pwc-archive/papers-with-abstracts huggingface dataset:
Training Support Vector Machines using Coresets
Authors: Cenk Baykal, Lucas Liebenwein, Wilko Schwarting
Date: 2017-08-13 00:00:00

We present a novel coreset construction algorithm for solving classification
tasks using Support Vector Machines (SVMs) in a computationally efficient
manner. A coreset is a weighted subset of the original data points that
provably approximates the original set. We show that coresets of size
polylogarithmic in $n$ and polynomial in $d$ exist for a set of $n$ input
points with $d$ features and present an $(\epsilon,\delta)$-FPRAS for
constructing coresets for scalable SVM training. Our method leverages the
insight that data points are often redundant and uses an importance sampling
scheme based on the sensitivity of each data point to construct coresets
efficiently. We evaluate the performance of our algorithm in accelerating SVM
training against real-world data sets and compare our algorithm to
state-of-the-art coreset approaches. Our empirical results show that our
approach outperforms a state-of-the-art coreset approach and uniform sampling
in enabling computational speedups while achieving low approximation error.
Egocentric Hand Detection Via Dynamic Region Growing
Authors: Shao Huang, WeiQiang Wang, Shengfeng He, Rynson W. H. Lau
Date: 2017-11-10 00:00:00
Tasks: Action Recognition, Gesture Recognition, Hand Detection, Superpixels, Temporal Action Localization

Egocentric videos, which mainly record the activities carried out by the
users of the wearable cameras, have drawn much research attentions in recent
years. Due to its lengthy content, a large number of ego-related applications
have been developed to abstract the captured videos. As the users are
accustomed to interacting with the target objects using their own hands while
their hands usually appear within their visual fields during the interaction,
an egocentric hand detection step is involved in tasks like gesture
recognition, action recognition and social interaction understanding. In this
work, we propose a dynamic region growing approach for hand region detection in
egocentric videos, by jointly considering hand-related motion and egocentric
cues. We first determine seed regions that most likely belong to the hand, by
analyzing the motion patterns across successive frames. The hand regions can
then be located by extending from the seed regions, according to the scores
computed for the adjacent superpixels. These scores are derived from four
egocentric cues: contrast, location, position consistency and appearance
continuity. We discuss how to apply the proposed method in real-life scenarios,
where multiple hands irregularly appear and disappear from the videos.
Experimental results on public datasets show that the proposed method achieves
superior performance compared with the state-of-the-art methods, especially in
complicated scenarios.
Communicative Capital for Prosthetic Agents
Authors: Patrick M. Pilarski, Richard S. Sutton, Kory W. Mathewson, Craig Sherstan, Adam S. R. Parker, Ann L. Edwards
Date: 2017-11-10 00:00:00

This work presents an overarching perspective on the role that machine
intelligence can play in enhancing human abilities, especially those that have
been diminished due to injury or illness. As a primary contribution, we develop
the hypothesis that assistive devices, and specifically artificial arms and
hands, can and should be viewed as agents in order for us to most effectively
improve their collaboration with their human users. We believe that increased
agency will enable more powerful interactions between human users and next
generation prosthetic devices, especially when the sensorimotor space of the
prosthetic technology greatly exceeds the conventional control and
communication channels available to a prosthetic user. To more concretely
examine an agency-based view on prosthetic devices, we propose a new schema for
interpreting the capacity of a human-machine collaboration as a function of
both the human's and machine's degrees of agency. We then introduce the idea of
communicative capital as a way of thinking about the communication resources
developed by a human and a machine during their ongoing interaction. Using this
schema of agency and capacity, we examine the benefits and disadvantages of
increasing the agency of a prosthetic limb. To do so, we present an analysis of
examples from the literature where building communicative capital has enabled a
progression of fruitful, task-directed interactions between prostheses and
their human users. We then describe further work that is needed to concretely
evaluate the hypothesis that prostheses are best thought of as agents. The
agent-based viewpoint developed in this article significantly extends current
thinking on how best to support the natural, functional use of increasingly
complex prosthetic enhancements, and opens the door for more powerful
interactions between humans and their assistive technologies.
Breast density classification with deep convolutional neural networks
Authors: Nan Wu, Krzysztof J. Geras, Yiqiu Shen, Jingyi Su, S. Gene Kim, Eric Kim, Stacey Wolfson, Linda Moy, Kyunghyun Cho
Date: 2017-11-10 00:00:00
Tasks: breast density classification, Classification, General Classification

Breast density classification is an essential part of breast cancer
screening. Although a lot of prior work considered this problem as a task for
learning algorithms, to our knowledge, all of them used small and not
clinically realistic data both for training and evaluation of their models. In
this work, we explore the limits of this task with a data set coming from over
200,000 breast cancer screening exams. We use this data to train and evaluate a
strong convolutional neural network classifier. In a reader study, we find that
our model can perform this task comparably to a human expert.
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation
Authors: Xiaomei Zhao, Yihong Wu, Guidong Song, Zhenye Li, Yazhuo Zhang, Yong Fan
Date: 2017-02-15 00:00:00
Tasks: Brain Tumor Segmentation, Image Segmentation, Segmentation, Semantic Segmentation, Tumor Segmentation

Accurate and reliable brain tumor segmentation is a critical component in
cancer diagnosis, treatment planning, and treatment outcome evaluation. Build
upon successful deep learning techniques, a novel brain tumor segmentation
method is developed by integrating fully convolutional neural networks (FCNNs)
and Conditional Random Fields (CRFs) in a unified framework to obtain
segmentation results with appearance and spatial consistency. We train a deep
learning based segmentation model using 2D image patches and image slices in
following steps: 1) training FCNNs using image patches; 2) training CRFs as
Recurrent Neural Networks (CRF-RNN) using image slices with parameters of FCNNs
fixed; and 3) fine-tuning the FCNNs and the CRF-RNN using image slices.
Particularly, we train 3 segmentation models using 2D image patches and slices
obtained in axial, coronal and sagittal views respectively, and combine them to
segment brain tumors using a voting based fusion strategy. Our method could
segment brain images slice-by-slice, much faster than those based on image
patches. We have evaluated our method based on imaging data provided by the
Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2013, BRATS 2015
and BRATS 2016. The experimental results have demonstrated that our method
could build a segmentation model with Flair, T1c, and T2 scans and achieve
competitive performance as those built with Flair, T1, T1c, and T2 scans.
Unsupervised Learning of Geometry with Edge-aware Depth-Normal Consistency
Authors: Zhenheng Yang, Peng Wang, Wei Xu, Liang Zhao, Ramakant Nevatia
Date: 2017-11-10 00:00:00
Tasks: Depth Estimation

Learning to reconstruct depths in a single image by watching unlabeled videos
via deep convolutional network (DCN) is attracting significant attention in
recent years. In this paper, we introduce a surface normal representation for
unsupervised depth estimation framework. Our estimated depths are constrained
to be compatible with predicted normals, yielding more robust geometry results.
Specifically, we formulate an edge-aware depth-normal consistency term, and
solve it by constructing a depth-to-normal layer and a normal-to-depth layer
inside of the DCN. The depth-to-normal layer takes estimated depths as input,
and computes normal directions using cross production based on neighboring
pixels. Then given the estimated normals, the normal-to-depth layer outputs a
regularized depth map through local planar smoothness. Both layers are computed
with awareness of edges inside the image to help address the issue of
depth/normal discontinuity and preserve sharp edges. Finally, to train the
network, we apply the photometric error and gradient smoothness for both depth
and normal predictions. We conducted experiments on both outdoor (KITTI) and
indoor (NYUv2) datasets, and show that our algorithm vastly outperforms state
of the art, which demonstrates the benefits from our approach.
Not all bytes are equal: Neural byte sieve for fuzzing
Authors: Mohit Rajpal, William Blum, Rishabh Singh
Date: 2017-11-10 00:00:00
Tasks: All, valid

Fuzzing is a popular dynamic program analysis technique used to find
vulnerabilities in complex software. Fuzzing involves presenting a target
program with crafted malicious input designed to cause crashes, buffer
overflows, memory errors, and exceptions. Crafting malicious inputs in an
efficient manner is a difficult open problem and often the best approach to
generating such inputs is through applying uniform random mutations to
pre-existing valid inputs (seed files). We present a learning technique that
uses neural networks to learn patterns in the input files from past fuzzing
explorations to guide future fuzzing explorations. In particular, the neural
models learn a function to predict good (and bad) locations in input files to
perform fuzzing mutations based on the past mutations and corresponding code
coverage information. We implement several neural models including LSTMs and
sequence-to-sequence models that can encode variable length input files. We
incorporate our models in the state-of-the-art AFL (American Fuzzy Lop) fuzzer
and show significant improvements in terms of code coverage, unique code paths,
and crashes for various input formats including ELF, PNG, PDF, and XML.
Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning
Authors: Anthony Perez, Christopher Yeh, George Azzari, Marshall Burke, David Lobell, Stefano Ermon
Date: 2017-11-10 00:00:00
Tasks: BIG-bench Machine Learning

Obtaining detailed and reliable data about local economic livelihoods in
developing countries is expensive, and data are consequently scarce. Previous
work has shown that it is possible to measure local-level economic livelihoods
using high-resolution satellite imagery. However, such imagery is relatively
expensive to acquire, often not updated frequently, and is mainly available for
recent years. We train CNN models on free and publicly available multispectral
daytime satellite images of the African continent from the Landsat 7 satellite,
which has collected imagery with global coverage for almost two decades. We
show that despite these images' lower resolution, we can achieve accuracies
that exceed previous benchmarks.
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon
Authors: Xin Dong, Shangyu Chen, Sinno Jialin Pan
Date: 2017-05-22 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Prediction

How to develop slim and accurate deep neural networks has become crucial for
real- world applications, especially for those employed in embedded systems.
Though previous work along this research line has shown some promising results,
most existing methods either fail to significantly compress a well-trained deep
network or require a heavy retraining process for the pruned deep network to
re-boost its prediction performance. In this paper, we propose a new layer-wise
pruning method for deep neural networks. In our proposed method, parameters of
each individual layer are pruned independently based on second order
derivatives of a layer-wise error function with respect to the corresponding
parameters. We prove that the final prediction performance drop after pruning
is bounded by a linear combination of the reconstructed errors caused at each
layer. Therefore, there is a guarantee that one only needs to perform a light
retraining process on the pruned network to resume its original prediction
performance. We conduct extensive experiments on benchmark datasets to
demonstrate the effectiveness of our pruning method compared with several
state-of-the-art baseline methods.
On Hash-Based Work Distribution Methods for Parallel Best-First Search
Authors: Yuu Jinnai, Alex Fukunaga
Date: 2017-06-10 00:00:00
Tasks: graph partitioning

Parallel best-first search algorithms such as Hash Distributed A* (HDA*)
distribute work among the processes using a global hash function. We analyze
the search and communication overheads of state-of-the-art hash-based parallel
best-first search algorithms, and show that although Zobrist hashing, the
standard hash function used by HDA*, achieves good load balance for many
domains, it incurs significant communication overhead since almost all
generated nodes are transferred to a different processor than their parents. We
propose Abstract Zobrist hashing, a new work distribution method for parallel
search which, instead of computing a hash value based on the raw features of a
state, uses a feature projection function to generate a set of abstract
features which results in a higher locality, resulting in reduced
communications overhead. We show that Abstract Zobrist hashing outperforms
previous methods on search domains using hand-coded, domain specific feature
projection functions. We then propose GRAZHDA*, a graph-partitioning based
approach to automatically generating feature projection functions. GRAZHDA*
seeks to approximate the partitioning of the actual search space graph by
partitioning the domain transition graph, an abstraction of the state space
graph. We show that GRAZHDA* outperforms previous methods on domain-independent
planning.
Stochastic Deep Learning in Memristive Networks
Authors: Anakha V Babu, Bipin Rajendran
Date: 2017-11-09 00:00:00
Tasks: Deep Learning

We study the performance of stochastically trained deep neural networks
(DNNs) whose synaptic weights are implemented using emerging memristive devices
that exhibit limited dynamic range, resolution, and variability in their
programming characteristics. We show that a key device parameter to optimize
the learning efficiency of DNNs is the variability in its programming
characteristics. DNNs with such memristive synapses, even with dynamic range as
low as $15$ and only $32$ discrete levels, when trained based on stochastic
updates suffer less than $3\%$ loss in accuracy compared to floating point
software baseline. We also study the performance of stochastic memristive DNNs
when used as inference engines with noise corrupted data and find that if the
device variability can be minimized, the relative degradation in performance
for the Stochastic DNN is better than that of the software baseline. Hence, our
study presents a new optimization corner for memristive devices for building
large noise-immune deep learning systems.
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
Authors: Victor Zhong, Caiming Xiong, Richard Socher
Date: 2017-08-31 00:00:00
Proceeding: ICLR 2018 1
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Text-To-SQL

A significant amount of the world's knowledge is stored in relational
databases. However, the ability for users to retrieve facts from a database is
limited due to a lack of understanding of query languages such as SQL. We
propose Seq2SQL, a deep neural network for translating natural language
questions to corresponding SQL queries. Our model leverages the structure of
SQL queries to significantly reduce the output space of generated queries.
Moreover, we use rewards from in-the-loop query execution over the database to
learn a policy to generate unordered parts of the query, which we show are less
suitable for optimization via cross entropy loss. In addition, we will publish
WikiSQL, a dataset of 80654 hand-annotated examples of questions and SQL
queries distributed across 24241 tables from Wikipedia. This dataset is
required to train our model and is an order of magnitude larger than comparable
datasets. By applying policy-based reinforcement learning with a query
execution environment to WikiSQL, our model Seq2SQL outperforms attentional
sequence to sequence models, improving execution accuracy from 35.9% to 59.4%
and logical form accuracy from 23.4% to 48.3%.
Learning and Real-time Classification of Hand-written Digits With Spiking Neural Networks
Authors: Shruti R. Kulkarni, John M. Alexiades, Bipin Rajendran
Date: 2017-11-09 00:00:00
Tasks: General Classification, GPU

We describe a novel spiking neural network (SNN) for automated, real-time
handwritten digit classification and its implementation on a GP-GPU platform.
Information processing within the network, from feature extraction to
classification is implemented by mimicking the basic aspects of neuronal spike
initiation and propagation in the brain. The feature extraction layer of the
SNN uses fixed synaptic weight maps to extract the key features of the image
and the classifier layer uses the recently developed NormAD approximate
gradient descent based supervised learning algorithm for spiking neural
networks to adjust the synaptic weights. On the standard MNIST database images
of handwritten digits, our network achieves an accuracy of 99.80% on the
training set and 98.06% on the test set, with nearly 7x fewer parameters
compared to the state-of-the-art spiking networks. We further use this network
in a GPU based user-interface system demonstrating real-time SNN simulation to
infer digits written by different users. On a test set of 500 such images, this
real-time platform achieves an accuracy exceeding 97% while making a prediction
within an SNN emulation time of less than 100ms.
Interpretable Vector AutoRegressions with Exogenous Time Series
Authors: Ines Wilms, Sumanta Basu, Jacob Bien, David S. Matteson
Date: 2017-11-09 00:00:00
Tasks: Management, Marketing, Time Series, Time Series Analysis

The Vector AutoRegressive (VAR) model is fundamental to the study of
multivariate time series. Although VAR models are intensively investigated by
many researchers, practitioners often show more interest in analyzing VARX
models that incorporate the impact of unmodeled exogenous variables (X) into
the VAR. However, since the parameter space grows quadratically with the number
of time series, estimation quickly becomes challenging. While several proposals
have been made to sparsely estimate large VAR models, the estimation of large
VARX models is under-explored. Moreover, typically these sparse proposals
involve a lasso-type penalty and do not incorporate lag selection into the
estimation procedure. As a consequence, the resulting models may be difficult
to interpret. In this paper, we propose a lag-based hierarchically sparse
estimator, called "HVARX", for large VARX models. We illustrate the usefulness
of HVARX on a cross-category management marketing application. Our results show
how it provides a highly interpretable model, and improves out-of-sample
forecast accuracy compared to a lasso-type approach.
Improving Deep Learning by Inverse Square Root Linear Units (ISRLUs)
Authors: Brad Carlile, Guy Delamarter, Paul Kinney, Akiko Marti, Brian Whitney
Date: 2017-10-27 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Learning

We introduce the "inverse square root linear unit" (ISRLU) to speed up
learning in deep neural networks. ISRLU has better performance than ELU but has
many of the same benefits. ISRLU and ELU have similar curves and
characteristics. Both have negative values, allowing them to push mean unit
activation closer to zero, and bring the normal gradient closer to the unit
natural gradient, ensuring a noise-robust deactivation state, lessening the
over fitting risk. The significant performance advantage of ISRLU on
traditional CPUs also carry over to more efficient HW implementations on HW/SW
codesign for CNNs/RNNs. In experiments with TensorFlow, ISRLU leads to faster
learning and better generalization than ReLU on CNNs. This work also suggests a
computationally efficient variant called the "inverse square root unit" (ISRU)
which can be used for RNNs. Many RNNs use either long short-term memory (LSTM)
and gated recurrent units (GRU) which are implemented with tanh and sigmoid
activation functions. ISRU has less com- putational complexity but still has a
similar curve to tanh and sigmoid.
Learning to Imagine Manipulation Goals for Robot Task Planning
Authors: Chris Paxton, Kapil Katyal, Christian Rupprecht, Raman Arora, Gregory D. Hager
Date: 2017-11-08 00:00:00
Tasks: Robot Task Planning, Task Planning

Prospection is an important part of how humans come up with new task plans,
but has not been explored in depth in robotics. Predicting multiple task-level
is a challenging problem that involves capturing both task semantics and
continuous variability over the state of the world. Ideally, we would combine
the ability of machine learning to leverage big data for learning the semantics
of a task, while using techniques from task planning to reliably generalize to
new environment. In this work, we propose a method for learning a model
encoding just such a representation for task planning. We learn a neural net
that encodes the $k$ most likely outcomes from high level actions from a given
world. Our approach creates comprehensible task plans that allow us to predict
changes to the environment many time steps into the future. We demonstrate this
approach via application to a stacking task in a cluttered environment, where
the robot must select between different colored blocks while avoiding
obstacles, in order to perform a task. We also show results on a simple
navigation task. Our algorithm generates realistic image and pose predictions
at multiple points in a given task.
An optimal hierarchical clustering approach to segmentation of mobile LiDAR point clouds
Authors: Sheng Xu, Ruisheng Wang, Han Zheng
Date: 2017-03-06 00:00:00
Tasks: Clustering, Point Cloud Segmentation, Segmentation

This paper proposes a hierarchical clustering approach for the segmentation
of mobile LiDAR point clouds. We perform the hierarchical clustering on
unorganized point clouds based on a proximity matrix. The dissimilarity measure
in the proximity matrix is calculated by the Euclidean distances between
clusters and the difference of normal vectors at given points. The main
contribution of this paper is that we succeed to optimize the combination of
clusters in the hierarchical clustering. The combination is obtained by
achieving the matching of a bipartite graph, and optimized by solving the
minimum-cost perfect matching. Results show that the proposed optimal
hierarchical clustering (OHC) succeeds to achieve the segmentation of multiple
individual objects automatically and outperforms the state-of-the-art LiDAR
point cloud segmentation approaches.