pwc-archive/papers-with-abstracts huggingface dataset:
Motion Equivariant Networks for Event Cameras with the Temporal Normalization Transform
Authors: Alex Zihao Zhu, ZiYun Wang, Kostas Daniilidis
Date: 2019-02-18 00:00:00
Tasks: Data Augmentation, General Classification, Optical Flow Estimation

In this work, we propose a novel transformation for events from an event
camera that is equivariant to optical flow under convolutions in the 3-D
spatiotemporal domain. Events are generated by changes in the image, which are
typically due to motion, either of the camera or the scene. As a result,
different motions result in a different set of events. For learning based tasks
based on a static scene such as classification which directly use the events,
we must either rely on the learning method to learn the underlying object
distinct from the motion, or to memorize all possible motions for each object
with extensive data augmentation. Instead, we propose a novel transformation of
the input event data which normalizes the $x$ and $y$ positions by the
timestamp of each event. We show that this transformation generates a
representation of the events that is equivariant to this motion when the
optical flow is constant, allowing a deep neural network to learn the
classification task without the need for expensive data augmentation. We test
our method on the event based N-MNIST dataset, as well as a novel dataset
N-MOVING-MNIST, with significantly more variety in motion compared to the
standard N-MNIST dataset. In all sequences, we demonstrate that our transformed
network is able to achieve similar or better performance compared to a network
with a standard volumetric event input, and performs significantly better when
the test set has a larger set of motions than seen at training.
Data augmentation for low resource sentiment analysis using generative adversarial networks
Authors: Rahul Gupta
Date: 2019-02-18 00:00:00
Tasks: Data Augmentation, Sentiment Analysis, Text Generation

Sentiment analysis is a task that may suffer from a lack of data in certain
cases, as the datasets are often generated and annotated by humans. In cases
where data is inadequate for training discriminative models, generate models
may aid training via data augmentation. Generative Adversarial Networks (GANs)
are one such model that has advanced the state of the art in several tasks,
including as image and text generation. In this paper, I train GAN models on
low resource datasets, then use them for the purpose of data augmentation
towards improving sentiment classifier generalization. Given the constraints of
limited data, I explore various techniques to train the GAN models. I also
present an analysis of the quality of generated GAN data as more training data
for the GAN is made available. In this analysis, the generated data is
evaluated as a test set (against a model trained on real data points) as well
as a training set to train classification models. Finally, I also conduct a
visual analysis by projecting the generated and the real data into a
two-dimensional space using the t-Distributed Stochastic Neighbor Embedding
(t-SNE) method.
Democratisation of Usable Machine Learning in Computer Vision
Authors: Raymond Bond, Ansgar Koene, Alan Dix, Jennifer Boger, Maurice D. Mulvenna, Mykola Galushka, Bethany Waterhouse Bradley, Fiona Browne, Hui Wang, Alexander Wong
Date: 2019-02-18 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

Many industries are now investing heavily in data science and automation to
replace manual tasks and/or to help with decision making, especially in the
realm of leveraging computer vision to automate many monitoring, inspection,
and surveillance tasks. This has resulted in the emergence of the 'data
scientist' who is conversant in statistical thinking, machine learning (ML),
computer vision, and computer programming. However, as ML becomes more
accessible to the general public and more aspects of ML become automated,
applications leveraging computer vision are increasingly being created by
non-experts with less opportunity for regulatory oversight. This points to the
overall need for more educated responsibility for these lay-users of usable ML
tools in order to mitigate potentially unethical ramifications. In this paper,
we undertake a SWOT analysis to study the strengths, weaknesses, opportunities,
and threats of building usable ML tools for mass adoption for important areas
leveraging ML such as computer vision. The paper proposes a set of data science
literacy criteria for educating and supporting lay-users in the responsible
development and deployment of ML applications.
End-to-end Lyrics Alignment for Polyphonic Music Using an Audio-to-Character Recognition Model
Authors: Daniel Stoller, Simon Durand, Sebastian Ewert
Date: 2019-02-18 00:00:00
Tasks: Retrieval, text-to-speech, Text to Speech

Time-aligned lyrics can enrich the music listening experience by enabling
karaoke, text-based song retrieval and intra-song navigation, and other
applications. Compared to text-to-speech alignment, lyrics alignment remains
highly challenging, despite many attempts to combine numerous sub-modules
including vocal separation and detection in an effort to break down the
problem. Furthermore, training required fine-grained annotations to be
available in some form. Here, we present a novel system based on a modified
Wave-U-Net architecture, which predicts character probabilities directly from
raw audio using learnt multi-scale representations of the various signal
components. There are no sub-modules whose interdependencies need to be
optimized. Our training procedure is designed to work with weak, line-level
annotations available in the real world. With a mean alignment error of 0.35s
on a standard dataset our system outperforms the state-of-the-art by an order
of magnitude.
Seven Myths in Machine Learning Research
Authors: Oscar Chang, Hod Lipson
Date: 2019-02-18 00:00:00
Tasks: BIG-bench Machine Learning

We present seven myths commonly believed to be true in machine learning
research, circa Feb 2019. This is an archival copy of the blog post at
https://crazyoscarchang.github.io/2019/02/16/seven-myths-in-machine-learning-research/
  Myth 1: TensorFlow is a Tensor manipulation library
  Myth 2: Image datasets are representative of real images found in the wild
  Myth 3: Machine Learning researchers do not use the test set for validation
  Myth 4: Every datapoint is used in training a neural network
  Myth 5: We need (batch) normalization to train very deep residual networks
  Myth 6: Attention $>$ Convolution
  Myth 7: Saliency maps are robust ways to interpret neural networks
Regularizing Black-box Models for Improved Interpretability
Authors: Gregory Plumb, Maruan Al-Shedivat, Angel Alexander Cabrera, Adam Perer, Eric Xing, Ameet Talwalkar
Date: 2019-02-18 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: BIG-bench Machine Learning, Interpretable Machine Learning

Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post-hoc explanation systems, whose explanation quality can be unpredictable. Our method, ExpO, is a hybridization of these approaches that regularizes a model for explanation quality at training time. Importantly, these regularizers are differentiable, model agnostic, and require no domain knowledge to define. We demonstrate that post-hoc explanations for ExpO-regularized models have better explanation quality, as measured by the common fidelity and stability metrics. We verify that improving these metrics leads to significantly more useful explanations with a user study on a realistic task.
Parenting: Safe Reinforcement Learning from Human Input
Authors: Christopher Frye, Ilya Feige
Date: 2019-02-18 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

Autonomous agents trained via reinforcement learning present numerous safety
concerns: reward hacking, negative side effects, and unsafe exploration, among
others. In the context of near-future autonomous agents, operating in
environments where humans understand the existing dangers, human involvement in
the learning process has proved a promising approach to AI Safety. Here we
demonstrate that a precise framework for learning from human input, loosely
inspired by the way humans parent children, solves a broad class of safety
problems in this context. We show that our Parenting algorithm solves these
problems in the relevant AI Safety gridworlds of Leike et al. (2017), that an
agent can learn to outperform its parent as it "matures", and that policies
learnt through Parenting are generalisable to new environments.
Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning
Authors: Mayank Agrawal, Joshua C. Peterson, Thomas L. Griffiths
Date: 2019-02-18 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

Large-scale behavioral datasets enable researchers to use complex machine learning algorithms to better predict human behavior, yet this increased predictive power does not always lead to a better understanding of the behavior in question. In this paper, we outline a data-driven, iterative procedure that allows cognitive scientists to use machine learning to generate models that are both interpretable and accurate. We demonstrate this method in the domain of moral decision-making, where standard experimental approaches often identify relevant principles that influence human judgments, but fail to generalize these findings to "real world" situations that place these principles in conflict. The recently released Moral Machine dataset allows us to build a powerful model that can predict the outcomes of these conflicts while remaining simple enough to explain the basis behind human decisions.
Comparing Apples and Oranges: Measuring Differences between Exploratory Data Mining Results
Authors: Nikolaj Tatti, Jilles Vreeken
Date: 2019-02-18 00:00:00

Deciding whether the results of two different mining algorithms provide
significantly different information is an important, yet understudied, open
problem in exploratory data mining. Whether the goal is to select the most
informative result for analysis, or to decide which mining approach will most
likely provide the most novel insight, it is essential that we can tell how
different the information is that different results by possibly different
methods provide.
  In this paper we take a first step towards comparing exploratory data mining
results on binary data. We propose to meaningfully convert results into sets of
noisy tiles, and compare between these sets by Maximum Entropy modelling and
Kullback-Leibler divergence, well-founded notions from Information Theory. We
so construct a measure that is highly flexible, and allows us to naturally
include background knowledge, such that differences in results can be measured
from the perspective of what a user already knows. Furthermore, adding to its
interpretability, it coincides with Jaccard dissimilarity when we only consider
exact tiles.
  Our approach provides a means to study and tell differences between results
of different exploratory data mining methods. As an application, we show that
our measure can also be used to identify which parts of results best redescribe
other results. Furthermore, we study its use for iterative data mining, where
one iteratively wants to find that result that will provide maximal novel
information. Experimental evaluation shows our measure gives meaningful
results, correctly identifies methods that are similar in nature, automatically
provides sound redescriptions of results, and is highly applicable for
iterative data mining.
Finding Robust Itemsets Under Subsampling
Authors: Nikolaj Tatti, Fabian Moerchen, Toon Calders
Date: 2019-02-18 00:00:00

Mining frequent patterns is plagued by the problem of pattern explosion
making pattern reduction techniques a key challenge in pattern mining. In this
paper we propose a novel theoretical framework for pattern reduction. We do
this by measuring the robustness of a property of an itemset such as closedness
or non-derivability. The robustness of a property is the probability that this
property holds on random subsets of the original data. We study four
properties: if an itemset is closed, free, non-derivable or totally shattered,
and demonstrate how to compute the robustness analytically without actually
sampling the data. Our concept of robustness has many advantages: Unlike
statistical approaches for reducing patterns, we do not assume a null
hypothesis or any noise model and in contrast to noise tolerant or approximate
patterns, the robust patterns for a given property are always a subset of the
patterns with this property. If the underlying property is monotonic, then the
measure is also monotonic, allowing us to efficiently mine robust itemsets. We
further derive a parameter-free technique for ranking itemsets that can be used
for top-$k$ approaches. Our experiments demonstrate that we can successfully
use the robustness measure to reduce the number of patterns and that ranking
yields interesting itemsets.
Classifying textual data: shallow, deep and ensemble methods
Authors: Laura Anderlucci, Lucia Guastadisegni, Cinzia Viroli
Date: 2019-02-18 00:00:00
Tasks: Classification, Deep Learning, General Classification, text-classification, Text Classification

This paper focuses on a comparative evaluation of the most common and modern
methods for text classification, including the recent deep learning strategies
and ensemble methods. The study is motivated by a challenging real data
problem, characterized by high-dimensional and extremely sparse data, deriving
from incoming calls to the customer care of an Italian phone company. We will
show that deep learning outperforms many classical (shallow) strategies but the
combination of shallow and deep learning methods in a unique ensemble
classifier may improve the robustness and the accuracy of "single"
classification methods.
Disentangling Factors of Variation via Generative Entangling
Authors: Guillaume Desjardins, Aaron Courville, Yoshua Bengio
Date: 2012-10-19 00:00:00
Tasks: General Classification

Here we propose a novel model family with the objective of learning to
disentangle the factors of variation in data. Our approach is based on the
spike-and-slab restricted Boltzmann machine which we generalize to include
higher-order interactions among multiple latent variables. Seen from a
generative perspective, the multiplicative interactions emulates the entangling
of factors of variation. Inference in the model can be seen as disentangling
these generative factors. Unlike previous attempts at disentangling latent
factors, the proposed model is trained using no supervised information
regarding the latent factors. We apply our model to the task of facial
expression classification.
MmWave MU-MIMO for Aerial Networks
Authors: Travis Cuvelier, Robert W. Heath Jr
Date: 2018-04-10 00:00:00

Millimeter wave offers high bandwidth for air-to-air (A2A) communication. In
this paper, we evaluate the rate performance of a multiuser MIMO (MU-MIMO)
configuration where several aircraft communicate with a central hub. We
consider a hybrid subarray architecture, single path channels, and realistic
atmospheric attenuation effects. We propose a mathematical framework for the
analysis of millimeter wave (mmWave) MU-MIMO networks. Via Monte Carlo
simulation, we demonstrate that mmWave is a promising technology for delivering
gigabit connectivity in next-generation aerial networks.
Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization
Authors: Yuxin Chen, Yuejie Chi, Jianqing Fan, Cong Ma, Yuling Yan
Date: 2019-02-20 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion

This paper studies noisy low-rank matrix completion: given partial and noisy entries of a large low-rank matrix, the goal is to estimate the underlying matrix faithfully and efficiently. Arguably one of the most popular paradigms to tackle this problem is convex relaxation, which achieves remarkable efficacy in practice. However, the theoretical support of this approach is still far from optimal in the noisy setting, falling short of explaining its empirical success. We make progress towards demystifying the practical efficacy of convex relaxation vis-\`a-vis random noise. When the rank and the condition number of the unknown matrix are bounded by a constant, we demonstrate that the convex programming approach achieves near-optimal estimation errors --- in terms of the Euclidean loss, the entrywise loss, and the spectral norm loss --- for a wide range of noise levels. All of this is enabled by bridging convex relaxation with the nonconvex Burer-Monteiro approach, a seemingly distinct algorithmic paradigm that is provably robust against noise. More specifically, we show that an approximate critical point of the nonconvex formulation serves as an extremely tight approximation of the convex solution, thus allowing us to transfer the desired statistical guarantees of the nonconvex approach to its convex counterpart.
Knowledge-based Analysis for Mortality Prediction from CT Images
Authors: Hengtao Guo, Uwe Kruger, Ge Wang, Mannudeep K. Kalra, Pingkun Yan
Date: 2019-02-20 00:00:00
Tasks: Clinical Knowledge, Lung Cancer Diagnosis, Mortality Prediction, Prediction

Recent studies have highlighted the high correlation between cardiovascular diseases (CVD) and lung cancer, and both are associated with significant morbidity and mortality. Low-Dose CT (LCDT) scans have led to significant improvements in the accuracy of lung cancer diagnosis and thus the reduction of cancer deaths. However, the high correlation between lung cancer and CVD has not been well explored for mortality prediction. This paper introduces a knowledge-based analytical method using deep convolutional neural network (CNN) for all-cause mortality prediction. The underlying approach combines structural image features extracted from CNNs, based on LDCT volume in different scale, and clinical knowledge obtained from quantitative measurements, to comprehensively predict the mortality risk of lung cancer screening subjects. The introduced method is referred to here as the Knowledge-based Analysis of Mortality Prediction Network, or KAMP-Net. It constitutes a collaborative framework that utilizes both imaging features and anatomical information, instead of completely relying on automatic feature extraction. Our work demonstrates the feasibility of incorporating quantitative clinical measurements to assist CNNs in all-cause mortality prediction from chest LDCT images. The results of this study confirm that radiologist defined features are an important complement to CNNs to achieve a more comprehensive feature extraction. Thus, the proposed KAMP-Net has shown to achieve a superior performance when compared to other methods. Our code is available at https://github.com/DIAL-RPI/KAMP-Net.
World Discovery Models
Authors: Mohammad Gheshlaghi Azar, Bilal Piot, Bernardo Avila Pires, Jean-bastien Grill, Florent Altché, Rémi Munos
Date: 2019-02-20 00:00:00

As humans we are driven by a strong desire for seeking novelty in our world.
Also upon observing a novel pattern we are capable of refining our
understanding of the world based on the new information---humans can discover
their world. The outstanding ability of the human mind for discovery has led to
many breakthroughs in science, art and technology. Here we investigate the
possibility of building an agent capable of discovering its world using the
modern AI technology. In particular we introduce NDIGO, Neural Differential
Information Gain Optimisation, a self-supervised discovery model that aims at
seeking new information to construct a global view of its world from partial
and noisy observations. Our experiments on some controlled 2-D navigation tasks
show that NDIGO outperforms state-of-the-art information-seeking methods in
terms of the quality of the learned representation. The improvement in
performance is particularly significant in the presence of white or structured
noise where other information-seeking methods follow the noise instead of
discovering their world.
ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing
Authors: Mark Neumann, Daniel King, Iz Beltagy, Waleed Ammar
Date: 2019-02-20 00:00:00
Proceeding: WS 2019 8

Despite recent advances in natural language processing, many statistical models for processing text perform extremely poorly under domain shift. Processing biomedical and clinical text is a critically important application area of natural language processing, for which there are few robust, practical, publicly available models. This paper describes scispaCy, a new tool for practical biomedical/scientific text processing, which heavily leverages the spaCy library. We detail the performance of two packages of models released in scispaCy and demonstrate their robustness on several tasks and datasets. Models and code are available at https://allenai.github.io/scispacy/
Feature Relevance Bounds for Ordinal Regression
Authors: Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl, Peter Tino, Barbara Hammer
Date: 2019-02-20 00:00:00
Tasks: regression

The increasing occurrence of ordinal data, mainly sociodemographic, led to a
renewed research interest in ordinal regression, i.e. the prediction of ordered
classes. Besides model accuracy, the interpretation of these models itself is
of high relevance, and existing approaches therefore enforce e.g. model
sparsity. For high dimensional or highly correlated data, however, this might
be misleading due to strong variable dependencies. In this contribution, we aim
for an identification of feature relevance bounds which - besides identifying
all relevant features - explicitly differentiates between strongly and weakly
relevant features.
LOSSGRAD: automatic learning rate in gradient descent
Authors: Bartosz Wójcik, Łukasz Maziarka, Jacek Tabor
Date: 2019-02-20 00:00:00

In this paper, we propose a simple, fast and easy to implement algorithm
LOSSGRAD (locally optimal step-size in gradient descent), which automatically
modifies the step-size in gradient descent during neural networks training.
Given a function $f$, a point $x$, and the gradient $\nabla_x f$ of $f$, we aim
to find the step-size $h$ which is (locally) optimal, i.e. satisfies: $$
h=arg\,min_{t \geq 0} f(x-t \nabla_x f). $$ Making use of quadratic
approximation, we show that the algorithm satisfies the above assumption. We
experimentally show that our method is insensitive to the choice of initial
learning rate while achieving results comparable to other methods.
On the effect of age perception biases for real age regression
Authors: Julio C. S. Jacques Junior, Cagri Ozcinar, Marina Marjanovic, Xavier Baró, Gholamreza Anbarjafari, Sergio Escalera
Date: 2019-02-20 00:00:00
Tasks: Age Estimation, regression

Automatic age estimation from facial images represents an important task in
computer vision. This paper analyses the effect of gender, age, ethnic, makeup
and expression attributes of faces as sources of bias to improve deep apparent
age prediction. Following recent works where it is shown that apparent age
labels benefit real age estimation, rather than direct real to real age
regression, our main contribution is the integration, in an end-to-end
architecture, of face attributes for apparent age prediction with an additional
loss for real age regression. Experimental results on the APPA-REAL dataset
indicate the proposed network successfully take advantage of the adopted
attributes to improve both apparent and real age estimation. Our model
outperformed a state-of-the-art architecture proposed to separately address
apparent and real age regression. Finally, we present preliminary results and
discussion of a proof of concept application using the proposed model to
regress the apparent age of an individual based on the gender of an external
observer.