pwc-archive/papers-with-abstracts huggingface dataset:
Censored Quantile Regression Forests
Authors: Alexander Hanbo Li, Jelena Bradic
Date: 2019-02-08 00:00:00
Tasks: quantile regression, regression

Random forests are powerful non-parametric regression method but are severely
limited in their usage in the presence of randomly censored observations, and
naively applied can exhibit poor predictive performance due to the incurred
biases. Based on a local adaptive representation of random forests, we develop
its regression adjustment for randomly censored regression quantile models.
Regression adjustment is based on new estimating equations that adapt to
censoring and lead to quantile score whenever the data do not exhibit
censoring. The proposed procedure named censored quantile regression forest,
allows us to estimate quantiles of time-to-event without any parametric
modeling assumption. We establish its consistency under mild model
specifications. Numerical studies showcase a clear advantage of the proposed
procedure.
Architecture Compression
Authors: Anubhav Ashok
Date: 2019-02-08 00:00:00
Tasks: Decoder, Model Compression

In this paper we propose a novel approach to model compression termed
Architecture Compression. Instead of operating on the weight or filter space of
the network like classical model compression methods, our approach operates on
the architecture space. A 1-D CNN encoder-decoder is trained to learn a mapping
from discrete architecture space to a continuous embedding and back.
Additionally, this embedding is jointly trained to regress accuracy and
parameter count in order to incorporate information about the architecture's
effectiveness on the dataset. During the compression phase, we first encode the
network and then perform gradient descent in continuous space to optimize a
compression objective function that maximizes accuracy and minimizes parameter
count. The final continuous feature is then mapped to a discrete architecture
using the decoder. We demonstrate the merits of this approach on visual
recognition tasks such as CIFAR-10, CIFAR-100, Fashion-MNIST and SVHN and
achieve a greater than 20x compression on CIFAR-10.
A simple and efficient architecture for trainable activation functions
Authors: Andrea Apicella, Francesco Isgrò, Roberto Prevete
Date: 2019-02-08 00:00:00

Learning automatically the best activation function for the task is an active
topic in neural network research. At the moment, despite promising results, it
is still difficult to determine a method for learning an activation function
that is at the same time theoretically simple and easy to implement. Moreover,
most of the methods proposed so far introduce new parameters or adopt different
learning techniques. In this work we propose a simple method to obtain trained
activation function which adds to the neural network local subnetworks with a
small amount of neurons. Experiments show that this approach could lead to
better result with respect to using a pre-defined activation function, without
introducing a large amount of extra parameters that need to be learned.
FERAtt: Facial Expression Recognition with Attention Net
Authors: Pedro D. Marrero Fernandez, Fidel A. Guerrero Peña, Tsang Ing Ren, Alexandre Cunha
Date: 2019-02-08 00:00:00
Tasks: Decoder, Facial Expression Recognition, Facial Expression Recognition (FER), General Classification

We present a new end-to-end network architecture for facial expression
recognition with an attention model. It focuses attention in the human face and
uses a Gaussian space representation for expression recognition. We devise this
architecture based on two fundamental complementary components: (1) facial
image correction and attention and (2) facial expression representation and
classification. The first component uses an encoder-decoder style network and a
convolutional feature extractor that are pixel-wise multiplied to obtain a
feature attention map. The second component is responsible for obtaining an
embedded representation and classification of the facial expression. We propose
a loss function that creates a Gaussian structure on the representation space.
To demonstrate the proposed method, we create two larger and more comprehensive
synthetic datasets using the traditional BU3DFE and CK+ facial datasets. We
compared results with the PreActResNet18 baseline. Our experiments on these
datasets have shown the superiority of our approach in recognizing facial
expressions.
Machine learning and chord based feature engineering for genre prediction in popular Brazilian music
Authors: Bruna D. Wundervald, Walmes M. Zeviani
Date: 2019-02-08 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering, Music Genre Recognition

Music genre can be hard to describe: many factors are involved, such as
style, music technique, and historical context. Some genres even have
overlapping characteristics. Looking for a better understanding of how music
genres are related to musical harmonic structures, we gathered data about the
music chords for thousands of popular Brazilian songs. Here, 'popular' does not
only refer to the genre named MPB (Brazilian Popular Music) but to nine
different genres that were considered particular to the Brazilian case. The
main goals of the present work are to extract and engineer harmonically related
features from chords data and to use it to classify popular Brazilian music
genres towards establishing a connection between harmonic relationships and
Brazilian genres. We also emphasize the generalization of the method for
obtaining the data, allowing for the replication and direct extension of this
work. Our final model is a combination of multiple classification trees, also
known as the random forest model. We found that features extracted from
harmonic elements can satisfactorily predict music genre for the Brazilian
case, as well as features obtained from the Spotify API. The variables
considered in this work also give an intuition about how they relate to the
genres.
Learning Ontologies with Epistemic Reasoning: The EL Case
Authors: Ana Ozaki, Nicolas Troquard
Date: 2019-02-08 00:00:00
Tasks: Epistemic Reasoning

We investigate the problem of learning description logic ontologies from
entailments via queries, using epistemic reasoning. We introduce a new learning
model consisting of epistemic membership and example queries and show that
polynomial learnability in this model coincides with polynomial learnability in
Angluin's exact learning model with membership and equivalence queries. We then
instantiate our learning framework to EL and show some complexity results for
an epistemic extension of EL where epistemic operators can be applied over the
axioms. Finally, we transfer known results for EL ontologies and its fragments
to our learning model based on epistemic reasoning.
Scalable Holistic Linear Regression
Authors: Dimitris Bertsimas, Michael Lingzhi Li
Date: 2019-02-08 00:00:00
Tasks: regression

We propose a new scalable algorithm for holistic linear regression building on Bertsimas & King (2016). Specifically, we develop new theory to model significance and multicollinearity as lazy constraints rather than checking the conditions iteratively. The resulting algorithm scales with the number of samples $n$ in the 10,000s, compared to the low 100s in the previous framework. Computational results on real and synthetic datasets show it greatly improves from previous algorithms in accuracy, false detection rate, computational time and scalability.
Does the "Artificial Intelligence Clinician" learn optimal treatment strategies for sepsis in intensive care?
Authors: Russell Jeter, Christopher Josef, Supreeth Shashikumar, Shamim Nemati
Date: 2019-02-08 00:00:00
Tasks: BIG-bench Machine Learning

From 2017 to 2018 the number of scientific publications found via PubMed
search using the keyword "Machine Learning" increased by 46% (4,317 to 6,307).
The results of studies involving machine learning, artificial intelligence
(AI), and big data have captured the attention of healthcare practitioners,
healthcare managers, and the public at a time when Western medicine grapples
with unmitigated cost increases and public demands for accountability. The
complexity involved in healthcare applications of machine learning and the size
of the associated data sets has afforded many researchers an uncontested
opportunity to satisfy these demands with relatively little oversight. In a
recent Nature Medicine article, "The Artificial Intelligence Clinician learns
optimal treatment strategies for sepsis in intensive care," Komorowski and his
coauthors propose methods to train an artificial intelligence clinician to
treat sepsis patients with vasopressors and IV fluids. In this post, we will
closely examine the claims laid out in this paper. In particular, we will study
the individual treatment profiles suggested by their AI Clinician to gain
insight into how their AI Clinician intends to treat patients on an individual
level.
Discovering Context Effects from Raw Choice Data
Authors: Arjun Seshadri, Alexander Peysakhovich, Johan Ugander
Date: 2019-02-08 00:00:00

Many applications in preference learning assume that decisions come from the maximization of a stable utility function. Yet a large experimental literature shows that individual choices and judgements can be affected by "irrelevant" aspects of the context in which they are made. An important class of such contexts is the composition of the choice set. In this work, our goal is to discover such choice set effects from raw choice data. We introduce an extension of the Multinomial Logit (MNL) model, called the context dependent random utility model (CDM), which allows for a particular class of choice set effects. We show that the CDM can be thought of as a second-order approximation to a general choice system, can be inferred optimally using maximum likelihood and, importantly, is easily interpretable. We apply the CDM to both real and simulated choice data to perform principled exploratory analyses for the presence of choice set effects.
FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary
Authors: Yingzhen Yang, Jiahui Yu, Nebojsa Jojic, Jun Huan, Thomas S. Huang
Date: 2019-02-08 00:00:00
Proceeding: ICLR 2020 1
Tasks: General Classification, image-classification, Image Classification, Neural Architecture Search, object-detection, Object Detection, Quantization

We present a novel method of compression of deep Convolutional Neural Networks (CNNs) by weight sharing through a new representation of convolutional filters. The proposed method reduces the number of parameters of each convolutional layer by learning a 1D vector termed Filter Summary (FS). The convolutional filters are located in FS as overlapping 1D segments, and nearby filters in FS share weights in their overlapping regions in a natural way. The resultant neural network based on such weight sharing scheme, termed Filter Summary CNNs or FSNet, has a FS in each convolution layer instead of a set of independent filters in the conventional convolution layer. FSNet has the same architecture as that of the baseline CNN to be compressed, and each convolution layer of FSNet has the same number of filters from FS as that of the basline CNN in the forward process. With compelling computational acceleration ratio, the parameter space of FSNet is much smaller than that of the baseline CNN. In addition, FSNet is quantization friendly. FSNet with weight quantization leads to even higher compression ratio without noticeable performance loss. We further propose Differentiable FSNet where the way filters share weights is learned in a differentiable and end-to-end manner. Experiments demonstrate the effectiveness of FSNet in compression of CNNs for computer vision tasks including image classification and object detection, and the effectiveness of DFSNet is evidenced by the task of Neural Architecture Search.
Skin Lesion Synthesis with Generative Adversarial Networks
Authors: Alceu Bissoto, Fábio Perez, Eduardo Valle, Sandra Avila
Date: 2019-02-08 00:00:00
Tasks: Cancer Classification, General Classification, Medical Image Generation, Skin Cancer Classification

Skin cancer is by far the most common type of cancer. Early detection is the
key to increase the chances for successful treatment significantly. Currently,
Deep Neural Networks are the state-of-the-art results on automated skin cancer
classification. To push the results further, we need to address the lack of
annotated data, which is expensive and require much effort from specialists. To
bypass this problem, we propose using Generative Adversarial Networks for
generating realistic synthetic skin lesion images. To the best of our
knowledge, our results are the first to show visually-appealing synthetic
images that comprise clinically-meaningful information.
Invariant-equivariant representation learning for multi-class data
Authors: Ilya Feige
Date: 2019-02-08 00:00:00
Proceeding: ICLR 2019 5
Tasks: Representation Learning

Representations learnt through deep neural networks tend to be highly informative, but opaque in terms of what information they learn to encode. We introduce an approach to probabilistic modelling that learns to represent data with two separate deep representations: an invariant representation that encodes the information of the class from which the data belongs, and an equivariant representation that encodes the symmetry transformation defining the particular data point within the class manifold (equivariant in the sense that the representation varies naturally with symmetry transformations). This approach is based primarily on the strategic routing of data through the two latent variables, and thus is conceptually transparent, easy to implement, and in-principle generally applicable to any data comprised of discrete classes of continuous distributions (e.g. objects in images, topics in language, individuals in behavioural data). We demonstrate qualitatively compelling representation learning and competitive quantitative performance, in both supervised and semi-supervised settings, versus comparable modelling approaches in the literature with little fine tuning.
Insertion Transformer: Flexible Sequence Generation via Insertion Operations
Authors: Mitchell Stern, William Chan, Jamie Kiros, Jakob Uszkoreit
Date: 2019-02-08 00:00:00
Tasks: Machine Translation, Translation, valid

We present the Insertion Transformer, an iterative, partially autoregressive
model for sequence generation based on insertion operations. Unlike typical
autoregressive models which rely on a fixed, often left-to-right ordering of
the output, our approach accommodates arbitrary orderings by allowing for
tokens to be inserted anywhere in the sequence during decoding. This
flexibility confers a number of advantages: for instance, not only can our
model be trained to follow specific orderings such as left-to-right generation
or a binary tree traversal, but it can also be trained to maximize entropy over
all valid insertions for robustness. In addition, our model seamlessly
accommodates both fully autoregressive generation (one insertion at a time) and
partially autoregressive generation (simultaneous insertions at multiple
locations). We validate our approach by analyzing its performance on the WMT
2014 English-German machine translation task under various settings for
training and decoding. We find that the Insertion Transformer outperforms many
prior non-autoregressive approaches to translation at comparable or better
levels of parallelism, and successfully recovers the performance of the
original Transformer while requiring only logarithmically many iterations
during decoding.
Ask Not What AI Can Do, But What AI Should Do: Towards a Framework of Task Delegability
Authors: Brian Lubars, Chenhao Tan
Date: 2019-02-08 00:00:00
Proceeding: NeurIPS 2019 12

While artificial intelligence (AI) holds promise for addressing societal challenges, issues of exactly which tasks to automate and to what extent to do so remain understudied. We approach this problem of task delegability from a human-centered perspective by developing a framework on human perception of task delegation to AI. We consider four high-level factors that can contribute to a delegation decision: motivation, difficulty, risk, and trust. To obtain an empirical understanding of human preferences in different tasks, we build a dataset of 100 tasks from academic papers, popular media portrayal of AI, and everyday life, and administer a survey based on our proposed framework. We find little preference for full AI control and a strong preference for machine-in-the-loop designs, in which humans play the leading role. Among the four factors, trust is the most correlated with human preferences of optimal human-machine delegation. This framework represents a first step towards characterizing human preferences of AI automation across tasks. We hope this work encourages future efforts towards understanding such individual attitudes; our goal is to inform the public and the AI research community rather than dictating any direction in technology development.
Speech enhancement with variational autoencoders and alpha-stable distributions
Authors: Simon Leglaive, Umut Simsekli, Antoine Liutkus, Laurent Girin, Radu Horaud
Date: 2019-02-08 00:00:00
Tasks: Speech Enhancement

This paper focuses on single-channel semi-supervised speech enhancement. We
learn a speaker-independent deep generative speech model using the framework of
variational autoencoders. The noise model remains unsupervised because we do
not assume prior knowledge of the noisy recording environment. In this context,
our contribution is to propose a noise model based on alpha-stable
distributions, instead of the more conventional Gaussian non-negative matrix
factorization approach found in previous studies. We develop a Monte Carlo
expectation-maximization algorithm for estimating the model parameters at test
time. Experimental results show the superiority of the proposed approach both
in terms of perceptual quality and intelligibility of the enhanced speech
signal.
Progressive Focus Search for the Static and Stochastic VRPTW with both Random Customers and Reveal Times
Authors: Michael Saint-Guillain, Christine Solnon, Yves Deville
Date: 2019-02-08 00:00:00
Tasks: Stochastic Optimization

Static stochastic VRPs aim at modeling real-life VRPs by considering
uncertainty on data. In particular, the SS-VRPTW-CR considers stochastic
customers with time windows and does not make any assumption on their reveal
times, which are stochastic as well. Based on customer request probabilities,
we look for an a priori solution composed preventive vehicle routes, minimizing
the expected number of unsatisfied customer requests at the end of the day. A
route describes a sequence of strategic vehicle relocations, from which nearby
requests can be rapidly reached. Instead of reoptimizing online, a so-called
recourse strategy defines the way the requests are handled, whenever they
appear. In this paper, we describe a new recourse strategy for the SS-VRPTW-CR,
improving vehicle routes by skipping useless parts. We show how to compute the
expected cost of a priori solutions, in pseudo-polynomial time, for this
recourse strategy. We introduce a new meta-heuristic, called Progressive Focus
Search (PFS), which may be combined with any local-search based algorithm for
solving static stochastic optimization problems. PFS accelerates the search by
using approximation factors: from an initial rough simplified problem, the
search progressively focuses to the actual problem description. We evaluate our
contributions on a new, real-world based, public benchmark.
Prediction of Dashed Café Wall illusion by the Classical Receptive Field Model
Authors: Nasim Nematzadeh, David M. W. Powers
Date: 2019-02-08 00:00:00

The Caf\'e Wall illusion is one of a class of tilt illusions where lines that are parallel appear to be tilted. We demonstrate that a simple Differences of Gaussian model provides an explanatory mechanism for the illusory tilt perceived in a family of Caf\'e Wall illusion generalizes to the dashed versions of Caf\'e Wall. Our explanation models the visual mechanisms in low level stages and the lateral inhibition of simple cells that can reveal tilt cues in Geometrical distortion illusions such as Tile illusions particularly Caf\'e Wall illusions. For this, we simulate the activations of the retinal/cortical simple cells in responses to these patterns based on a Classical Receptive Field (CRF) model (referred to as Vis-CRF) to explain tilt effects in these illusions. Previously, it was assumed that all these visual experiences of tilt arise from the orientation selectivity properties described for more complex cortical cells. An estimation of an overall tilt angle perceived in these illusions is based on the integration of the local tilts detected by simple cells which is presumed to be a key mechanism utilized by the complex cells to create our final perception of tilt.
Land Use Classification Using Multi-neighborhood LBPs
Authors: Harjot Singh Parmar
Date: 2019-02-07 00:00:00
Tasks: Classification, General Classification

In this paper we propose the use of multiple local binary patterns(LBPs) to
effectively classify land use images. We use the UC Merced 21 class land use
image dataset. Task is challenging for classification as the dataset contains
intra class variability and inter class similarities. Our proposed method of
using multi-neighborhood LBPs combined with nearest neighbor classifier is able
to achieve an accuracy of 77.76%. Further class wise analysis is conducted and
suitable suggestion are made for further improvements to classification
accuracy.
Learning V1 Simple Cells with Vector Representation of Local Content and Matrix Representation of Local Motion
Authors: Ruiqi Gao, Jianwen Xie, Siyuan Huang, Yufan Ren, Song-Chun Zhu, Ying Nian Wu
Date: 2019-01-24 00:00:00
Tasks: Optical Flow Estimation

This paper proposes a representational model for image pairs such as consecutive video frames that are related by local pixel displacements, in the hope that the model may shed light on motion perception in primary visual cortex (V1). The model couples the following two components: (1) the vector representations of local contents of images and (2) the matrix representations of local pixel displacements caused by the relative motions between the agent and the objects in the 3D scene. When the image frame undergoes changes due to local pixel displacements, the vectors are multiplied by the matrices that represent the local displacements. Thus the vector representation is equivariant as it varies according to the local displacements. Our experiments show that our model can learn Gabor-like filter pairs of quadrature phases. The profiles of the learned filters match those of simple cells in Macaque V1. Moreover, we demonstrate that the model can learn to infer local motions in either a supervised or unsupervised manner. With such a simple model, we achieve competitive results on optical flow estimation.
Dynamic Clearing and Contagion in Financial Networks
Authors: Tathagata Banerjee, Alex Bernstein, Zachary Feinstein
Date: 2018-01-06 00:00:00

In this paper we introduce a generalized extension of the Eisenberg-Noe model of financial contagion to allow for time dynamics of the interbank liabilities, including a dynamic examination of default risk. This framework separates the cash account and long-term capital account to more accurately model the health of a financial institution. In doing so, such a system allows us to distinguish between delinquency and default as well as between defaults resulting from either insolvency or illiquidity.