pwc-archive/papers-with-abstracts huggingface dataset:
Lip Reading Using Convolutional Auto Encoders as Feature Extractor
Authors: Dharin Parekh, Ankitesh Gupta, Shharrnam Chhatpar, Anmol Yash Kumar, Manasi Kulkarni
Date: 2018-05-31 00:00:00
Tasks: Classification, General Classification, Lip Reading, Sentence

Visual recognition of speech using the lip movement is called Lip-reading.
Recent developments in this nascent field uses different neural networks as
feature extractors which serve as input to a model which can map the temporal
relationship and classify. Though end to end sentence level Lip-reading is the
current trend, we proposed a new model which employs word level classification
and breaks the set benchmarks for standard datasets. In our model we use
convolutional autoencoders as feature extractors which are then fed to a Long
short-term memory model. We tested our proposed model on BBC's LRW dataset,
MIRACL-VC1 and GRID dataset. Achieving a classification accuracy of 98% on
MIRACL-VC1 as compared to 93.4% of the set benchmark (Rekik et al., 2014). On
BBC's LRW the proposed model performed better than the baseline model of
convolutional neural networks and Long short-term memory model (Garg et al.,
2016). Showing the features learned by the models we clearly indicate how the
proposed model works better than the baseline model. The same model can also be
extended for end to end sentence level classification.
Reinforced Continual Learning
Authors: Ju Xu, Zhanxing Zhu
Date: 2018-05-31 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Continual Learning, General Classification, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Most artificial intelligence models have limiting ability to solve new tasks
faster, without forgetting previously acquired knowledge. The recently emerging
paradigm of continual learning aims to solve this issue, in which the model
learns various tasks in a sequential fashion. In this work, a novel approach
for continual learning is proposed, which searches for the best neural
architecture for each coming task via sophisticatedly designed reinforcement
learning strategies. We name it as Reinforced Continual Learning. Our method
not only has good performance on preventing catastrophic forgetting but also
fits new tasks well. The experiments on sequential classification tasks for
variants of MNIST and CIFAR-100 datasets demonstrate that the proposed approach
outperforms existing continual learning alternatives for deep networks.
Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization
Authors: Jonathan Scarlett, Ilijia Bogunovic, Volkan Cevher
Date: 2017-05-31 00:00:00

In this paper, we consider the problem of sequentially optimizing a black-box
function $f$ based on noisy samples and bandit feedback. We assume that $f$ is
smooth in the sense of having a bounded norm in some reproducing kernel Hilbert
space (RKHS), yielding a commonly-considered non-Bayesian form of Gaussian
process bandit optimization. We provide algorithm-independent lower bounds on
the simple regret, measuring the suboptimality of a single point reported after
$T$ rounds, and on the cumulative regret, measuring the sum of regrets over the
$T$ chosen points. For the isotropic squared-exponential kernel in $d$
dimensions, we find that an average simple regret of $\epsilon$ requires $T =
\Omega\big(\frac{1}{\epsilon^2} (\log\frac{1}{\epsilon})^{d/2}\big)$, and the
average cumulative regret is at least $\Omega\big( \sqrt{T(\log T)^{d/2}}
\big)$, thus matching existing upper bounds up to the replacement of $d/2$ by
$2d+O(1)$ in both cases. For the Mat\'ern-$\nu$ kernel, we give analogous
bounds of the form $\Omega\big( (\frac{1}{\epsilon})^{2+d/\nu}\big)$ and
$\Omega\big( T^{\frac{\nu + d}{2\nu + d}} \big)$, and discuss the resulting
gaps to the existing upper bounds.
Forgetting Memories and their Attractiveness
Authors: Enzo Marinari
Date: 2018-05-31 00:00:00

We study numerically the memory which forgets, introduced in 1986 by Parisi
by bounding the synaptic strength, with a mechanism which avoid confusion,
allows to remember the pattern learned more recently and has a physiologically
very well defined meaning. We analyze a number of features of the learning at
finite number of neurons and finite number of patterns. We discuss how the
system behaves in the large but finite N limit. We analyze the basin of
attraction of the patterns that have been learned, and we show that it is
exponentially small in the age of the pattern. This is a clearly non
physiological feature of the model.
Light Field Denoising via Anisotropic Parallax Analysis in a CNN Framework
Authors: Jie Chen, Junhui Hou, Lap-Pui Chau
Date: 2018-05-31 00:00:00
Tasks: Denoising

Light field (LF) cameras provide perspective information of scenes by taking
directional measurements of the focusing light rays. The raw outputs are
usually dark with additive camera noise, which impedes subsequent processing
and applications. We propose a novel LF denoising framework based on
anisotropic parallax analysis (APA). Two convolutional neural networks are
jointly designed for the task: first, the structural parallax synthesis network
predicts the parallax details for the entire LF based on a set of anisotropic
parallax features. These novel features can efficiently capture the high
frequency perspective components of a LF from noisy observations. Second, the
view-dependent detail compensation network restores non-Lambertian variation to
each LF view by involving view-specific spatial energies. Extensive experiments
show that the proposed APA LF denoiser provides a much better denoising
performance than state-of-the-art methods in terms of visual quality and in
preservation of parallax details.
Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches
Authors: Simon Olofsson, Marc Peter Deisenroth, Ruth Misener
Date: 2018-02-12 00:00:00
Proceeding: ICML 2018 7
Tasks: Marketing

Healthcare companies must submit pharmaceutical drugs or medical devices to
regulatory bodies before marketing new technology. Regulatory bodies frequently
require transparent and interpretable computational modelling to justify a new
healthcare technology, but researchers may have several competing models for a
biological system and too little data to discriminate between the models. In
design of experiments for model discrimination, the goal is to design maximally
informative physical experiments in order to discriminate between rival
predictive models. Prior work has focused either on analytical approaches,
which cannot manage all functions, or on data-driven approaches, which may have
computational difficulties or lack interpretable marginal predictive
distributions. We develop a methodology introducing Gaussian process surrogates
in lieu of the original mechanistic models. We thereby extend existing design
and model discrimination methods developed for analytical models to cases of
non-analytical models in a computationally efficient manner.
Deep-Energy: Unsupervised Training of Deep Neural Networks
Authors: Alona Golts, Daniel Freedman, Michael Elad
Date: 2018-05-31 00:00:00
Tasks: Image Dehazing, Image Matting, Single Image Dehazing

The success of deep learning has been due, in no small part, to the availability of large annotated datasets. Thus, a major bottleneck in current learning pipelines is the time-consuming human annotation of data. In scenarios where such input-output pairs cannot be collected, simulation is often used instead, leading to a domain-shift between synthesized and real-world data. This work offers an unsupervised alternative that relies on the availability of task-specific energy functions, replacing the generic supervised loss. Such energy functions are assumed to lead to the desired label as their minimizer given the input. The proposed approach, termed "Deep Energy", trains a Deep Neural Network (DNN) to approximate this minimization for any chosen input. Once trained, a simple and fast feed-forward computation provides the inferred label. This approach allows us to perform unsupervised training of DNNs with real-world inputs only, and without the need for manually-annotated labels, nor synthetically created data. "Deep Energy" is demonstrated in this paper on three different tasks -- seeded segmentation, image matting and single image dehazing -- exposing its generality and wide applicability. Our experiments show that the solution provided by the network is often much better in quality than the one obtained by a direct minimization of the energy function, suggesting an added regularization property in our scheme.
Classification of volcanic ash particles using a convolutional neural network and probability
Authors: Daigo Shoji, Rina Noguchi, Shizuka Otsuki, Hideitsu Hino
Date: 2018-05-31 00:00:00
Tasks: Clustering, General Classification

Analyses of volcanic ash are typically performed either by qualitatively
classifying ash particles by eye or by quantitatively parameterizing its shape
and texture. While complex shapes can be classified through qualitative
analyses, the results are subjective due to the difficulty of categorizing
complex shapes into a single class. Although quantitative analyses are
objective, selection of shape parameters is required. Here, we applied a
convolutional neural network (CNN) for the classification of volcanic ash.
First, we defined four basal particle shapes (blocky, vesicular, elongated,
rounded) generated by different eruption mechanisms (e.g., brittle
fragmentation), and then trained the CNN using particles composed of only one
basal shape. The CNN could recognize the basal shapes with over 90% accuracy.
Using the trained network, we classified ash particles composed of multiple
basal shapes based on the output of the network, which can be interpreted as a
mixing ratio of the four basal shapes. Clustering of samples by the averaged
probabilities and the intensity is consistent with the eruption type. The
mixing ratio output by the CNN can be used to quantitatively classify complex
shapes in nature without categorizing forcibly and without the need for shape
parameters, which may lead to a new taxonomy.
DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder
Authors: Xiaodong Gu, Kyunghyun Cho, Jung-Woo Ha, Sunghun Kim
Date: 2018-05-31 00:00:00
Proceeding: ICLR 2019 5
Tasks: Response Generation

Variational autoencoders~(VAEs) have shown a promise in data-driven
conversation modeling. However, most VAE conversation models match the
approximate posterior distribution over the latent variables to a simple prior
such as standard normal distribution, thereby restricting the generated
responses to a relatively simple (e.g., unimodal) scope. In this paper, we
propose DialogWAE, a conditional Wasserstein autoencoder~(WAE) specially
designed for dialogue modeling. Unlike VAEs that impose a simple distribution
over the latent variables, DialogWAE models the distribution of data by
training a GAN within the latent variable space. Specifically, our model
samples from the prior and posterior distributions over the latent variables by
transforming context-dependent random noise using neural networks and minimizes
the Wasserstein distance between the two distributions. We further develop a
Gaussian mixture prior network to enrich the latent space. Experiments on two
popular datasets show that DialogWAE outperforms the state-of-the-art
approaches in generating more coherent, informative and diverse responses.
Crowdsourcing for Reminiscence Chatbot Design
Authors: Svetlana Nikitina, Florian Daniel, Marcos Baez, Fabio Casati
Date: 2018-05-31 00:00:00
Tasks: Chatbot

In this work-in-progress paper we discuss the challenges in identifying
effective and scalable crowd-based strategies for designing content,
conversation logic, and meaningful metrics for a reminiscence chatbot targeted
at older adults. We formalize the problem and outline the main research
questions that drive the research agenda in chatbot design for reminiscence and
for relational agents for older adults in general.
Hallucinating robots: Inferring Obstacle Distances from Partial Laser Measurements
Authors: Jens Lundell, Francesco Verdoja, Ville Kyrki
Date: 2018-05-31 00:00:00

Many mobile robots rely on 2D laser scanners for localization, mapping, and
navigation. However, those sensors are unable to correctly provide distance to
obstacles such as glass panels and tables whose actual occupancy is invisible
at the height the sensor is measuring. In this work, instead of estimating the
distance to obstacles from richer sensor readings such as 3D lasers or RGBD
sensors, we present a method to estimate the distance directly from raw 2D
laser data. To learn a mapping from raw 2D laser distances to obstacle
distances we frame the problem as a learning task and train a neural network
formed as an autoencoder. A novel configuration of network hyperparameters is
proposed for the task at hand and is quantitatively validated on a test set.
Finally, we qualitatively demonstrate in real time on a Care-O-bot 4 that the
trained network can successfully infer obstacle distances from partial 2D laser
readings.
On representation power of neural network-based graph embedding and beyond
Authors: Akifumi Okuno, Hidetoshi Shimodaira
Date: 2018-05-31 00:00:00
Tasks: Graph Embedding

We consider the representation power of siamese-style similarity functions
used in neural network-based graph embedding. The inner product similarity
(IPS) with feature vectors computed via neural networks is commonly used for
representing the strength of association between two nodes. However, only a
little work has been done on the representation capability of IPS. A very
recent work shed light on the nature of IPS and reveals that IPS has the
capability of approximating any positive definite (PD) similarities. However, a
simple example demonstrates the fundamental limitation of IPS to approximate
non-PD similarities. We then propose a novel model named Shifted IPS (SIPS)
that approximates any Conditionally PD (CPD) similarities arbitrary well. CPD
is a generalization of PD with many examples such as negative Poincar\'e
distance and negative Wasserstein distance, thus SIPS has a potential impact to
significantly improve the applicability of graph embedding without taking great
care in configuring the similarity function. Our numerical experiments
demonstrate the SIPS's superiority over IPS. In theory, we further extend SIPS
beyond CPD by considering the inner product in Minkowski space so that it
approximates more general similarities.
Simultaneous Optical Flow and Segmentation (SOFAS) using Dynamic Vision Sensor
Authors: Timo Stoffregen, Lindsay Kleeman
Date: 2018-05-31 00:00:00
Tasks: Optical Flow Estimation

We present an algorithm (SOFAS) to estimate the optical flow of events
generated by a dynamic vision sensor (DVS). Where traditional cameras produce
frames at a fixed rate, DVSs produce asynchronous events in response to
intensity changes with a high temporal resolution. Our algorithm uses the fact
that events are generated by edges in the scene to not only estimate the
optical flow but also to simultaneously segment the image into objects which
are travelling at the same velocity. This way it is able to avoid the aperture
problem which affects other implementations such as Lucas-Kanade. Finally, we
show that SOFAS produces more accurate results than traditional optic flow
algorithms.
Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators
Authors: Isao Ishikawa, Keisuke Fujii, Masahiro Ikeda, Yuka Hashimoto, Yoshinobu Kawahara
Date: 2018-05-31 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Time Series, Time Series Analysis

The development of a metric for structural data is a long-term problem in
pattern recognition and machine learning. In this paper, we develop a general
metric for comparing nonlinear dynamical systems that is defined with
Perron-Frobenius operators in reproducing kernel Hilbert spaces. Our metric
includes the existing fundamental metrics for dynamical systems, which are
basically defined with principal angles between some appropriately-chosen
subspaces, as its special cases. We also describe the estimation of our metric
from finite data. We empirically illustrate our metric with an example of
rotation dynamics in a unit disk in a complex plane, and evaluate the
performance with real-world time-series data.
DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation
Authors: Jimmy Wu, Bolei Zhou, Diondra Peck, Scott Hsieh, Vandana Dialani, Lester Mackey, Genevieve Patterson
Date: 2018-05-31 00:00:00
Tasks: Classification, General Classification, Medical Diagnosis

We propose DeepMiner, a framework to discover interpretable representations in deep neural networks and to build explanations for medical predictions. By probing convolutional neural networks (CNNs) trained to classify cancer in mammograms, we show that many individual units in the final convolutional layer of a CNN respond strongly to diseased tissue concepts specified by the BI-RADS lexicon. After expert annotation of the interpretable units, our proposed method is able to generate explanations for CNN mammogram classification that are consistent with ground truth radiology reports on the Digital Database for Screening Mammography. We show that DeepMiner not only enables better understanding of the nuances of CNN classification decisions but also possibly discovers new visual knowledge relevant to medical diagnosis.
A Divide-and-Conquer Approach to Geometric Sampling for Active Learning
Authors: Xiaofeng Cao
Date: 2018-05-31 00:00:00
Tasks: Active Learning, Boundary Detection, General Classification

Active learning (AL) repeatedly trains the classifier with the minimum labeling budget to improve the current classification model. The training process is usually supervised by an uncertainty evaluation strategy. However, the uncertainty evaluation always suffers from performance degeneration when the initial labeled set has insufficient labels. To completely eliminate the dependence on the uncertainty evaluation sampling in AL, this paper proposes a divide-and-conquer idea that directly transfers the AL sampling as the geometric sampling over the clusters. By dividing the points of the clusters into cluster boundary and core points, we theoretically discuss their margin distance and {hypothesis relationship}. With the advantages of cluster boundary points in the above two properties, we propose a Geometric Active Learning (GAL) algorithm by knight's tour. Experimental studies of the two reported experimental tasks including cluster boundary detection and AL classification show that the proposed GAL method significantly outperforms the state-of-the-art baselines.
MBA: Mini-Batch AUC Optimization
Authors: San Gultekin, Avishek Saha, Adwait Ratnaparkhi, John Paisley
Date: 2018-05-29 00:00:00

Area under the receiver operating characteristics curve (AUC) is an important
metric for a wide range of signal processing and machine learning problems, and
scalable methods for optimizing AUC have recently been proposed. However,
handling very large datasets remains an open challenge for this problem. This
paper proposes a novel approach to AUC maximization, based on sampling
mini-batches of positive/negative instance pairs and computing U-statistics to
approximate a global risk minimization problem. The resulting algorithm is
simple, fast, and learning-rate free. We show that the number of samples
required for good performance is independent of the number of pairs available,
which is a quadratic function of the positive and negative instances. Extensive
experiments show the practical utility of the proposed method.
Multiaccuracy: Black-Box Post-Processing for Fairness in Classification
Authors: Michael P. Kim, Amirata Ghorbani, James Zou
Date: 2018-05-31 00:00:00
Tasks: Classification, Fairness, General Classification, image-classification, Image Classification

Prediction systems are successfully deployed in applications ranging from
disease diagnosis, to predicting credit worthiness, to image recognition. Even
when the overall accuracy is high, these systems may exhibit systematic biases
that harm specific subpopulations; such biases may arise inadvertently due to
underrepresentation in the data used to train a machine-learning model, or as
the result of intentional malicious discrimination. We develop a rigorous
framework of *multiaccuracy* auditing and post-processing to ensure accurate
predictions across *identifiable subgroups*.
  Our algorithm, MULTIACCURACY-BOOST, works in any setting where we have
black-box access to a predictor and a relatively small set of labeled data for
auditing; importantly, this black-box framework allows for improved fairness
and accountability of predictions, even when the predictor is minimally
transparent. We prove that MULTIACCURACY-BOOST converges efficiently and show
that if the initial model is accurate on an identifiable subgroup, then the
post-processed model will be also. We experimentally demonstrate the
effectiveness of the approach to improve the accuracy among minority subgroups
in diverse applications (image classification, finance, population health).
Interestingly, MULTIACCURACY-BOOST can improve subpopulation accuracy (e.g. for
"black women") even when the sensitive features (e.g. "race", "gender") are not
given to the algorithm explicitly.
Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data
Authors: Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael. I. Jordan
Date: 2018-05-31 00:00:00
Tasks: General Classification, text-classification, Text Classification

We present a probabilistic framework for studying adversarial attacks on
discrete data. Based on this framework, we derive a perturbation-based method,
Greedy Attack, and a scalable learning-based method, Gumbel Attack, that
illustrate various tradeoffs in the design of attacks. We demonstrate the
effectiveness of these methods using both quantitative metrics and human
evaluation on various state-of-the-art models for text classification,
including a word-based CNN, a character-based CNN and an LSTM. As as example of
our results, we show that the accuracy of character-based convolutional
networks drops to the level of random selection by modifying only five
characters through Greedy Attack.
Fast Context-Annotated Classification of Different Types of Web Service Descriptions
Authors: Serguei A. Mokhov, Joey Paquet, Arash Khodadadi
Date: 2018-05-31 00:00:00
Tasks: Classification, Cloud Computing, General Classification, Service Composition

In the recent rapid growth of web services, IoT, and cloud computing, many
web services and APIs appeared on the web. With the failure of global UDDI
registries, different service repositories started to appear, trying to list
and categorize various types of web services for client applications' discover
and use. In order to increase the effectiveness and speed up the task of
finding compatible Web Services in the brokerage when performing service
composition or suggesting Web Services to the requests, high-level
functionality of the service needs to be determined. Due to the lack of
structured support for specifying such functionality, classification of
services into a set of abstract categories is necessary. We employ a wide range
of Machine Learning and Signal Processing algorithms and techniques in order to
find the highest precision achievable in the scope of this article for the fast
classification of three type of service descriptions: WSDL, REST, and WADL. In
addition, we complement our approach by showing the importance and effect of
contextual information on the classification of the service descriptions and
show that it improves the accuracy in 5 different categories of services.