pwc-archive/papers-with-abstracts huggingface dataset:
Informative Gene Selection for Microarray Classification via Adaptive Elastic Net with Conditional Mutual Information
Authors: Xin-Guang Yang, Yongjin Lu
Date: 2018-06-05 00:00:00
Tasks: General Classification, Microarray Classification

Due to the advantage of achieving a better performance under weak
regularization, elastic net has attracted wide attention in statistics, machine
learning, bioinformatics, and other fields. In particular, a variation of the
elastic net, adaptive elastic net (AEN), integrates the adaptive grouping
effect. In this paper, we aim to develop a new algorithm: Adaptive Elastic Net
with Conditional Mutual Information (AEN-CMI) that further improves AEN by
incorporating conditional mutual information into the gene selection process.
We apply this new algorithm to screen significant genes for two kinds of
cancers: colon cancer and leukemia. Compared with other algorithms including
Support Vector Machine, Classic Elastic Net and Adaptive Elastic Net, the
proposed algorithm, AEN-CMI, obtains the best classification performance using
the least number of genes.
Visually grounded cross-lingual keyword spotting in speech
Authors: Herman Kamper, Michael Roth
Date: 2018-06-13 00:00:00
Tasks: Keyword Spotting, Visual Grounding

Recent work considered how images paired with speech can be used as
supervision for building speech systems when transcriptions are not available.
We ask whether visual grounding can be used for cross-lingual keyword spotting:
given a text keyword in one language, the task is to retrieve spoken utterances
containing that keyword in another language. This could enable searching
through speech in a low-resource language using text queries in a high-resource
language. As a proof-of-concept, we use English speech with German queries: we
use a German visual tagger to add keyword labels to each training image, and
then train a neural network to map English speech to German keywords. Without
seeing parallel speech-transcriptions or translations, the model achieves a
precision at ten of 58%. We show that most erroneous retrievals contain
equivalent or semantically relevant keywords; excluding these would improve
P@10 to 91%.
Hyperdrive: A Multi-Chip Systolically Scalable Binary-Weight CNN Inference Engine
Authors: Renzo Andri, Lukas Cavigelli, Davide Rossi, Luca Benini
Date: 2018-03-05 00:00:00
Tasks: Quantization

Deep neural networks have achieved impressive results in computer vision and
machine learning. Unfortunately, state-of-the-art networks are extremely
compute and memory intensive which makes them unsuitable for mW-devices such as
IoT end-nodes. Aggressive quantization of these networks dramatically reduces
the computation and memory footprint. Binary-weight neural networks (BWNs)
follow this trend, pushing weight quantization to the limit. Hardware
accelerators for BWNs presented up to now have focused on core efficiency,
disregarding I/O bandwidth and system-level efficiency that are crucial for
deployment of accelerators in ultra-low power devices. We present Hyperdrive: a
BWN accelerator dramatically reducing the I/O bandwidth exploiting a novel
binary-weight streaming approach, which can be used for arbitrarily sized
convolutional neural network architecture and input resolution by exploiting
the natural scalability of the compute units both at chip-level and
system-level by arranging Hyperdrive chips systolically in a 2D mesh while
processing the entire feature map together in parallel. Hyperdrive achieves 4.3
TOp/s/W system-level efficiency (i.e., including I/Os)---3.1x higher than
state-of-the-art BWN accelerators, even if its core uses resource-intensive
FP16 arithmetic for increased robustness.
Self-Supervised Feature Learning by Learning to Spot Artifacts
Authors: Simon Jenni, Paolo Favaro
Date: 2018-06-13 00:00:00
Proceeding: CVPR 2018 6
Tasks: Decoder, Self-Supervised Learning

We introduce a novel self-supervised learning method based on adversarial
training. Our objective is to train a discriminator network to distinguish real
images from images with synthetic artifacts, and then to extract features from
its intermediate layers that can be transferred to other data domains and
tasks. To generate images with artifacts, we pre-train a high-capacity
autoencoder and then we use a damage and repair strategy: First, we freeze the
autoencoder and damage the output of the encoder by randomly dropping its
entries. Second, we augment the decoder with a repair network, and train it in
an adversarial manner against the discriminator. The repair network helps
generate more realistic images by inpainting the dropped feature entries. To
make the discriminator focus on the artifacts, we also make it predict what
entries in the feature were dropped. We demonstrate experimentally that
features learned by creating and spotting artifacts achieve state of the art
performance in several benchmarks.
Brain-Computer Interface with Corrupted EEG Data: A Tensor Completion Approach
Authors: Jordi Sole-Casals, Cesar F. Caiafa, Qibin Zhao, Adrzej Cichocki
Date: 2018-06-13 00:00:00
Tasks: Brain Computer Interface, Classification, EEG, Electroencephalogram (EEG), General Classification, Motor Imagery, Tensor Decomposition

One of the current issues in Brain-Computer Interface is how to deal with
noisy Electroencephalography measurements organized as multidimensional
datasets. On the other hand, recently, significant advances have been made in
multidimensional signal completion algorithms that exploit tensor decomposition
models to capture the intricate relationship among entries in a
multidimensional signal. We propose to use tensor completion applied to EEG
data for improving the classification performance in a motor imagery BCI system
with corrupted measurements. Noisy measurements are considered as unknowns that
are inferred from a tensor decomposition model. We evaluate the performance of
four recently proposed tensor completion algorithms plus a simple interpolation
strategy, first with random missing entries and then with missing samples
constrained to have a specific structure (random missing channels), which is a
more realistic assumption in BCI Applications. We measured the ability of these
algorithms to reconstruct the tensor from observed data. Then, we tested the
classification accuracy of imagined movement in a BCI experiment with missing
samples. We show that for random missing entries, all tensor completion
algorithms can recover missing samples increasing the classification
performance compared to a simple interpolation approach. For the random missing
channels case, we show that tensor completion algorithms help to reconstruct
missing channels, significantly improving the accuracy in the classification of
motor imagery, however, not at the same level as clean data. Tensor completion
algorithms are useful in real BCI applications. The proposed strategy could
allow using motor imagery BCI systems even when EEG data is highly affected by
missing channels and/or samples, avoiding the need of new acquisitions in the
calibration stage.
Safe learning-based optimal motion planning for automated driving
Authors: Zlatan Ajanovic, Bakir Lacevic, Georg Stettinger, Daniel Watzenig, Martin Horn
Date: 2018-05-25 00:00:00
Tasks: BIG-bench Machine Learning, Motion Planning, Optimal Motion Planning

This paper presents preliminary work on learning the search heuristic for the
optimal motion planning for automated driving in urban traffic. Previous work
considered search-based optimal motion planning framework (SBOMP) that utilized
numerical or model-based heuristics that did not consider dynamic obstacles.
Optimal solution was still guaranteed since dynamic obstacles can only increase
the cost. However, significant variations in the search efficiency are observed
depending whether dynamic obstacles are present or not. This paper introduces
machine learning (ML) based heuristic that takes into account dynamic
obstacles, thus adding to the performance consistency for achieving real-time
implementation.
Gradient-based Camera Exposure Control for Outdoor Mobile Platforms
Authors: Inwook Shim, Tae-Hyun Oh, Joon-Young Lee, Jinwook Choi, Dong-Geol Choi, In So Kweon
Date: 2017-08-24 00:00:00
Tasks: Pedestrian Detection, Stereo Matching, Stereo Matching Hand, Visual Odometry

We introduce a novel method to automatically adjust camera exposure for image
processing and computer vision applications on mobile robot platforms. Because
most image processing algorithms rely heavily on low-level image features that
are based mainly on local gradient information, we consider that gradient
quantity can determine the proper exposure level, allowing a camera to capture
the important image features in a manner robust to illumination conditions. We
then extend this concept to a multi-camera system and present a new control
algorithm to achieve both brightness consistency between adjacent cameras and a
proper exposure level for each camera. We implement our prototype system with
off-the-shelf machine-vision cameras and demonstrate the effectiveness of the
proposed algorithms on practical applications, including pedestrian detection,
visual odometry, surround-view imaging, panoramic imaging and stereo matching.
Fast Model Identification via Physics Engines for Data-Efficient Policy Search
Authors: Shaojun Zhu, Andrew Kimmel, Kostas E. Bekris, Abdeslam Boularias
Date: 2017-10-24 00:00:00
Tasks: Bayesian Optimization, Friction, Model-based Reinforcement Learning, Reinforcement Learning

This paper presents a method for identifying mechanical parameters of robots
or objects, such as their mass and friction coefficients. Key features are the
use of off-the-shelf physics engines and the adaptation of a Bayesian
optimization technique towards minimizing the number of real-world experiments
needed for model-based reinforcement learning. The proposed framework
reproduces in a physics engine experiments performed on a real robot and
optimizes the model's mechanical parameters so as to match real-world
trajectories. The optimized model is then used for learning a policy in
simulation, before real-world deployment. It is well understood, however, that
it is hard to exactly reproduce real trajectories in simulation. Moreover, a
near-optimal policy can be frequently found with an imperfect model. Therefore,
this work proposes a strategy for identifying a model that is just good enough
to approximate the value of a locally optimal policy with a certain confidence,
instead of wasting effort on identifying the most accurate model. Evaluations,
performed both in simulation and on a real robotic manipulation task, indicate
that the proposed strategy results in an overall time-efficient, integrated
model identification and learning solution, which significantly improves the
data-efficiency of existing policy search algorithms.
Generating Sentences Using a Dynamic Canvas
Authors: Harshil Shah, Bowen Zheng, David Barber
Date: 2018-06-13 00:00:00
Tasks: Sentence

We introduce the Attentive Unsupervised Text (W)riter (AUTR), which is a word
level generative model for natural language. It uses a recurrent neural network
with a dynamic attention and canvas memory mechanism to iteratively construct
sentences. By viewing the state of the memory at intermediate stages and where
the model is placing its attention, we gain insight into how it constructs
sentences. We demonstrate that AUTR learns a meaningful latent representation
for each sentence, and achieves competitive log-likelihood lower bounds whilst
being computationally efficient. It is effective at generating and
reconstructing sentences, as well as imputing missing words.
Co-evolutionary multi-task learning for dynamic time series prediction
Authors: Rohitash Chandra, Yew-Soon Ong, Chi-Keong Goh
Date: 2017-02-27 00:00:00
Tasks: Evolutionary Algorithms, Multi-Task Learning, Prediction, Time Series, Time Series Analysis, Time Series Prediction

Time series prediction typically consists of a data reconstruction phase
where the time series is broken into overlapping windows known as the timespan.
The size of the timespan can be seen as a way of determining the extent of past
information required for an effective prediction. In certain applications such
as the prediction of wind-intensity of storms and cyclones, prediction models
need to be dynamic in accommodating different values of the timespan. These
applications require robust prediction as soon as the event takes place. We
identify a new category of problem called dynamic time series prediction that
requires a model to give prediction when presented with varying lengths of the
timespan. In this paper, we propose a co-evolutionary multi-task learning
method that provides a synergy between multi-task learning and co-evolutionary
algorithms to address dynamic time series prediction. The method features
effective use of building blocks of knowledge inspired by dynamic programming
and multi-task learning. It enables neural networks to retain modularity during
training for making a decision in situations even when certain inputs are
missing. The effectiveness of the method is demonstrated using one-step-ahead
chaotic time series and tropical cyclone wind-intensity prediction.
Plug-and-Play Unplugged: Optimization Free Reconstruction using Consensus Equilibrium
Authors: Gregery T. Buzzard, Stanley H. Chan, Suhas Sreehari, Charles A. Bouman
Date: 2017-05-24 00:00:00
Tasks: Denoising, Image Reconstruction

Regularized inversion methods for image reconstruction are used widely due to
their tractability and ability to combine complex physical sensor models with
useful regularity criteria. Such methods motivated the recently developed
Plug-and-Play prior method, which provides a framework to use advanced
denoising algorithms as regularizers in inversion. However, the need to
formulate regularized inversion as the solution to an optimization problem
limits the possible regularity conditions and physical sensor models.
  In this paper, we introduce Consensus Equilibrium (CE), which generalizes
regularized inversion to include a much wider variety of both forward
components and prior components without the need for either to be expressed
with a cost function. CE is based on the solution of a set of equilibrium
equations that balance data fit and regularity. In this framework, the problem
of MAP estimation in regularized inversion is replaced by the problem of
solving these equilibrium equations, which can be approached in multiple ways.
  The key contribution of CE is to provide a novel framework for fusing
multiple heterogeneous models of physical sensors or models learned from data.
We describe the derivation of the CE equations and prove that the solution of
the CE equations generalizes the standard MAP estimate under appropriate
circumstances.
  We also discuss algorithms for solving the CE equations, including ADMM with
a novel form of preconditioning and Newton's method. We give examples to
illustrate consensus equilibrium and the convergence properties of these
algorithms and demonstrate this method on some toy problems and on a denoising
example in which we use an array of convolutional neural network denoisers,
none of which is tuned to match the noise level in a noisy image but which in
consensus can achieve a better result than any of them individually.
OpenEDGAR: Open Source Software for SEC EDGAR Analysis
Authors: Michael J Bommarito II, Daniel Martin Katz, Eric M Detterman
Date: 2018-06-13 00:00:00
Tasks: Retrieval

OpenEDGAR is an open source Python framework designed to rapidly construct
research databases based on the Electronic Data Gathering, Analysis, and
Retrieval (EDGAR) system operated by the US Securities and Exchange Commission
(SEC). OpenEDGAR is built on the Django application framework, supports
distributed compute across one or more servers, and includes functionality to
(i) retrieve and parse index and filing data from EDGAR, (ii) build tables for
key metadata like form type and filer, (iii) retrieve, parse, and update CIK to
ticker and industry mappings, (iv) extract content and metadata from filing
documents, and (v) search filing document contents. OpenEDGAR is designed for
use in both academic research and industrial applications, and is distributed
under MIT License at https://github.com/LexPredict/openedgar.
Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders
Authors: Xiaoran Chen, Ender Konukoglu
Date: 2018-06-13 00:00:00
Tasks: Anomaly Detection, Lesion Detection

Lesion detection in brain Magnetic Resonance Images (MRI) remains a
challenging task. State-of-the-art approaches are mostly based on supervised
learning making use of large annotated datasets. Human beings, on the other
hand, even non-experts, can detect most abnormal lesions after seeing a handful
of healthy brain images. Replicating this capability of using prior information
on the appearance of healthy brain structure to detect lesions can help
computers achieve human level abnormality detection, specifically reducing the
need for numerous labeled examples and bettering generalization of previously
unseen lesions. To this end, we study detection of lesion regions in an
unsupervised manner by learning data distribution of brain MRI of healthy
subjects using auto-encoder based methods. We hypothesize that one of the main
limitations of the current models is the lack of consistency in latent
representation. We propose a simple yet effective constraint that helps mapping
of an image bearing lesion close to its corresponding healthy image in the
latent space. We use the Human Connectome Project dataset to learn distribution
of healthy-appearing brain MRI and report improved detection, in terms of AUC,
of the lesions in the BRATS challenge dataset.
Crowd-Powered Data Mining
Authors: Chengliang Chai, Ju Fan, Guoliang Li, Jiannan Wang, Yudian Zheng
Date: 2018-06-13 00:00:00
Tasks: Clustering, General Classification, image-classification, Image Classification, Sentiment Analysis, Transfer Learning

Many data mining tasks cannot be completely addressed by auto- mated
processes, such as sentiment analysis and image classification. Crowdsourcing
is an effective way to harness the human cognitive ability to process these
machine-hard tasks. Thanks to public crowdsourcing platforms, e.g., Amazon
Mechanical Turk and Crowd- Flower, we can easily involve hundreds of thousands
of ordinary workers (i.e., the crowd) to address these machine-hard tasks. In
this tutorial, we will survey and synthesize a wide spectrum of existing
studies on crowd-powered data mining. We first give an overview of
crowdsourcing, and then summarize the fundamental techniques, including quality
control, cost control, and latency control, which must be considered in
crowdsourced data mining. Next we review crowd-powered data mining operations,
including classification, clustering, pattern mining, machine learning using
the crowd (including deep learning, transfer learning and semi-supervised
learning) and knowledge discovery. Finally, we provide the emerging challenges
in crowdsourced data mining.
One-Shot Segmentation in Clutter
Authors: Claudio Michaelis, Matthias Bethge, Alexander S. Ecker
Date: 2018-03-26 00:00:00
Proceeding: ICML 2018 7
Tasks: Foreground Segmentation, object-detection, One-Shot Segmentation, Segmentation

We tackle the problem of one-shot segmentation: finding and segmenting a
previously unseen object in a cluttered scene based on a single instruction
example. We propose a novel dataset, which we call $\textit{cluttered
Omniglot}$. Using a baseline architecture combining a Siamese embedding for
detection with a U-net for segmentation we show that increasing levels of
clutter make the task progressively harder. Using oracle models with access to
various amounts of ground-truth information, we evaluate different aspects of
the problem and show that in this kind of visual search task, detection and
segmentation are two intertwined problems, the solution to each of which helps
solving the other. We therefore introduce $\textit{MaskNet}$, an improved model
that attends to multiple candidate locations, generates segmentation proposals
to mask out background clutter and selects among the segmented objects. Our
findings suggest that such image recognition models based on an iterative
refinement of object detection and foreground segmentation may provide a way to
deal with highly cluttered scenes.
The streaming rollout of deep networks - towards fully model-parallel execution
Authors: Volker Fischer, Jan Köhler, Thomas Pfeil
Date: 2018-06-13 00:00:00
Proceeding: NeurIPS 2018 12

Deep neural networks, and in particular recurrent networks, are promising
candidates to control autonomous agents that interact in real-time with the
physical world. However, this requires a seamless integration of temporal
features into the network's architecture. For the training of and inference
with recurrent neural networks, they are usually rolled out over time, and
different rollouts exist. Conventionally during inference, the layers of a
network are computed in a sequential manner resulting in sparse temporal
integration of information and long response times. In this study, we present a
theoretical framework to describe rollouts, the level of model-parallelization
they induce, and demonstrate differences in solving specific tasks. We prove
that certain rollouts, also for networks with only skip and no recurrent
connections, enable earlier and more frequent responses, and show empirically
that these early responses have better performance. The streaming rollout
maximizes these properties and enables a fully parallel execution of the
network reducing runtime on massively parallel devices. Finally, we provide an
open-source toolbox to design, train, evaluate, and interact with streaming
rollouts.
Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making
Authors: Hoda Heidari, Claudio Ferrari, Krishna P. Gummadi, Andreas Krause
Date: 2018-06-13 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Decision Making, Fairness

We draw attention to an important, yet largely overlooked aspect of
evaluating fairness for automated decision making systems---namely risk and
welfare considerations. Our proposed family of measures corresponds to the
long-established formulations of cardinal social welfare in economics, and is
justified by the Rawlsian conception of fairness behind a veil of ignorance.
The convex formulation of our welfare-based measures of fairness allows us to
integrate them as a constraint into any convex loss minimization pipeline. Our
empirical analysis reveals interesting trade-offs between our proposal and (a)
prediction accuracy, (b) group discrimination, and (c) Dwork et al.'s notion of
individual fairness. Furthermore and perhaps most importantly, our work
provides both heuristic justification and empirical evidence suggesting that a
lower-bound on our measures often leads to bounded inequality in algorithmic
outcomes; hence presenting the first computationally feasible mechanism for
bounding individual-level inequality.
Expression Empowered ResiDen Network for Facial Action Unit Detection
Authors: Shreyank Jyoti, Abhinav Dhall
Date: 2018-06-13 00:00:00
Tasks: Action Unit Detection, Facial Action Unit Detection, Facial Expression Recognition, Facial Expression Recognition (FER)

The paper explores the topic of Facial Action Unit (FAU) detection in the
wild. In particular, we are interested in answering the following questions:
(1) how useful are residual connections across dense blocks for face analysis?
(2) how useful is the information from a network trained for categorical Facial
Expression Recognition (FER) for the task of FAU detection? The proposed
network (ResiDen) exploits dense blocks along with residual connections and
uses auxiliary information from a FER network. The experiments are performed on
the EmotionNet and DISFA datasets. The experiments show the usefulness of
facial expression information for AU detection. The proposed network achieves
state-of-art results on the two databases. Analysis of the results for cross
database protocol shows the effectiveness of the network.