pwc-archive/papers-with-abstracts huggingface dataset:
Characterizing Driving Styles with Deep Learning
Authors: Weishan Dong, Jian Li, Renjie Yao, Changsheng Li, Ting Yuan, Lanjun Wang
Date: 2016-07-13 00:00:00
Tasks: Autonomous Driving, Deep Learning, Driver Identification

Characterizing driving styles of human drivers using vehicle sensor data,
e.g., GPS, is an interesting research problem and an important real-world
requirement from automotive industries. A good representation of driving
features can be highly valuable for autonomous driving, auto insurance, and
many other application scenarios. However, traditional methods mainly rely on
handcrafted features, which limit machine learning algorithms to achieve a
better performance. In this paper, we propose a novel deep learning solution to
this problem, which could be the first attempt of extending deep learning to
driving behavior analysis based on GPS data. The proposed approach can
effectively extract high level and interpretable features describing complex
driving patterns. It also requires significantly less human experience and
work. The power of the learned driving style representations are validated
through the driver identification problem using a large real dataset.
4D Crop Monitoring: Spatio-Temporal Reconstruction for Agriculture
Authors: Jing Dong, John Gary Burnham, Byron Boots, Glen C. Rains, Frank Dellaert
Date: 2016-10-08 00:00:00
Tasks: 4D reconstruction

Autonomous crop monitoring at high spatial and temporal resolution is a
critical problem in precision agriculture. While Structure from Motion and
Multi-View Stereo algorithms can finely reconstruct the 3D structure of a field
with low-cost image sensors, these algorithms fail to capture the dynamic
nature of continuously growing crops. In this paper we propose a 4D
reconstruction approach to crop monitoring, which employs a spatio-temporal
model of dynamic scenes that is useful for precision agriculture applications.
Additionally, we provide a robust data association algorithm to address the
problem of large appearance changes due to scenes being viewed from different
angles at different points in time, which is critical to achieving 4D
reconstruction. Finally, we collected a high quality dataset with ground truth
statistics to evaluate the performance of our method. We demonstrate that our
4D reconstruction approach provides models that are qualitatively correct with
respect to visual appearance and quantitatively accurate when measured against
the ground truth geometric properties of the monitored crops.
Deep Convolutional Networks as Models of Generalization and Blending Within Visual Creativity
Authors: Graeme McCaig, Steve DiPaola, Liane Gabora
Date: 2016-10-08 00:00:00

We examine two recent artificial intelligence (AI) based deep learning algorithms for visual blending in convolutional neural networks (Mordvintsev et al. 2015, Gatys et al. 2015). To investigate the potential value of these algorithms as tools for computational creativity research, we explain and schematize the essential aspects of the algorithms' operation and give visual examples of their output. We discuss the relationship of the two algorithms to human cognitive science theories of creativity such as conceptual blending theory and honing theory, and characterize the algorithms with respect to generation of novelty and aesthetic quality.
Learning What and Where to Draw
Authors: Scott Reed, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele, Honglak Lee
Date: 2016-10-08 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Image Generation, Text-to-Image Generation

Generative Adversarial Networks (GANs) have recently demonstrated the
capability to synthesize compelling real-world images, such as room interiors,
album covers, manga, faces, birds, and flowers. While existing models can
synthesize images based on global constraints such as a class label or caption,
they do not provide control over pose or object location. We propose a new
model, the Generative Adversarial What-Where Network (GAWWN), that synthesizes
images given instructions describing what content to draw in which location. We
show high-quality 128 x 128 image synthesis on the Caltech-UCSD Birds dataset,
conditioned on both informal text descriptions and also object location. Our
system exposes control over both the bounding box around the bird and its
constituent parts. By modeling the conditional distributions over part
locations, our system also enables conditioning on arbitrary subsets of parts
(e.g. only the beak and tail), yielding an efficient interface for picking part
locations. We also show preliminary results on the more challenging domain of
text- and location-controllable synthesis of images of human actions on the
MPII Human Pose dataset.
Theoretical Evaluation of Feature Selection Methods based on Mutual Information
Authors: Cláudia Pascoal, M. Rosário Oliveira, António Pacheco, Rui Valadas
Date: 2016-09-21 00:00:00
Tasks: feature selection, Mutual Information Estimation

Feature selection methods are usually evaluated by wrapping specific
classifiers and datasets in the evaluation process, resulting very often in
unfair comparisons between methods. In this work, we develop a theoretical
framework that allows obtaining the true feature ordering of two-dimensional
sequential forward feature selection methods based on mutual information, which
is independent of entropy or mutual information estimation methods,
classifiers, or datasets, and leads to an undoubtful comparison of the methods.
Moreover, the theoretical framework unveils problems intrinsic to some methods
that are otherwise difficult to detect, namely inconsistencies in the
construction of the objective function used to select the candidate features,
due to various types of indeterminations and to the possibility of the entropy
of continuous random variables taking null and negative values.
ResearchDoom and CocoDoom: Learning Computer Vision with Games
Authors: A. Mahendran, H. Bilen, J. F. Henriques, A. Vedaldi
Date: 2016-10-07 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation, Motion Estimation, Object Recognition, Scene Segmentation, Segmentation

In this short note we introduce ResearchDoom, an implementation of the Doom
first-person shooter that can extract detailed metadata from the game. We also
introduce the CocoDoom dataset, a collection of pre-recorded data extracted
from Doom gaming sessions along with annotations in the MS Coco format.
ResearchDoom and CocoDoom can be used to train and evaluate a variety of
computer vision methods such as object recognition, detection and segmentation
at the level of instances and categories, tracking, ego-motion estimation,
monocular depth estimation and scene segmentation. The code and data are
available at http://www.robots.ox.ac.uk/~vgg/research/researchdoom.
A Novel Representation of Neural Networks
Authors: Anthony Caterini, Dong Eui Chang
Date: 2016-10-05 00:00:00

Deep Neural Networks (DNNs) have become very popular for prediction in many
areas. Their strength is in representation with a high number of parameters
that are commonly learned via gradient descent or similar optimization methods.
However, the representation is non-standardized, and the gradient calculation
methods are often performed using component-based approaches that break
parameters down into scalar units, instead of considering the parameters as
whole entities. In this work, these problems are addressed. Standard notation
is used to represent DNNs in a compact framework. Gradients of DNN loss
functions are calculated directly over the inner product space on which the
parameters are defined. This framework is general and is applied to two common
network types: the Multilayer Perceptron and the Deep Autoencoder.
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
Authors: Ashwin K. Vijayakumar, Michael Cogswell, Ramprasath R. Selvaraju, Qing Sun, Stefan Lee, David Crandall, Dhruv Batra
Date: 2016-10-07 00:00:00
Tasks: Diversity, Image Captioning, Machine Translation, Question Generation, Question-Generation, Text Generation, Time Series Analysis

Neural sequence models are widely used to model time-series data. Equally
ubiquitous is the usage of beam search (BS) as an approximate inference
algorithm to decode output sequences from these models. BS explores the search
space in a greedy left-right fashion retaining only the top-B candidates -
resulting in sequences that differ only slightly from each other. Producing
lists of nearly identical sequences is not only computationally wasteful but
also typically fails to capture the inherent ambiguity of complex AI tasks. To
overcome this problem, we propose Diverse Beam Search (DBS), an alternative to
BS that decodes a list of diverse outputs by optimizing for a
diversity-augmented objective. We observe that our method finds better top-1
solutions by controlling for the exploration and exploitation of the search
space - implying that DBS is a better search algorithm. Moreover, these gains
are achieved with minimal computational or memory over- head as compared to
beam search. To demonstrate the broad applicability of our method, we present
results on image captioning, machine translation and visual question generation
using both standard quantitative metrics and qualitative human studies.
Further, we study the role of diversity for image-grounded language generation
tasks as the complexity of the image changes. We observe that our method
consistently outperforms BS and previously proposed techniques for diverse
decoding from neural sequence models.
Indoor Space Recognition using Deep Convolutional Neural Network: A Case Study at MIT Campus
Authors: Fan Zhang, Fabio Duarte, Ruixian Ma, Dimitrios Milioris, Hui Lin, Carlo Ratti
Date: 2016-10-07 00:00:00
Tasks: Scene Recognition

In this paper, we propose a robust and parsimonious approach using Deep
Convolutional Neural Network (DCNN) to recognize and interpret interior space.
DCNN has achieved incredible success in object and scene recognition. In this
study we design and train a DCNN to classify a pre-zoning indoor space, and
from a single phone photo to recognize the learned space features, with no need
of additional assistive technology. We collect more than 600,000 images inside
MIT campus buildings to train our DCNN model, and achieved 97.9% accuracy in
validation dataset and 81.7% accuracy in test dataset based on spatial-scale
fixed model. Furthermore, the recognition accuracy and spatial resolution can
be potentially improved through multiscale classification model. We identify
the discriminative image regions through Class Activating Mapping (CAM)
technique, to observe the model's behavior in how to recognize space and
interpret it in an abstract way. By evaluating the results with
misclassification matrix, we investigate the visual spatial feature of interior
space by looking into its visual similarity and visual distinctiveness, giving
insights into interior design and human indoor perception and wayfinding
research. The contribution of this paper is threefold. First, we propose a
robust and parsimonious approach for indoor navigation using DCNN. Second, we
demonstrate that DCNN also has a potential capability in space feature learning
and recognition, even under severe appearance changes. Third, we introduce a
DCNN based approach to look into the visual similarity and visual
distinctiveness of interior space.
Equality of Opportunity in Supervised Learning
Authors: Moritz Hardt, Eric Price, Nathan Srebro
Date: 2016-10-07 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Attribute, General Classification

We propose a criterion for discrimination against a specified sensitive
attribute in supervised learning, where the goal is to predict some target
based on available features. Assuming data about the predictor, target, and
membership in the protected group are available, we show how to optimally
adjust any learned predictor so as to remove discrimination according to our
definition. Our framework also improves incentives by shifting the cost of poor
classification from disadvantaged groups to the decision maker, who can respond
by improving the classification accuracy.
  In line with other studies, our notion is oblivious: it depends only on the
joint statistics of the predictor, the target and the protected attribute, but
not on interpretation of individualfeatures. We study the inherent limits of
defining and identifying biases based on such oblivious measures, outlining
what can and cannot be inferred from different oblivious tests.
  We illustrate our notion using a case study of FICO credit scores.
Distributed Averaging CNN-ELM for Big Data
Authors: Arif Budiman, Mohamad Ivan Fanany, Chan Basaruddin
Date: 2016-10-07 00:00:00
Tasks: BIG-bench Machine Learning

Increasing the scalability of machine learning to handle big volume of data
is a challenging task. The scale up approach has some limitations. In this
paper, we proposed a scale out approach for CNN-ELM based on MapReduce on
classifier level. Map process is the CNN-ELM training for certain partition of
data. It involves many CNN-ELM models that can be trained asynchronously.
Reduce process is the averaging of all CNN-ELM weights as final training
result. This approach can save a lot of training time than single CNN-ELM
models trained alone. This approach also increased the scalability of machine
learning by combining scale out and scale up approaches. We verified our method
in extended MNIST data set and not-MNIST data set experiment. However, it has
some drawbacks by additional iteration learning parameters that need to be
carefully taken and training data distribution that need to be carefully
selected. Further researches to use more complex image data set are required.
Combining local and global smoothing in multivariate density estimation
Authors: Adelchi Azzalini
Date: 2016-10-07 00:00:00
Tasks: Clustering, Density Estimation

Non-parametric estimation of a multivariate density estimation is tackled via
a method which combines traditional local smoothing with a form of global
smoothing but without imposing a rigid structure. Simulation work delivers
encouraging indications on the effectiveness of the method. An application to
density-based clustering illustrates a possible usage.
Phase-Mapper: An AI Platform to Accelerate High Throughput Materials Discovery
Authors: Yexiang Xue, Junwen Bai, Ronan Le Bras, Brendan Rappazzo, Richard Bernstein, Johan Bjorck, Liane Longpre, Santosh K. Suram, Robert B. van Dover, John Gregoire, Carla P. Gomes
Date: 2016-10-03 00:00:00
Tasks: Vocal Bursts Intensity Prediction

High-Throughput materials discovery involves the rapid synthesis,
measurement, and characterization of many different but structurally-related
materials. A key problem in materials discovery, the phase map identification
problem, involves the determination of the crystal phase diagram from the
materials' composition and structural characterization data. We present
Phase-Mapper, a novel AI platform to solve the phase map identification problem
that allows humans to interact with both the data and products of AI
algorithms, including the incorporation of human feedback to constrain or
initialize solutions. Phase-Mapper affords incorporation of any spectral
demixing algorithm, including our novel solver, AgileFD, which is based on a
convolutive non-negative matrix factorization algorithm. AgileFD can
incorporate constraints to capture the physics of the materials as well as
human feedback. We compare three solver variants with previously proposed
methods in a large-scale experiment involving 20 synthetic systems,
demonstrating the efficacy of imposing physical constrains using AgileFD.
Phase-Mapper has also been used by materials scientists to solve a wide variety
of phase diagrams, including the previously unsolved Nb-Mn-V oxide system,
which is provided here as an illustrative example.
Inductive Coherence
Authors: Scott Garrabrant, Benya Fallenstein, Abram Demski, Nate Soares
Date: 2016-04-18 00:00:00
Tasks: Negation, Sentence

While probability theory is normally applied to external environments, there
has been some recent interest in probabilistic modeling of the outputs of
computations that are too expensive to run. Since mathematical logic is a
powerful tool for reasoning about computer programs, we consider this problem
from the perspective of integrating probability and logic. Recent work on
assigning probabilities to mathematical statements has used the concept of
coherent distributions, which satisfy logical constraints such as the
probability of a sentence and its negation summing to one. Although there are
algorithms which converge to a coherent probability distribution in the limit,
this yields only weak guarantees about finite approximations of these
distributions. In our setting, this is a significant limitation: Coherent
distributions assign probability one to all statements provable in a specific
logical theory, such as Peano Arithmetic, which can prove what the output of
any terminating computation is; thus, a coherent distribution must assign
probability one to the output of any terminating computation. To model
uncertainty about computations, we propose to work with approximations to
coherent distributions. We introduce inductive coherence, a strengthening of
coherence that provides appropriate constraints on finite approximations, and
propose an algorithm which satisfies this criterion.
Adaptive Convolutional ELM For Concept Drift Handling in Online Stream Data
Authors: Arif Budiman, Mohamad Ivan Fanany, Chan Basaruddin
Date: 2016-10-07 00:00:00

In big data era, the data continuously generated and its distribution may
keep changes overtime. These challenges in online stream of data are known as
concept drift. In this paper, we proposed the Adaptive Convolutional ELM method
(ACNNELM) as enhancement of Convolutional Neural Network (CNN) with a hybrid
Extreme Learning Machine (ELM) model plus adaptive capability. This method is
aimed for concept drift handling. We enhanced the CNN as convolutional
hiererchical features representation learner combined with Elastic ELM
(E$^2$LM) as a parallel supervised classifier. We propose an Adaptive OS-ELM
(AOS-ELM) for concept drift adaptability in classifier level (named ACNNELM-1)
and matrices concatenation ensembles for concept drift adaptability in ensemble
level (named ACNNELM-2). Our proposed Adaptive CNNELM is flexible that works
well in classifier level and ensemble level while most current methods only
proposed to work on either one of the levels.
  We verified our method in extended MNIST data set and not MNIST data set. We
set the experiment to simulate virtual drift, real drift, and hybrid drift
event and we demonstrated how our CNNELM adaptability works. Our proposed
method works well and gives better accuracy, computation scalability, and
concept drifts adaptability compared to the regular ELM and CNN. Further
researches are still required to study the optimum parameters and to use more
varied image data set.
Sequential Low-Rank Change Detection
Authors: Yao Xie, Lee Seversky
Date: 2016-10-03 00:00:00
Tasks: Change Detection, Dimensionality Reduction

Detecting emergence of a low-rank signal from high-dimensional data is an
important problem arising from many applications such as camera surveillance
and swarm monitoring using sensors. We consider a procedure based on the
largest eigenvalue of the sample covariance matrix over a sliding window to
detect the change. To achieve dimensionality reduction, we present a
sketching-based approach for rank change detection using the low-dimensional
linear sketches of the original high-dimensional observations. The premise is
that when the sketching matrix is a random Gaussian matrix, and the dimension
of the sketching vector is sufficiently large, the rank of sample covariance
matrix for these sketches equals the rank of the original sample covariance
matrix with high probability. Hence, we may be able to detect the low-rank
change using sample covariance matrices of the sketches without having to
recover the original covariance matrix. We character the performance of the
largest eigenvalue statistic in terms of the false-alarm-rate and the expected
detection delay, and present an efficient online implementation via subspace
tracking.
Pseudo-Bayesian Robust PCA: Algorithms and Analyses
Authors: Tae-Hyun Oh, Yasuyuki Matsushita, In So Kweon, David Wipf
Date: 2015-12-07 00:00:00
Tasks: Matrix Completion

Commonly used in computer vision and other applications, robust PCA
represents an algorithmic attempt to reduce the sensitivity of classical PCA to
outliers. The basic idea is to learn a decomposition of some data matrix of
interest into low rank and sparse components, the latter representing unwanted
outliers. Although the resulting optimization problem is typically NP-hard,
convex relaxations provide a computationally-expedient alternative with
theoretical support. However, in practical regimes performance guarantees break
down and a variety of non-convex alternatives, including Bayesian-inspired
models, have been proposed to boost estimation quality. Unfortunately though,
without additional a priori knowledge none of these methods can significantly
expand the critical operational range such that exact principal subspace
recovery is possible. Into this mix we propose a novel pseudo-Bayesian
algorithm that explicitly compensates for design weaknesses in many existing
non-convex approaches leading to state-of-the-art performance with a sound
analytical foundation. Surprisingly, our algorithm can even outperform convex
matrix completion despite the fact that the latter is provided with perfect
knowledge of which entries are not corrupted.
Online Isotonic Regression
Authors: Wojciech Kotłowski, Wouter M. Koolen, Alan Malek
Date: 2016-03-14 00:00:00
Tasks: regression

We consider the online version of the isotonic regression problem. Given a
set of linearly ordered points (e.g., on the real line), the learner must
predict labels sequentially at adversarially chosen positions and is evaluated
by her total squared loss compared against the best isotonic (non-decreasing)
function in hindsight. We survey several standard online learning algorithms
and show that none of them achieve the optimal regret exponent; in fact, most
of them (including Online Gradient Descent, Follow the Leader and Exponential
Weights) incur linear regret. We then prove that the Exponential Weights
algorithm played over a covering net of isotonic functions has a regret bounded
by $O\big(T^{1/3} \log^{2/3}(T)\big)$ and present a matching $\Omega(T^{1/3})$
lower bound on regret. We provide a computationally efficient version of this
algorithm. We also analyze the noise-free case, in which the revealed labels
are isotonic, and show that the bound can be improved to $O(\log T)$ or even to
$O(1)$ (when the labels are revealed in isotonic order). Finally, we extend the
analysis beyond squared loss and give bounds for entropic loss and absolute
loss.