preferred
residential zone. Then, three transport development scenarios including
construction a new highway, subway and bus rapid transit (BRT) line are
simulated and resulting changes in residence choice of agents are evaluated.
Results show that transport development scenarios significantly affect
residence choice behavior of different socio-economic categories of renter
households and lead to considerable changes in the residential demand,
composition of residents, mean income level and mean car ownership in their
vicinities.
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs
Authors: Diviyan Kalainathan, Olivier Goudet, Isabelle Guyon, David Lopez-Paz, Michèle Sebag
Date: 2018-03-13 00:00:00
Tasks: Causal Discovery

A new causal discovery method, Structural Agnostic Modeling (SAM), is presented in this paper. Leveraging both conditional independencies and distributional asymmetries, SAM aims to find the underlying causal structure from observational data. The approach is based on a game between different players estimating each variable distribution conditionally to the others as a neural net, and an adversary aimed at discriminating the generated data against the original data. A learning criterion combining distribution estimation, sparsity and acyclicity constraints is used to enforce the optimization of the graph structure and parameters through stochastic gradient descent. SAM is extensively experimentally validated on synthetic and real data.
Using Parameterized Black-Box Priors to Scale Up Model-Based Policy Search for Robotics
Authors: Konstantinos Chatzilygeroudis, Jean-Baptiste Mouret
Date: 2017-09-20 00:00:00
Tasks: Continuous Control, Reinforcement Learning

The most data-efficient algorithms for reinforcement learning in robotics are
model-based policy search algorithms, which alternate between learning a
dynamical model of the robot and optimizing a policy to maximize the expected
return given the model and its uncertainties. Among the few proposed
approaches, the recently introduced Black-DROPS algorithm exploits a black-box
optimization algorithm to achieve both high data-efficiency and good
computation times when several cores are used; nevertheless, like all
model-based policy search approaches, Black-DROPS does not scale to high
dimensional state/action spaces. In this paper, we introduce a new model
learning procedure in Black-DROPS that leverages parameterized black-box priors
to (1) scale up to high-dimensional systems, and (2) be robust to large
inaccuracies of the prior information. We demonstrate the effectiveness of our
approach with the "pendubot" swing-up task in simulation and with a physical
hexapod robot (48D state space, 18D action space) that has to walk forward as
fast as possible. The results show that our new algorithm is more
data-efficient than previous model-based policy search algorithms (with and
without priors) and that it can allow a physical 6-legged robot to learn new
gaits in only 16 to 30 seconds of interaction time.
An Agent-Based Simulation of Residential Location Choice of Tenants in Tehran, Iran
Authors: A. Shirzadi Babakan, A. Alimohammadi
Date: 2018-03-13 00:00:00

Residential location choice modeling is one of the substantial components of
land use and transportation models. While numerous aggregated mathematical and
statistical approaches have been developed to model the residence choice
behavior of households, disaggregated approaches such as the agent-based
modeling have shown interesting capabilities. In this article, a novel
agent-based approach is developed to simulate the residential location choice
of tenants in Tehran, the capital of Iran. Tenants are considered as agents who
select their desired residential alternatives according to their
characteristics and preferences for various criteria such as the rent,
accessibility to different services and facilities, environmental pollution,
and distance from their workplace and former residence. The choice set of
agents is limited to their desired residential alternatives by applying a
constrained NSGA-II algorithm. Then, agents compete with each other to select
their final residence among their alternatives. Results of the proposed
approach are validated by comparing simulated and actual residences of a sample
of tenants. Results show that the proposed approach is able to accurately
simulate the residence of 59.3% of tenants at the traffic analysis zone level.
Large Scale Automated Forecasting for Monitoring Network Safety and Security
Authors: Roi Naveiro, Simón Rodríguez, David Ríos Insua
Date: 2018-02-19 00:00:00

Real time large scale streaming data pose major challenges to forecasting, in
particular defying the presence of human experts to perform the corresponding
analysis. We present here a class of models and methods used to develop an
automated, scalable and versatile system for large scale forecasting oriented
towards safety and security monitoring. Our system provides short and long term
forecasts and uses them to detect safety and security issues in relation with
multiple internet connected devices well in advance they might take place.
Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection
Authors: Yu-Xing Tang, Josiah Wang, Xiaofang Wang, Boyang Gao, Emmanuel Dellandrea, Robert Gaizauskas, Liming Chen
Date: 2018-01-09 00:00:00
Tasks: Object, object-detection, Object Detection, Semi-Supervised Object Detection, Transfer Learning

Deep CNN-based object detection systems have achieved remarkable success on
several large-scale object detection benchmarks. However, training such
detectors requires a large number of labeled bounding boxes, which are more
difficult to obtain than image-level annotations. Previous work addresses this
issue by transforming image-level classifiers into object detectors. This is
done by modeling the differences between the two on categories with both
image-level and bounding box annotations, and transferring this information to
convert classifiers to detectors for categories without bounding box
annotations. We improve this previous work by incorporating knowledge about
object similarities from visual and semantic domains during the transfer
process. The intuition behind our proposed method is that visually and
semantically similar categories should exhibit more common transferable
properties than dissimilar categories, e.g. a better detector would result by
transforming the differences between a dog classifier and a dog detector onto
the cat class, than would by transforming from the violin class. Experimental
results on the challenging ILSVRC2013 detection dataset demonstrate that each
of our proposed object similarity based knowledge transfer methods outperforms
the baseline methods. We found strong evidence that visual similarity and
semantic relatedness are complementary for the task, and when combined notably
improve detection, achieving state-of-the-art detection performance in a
semi-supervised setting.
Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation
Authors: Xiaowei Xu, Qing Lu, Yu Hu, Lin Yang, Sharon Hu, Danny Chen, Yiyu Shi
Date: 2018-03-13 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Segmentation, Quantization, Segmentation, Semantic Segmentation

With pervasive applications of medical imaging in health-care, biomedical
image segmentation plays a central role in quantitative analysis, clinical
diagno- sis, and medical intervention. Since manual anno- tation su ers limited
reproducibility, arduous e orts, and excessive time, automatic segmentation is
desired to process increasingly larger scale histopathological data. Recently,
deep neural networks (DNNs), par- ticularly fully convolutional networks
(FCNs), have been widely applied to biomedical image segmenta- tion, attaining
much improved performance. At the same time, quantization of DNNs has become an
ac- tive research topic, which aims to represent weights with less memory
(precision) to considerably reduce memory and computation requirements of DNNs
while maintaining acceptable accuracy. In this paper, we apply quantization
techniques to FCNs for accurate biomedical image segmentation. Unlike existing
litera- ture on quantization which primarily targets memory and computation
complexity reduction, we apply quan- tization as a method to reduce over tting
in FCNs for better accuracy. Speci cally, we focus on a state-of- the-art
segmentation framework, suggestive annotation [22], which judiciously extracts
representative annota- tion samples from the original training dataset, obtain-
ing an e ective small-sized balanced training dataset. We develop two new
quantization processes for this framework: (1) suggestive annotation with
quantiza- tion for highly representative training samples, and (2) network
training with quantization for high accuracy. Extensive experiments on the
MICCAI Gland dataset show that both quantization processes can improve the
segmentation performance, and our proposed method exceeds the current
state-of-the-art performance by up to 1%. In addition, our method has a
reduction of up to 6.4x on memory usage.
Learning to Recognize Musical Genre from Audio
Authors: Michaël Defferrard, Sharada P. Mohanty, Sean F. Carroll, Marcel Salathé
Date: 2018-03-13 00:00:00
Tasks: Music Genre Recognition

We here summarize our experience running a challenge with open data for
musical genre recognition. Those notes motivate the task and the challenge
design, show some statistics about the submissions, and present the results.
Causal inference for data-driven debugging and decision making in cloud computing
Authors: Philipp Geiger, Lucian Carata, Bernhard Schoelkopf
Date: 2016-03-04 00:00:00
Tasks: Causal Inference, Cloud Computing, counterfactual, Decision Making, Privacy Preserving

Cloud computing involves complex technical and economical systems and interactions. This brings about various challenges, two of which are: (1) debugging and control to optimize the performance of computing systems, with the help of sandbox experiments, and (2) privacy-preserving prediction of the cost of ``spot'' resources for decision making of cloud clients. In this paper, we formalize debugging by counterfactual probabilities and control by post-(soft-)interventional probabilities. We prove that counterfactuals can approximately be calculated from a ``stochastic'' graphical causal model (while they are originally defined only for ``deterministic'' functional causal models), and based on this sketch a data-driven approach to address problem (1). To address problem (2), we formalize bidding by post-(soft-)interventional probabilities and present a simple mathematical result on approximate integration of ``incomplete'' conditional probability distributions. We show how this can be used by cloud clients to trade off privacy against predictability of the outcome of their bidding actions in a toy scenario. We report experiments on simulated and real data.
Occam's razor is insufficient to infer the preferences of irrational agents
Authors: Stuart Armstrong, Sören Mindermann
Date: 2017-12-15 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Inverse reinforcement learning (IRL) attempts to infer human rewards or
preferences from observed behavior. Since human planning systematically
deviates from rationality, several approaches have been tried to account for
specific human shortcomings. However, the general problem of inferring the
reward function of an agent of unknown rationality has received little
attention. Unlike the well-known ambiguity problems in IRL, this one is
practically relevant but cannot be resolved by observing the agent's policy in
enough environments. This paper shows (1) that a No Free Lunch result implies
it is impossible to uniquely decompose a policy into a planning algorithm and
reward function, and (2) that even with a reasonable simplicity prior/Occam's
razor on the set of decompositions, we cannot distinguish between the true
decomposition and others that lead to high regret. To address this, we need
simple `normative' assumptions, which cannot be deduced exclusively from
observations.
Experimental and causal view on information integration in autonomous agents
Authors: Philipp Geiger, Katja Hofmann, Bernhard Schölkopf
Date: 2016-06-14 00:00:00
Tasks: Decision Making, Self-Driving Cars, Transfer Learning

The amount of digitally available but heterogeneous information about the
world is remarkable, and new technologies such as self-driving cars, smart
homes, or the internet of things may further increase it. In this paper we
present preliminary ideas about certain aspects of the problem of how such
heterogeneous information can be harnessed by autonomous agents. After
discussing potentials and limitations of some existing approaches, we
investigate how \emph{experiments} can help to obtain a better understanding of
the problem. Specifically, we present a simple agent that integrates video data
from a different agent, and implement and evaluate a version of it on the novel
experimentation platform \emph{Malmo}. The focus of a second investigation is
on how information about the hardware of different agents, the agents' sensory
data, and \emph{causal} information can be utilized for knowledge transfer
between agents and subsequently more data-efficient decision making. Finally,
we discuss potential future steps w.r.t.\ theory and experimentation, and
formulate open questions.
IDEL: In-Database Entity Linking with Neural Embeddings
Authors: Torsten Kilias, Alexander Löser, Felix A. Gers, Richard Koopmanschap, Ying Zhang, Martin Kersten
Date: 2018-03-13 00:00:00
Tasks: Entity Linking, Retrieval

We present a novel architecture, In-Database Entity Linking (IDEL), in which
we integrate the analytics-optimized RDBMS MonetDB with neural text mining
abilities. Our system design abstracts core tasks of most neural entity linking
systems for MonetDB. To the best of our knowledge, this is the first defacto
implemented system integrating entity-linking in a database. We leverage the
ability of MonetDB to support in-database-analytics with user defined functions
(UDFs) implemented in Python. These functions call machine learning libraries
for neural text mining, such as TensorFlow. The system achieves zero cost for
data shipping and transformation by utilizing MonetDB's ability to embed Python
processes in the database kernel and exchange data in NumPy arrays. IDEL
represents text and relational data in a joint vector space with neural
embeddings and can compensate errors with ambiguous entity representations. For
detecting matching entities, we propose a novel similarity function based on
joint neural embeddings which are learned via minimizing pairwise contrastive
ranking loss. This function utilizes a high dimensional index structures for
fast retrieval of matching entities. Our first implementation and experiments
using the WebNLG corpus show the effectiveness and the potentials of IDEL.
ViTac: Feature Sharing between Vision and Tactile Sensing for Cloth Texture Recognition
Authors: Shan Luo, Wenzhen Yuan, Edward Adelson, Anthony G. Cohn, Raul Fuentes
Date: 2018-02-21 00:00:00

Vision and touch are two of the important sensing modalities for humans and
they offer complementary information for sensing the environment. Robots could
also benefit from such multi-modal sensing ability. In this paper, addressing
for the first time (to the best of our knowledge) texture recognition from
tactile images and vision, we propose a new fusion method named Deep Maximum
Covariance Analysis (DMCA) to learn a joint latent space for sharing features
through vision and tactile sensing. The features of camera images and tactile
data acquired from a GelSight sensor are learned by deep neural networks. But
the learned features are of a high dimensionality and are redundant due to the
differences between the two sensing modalities, which deteriorates the
perception performance. To address this, the learned features are paired using
maximum covariance analysis. Results of the algorithm on a newly collected
dataset of paired visual and tactile data relating to cloth textures show that
a good recognition performance of greater than 90\% can be achieved by using
the proposed DMCA framework. In addition, we find that the perception
performance of either vision or tactile sensing can be improved by employing
the shared representation space, compared to learning from unimodal data.
Expert identification of visual primitives used by CNNs during mammogram classification
Authors: Jimmy Wu, Diondra Peck, Scott Hsieh, Vandana Dialani, Constance D. Lehman, Bolei Zhou, Vasilis Syrgkanis, Lester Mackey, Genevieve Patterson
Date: 2018-03-13 00:00:00
Tasks: Classification, General Classification

This work interprets the internal representations of deep neural networks
trained for classification of diseased tissue in 2D mammograms. We propose an
expert-in-the-loop interpretation method to label the behavior of internal
units in convolutional neural networks (CNNs). Expert radiologists identify
that the visual patterns detected by the units are correlated with meaningful
medical phenomena such as mass tissue and calcificated vessels. We demonstrate
that several trained CNN models are able to produce explanatory descriptions to
support the final classification decisions. We view this as an important first
step toward interpreting the internal representations of medical classification
CNNs and explaining their predictions.