pwc-archive/papers-with-abstracts huggingface dataset:
New Fairness Metrics for Recommendation that Embrace Differences
Authors: Sirui Yao, Bert Huang
Date: 2017-06-29 00:00:00
Tasks: Collaborative Filtering, Fairness, Recommendation Systems

We study fairness in collaborative-filtering recommender systems, which are
sensitive to discrimination that exists in historical data. Biased data can
lead collaborative filtering methods to make unfair predictions against
minority groups of users. We identify the insufficiency of existing fairness
metrics and propose four new metrics that address different forms of
unfairness. These fairness metrics can be optimized by adding fairness terms to
the learning objective. Experiments on synthetic and real data show that our
new metrics can better measure fairness than the baseline, and that the
fairness objectives effectively help reduce unfairness.
Quasi-Oracle Estimation of Heterogeneous Treatment Effects
Authors: Xinkun Nie, Stefan Wager
Date: 2017-12-13 00:00:00
Tasks: Heterogeneous Treatment Effect Estimation, regression

Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical challenges, such as personalized medicine and optimal resource allocation. In this paper, we develop a general class of two-step algorithms for heterogeneous treatment effect estimation in observational studies. We first estimate marginal effects and treatment propensities in order to form an objective function that isolates the causal component of the signal. Then, we optimize this data-adaptive objective function. Our approach has several advantages over existing methods. From a practical perspective, our method is flexible and easy to use: In both steps, we can use any loss-minimization method, e.g., penalized regression, deep neural networks, or boosting; moreover, these methods can be fine-tuned by cross validation. Meanwhile, in the case of penalized kernel regression, we show that our method has a quasi-oracle property: Even if the pilot estimates for marginal effects and treatment propensities are not particularly accurate, we achieve the same error bounds as an oracle who has a priori knowledge of these two nuisance components. We implement variants of our approach based on penalized regression, kernel ridge regression, and boosting in a variety of simulation setups, and find promising performance relative to existing baselines.
FFT-Based Deep Learning Deployment in Embedded Systems
Authors: Sheng Lin, Ning Liu, Mahdi Nazemi, Hongjia Li, Caiwen Ding, Yanzhi Wang, Massoud Pedram
Date: 2017-12-13 00:00:00
Tasks: Deep Learning, speech-recognition, Speech Recognition

Deep learning has delivered its powerfulness in many application domains,
especially in image and speech recognition. As the backbone of deep learning,
deep neural networks (DNNs) consist of multiple layers of various types with
hundreds to thousands of neurons. Embedded platforms are now becoming essential
for deep learning deployment due to their portability, versatility, and energy
efficiency. The large model size of DNNs, while providing excellent accuracy,
also burdens the embedded platforms with intensive computation and storage.
Researchers have investigated on reducing DNN model size with negligible
accuracy loss. This work proposes a Fast Fourier Transform (FFT)-based DNN
training and inference model suitable for embedded platforms with reduced
asymptotic complexity of both computation and storage, making our approach
distinguished from existing approaches. We develop the training and inference
algorithms based on FFT as the computing kernel and deploy the FFT-based
inference model on embedded platforms achieving extraordinary processing speed.
Reasoning in Systems with Elements that Randomly Switch Characteristics
Authors: Subhash Kak
Date: 2017-12-13 00:00:00

We examine the issue of stability of probability in reasoning about complex
systems with uncertainty in structure. Normally, propositions are viewed as
probability functions on an abstract random graph where it is implicitly
assumed that the nodes of the graph have stable properties. But what if some of
the nodes change their characteristics? This is a situation that cannot be
covered by abstractions of either static or dynamic sets when these changes
take place at regular intervals. We propose the use of sets with elements that
change, and modular forms are proposed to account for one type of such change.
An expression for the dependence of the mean on the probability of the
switching elements has been determined. The system is also analyzed from the
perspective of decision between different hypotheses. Such sets are likely to
be of use in complex system queries and in analysis of surveys.
Spatial-temporal wind field prediction by Artificial Neural Networks
Authors: Jianan Cao, David J. Farnham, Upmanu Lall
Date: 2017-12-13 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis, Time Series Forecasting

The prediction of near surface wind speed is becoming increasingly vital for
the operation of electrical energy grids as the capacity of installed wind
power grows. The majority of predictive wind speed modeling has focused on
point-based time-series forecasting. Effectively balancing demand and supply in
the presence of distributed wind turbine electricity generation, however,
requires the prediction of wind fields in space and time. Additionally,
predictions of full wind fields are particularly useful for future power
planning such as the optimization of electricity power supply systems. In this
paper, we propose a composite artificial neural network (ANN) model to predict
the 6-hour and 24-hour ahead average wind speed over a large area (~3.15*106
km2). The ANN model consists of a convolutional input layer, a Long Short-Term
Memory (LSTM) hidden layer, and a transposed convolutional layer as the output
layer. We compare the ANN model with two non-parametric models, a null
persistence model and a mean value model, and find that the ANN model has
substantially smaller error than each of these models. Additionally, the ANN
model also generally performs better than integrated autoregressive moving
average models, which are trained for optimal performance in specific
locations.
Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification
Authors: Saining Xie, Chen Sun, Jonathan Huang, Zhuowen Tu, Kevin Murphy
Date: 2017-12-13 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Classification, Action Detection, Action Recognition, Classification, General Classification, image-classification, Image Classification, Representation Learning, Video Classification

Despite the steady progress in video analysis led by the adoption of
convolutional neural networks (CNNs), the relative improvement has been less
drastic as that in 2D static image classification. Three main challenges exist
including spatial (image) feature representation, temporal information
representation, and model/computation complexity. It was recently shown by
Carreira and Zisserman that 3D CNNs, inflated from 2D networks and pretrained
on ImageNet, could be a promising way for spatial and temporal representation
learning. However, as for model/computation complexity, 3D CNNs are much more
expensive than 2D CNNs and prone to overfit. We seek a balance between speed
and accuracy by building an effective and efficient video classification system
through systematic exploration of critical network design choices. In
particular, we show that it is possible to replace many of the 3D convolutions
by low-cost 2D convolutions. Rather surprisingly, best result (in both speed
and accuracy) is achieved when replacing the 3D convolutions at the bottom of
the network, suggesting that temporal representation learning on high-level
semantic features is more useful. Our conclusion generalizes to datasets with
very different properties. When combined with several other cost-effective
designs including separable spatial/temporal convolution and feature gating,
our system results in an effective video classification system that that
produces very competitive results on several action classification benchmarks
(Kinetics, Something-something, UCF101 and HMDB), as well as two action
detection (localization) benchmarks (JHMDB and UCF101-24).
MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features
Authors: Liang-Chieh Chen, Alexander Hermans, George Papandreou, Florian Schroff, Peng Wang, Hartwig Adam
Date: 2017-12-13 00:00:00
Proceeding: CVPR 2018 6
Tasks: Instance Segmentation, Object, object-detection, Object Detection, Prediction, Segmentation, Semantic Segmentation

In this work, we tackle the problem of instance segmentation, the task of
simultaneously solving object detection and semantic segmentation. Towards this
goal, we present a model, called MaskLab, which produces three outputs: box
detection, semantic segmentation, and direction prediction. Building on top of
the Faster-RCNN object detector, the predicted boxes provide accurate
localization of object instances. Within each region of interest, MaskLab
performs foreground/background segmentation by combining semantic and direction
prediction. Semantic segmentation assists the model in distinguishing between
objects of different semantic classes including background, while the direction
prediction, estimating each pixel's direction towards its corresponding center,
allows separating instances of the same semantic class. Moreover, we explore
the effect of incorporating recent successful methods from both segmentation
and detection (i.e. atrous convolution and hypercolumn). Our proposed model is
evaluated on the COCO instance segmentation benchmark and shows comparable
performance with other state-of-art models.
Symbol detection in online handwritten graphics using Faster R-CNN
Authors: Frank D. Julca-Aguilar, Nina S. T. Hirata
Date: 2017-12-13 00:00:00
Tasks: object-detection, Object Detection

Symbol detection techniques in online handwritten graphics (e.g. diagrams and
mathematical expressions) consist of methods specifically designed for a single
graphic type. In this work, we evaluate the Faster R-CNN object detection
algorithm as a general method for detection of symbols in handwritten graphics.
We evaluate different configurations of the Faster R-CNN method, and point out
issues relative to the handwritten nature of the data. Considering the online
recognition context, we evaluate efficiency and accuracy trade-offs of using
Deep Neural Networks of different complexities as feature extractors. We
evaluate the method on publicly available flowchart and mathematical expression
(CROHME-2016) datasets. Results show that Faster R-CNN can be effectively used
on both datasets, enabling the possibility of developing general methods for
symbol detection, and furthermore, general graphic understanding methods that
could be built on top of the algorithm.
Ballpark Crowdsourcing: The Wisdom of Rough Group Comparisons
Authors: Tom Hope, Dafna Shahaf
Date: 2017-12-13 00:00:00
Tasks: BIG-bench Machine Learning

Crowdsourcing has become a popular method for collecting labeled training
data. However, in many practical scenarios traditional labeling can be
difficult for crowdworkers (for example, if the data is high-dimensional or
unintuitive, or the labels are continuous).
  In this work, we develop a novel model for crowdsourcing that can complement
standard practices by exploiting people's intuitions about groups and relations
between them. We employ a recent machine learning setting, called Ballpark
Learning, that can estimate individual labels given only coarse, aggregated
signal over groups of data points. To address the important case of continuous
labels, we extend the Ballpark setting (which focused on classification) to
regression problems. We formulate the problem as a convex optimization problem
and propose fast, simple methods with an innate robustness to outliers.
  We evaluate our methods on real-world datasets, demonstrating how useful
constraints about groups can be harnessed from a crowd of non-experts. Our
methods can rival supervised models trained on many true labels, and can obtain
considerably better results from the crowd than a standard label-collection
process (for a lower price). By collecting rough guesses on groups of instances
and using machine learning to infer the individual labels, our lightweight
framework is able to address core crowdsourcing challenges and train machine
learning models in a cost-effective way.
Logarithmic distributions prove that intrinsic learning is Hebbian
Authors: Gabriele Scheler
Date: 2014-10-21 00:00:00
Tasks: Hippocampus

In this paper, we present data for the lognormal distributions of spike
rates, synaptic weights and intrinsic excitability (gain) for neurons in
various brain areas, such as auditory or visual cortex, hippocampus,
cerebellum, striatum, midbrain nuclei. We find a remarkable consistency of
heavy-tailed, specifically lognormal, distributions for rates, weights and
gains in all brain areas examined. The difference between strongly recurrent
and feed-forward connectivity (cortex vs. striatum and cerebellum),
neurotransmitter (GABA (striatum) or glutamate (cortex)) or the level of
activation (low in cortex, high in Purkinje cells and midbrain nuclei) turns
out to be irrelevant for this feature. Logarithmic scale distribution of
weights and gains appears to be a general, functional property in all cases
analyzed. We then created a generic neural model to investigate adaptive
learning rules that create and maintain lognormal distributions. We
conclusively demonstrate that not only weights, but also intrinsic gains, need
to have strong Hebbian learning in order to produce and maintain the
experimentally attested distributions. This provides a solution to the
long-standing question about the type of plasticity exhibited by intrinsic
excitability.
J-MOD$^{2}$: Joint Monocular Obstacle Detection and Depth Estimation
Authors: Michele Mancini, Gabriele Costante, Paolo Valigi, Thomas A. Ciarfuglia
Date: 2017-09-25 00:00:00
Tasks: Depth Estimation, Scene Understanding, Semantic Segmentation

In this work, we propose an end-to-end deep architecture that jointly learns
to detect obstacles and estimate their depth for MAV flight applications. Most
of the existing approaches either rely on Visual SLAM systems or on depth
estimation models to build 3D maps and detect obstacles. However, for the task
of avoiding obstacles this level of complexity is not required. Recent works
have proposed multi task architectures to both perform scene understanding and
depth estimation. We follow their track and propose a specific architecture to
jointly estimate depth and obstacles, without the need to compute a global map,
but maintaining compatibility with a global SLAM system if needed. The network
architecture is devised to exploit the joint information of the obstacle
detection task, that produces more reliable bounding boxes, with the depth
estimation one, increasing the robustness of both to scenario changes. We call
this architecture J-MOD$^{2}$. We test the effectiveness of our approach with
experiments on sequences with different appearance and focal lengths and
compare it to SotA multi task methods that jointly perform semantic
segmentation and depth estimation. In addition, we show the integration in a
full system using a set of simulated navigation experiments where a MAV
explores an unknown scenario and plans safe trajectories by using our detection
model.
GMM-Based Synthetic Samples for Classification of Hyperspectral Images With Limited Training Data
Authors: AmirAbbas Davari, Erchan Aptoula, Berrin Yanikoglu, Andreas Maier, Christian Riess
Date: 2017-12-13 00:00:00
Tasks: Classification Of Hyperspectral Images, General Classification, Multi-class Classification

The amount of training data that is required to train a classifier scales
with the dimensionality of the feature data. In hyperspectral remote sensing,
feature data can potentially become very high dimensional. However, the amount
of training data is oftentimes limited. Thus, one of the core challenges in
hyperspectral remote sensing is how to perform multi-class classification using
only relatively few training data points.
  In this work, we address this issue by enriching the feature matrix with
synthetically generated sample points. This synthetic data is sampled from a
GMM fitted to each class of the limited training data. Although, the true
distribution of features may not be perfectly modeled by the fitted GMM, we
demonstrate that a moderate augmentation by these synthetic samples can
effectively replace a part of the missing training samples. We show the
efficacy of the proposed approach on two hyperspectral datasets. The median
gain in classification performance is $5\%$. It is also encouraging that this
performance gain is remarkably stable for large variations in the number of
added samples, which makes it much easier to apply this method to real-world
applications.
Multiple testing for outlier detection in functional data
Authors: Clémentine Barreyre, Béatrice Laurent, Jean-Michel Loubes, Bertrand Cabon, Loïc Boussouf
Date: 2017-12-13 00:00:00
Tasks: Dimensionality Reduction, Outlier Detection

We propose a novel procedure for outlier detection in functional data, in a
semi-supervised framework. As the data is functional, we consider the
coefficients obtained after projecting the observations onto orthonormal bases
(wavelet, PCA). A multiple testing procedure based on the two-sample test is
defined in order to highlight the levels of the coefficients on which the
outliers appear as significantly different to the normal data. The selected
coefficients are then called features for the outlier detection, on which we
compute the Local Outlier Factor to highlight the outliers. This procedure to
select the features is applied on simulated data that mimic the behaviour of
space telemetries, and compared with existing dimension reduction techniques.
Exponential convergence of testing error for stochastic gradient methods
Authors: Loucas Pillaud-Vivien, Alessandro Rudi, Francis Bach
Date: 2017-12-13 00:00:00
Tasks: Binary Classification, Classification, General Classification

We consider binary classification problems with positive definite kernels and
square loss, and study the convergence rates of stochastic gradient methods. We
show that while the excess testing loss (squared loss) converges slowly to zero
as the number of observations (and thus iterations) goes to infinity, the
testing error (classification error) converges exponentially fast if low-noise
conditions are assumed.
Mathematics of Deep Learning
Authors: Rene Vidal, Joan Bruna, Raja Giryes, Stefano Soatto
Date: 2017-12-13 00:00:00
Tasks: Deep Learning, General Classification, Representation Learning

Recently there has been a dramatic increase in the performance of recognition
systems due to the introduction of deep architectures for representation
learning and classification. However, the mathematical reasons for this success
remain elusive. This tutorial will review recent work that aims to provide a
mathematical justification for several properties of deep networks, such as
global optimality, geometric stability, and invariance of the learned
representations.
Regularization and Optimization strategies in Deep Convolutional Neural Network
Authors: Pushparaja Murugan, Shanmugasundaram Durairaj
Date: 2017-12-13 00:00:00

Convolution Neural Networks, known as ConvNets exceptionally perform well in
many complex machine learning tasks. The architecture of ConvNets demands the
huge and rich amount of data and involves with a vast number of parameters that
leads the learning takes to be computationally expensive, slow convergence
towards the global minima, trap in local minima with poor predictions. In some
cases, architecture overfits the data and make the architecture difficult to
generalise for new samples that were not in the training set samples. To
address these limitations, many regularization and optimization strategies are
developed for the past few years. Also, studies suggested that these techniques
significantly increase the performance of the networks as well as reducing the
computational cost. In implementing these techniques, one must thoroughly
understand the theoretical concept of how this technique works in increasing
the expressive power of the networks. This article is intended to provide the
theoretical concepts and mathematical formulation of the most commonly used
strategies in developing a ConvNet architecture.
A Quantum Extension of Variational Bayes Inference
Authors: Hideyuki Miyahara, Yuki Sughiyama
Date: 2017-12-13 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

Variational Bayes (VB) inference is one of the most important algorithms in
machine learning and widely used in engineering and industry. However, VB is
known to suffer from the problem of local optima. In this Letter, we generalize
VB by using quantum mechanics, and propose a new algorithm, which we call
quantum annealing variational Bayes (QAVB) inference. We then show that QAVB
drastically improve the performance of VB by applying them to a clustering
problem described by a Gaussian mixture model. Finally, we discuss an intuitive
understanding on how QAVB works well.
An Enhanced Hybrid MobileNet
Authors: Hong-Yen Chen, Chung-Yen Su
Date: 2017-12-13 00:00:00

Complicated and deep neural network models can achieve high accuracy for image recognition. However, they require a huge amount of computations and model parameters, which are not suitable for mobile and embedded devices. Therefore, MobileNet was proposed, which can reduce the number of parameters and computational cost dramatically. The main idea of MobileNet is to use a depthwise separable convolution. Two hyper-parameters, a width multiplier and a resolution multiplier are used to the trade-off between the accuracy and the latency. In this paper, we propose a new architecture to improve the MobileNet. Instead of using the resolution multiplier, we use a depth multiplier and combine with either Fractional Max Pooling or the max pooling. Experimental results on CIFAR database show that the proposed architecture can reduce the amount of computational cost and increase the accuracy simultaneously.