pwc-archive/papers-with-abstracts huggingface dataset:
Detecting Irregular Patterns in IoT Streaming Data for Fall Detection
Authors: Sazia Mahfuz, Haruna Isah, Farhana Zulkernine, Peter Nicholls
Date: 2018-11-16 00:00:00

Detecting patterns in real time streaming data has been an interesting and
challenging data analytics problem. With the proliferation of a variety of
sensor devices, real-time analytics of data from the Internet of Things (IoT)
to learn regular and irregular patterns has become an important machine
learning problem to enable predictive analytics for automated notification and
decision support. In this work, we address the problem of learning an irregular
human activity pattern, fall, from streaming IoT data from wearable sensors. We
present a deep neural network model for detecting fall based on accelerometer
data giving 98.75 percent accuracy using an online physical activity monitoring
dataset called "MobiAct", which was published by Vavoulas et al. The initial
model was developed using IBM Watson studio and then later transferred and
deployed on IBM Cloud with the streaming analytics service supported by IBM
Streams for monitoring real-time IoT data. We also present the systems
architecture of the real-time fall detection framework that we intend to use
with mbientlabs wearable health monitoring sensors for real time patient
monitoring at retirement homes or rehabilitation clinics.
AclNet: efficient end-to-end audio classification CNN
Authors: Jonathan J Huang, Juan Jose Alvarado Leanos
Date: 2018-11-16 00:00:00
Tasks: Audio Classification, Classification, Data Augmentation, General Classification

We propose an efficient end-to-end convolutional neural network architecture,
AclNet, for audio classification. When trained with our data augmentation and
regularization, we achieved state-of-the-art performance on the ESC-50 corpus
with 85:65% accuracy. Our network allows configurations such that memory and
compute requirements are drastically reduced, and a tradeoff analysis of
accuracy and complexity is presented. The analysis shows high accuracy at
significantly reduced computational complexity compared to existing solutions.
For example, a configuration with only 155k parameters and 49:3 million
multiply-adds per second is 81:75%, exceeding human accuracy of 81:3%. This
improved efficiency can enable always-on inference in energy-efficient
platforms.
Composite Binary Decomposition Networks
Authors: You Qiaoben, Zheng Wang, Jianguo Li, Yinpeng Dong, Yu-Gang Jiang, Jun Zhu
Date: 2018-11-16 00:00:00
Tasks: General Classification, image-classification, Image Classification, object-detection, Object Detection, Semantic Segmentation

Binary neural networks have great resource and computing efficiency, while
suffer from long training procedure and non-negligible accuracy drops, when
comparing to the full-precision counterparts. In this paper, we propose the
composite binary decomposition networks (CBDNet), which first compose
real-valued tensor of each layer with a limited number of binary tensors, and
then decompose some conditioned binary tensors into two low-rank binary
tensors, so that the number of parameters and operations are greatly reduced
comparing to the original ones. Experiments demonstrate the effectiveness of
the proposed method, as CBDNet can approximate image classification network
ResNet-18 using 5.25 bits, VGG-16 using 5.47 bits, DenseNet-121 using 5.72
bits, object detection networks SSD300 using 4.38 bits, and semantic
segmentation networks SegNet using 5.18 bits, all with minor accuracy drops.
Ground Plane Polling for 6DoF Pose Estimation of Objects on the Road
Authors: Akshay Rangesh, Mohan M. Trivedi
Date: 2018-11-16 00:00:00
Tasks: Object, Pose Estimation

This paper introduces an approach to produce accurate 3D detection boxes for objects on the ground using single monocular images. We do so by merging 2D visual cues, 3D object dimensions, and ground plane constraints to produce boxes that are robust against small errors and incorrect predictions. First, we train a single-shot convolutional neural network (CNN) that produces multiple visual and geometric cues of interest: 2D bounding boxes, 2D keypoints of interest, coarse object orientations and object dimensions. Subsets of these cues are then used to poll probable ground planes from a pre-computed database of ground planes, to identify the "best fit" plane with highest consensus. Once identified, the "best fit" plane provides enough constraints to successfully construct the desired 3D detection box, without directly predicting the 6DoF pose of the object. The entire ground plane polling (GPP) procedure is constructed as a non-parametrized layer of the CNN that outputs the desired "best fit" plane and the corresponding 3D keypoints, which together define the final 3D bounding box. Doing so allows us to poll thousands of different ground plane configurations without adding considerable overhead, while also creating a single CNN that directly produces the desired output without the need for post processing. We evaluate our method on the 2D detection and orientation estimation benchmark from the challenging KITTI dataset, and provide additional comparisons for 3D metrics of importance. This single-stage, single-pass CNN results in superior localization and orientation estimation compared to more complex and computationally expensive monocular approaches.
Spatial-temporal Multi-Task Learning for Within-field Cotton Yield Prediction
Authors: Long Nguyen, Jia Zhen, Zhe Lin, Hanxiang Du, Zhou Yang, Wenxuan Guo, Fang Jin
Date: 2018-11-16 00:00:00
Tasks: Crop Yield Prediction, Management, Multi-Task Learning

Understanding and accurately predicting within-field spatial variability of
crop yield play a key role in site-specific management of crop inputs such as
irrigation water and fertilizer for optimized crop production. However, such a
task is challenged by the complex interaction between crop growth and
environmental and managerial factors, such as climate, soil conditions,
tillage, and irrigation. In this paper, we present a novel Spatial-temporal
Multi-Task Learning algorithms for within-field crop yield prediction in west
Texas from 2001 to 2003. This algorithm integrates multiple heterogeneous data
sources to learn different features simultaneously, and to aggregate
spatial-temporal features by introducing a weighted regularizer to the loss
functions. Our comprehensive experimental results consistently outperform the
results of other conventional methods, and suggest a promising approach, which
improves the landscape of crop prediction research fields.
Optical Flow Based Background Subtraction with a Moving Camera: Application to Autonomous Driving
Authors: Sotirios Diamantas, Kostas Alexis
Date: 2018-11-16 00:00:00
Tasks: Autonomous Driving, Optical Flow Estimation, regression

In this research we present a novel algorithm for background subtraction
using a moving camera. Our algorithm is based purely on visual information
obtained from a camera mounted on an electric bus, operating in downtown Reno
which automatically detects moving objects of interest with the view to provide
a fully autonomous vehicle. In our approach we exploit the optical flow vectors
generated by the motion of the camera while keeping parameter assumptions a
minimum. At first, we estimate the Focus of Expansion, which is used to model
and simulate 3D points given the intrinsic parameters of the camera, and
perform multiple linear regression to estimate the regression equation
parameters and implement on the real data set of every frame to identify moving
objects. We validated our algorithm using data taken from a common bus route.
Neural network state estimation for full quantum state tomography
Authors: Qian Xu, Shuqi Xu
Date: 2018-11-16 00:00:00
Tasks: BIG-bench Machine Learning, Quantum State Tomography, State Estimation

An efficient state estimation model, neural network estimation (NNE),
empowered by machine learning techniques, is presented for full quantum state
tomography (FQST). A parameterized function based on neural network is applied
to map the measurement outcomes to the estimated quantum states. Parameters are
updated with supervised learning procedures. From the computational complexity
perspective our algorithm is the most efficient one among existing state
estimation algorithms for full quantum state tomography. We perform numerical
tests to prove both the accuracy and scalability of our model.
Mean Square Prediction Error of Misspecified Gaussian Process Models
Authors: Thomas Beckers, Jonas Umlauft, Sandra Hirche
Date: 2018-11-16 00:00:00
Tasks: Prediction, regression, valid

Nonparametric modeling approaches show very promising results in the area of
system identification and control. A naturally provided model confidence is
highly relevant for system-theoretical considerations to provide guarantees for
application scenarios. Gaussian process regression represents one approach
which provides such an indicator for the model confidence. However, this
measure is only valid if the covariance function and its hyperparameters fit
the underlying data generating process. In this paper, we derive an upper bound
for the mean square prediction error of misspecified Gaussian process models
based on a pseudo-concave optimization problem. We present application
scenarios and a simulation to compare the derived upper bound with the true
mean square error.
Detecting The Objects on The Road Using Modular Lightweight Network
Authors: Sen Cao, Yazhou Liu, Pongsak Lasang, ShengMei Shen
Date: 2018-11-16 00:00:00
Tasks: object-detection, Object Detection

This paper presents a modular lightweight network model for road objects
detection, such as car, pedestrian and cyclist, especially when they are far
away from the camera and their sizes are small. Great advances have been made
for the deep networks, but small objects detection is still a challenging task.
In order to solve this problem, majority of existing methods utilize
complicated network or bigger image size, which generally leads to higher
computation cost. The proposed network model is referred to as modular feature
fusion detector (MFFD), using a fast and efficient network architecture for
detecting small objects. The contribution lies in the following aspects: 1) Two
base modules have been designed for efficient computation: Front module reduce
the information loss from raw input images; Tinier module decrease model size
and computation cost, while ensuring the detection accuracy. 2) By stacking the
base modules, we design a context features fusion framework for multi-scale
object detection. 3) The propose method is efficient in terms of model size and
computation cost, which is applicable for resource limited devices, such as
embedded systems for advanced driver assistance systems (ADAS). Comparisons
with the state-of-the-arts on the challenging KITTI dataset reveal the
superiority of the proposed method. Especially, 100 fps can be achieved on the
embedded GPUs such as Jetson TX2.
Nudging Neural Conversational Model with Domain Knowledge
Authors: Sungjin Lee
Date: 2018-11-15 00:00:00
Tasks: model

Neural conversation models are attractive because one can train a model
directly on dialog examples with minimal labeling. With a small amount of data,
however, they often fail to generalize over test data since they tend to
capture spurious features instead of semantically meaningful domain knowledge.
To address this issue, we propose a novel approach that allows any human
teachers to transfer their domain knowledge to the conversation model in the
form of natural language rules. We tested our method with three different
dialog datasets. The improved performance across all domains demonstrates the
efficacy of our proposed method.
A note on hyperparameters in black-box adversarial examples
Authors: Jamie Hayes
Date: 2018-11-15 00:00:00

Since Biggio et al. (2013) and Szegedy et al. (2013) first drew attention to
adversarial examples, there has been a flood of research into defending and
attacking machine learning models. However, almost all proposed attacks assume
white-box access to a model. In other words, the attacker is assumed to have
perfect knowledge of the models weights and architecture. With this insider
knowledge, a white-box attack can leverage gradient information to craft
adversarial examples. Black-box attacks assume no knowledge of the model
weights or architecture. These attacks craft adversarial examples using
information only contained in the logits or hard classification label. Here, we
assume the attacker can use the logits in order to find an adversarial example.
Empirically, we show that 2-sided stochastic gradient estimation techniques are
not sensitive to scaling parameters, and can be used to mount powerful
black-box attacks requiring relatively few model queries.
Context-Dependent Upper-Confidence Bounds for Directed Exploration
Authors: Raksha Kumaraswamy, Matthew Schlegel, Adam White, Martha White
Date: 2018-11-15 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Efficient Exploration, Reinforcement Learning

Directed exploration strategies for reinforcement learning are critical for learning an optimal policy in a minimal number of interactions with the environment. Many algorithms use optimism to direct exploration, either through visitation estimates or upper confidence bounds, as opposed to data-inefficient strategies like \epsilon-greedy that use random, undirected exploration. Most data-efficient exploration methods require significant computation, typically relying on a learned model to guide exploration. Least-squares methods have the potential to provide some of the data-efficiency benefits of model-based approaches -- because they summarize past interactions -- with the computation closer to that of model-free approaches. In this work, we provide a novel, computationally efficient, incremental exploration strategy, leveraging this property of least-squares temporal difference learning (LSTD). We derive upper confidence bounds on the action-values learned by LSTD, with context-dependent (or state-dependent) noise variance. Such context-dependent noise focuses exploration on a subset of variable states, and allows for reduced exploration in other states. We empirically demonstrate that our algorithm can converge more quickly than other incremental exploration strategies using confidence estimates on action-values.
The Utility of Sparse Representations for Control in Reinforcement Learning
Authors: Vincent Liu, Raksha Kumaraswamy, Lei Le, Martha White
Date: 2018-11-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We investigate sparse representations for control in reinforcement learning.
While these representations are widely used in computer vision, their
prevalence in reinforcement learning is limited to sparse coding where
extracting representations for new data can be computationally intensive. Here,
we begin by demonstrating that learning a control policy incrementally with a
representation from a standard neural network fails in classic control domains,
whereas learning with a representation obtained from a neural network that has
sparsity properties enforced is effective. We provide evidence that the reason
for this is that the sparse representation provides locality, and so avoids
catastrophic interference, and particularly keeps consistent, stable values for
bootstrapping. We then discuss how to learn such sparse representations. We
explore the idea of Distributional Regularizers, where the activation of hidden
nodes is encouraged to match a particular distribution that results in sparse
activation across time. We identify a simple but effective way to obtain sparse
representations, not afforded by previously proposed strategies, making it more
practical for further investigation into sparse representations for
reinforcement learning.
Concept-Oriented Deep Learning: Generative Concept Representations
Authors: Daniel T. Chang
Date: 2018-11-15 00:00:00
Tasks: Deep Learning

Generative concept representations have three major advantages over
discriminative ones: they can represent uncertainty, they support integration
of learning and reasoning, and they are good for unsupervised and
semi-supervised learning. We discuss probabilistic and generative deep
learning, which generative concept representations are based on, and the use of
variational autoencoders and generative adversarial networks for learning
generative concept representations, particularly for concepts whose data are
sequences, structured data or graphs.
Streaming End-to-end Speech Recognition For Mobile Devices
Authors: Yanzhang He, Tara N. Sainath, Rohit Prabhavalkar, Ian McGraw, Raziel Alvarez, Ding Zhao, David Rybach, Anjuli Kannan, Yonghui Wu, Ruoming Pang, Qiao Liang, Deepti Bhatia, Yuan Shangguan, Bo Li, Golan Pundak, Khe Chai Sim, Tom Bagby, Shuo-Yiin Chang, Kanishka Rao, Alexander Gruenstein
Date: 2018-11-15 00:00:00
Tasks: speech-recognition, Speech Recognition

End-to-end (E2E) models, which directly predict output character sequences
given input speech, are good candidates for on-device speech recognition. E2E
models, however, present numerous challenges: In order to be truly useful, such
models must decode speech utterances in a streaming fashion, in real time; they
must be robust to the long tail of use cases; they must be able to leverage
user-specific context (e.g., contact lists); and above all, they must be
extremely accurate. In this work, we describe our efforts at building an E2E
speech recognizer using a recurrent neural network transducer. In experimental
evaluations, we find that the proposed approach can outperform a conventional
CTC-based model in terms of both latency and accuracy in a number of evaluation
categories.
A Spectral View of Adversarially Robust Features
Authors: Shivam Garg, Vatsal Sharan, Brian Hu Zhang, Gregory Valiant
Date: 2018-11-15 00:00:00
Proceeding: NeurIPS 2018 12

Given the apparent difficulty of learning models that are robust to adversarial perturbations, we propose tackling the simpler problem of developing adversarially robust features. Specifically, given a dataset and metric of interest, the goal is to return a function (or multiple functions) that 1) is robust to adversarial perturbations, and 2) has significant variation across the datapoints. We establish strong connections between adversarially robust features and a natural spectral property of the geometry of the dataset and metric of interest. This connection can be leveraged to provide both robust features, and a lower bound on the robustness of any function that has significant variance across the dataset. Finally, we provide empirical evidence that the adversarially robust features given by this spectral approach can be fruitfully leveraged to learn a robust (and accurate) model.
Economics of Human-AI Ecosystem: Value Bias and Lost Utility in Multi-Dimensional Gaps
Authors: Daniel Muller
Date: 2018-11-15 00:00:00
Tasks: Decision Making

In recent years, artificial intelligence (AI) decision-making and autonomous
systems became an integrated part of the economy, industry, and society. The
evolving economy of the human-AI ecosystem raising concerns regarding the risks
and values inherited in AI systems. This paper investigates the dynamics of
creation and exchange of values and points out gaps in perception of
cost-value, knowledge, space and time dimensions. It shows aspects of value
bias in human perception of achievements and costs that encoded in AI systems.
It also proposes rethinking hard goals definitions and cost-optimal
problem-solving principles in the lens of effectiveness and efficiency in the
development of trusted machines. The paper suggests a value-driven with cost
awareness strategy and principles for problem-solving and planning of effective
research progress to address real-world problems that involve diverse forms of
achievements, investments, and survival scenarios.
Conditional GANs for Multi-Illuminant Color Constancy: Revolution or Yet Another Approach?
Authors: Oleksii Sidorov
Date: 2018-11-15 00:00:00
Tasks: Color Constancy, Generative Adversarial Network, Shadow Detection, Shadow Detection And Removal, Shadow Removal

Non-uniform and multi-illuminant color constancy are important tasks, the
solution of which will allow to discard information about lighting conditions
in the image. Non-uniform illumination and shadows distort colors of real-world
objects and mostly do not contain valuable information. Thus, many computer
vision and image processing techniques would benefit from automatic discarding
of this information at the pre-processing step. In this work we propose novel
view on this classical problem via generative end-to-end algorithm based on
image conditioned Generative Adversarial Network. We also demonstrate the
potential of the given approach for joint shadow detection and removal. Forced
by the lack of training data, we render the largest existing shadow removal
dataset and make it publicly available. It consists of approximately 6,000
pairs of wide field of view synthetic images with and without shadows.
Unconstrained Submodular Maximization with Constant Adaptive Complexity
Authors: Lin Chen, Moran Feldman, Amin Karbasi
Date: 2018-11-15 00:00:00

In this paper, we consider the unconstrained submodular maximization problem.
We propose the first algorithm for this problem that achieves a tight
$(1/2-\varepsilon)$-approximation guarantee using $\tilde{O}(\varepsilon^{-1})$
adaptive rounds and a linear number of function evaluations. No previously
known algorithm for this problem achieves an approximation ratio better than
$1/3$ using less than $\Omega(n)$ rounds of adaptivity, where $n$ is the size
of the ground set. Moreover, our algorithm easily extends to the maximization
of a non-negative continuous DR-submodular function subject to a box constraint
and achieves a tight $(1/2-\varepsilon)$-approximation guarantee for this
problem while keeping the same adaptive and query complexities.
On Generality and Knowledge Transferability in Cross-Domain Duplicate Question Detection for Heterogeneous Community Question Answering
Authors: Mohomed Shazan Mohomed Jabbar, Luke Kumar, Hamman Samuel, Mi-Young Kim, Sankalp Prabhakar, Randy Goebel, Osmar Zaïane
Date: 2018-11-15 00:00:00
Tasks: Community Question Answering, Domain Adaptation, General Classification, Question Answering, Retrieval, Transfer Learning

Duplicate question detection is an ongoing challenge in community question
answering because semantically equivalent questions can have significantly
different words and structures. In addition, the identification of duplicate
questions can reduce the resources required for retrieval, when the same
questions are not repeated. This study compares the performance of deep neural
networks and gradient tree boosting, and explores the possibility of domain
adaptation with transfer learning to improve the under-performing target
domains for the text-pair duplicates classification task, using three
heterogeneous datasets: general-purpose Quora, technical Ask Ubuntu, and
academic English Stack Exchange. Ultimately, our study exposes the alternative
hypothesis that the meaning of a "duplicate" is not inherently general-purpose,
but rather is dependent on the domain of learning, hence reducing the chance of
transfer learning through adapting to the domain.