pwc-archive/papers-with-abstracts huggingface dataset:
Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved
Authors: Jiahao Chen, Nathan Kallus, Xiaojie Mao, Geoffry Svacha, Madeleine Udell
Date: 2018-11-27 00:00:00
Tasks: Decision Making, Fairness, Imputation, Missing Labels

Assessing the fairness of a decision making system with respect to a
protected class, such as gender or race, is challenging when class membership
labels are unavailable. Probabilistic models for predicting the protected class
based on observable proxies, such as surname and geolocation for race, are
sometimes used to impute these missing labels for compliance assessments.
Empirically, these methods are observed to exaggerate disparities, but the
reason why is unknown. In this paper, we decompose the biases in estimating
outcome disparity via threshold-based imputation into multiple interpretable
bias sources, allowing us to explain when over- or underestimation occurs. We
also propose an alternative weighted estimator that uses soft classification,
and show that its bias arises simply from the conditional covariance of the
outcome with the true class membership. Finally, we illustrate our results with
numerical simulations and a public dataset of mortgage applications, using
geolocation as a proxy for race. We confirm that the bias of threshold-based
imputation is generally upward, but its magnitude varies strongly with the
threshold chosen. Our new weighted estimator tends to have a negative bias that
is much simpler to analyze and reason about.
Knots in random neural networks
Authors: Kevin K. Chen, Anthony C. Gamst, Alden K. Walker
Date: 2018-11-27 00:00:00

The weights of a neural network are typically initialized at random, and one
can think of the functions produced by such a network as having been generated
by a prior over some function space. Studying random networks, then, is useful
for a Bayesian understanding of the network evolution in early stages of
training. In particular, one can investigate why neural networks with huge
numbers of parameters do not immediately overfit. We analyze the properties of
random scalar-input feed-forward rectified linear unit architectures, which are
random linear splines. With weights and biases sampled from certain common
distributions, empirical tests show that the number of knots in the spline
produced by the network is equal to the number of neurons, to very close
approximation. We describe our progress towards a completely analytic
explanation of this phenomenon. In particular, we show that random single-layer
neural networks are equivalent to integrated random walks with variable step
sizes. That each neuron produces one knot on average is equivalent to the
associated integrated random walk having one zero crossing on average. We
explore how properties of the integrated random walk, including the step sizes
and initial conditions, affect the number of crossings. The number of knots in
random neural networks can be related to the behavior of extreme learning
machines, but it also establishes a prior preventing optimizers from
immediately overfitting to noisy training data.
The Structure of Optimal Private Tests for Simple Hypotheses
Authors: Clément L. Canonne, Gautam Kamath, Audra McMillan, Adam Smith, Jonathan Ullman
Date: 2018-11-27 00:00:00
Tasks: Change Point Detection, Generalization Bounds, LEMMA, Two-sample testing

Hypothesis testing plays a central role in statistical inference, and is used
in many settings where privacy concerns are paramount. This work answers a
basic question about privately testing simple hypotheses: given two
distributions $P$ and $Q$, and a privacy level $\varepsilon$, how many i.i.d.
samples are needed to distinguish $P$ from $Q$ subject to
$\varepsilon$-differential privacy, and what sort of tests have optimal sample
complexity? Specifically, we characterize this sample complexity up to constant
factors in terms of the structure of $P$ and $Q$ and the privacy level
$\varepsilon$, and show that this sample complexity is achieved by a certain
randomized and clamped variant of the log-likelihood ratio test. Our result is
an analogue of the classical Neyman-Pearson lemma in the setting of private
hypothesis testing. We also give an application of our result to the private
change-point detection. Our characterization applies more generally to
hypothesis tests satisfying essentially any notion of algorithmic stability,
which is known to imply strong generalization bounds in adaptive data analysis,
and thus our results have applications even when privacy is not a primary
concern.
Understanding and Improving Kernel Local Descriptors
Authors: Arun Mukundan, Giorgos Tolias, Andrei Bursuc, Hervé Jégou, Ondřej Chum
Date: 2018-11-27 00:00:00
Tasks: Position

We propose a multiple-kernel local-patch descriptor based on efficient match
kernels from pixel gradients. It combines two parametrizations of gradient
position and direction, each parametrization provides robustness to a different
type of patch mis-registration: polar parametrization for noise in the patch
dominant orientation detection, Cartesian for imprecise location of the feature
point. Combined with whitening of the descriptor space, that is learned with or
without supervision, the performance is significantly improved. We analyze the
effect of the whitening on patch similarity and demonstrate its semantic
meaning. Our unsupervised variant is the best performing descriptor constructed
without the need of labeled data. Despite the simplicity of the proposed
descriptor, it competes well with deep learning approaches on a number of
different tasks.
Reliable uncertainty estimate for antibiotic resistance classification with Stochastic Gradient Langevin Dynamics
Authors: Md-Nafiz Hamid, Iddo Friedberg
Date: 2018-11-27 00:00:00
Tasks: Deep Learning, General Classification

Antibiotic resistance monitoring is of paramount importance in the face of
this on-going global epidemic. Deep learning models trained with traditional
optimization algorithms (e.g. Adam, SGD) provide poor posterior estimates when
tested against out-of-distribution (OoD) antibiotic resistant/non-resistant
genes. In this paper, we introduce a deep learning model trained with
Stochastic Gradient Langevin Dynamics (SGLD) to classify antibiotic resistant
genes. The model provides better uncertainty estimates when tested against OoD
data compared to traditional optimization methods such as Adam.
SOC: hunting the underground inside story of the ethereum Social-network Opinion and Comment
Authors: TonTon Hsien-De Huang, Po-Wei Hong, Ying-Tse Lee, Yi-Lun Wang, Chi-Leong Lok, Hung-Yu Kao
Date: 2018-11-27 00:00:00
Tasks: Sentiment Analysis

The cryptocurrency is attracting more and more attention because of the
blockchain technology. Ethereum is gaining a significant popularity in
blockchain community, mainly due to the fact that it is designed in a way that
enables developers to write smart contracts and decentralized applications
(Dapps). There are many kinds of cryptocurrency information on the social
network. The risks and fraud problems behind it have pushed many countries
including the United States, South Korea, and China to make warnings and set up
corresponding regulations. However, the security of Ethereum smart contracts
has not gained much attention. Through the Deep Learning approach, we propose a
method of sentiment analysis for Ethereum's community comments. In this
research, we first collected the users' cryptocurrency comments from the social
network and then fed to our LSTM + CNN model for training. Then we made
prediction through sentiment analysis. With our research result, we have
demonstrated that both the precision and the recall of sentiment analysis can
achieve 0.80+. More importantly, we deploy our sentiment analysis1 on
RatingToken and Coin Master (mobile application of Cheetah Mobile Blockchain
Security Center23). We can effectively provide detail information to resolve
the risks of being fake and fraud problems.
Real-time high speed motion prediction using fast aperture-robust event-driven visual flow
Authors: Himanshu Akolkar, Sio-Hoi Ieng, Ryad Benosman
Date: 2018-11-27 00:00:00
Tasks: Motion Estimation, motion prediction, Optical Flow Estimation

Optical flow is a crucial component of the feature space for early visual processing of dynamic scenes especially in new applications such as self-driving vehicles, drones and autonomous robots. The dynamic vision sensors are well suited for such applications because of their asynchronous, sparse and temporally precise representation of the visual dynamics. Many algorithms proposed for computing visual flow for these sensors suffer from the aperture problem as the direction of the estimated flow is governed by the curvature of the object rather than the true motion direction. Some methods that do overcome this problem by temporal windowing under-utilize the true precise temporal nature of the dynamic sensors. In this paper, we propose a novel multi-scale plane fitting based visual flow algorithm that is robust to the aperture problem and also computationally fast and efficient. Our algorithm performs well in many scenarios ranging from fixed camera recording simple geometric shapes to real world scenarios such as camera mounted on a moving car and can successfully perform event-by-event motion estimation of objects in the scene to allow for predictions of upto 500 ms i.e. equivalent to 10 to 25 frames with traditional cameras.
Unprocessing Images for Learned Raw Denoising
Authors: Tim Brooks, Ben Mildenhall, Tianfan Xue, Jiawen Chen, Dillon Sharlet, Jonathan T. Barron
Date: 2018-11-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: Denoising, Image Denoising, Noise Estimation, Tone Mapping

Machine learning techniques work best when the data used for training
resembles the data used for evaluation. This holds true for learned
single-image denoising algorithms, which are applied to real raw camera sensor
readings but, due to practical constraints, are often trained on synthetic
image data. Though it is understood that generalizing from synthetic to real
data requires careful consideration of the noise properties of image sensors,
the other aspects of a camera's image processing pipeline (gain, color
correction, tone mapping, etc) are often overlooked, despite their significant
effect on how raw measurements are transformed into finished images. To address
this, we present a technique to "unprocess" images by inverting each step of an
image processing pipeline, thereby allowing us to synthesize realistic raw
sensor measurements from commonly available internet photos. We additionally
model the relevant components of an image processing pipeline when evaluating
our loss function, which allows training to be aware of all relevant
photometric processing that will occur after denoising. By processing and
unprocessing model outputs and training data in this way, we are able to train
a simple convolutional neural network that has 14%-38% lower error rates and is
9x-18x faster than the previous state of the art on the Darmstadt Noise
Dataset, and generalizes to sensors outside of that dataset as well.
LEASGD: an Efficient and Privacy-Preserving Decentralized Algorithm for Distributed Learning
Authors: Hsin-Pai Cheng, Patrick Yu, Haojing Hu, Feng Yan, Shi-Yu Li, Hai Li, Yiran Chen
Date: 2018-11-27 00:00:00
Tasks: Privacy Preserving

Distributed learning systems have enabled training large-scale models over
large amount of data in significantly shorter time. In this paper, we focus on
decentralized distributed deep learning systems and aim to achieve differential
privacy with good convergence rate and low communication cost. To achieve this
goal, we propose a new learning algorithm LEASGD (Leader-Follower Elastic
Averaging Stochastic Gradient Descent), which is driven by a novel
Leader-Follower topology and a differential privacy model.We provide a
theoretical analysis of the convergence rate and the trade-off between the
performance and privacy in the private setting.The experimental results show
that LEASGD outperforms state-of-the-art decentralized learning algorithm DPSGD
by achieving steadily lower loss within the same iterations and by reducing the
communication cost by 30%. In addition, LEASGD spends less differential privacy
budget and has higher final accuracy result than DPSGD under private setting.
Bayesian graph convolutional neural networks for semi-supervised classification
Authors: Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Üstebay
Date: 2018-11-27 00:00:00
Tasks: Classification, General Classification, Graph Classification, Matrix Completion

Recently, techniques for applying convolutional neural networks to
graph-structured data have emerged. Graph convolutional neural networks (GCNNs)
have been used to address node and graph classification and matrix completion.
Although the performance has been impressive, the current implementations have
limited capability to incorporate uncertainty in the graph structure. Almost
all GCNNs process a graph as though it is a ground-truth depiction of the
relationship between nodes, but often the graphs employed in applications are
themselves derived from noisy data or modelling assumptions. Spurious edges may
be included; other edges may be missing between nodes that have very strong
relationships. In this paper we adopt a Bayesian approach, viewing the observed
graph as a realization from a parametric family of random graphs. We then
target inference of the joint posterior of the random graph parameters and the
node (or graph) labels. We present the Bayesian GCNN framework and develop an
iterative learning procedure for the case of assortative mixed-membership
stochastic block models. We present the results of experiments that demonstrate
that the Bayesian formulation can provide better performance when there are
very few labels available during the training process.
Learning to detect dysarthria from raw speech
Authors: Juliette Millet, Neil Zeghidour
Date: 2018-11-27 00:00:00
Tasks: General Classification, Sentence, speech-recognition, Speech Recognition

Speech classifiers of paralinguistic traits traditionally learn from diverse
hand-crafted low-level features, by selecting the relevant information for the
task at hand. We explore an alternative to this selection, by learning jointly
the classifier, and the feature extraction. Recent work on speech recognition
has shown improved performance over speech features by learning from the
waveform. We extend this approach to paralinguistic classification and propose
a neural network that can learn a filterbank, a normalization factor and a
compression power from the raw speech, jointly with the rest of the
architecture. We apply this model to dysarthria detection from sentence-level
audio recordings. Starting from a strong attention-based baseline on which
mel-filterbanks outperform standard low-level descriptors, we show that
learning the filters or the normalization and compression improves over fixed
features by 10% absolute accuracy. We also observe a gain over OpenSmile
features by learning jointly the feature extraction, the normalization, and the
compression factor with the architecture. This constitutes a first attempt at
learning jointly all these operations from raw audio for a speech
classification task.
Bilinear Parameterization For Differentiable Rank-Regularization
Authors: Marcus Valtonen Örnhag, Carl Olsson, Anders Heyden
Date: 2018-11-27 00:00:00
Tasks: Second-order methods

Low rank approximation is a commonly occurring problem in many computer vision and machine learning applications. There are two common ways of optimizing the resulting models. Either the set of matrices with a given rank can be explicitly parametrized using a bilinear factorization, or low rank can be implicitly enforced using regularization terms penalizing non-zero singular values. While the former approach results in differentiable problems that can be efficiently optimized using local quadratic approximation, the latter is typically not differentiable (sometimes even discontinuous) and requires first order subgradient or splitting methods. It is well known that gradient based methods exhibit slow convergence for ill-conditioned problems. In this paper we show how many non-differentiable regularization methods can be reformulated into smooth objectives using bilinear parameterization. This allows us to use standard second order methods, such as Levenberg--Marquardt (LM) and Variable Projection (VarPro), to achieve accurate solutions for ill-conditioned cases. We show on several real and synthetic experiments that our second order formulation converges to substantially more accurate solutions than competing state-of-the-art methods.
Generative Adversarial Network Training is a Continual Learning Problem
Authors: Kevin J Liang, Chunyuan Li, Guoyin Wang, Lawrence Carin
Date: 2018-11-27 00:00:00
Proceeding: ICLR 2019 5
Tasks: Continual Learning, Generative Adversarial Network, Text Generation

Generative Adversarial Networks (GANs) have proven to be a powerful framework
for learning to draw samples from complex distributions. However, GANs are also
notoriously difficult to train, with mode collapse and oscillations a common
problem. We hypothesize that this is at least in part due to the evolution of
the generator distribution and the catastrophic forgetting tendency of neural
networks, which leads to the discriminator losing the ability to remember
synthesized samples from previous instantiations of the generator. Recognizing
this, our contributions are twofold. First, we show that GAN training makes for
a more interesting and realistic benchmark for continual learning methods
evaluation than some of the more canonical datasets. Second, we propose
leveraging continual learning techniques to augment the discriminator,
preserving its ability to recognize previous generator samples. We show that
the resulting methods add only a light amount of computation, involve minimal
changes to the model, and result in better overall performance on the examined
image and text generation tasks.
Automatic Face Aging in Videos via Deep Reinforcement Learning
Authors: Chi Nhan Duong, Khoa Luu, Kha Gia Quach, Nghia Nguyen, Eric Patterson, Tien D. Bui, Ngan Le
Date: 2018-11-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: Deep Reinforcement Learning, Face Verification, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper presents a novel approach to synthesize automatically
age-progressed facial images in video sequences using Deep Reinforcement
Learning. The proposed method models facial structures and the longitudinal
face-aging process of given subjects coherently across video frames. The
approach is optimized using a long-term reward, Reinforcement Learning function
with deep feature extraction from Deep Convolutional Neural Network. Unlike
previous age-progression methods that are only able to synthesize an aged
likeness of a face from a single input image, the proposed approach is capable
of age-progressing facial likenesses in videos with consistently synthesized
facial features across frames. In addition, the deep reinforcement learning
method guarantees preservation of the visual identity of input faces after
age-progression. Results on videos of our new collected aging face AGFW-v2
database demonstrate the advantages of the proposed solution in terms of both
quality of age-progressed faces, temporal smoothness, and cross-age face
verification.
MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices
Authors: Chi Nhan Duong, Kha Gia Quach, Ibsa Jalata, Ngan Le, Khoa Luu
Date: 2018-11-27 00:00:00
Tasks: Deep Learning, Face Recognition, GPU

Deep neural networks have been widely used in numerous computer vision
applications, particularly in face recognition. However, deploying deep neural
network face recognition on mobile devices has recently become a trend but
still limited since most high-accuracy deep models are both time and GPU
consumption in the inference stage. Therefore, developing a lightweight deep
neural network is one of the most practical solutions to deploy face
recognition on mobile devices. Such the lightweight deep neural network
requires efficient memory with small number of weights representation and low
cost operators. In this paper, a novel deep neural network named MobiFace, a
simple but effective approach, is proposed for productively deploying face
recognition on mobile devices. The experimental results have shown that our
lightweight MobiFace is able to achieve high performance with 99.73% on LFW
database and 91.3% on large-scale challenging Megaface database. It is also
eventually competitive against large-scale deep-networks face recognition while
significant reducing computational time and memory consumption.
Robust Classification of Financial Risk
Authors: Suproteem K. Sarkar, Kojin Oshiba, Daniel Giebisch, Yaron Singer
Date: 2018-11-27 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Decision Making, General Classification, Robust classification

Algorithms are increasingly common components of high-impact decision-making,
and a growing body of literature on adversarial examples in laboratory settings
indicates that standard machine learning models are not robust. This suggests
that real-world systems are also susceptible to manipulation or
misclassification, which especially poses a challenge to machine learning
models used in financial services. We use the loan grade classification problem
to explore how machine learning models are sensitive to small changes in
user-reported data, using adversarial attacks documented in the literature and
an original, domain-specific attack. Our work shows that a robust optimization
algorithm can build models for financial services that are resistant to
misclassification on perturbations. To the best of our knowledge, this is the
first study of adversarial attacks and defenses for deep learning in financial
services.
Refined WaveNet Vocoder for Variational Autoencoder Based Voice Conversion
Authors: Wen-Chin Huang, Yi-Chiao Wu, Hsin-Te Hwang, Patrick Lumban Tobing, Tomoki Hayashi, Kazuhiro Kobayashi, Tomoki Toda, Yu Tsao, Hsin-Min Wang
Date: 2018-11-27 00:00:00
Tasks: Voice Conversion

This paper presents a refinement framework of WaveNet vocoders for variational autoencoder (VAE) based voice conversion (VC), which reduces the quality distortion caused by the mismatch between the training data and testing data. Conventional WaveNet vocoders are trained with natural acoustic features but conditioned on the converted features in the conversion stage for VC, and such a mismatch often causes significant quality and similarity degradation. In this work, we take advantage of the particular structure of VAEs to refine WaveNet vocoders with the self-reconstructed features generated by VAE, which are of similar characteristics with the converted features while having the same temporal structure with the target natural features. We analyze these features and show that the self-reconstructed features are similar to the converted features. Objective and subjective experimental results demonstrate the effectiveness of our proposed framework.