pwc-archive/papers-with-abstracts huggingface dataset:
Crowd-Assisted Polyp Annotation of Virtual Colonoscopy Videos
Authors: Ji Hwan Park, Saad Nadeem, Joseph Marino, Kevin Baker, Matthew Barish, Arie Kaufman
Date: 2018-09-17 00:00:00
Tasks: Navigate

Virtual colonoscopy (VC) allows a radiologist to navigate through a 3D colon
model reconstructed from a computed tomography scan of the abdomen, looking for
polyps, the precursors of colon cancer. Polyps are seen as protrusions on the
colon wall and haustral folds, visible in the VC fly-through videos. A complete
review of the colon surface requires full navigation from the rectum to the
cecum in antegrade and retrograde directions, which is a tedious task that
takes an average of 30 minutes. Crowdsourcing is a technique for non-expert
users to perform certain tasks, such as image or video annotation. In this
work, we use crowdsourcing for the examination of complete VC fly-through
videos for polyp annotation by non-experts. The motivation for this is to
potentially help the radiologist reach a diagnosis in a shorter period of time,
and provide a stronger confirmation of the eventual diagnosis. The
crowdsourcing interface includes an interactive tool for the crowd to annotate
suspected polyps in the video with an enclosing box. Using our workflow, we
achieve an overall polyps-per-patient sensitivity of 87.88% (95.65% for polyps
$\geq$5mm and 70% for polyps $<$5mm). We also demonstrate the efficacy and
effectiveness of a non-expert user in detecting and annotating polyps and
discuss their possibility in aiding radiologists in VC examinations.
Adversarial Imitation via Variational Inverse Reinforcement Learning
Authors: Ahmed H. Qureshi, Byron Boots, Michael C. Yip
Date: 2018-09-17 00:00:00
Proceeding: ICLR 2019 5
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

We consider a problem of learning the reward and policy from expert examples
under unknown dynamics. Our proposed method builds on the framework of
generative adversarial networks and introduces the empowerment-regularized
maximum-entropy inverse reinforcement learning to learn near-optimal rewards
and policies. Empowerment-based regularization prevents the policy from
overfitting to expert demonstrations, which advantageously leads to more
generalized behaviors that result in learning near-optimal rewards. Our method
simultaneously learns empowerment through variational information maximization
along with the reward and policy under the adversarial learning formulation. We
evaluate our approach on various high-dimensional complex control tasks. We
also test our learned rewards in challenging transfer learning problems where
training and testing environments are made to be different from each other in
terms of dynamics or structure. The results show that our proposed method not
only learns near-optimal rewards and policies that are matching expert behavior
but also performs significantly better than state-of-the-art inverse
reinforcement learning algorithms.
Crowdsourcing Lung Nodules Detection and Annotation
Authors: Saeed Boorboor, Saad Nadeem, Ji Hwan Park, Kevin Baker, Arie Kaufman
Date: 2018-09-17 00:00:00
Tasks: Computed Tomography (CT), Lung Nodule Detection, Sensitivity

We present crowdsourcing as an additional modality to aid radiologists in the
diagnosis of lung cancer from clinical chest computed tomography (CT) scans.
More specifically, a complete workflow is introduced which can help maximize
the sensitivity of lung nodule detection by utilizing the collective
intelligence of the crowd. We combine the concept of overlapping thin-slab
maximum intensity projections (TS-MIPs) and cine viewing to render short videos
that can be outsourced as an annotation task to the crowd. These videos are
generated by linearly interpolating overlapping TS-MIPs of CT slices through
the depth of each quadrant of a patient's lung. The resultant videos are
outsourced to an online community of non-expert users who, after a brief
tutorial, annotate suspected nodules in these video segments. Using our
crowdsourcing workflow, we achieved a lung nodule detection sensitivity of over
90% for 20 patient CT datasets (containing 178 lung nodules with sizes between
1-30mm), and only 47 false positives from a total of 1021 annotations on
nodules of all sizes (96% sensitivity for nodules$>$4mm). These results show
that crowdsourcing can be a robust and scalable modality to aid radiologists in
screening for lung cancer, directly or in combination with computer-aided
detection (CAD) algorithms. For CAD algorithms, the presented workflow can
provide highly accurate training data to overcome the high false-positive rate
(per scan) problem. We also provide, for the first time, analysis on nodule
size and position which can help improve CAD algorithms.
Hidden Markov Model Estimation-Based Q-learning for Partially Observable Markov Decision Process
Authors: Hyung-Jin Yoon, Donghwan Lee, Naira Hovakimyan
Date: 2018-09-17 00:00:00
Tasks: Q-Learning

The objective is to study an on-line Hidden Markov model (HMM)
estimation-based Q-learning algorithm for partially observable Markov decision
process (POMDP) on finite state and action sets. When the full state
observation is available, Q-learning finds the optimal action-value function
given the current action (Q function). However, Q-learning can perform poorly
when the full state observation is not available. In this paper, we formulate
the POMDP estimation into a HMM estimation problem and propose a recursive
algorithm to estimate both the POMDP parameter and Q function concurrently.
Also, we show that the POMDP estimation converges to a set of stationary points
for the maximum likelihood estimate, and the Q function estimation converges to
a fixed point that satisfies the Bellman optimality equation weighted on the
invariant distribution of the state belief determined by the HMM estimation
process.
Segmenting root systems in X-ray computed tomography images using level sets
Authors: Amy Tabb, Keith E. Duncan, Christopher N. Topp
Date: 2018-09-17 00:00:00
Tasks: Segmentation

The segmentation of plant roots from soil and other growing media in X-ray
computed tomography images is needed to effectively study the root system
architecture without excavation. However, segmentation is a challenging problem
in this context because the root and non-root regions share similar features.
In this paper, we describe a method based on level sets and specifically
adapted for this segmentation problem. In particular, we deal with the issues
of using a level sets approach on large image volumes for root segmentation,
and track active regions of the front using an occupancy grid. This method
allows for straightforward modifications to a narrow-band algorithm such that
excessive forward and backward movements of the front can be avoided, distance
map computations in a narrow band context can be done in linear time through
modification of Meijster et al.'s distance transform algorithm, and regions of
the image volume are iteratively used to estimate distributions for root versus
non-root classes. Results are shown of three plant species of different
maturity levels, grown in three different media. Our method compares favorably
to a state-of-the-art method for root segmentation in X-ray CT image volumes.
Déjà Vu: an empirical evaluation of the memorization properties of ConvNets
Authors: Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Hervé Jégou
Date: 2018-09-17 00:00:00
Proceeding: ICLR 2019 5
Tasks: Data Augmentation, Memorization

Convolutional neural networks memorize part of their training data, which is
why strategies such as data augmentation and drop-out are employed to mitigate
overfitting. This paper considers the related question of "membership
inference", where the goal is to determine if an image was used during
training. We consider it under three complementary angles. We show how to
detect which dataset was used to train a model, and in particular whether some
validation images were used at train time. We then analyze explicit
memorization and extend classical random label experiments to the problem of
learning a model that predicts if an image belongs to an arbitrary set.
Finally, we propose a new approach to infer membership when a few of the top
layers are not available or have been fine-tuned, and show that lower layers
still carry information about the training samples. To support our findings, we
conduct large-scale experiments on Imagenet and subsets of YFCC-100M with
modern architectures such as VGG and Resnet.
Scattering Networks for Hybrid Representation Learning
Authors: Edouard Oyallon, Sergey Zagoruyko, Gabriel Huang, Nikos Komodakis, Simon Lacoste-Julien, Matthew Blaschko, Eugene Belilovsky
Date: 2018-09-17 00:00:00
Tasks: Representation Learning

Scattering networks are a class of designed Convolutional Neural Networks
(CNNs) with fixed weights. We argue they can serve as generic representations
for modelling images. In particular, by working in scattering space, we achieve
competitive results both for supervised and unsupervised learning tasks, while
making progress towards constructing more interpretable CNNs. For supervised
learning, we demonstrate that the early layers of CNNs do not necessarily need
to be learned, and can be replaced with a scattering network instead. Indeed,
using hybrid architectures, we achieve the best results with predefined
representations to-date, while being competitive with end-to-end learned CNNs.
Specifically, even applying a shallow cascade of small-windowed scattering
coefficients followed by 1$\times$1-convolutions results in AlexNet accuracy on
the ILSVRC2012 classification task. Moreover, by combining scattering networks
with deep residual networks, we achieve a single-crop top-5 error of 11.4% on
ILSVRC2012. Also, we show they can yield excellent performance in the small
sample regime on CIFAR-10 and STL-10 datasets, exceeding their end-to-end
counterparts, through their ability to incorporate geometrical priors. For
unsupervised learning, scattering coefficients can be a competitive
representation that permits image recovery. We use this fact to train hybrid
GANs to generate images. Finally, we empirically analyze several properties
related to stability and reconstruction of images from scattering coefficients.
Generative x-vectors for text-independent speaker verification
Authors: Longting Xu, Rohan Kumar Das, Emre Yilmaz, Jichen Yang, Haizhou Li
Date: 2018-09-17 00:00:00
Tasks: Speaker Verification, Text-Independent Speaker Verification

Speaker verification (SV) systems using deep neural network embeddings,
so-called the x-vector systems, are becoming popular due to its good
performance superior to the i-vector systems. The fusion of these systems
provides improved performance benefiting both from the discriminatively trained
x-vectors and generative i-vectors capturing distinct speaker characteristics.
In this paper, we propose a novel method to include the complementary
information of i-vector and x-vector, that is called generative x-vector. The
generative x-vector utilizes a transformation model learned from the i-vector
and x-vector representations of the background data. Canonical correlation
analysis is applied to derive this transformation model, which is later used to
transform the standard x-vectors of the enrollment and test segments to the
corresponding generative x-vectors. The SV experiments performed on the NIST
SRE 2010 dataset demonstrate that the system using generative x-vectors
provides considerably better performance than the baseline i-vector and
x-vector systems. Furthermore, the generative x-vectors outperform the fusion
of i-vector and x-vector systems for long-duration utterances, while yielding
comparable results for short-duration utterances.
Decision-support for the Masses by Enabling Conversations with Open Data
Authors: Biplav Srivastava
Date: 2018-09-16 00:00:00

Open data refers to data that is freely available for reuse. Although there
has been rapid increase in availability of open data to public in the last
decade, this has not translated into better decision-support tools for them. We
propose intelligent conversation generators as a grand challenge that would
automatically create data-driven conversation interfaces (CIs), also known as
chatbots or dialog systems, from open data and deliver personalized analytical
insights to users based on their contextual needs. Such generators will not
only help bring Artificial Intelligence (AI)-based solutions for important
societal problems to the masses but also advance AI by providing an integrative
testbed for human-centric AI and filling gaps in the state-of-art towards this
aim.
Improvements on Hindsight Learning
Authors: Ameet Deshpande, Srikanth Sarma, Ashutosh Jha, Balaraman Ravindran
Date: 2018-09-16 00:00:00
Tasks: Policy Gradient Methods, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Sparse reward problems are one of the biggest challenges in Reinforcement
Learning. Goal-directed tasks are one such sparse reward problems where a
reward signal is received only when the goal is reached. One promising way to
train an agent to perform goal-directed tasks is to use Hindsight Learning
approaches. In these approaches, even when an agent fails to reach the desired
goal, the agent learns to reach the goal it achieved instead. Doing this over
multiple trajectories while generalizing the policy learned from the achieved
goals, the agent learns a goal conditioned policy to reach any goal. One such
approach is Hindsight Experience replay which uses an off-policy Reinforcement
Learning algorithm to learn a goal conditioned policy. In this approach, a
replay of the past transitions happens in a uniformly random fashion. Another
approach is to use a Hindsight version of the policy gradients to directly
learn a policy. In this work, we discuss different ways to replay past
transitions to improve learning in hindsight experience replay focusing on
prioritized variants in particular. Also, we implement the Hindsight Policy
gradient methods to robotic tasks.
Systems of bounded rational agents with information-theoretic constraints
Authors: Sebastian Gottwald, Daniel A. Braun
Date: 2018-09-16 00:00:00

Specialization and hierarchical organization are important features of
efficient collaboration in economical, artificial, and biological systems.
Here, we investigate the hypothesis that both features can be explained by the
fact that each entity of such a system is limited in a certain way. We propose
an information-theoretic approach based on a Free Energy principle, in order to
computationally analyze systems of bounded rational agents that deal with such
limitations optimally. We find that specialization allows to focus on fewer
tasks, thus leading to a more efficient execution, but in turn requires
coordination in hierarchical structures of specialized experts and coordinating
units. Our results suggest that hierarchical architectures of specialized units
at lower levels that are coordinated by units at higher levels are optimal,
given that each unit's information-processing capability is limited and
conforms to constraints on complexity costs.
A Fog Robotic System for Dynamic Visual Servoing
Authors: Nan Tian, Jinfa Chen, Mas Ma, Robert Zhang, Bill Huang, Ken Goldberg, Somayeh Sojoudi
Date: 2018-09-16 00:00:00
Tasks: Object Recognition

Cloud Robotics is a paradigm where distributed robots are connected to cloud
services via networks to access unlimited computation power, at the cost of
network communication. However, due to limitations such as network latency and
variability, it is difficult to control dynamic, human compliant service robots
directly from the cloud. In this work, by leveraging asynchronous protocol with
a heartbeat signal, we combine cloud robotics with a smart edge device to build
a Fog Robotic system. We use the system to enable robust teleoperation of a
dynamic self-balancing robot from the cloud. We first use the system to pick up
boxes from static locations, a task commonly performed in warehouse logistics.
To make cloud teleoperation more efficient, we deploy image based visual
servoing (IBVS) to perform box pickups automatically. Visual feedbacks,
including apriltag recognition and tracking, are performed in the cloud to
emulate a Fog Robotic object recognition system for IBVS. We demonstrate the
feasibility of real-time dynamic automation system using this cloud-edge
hybrid, which opens up possibilities of deploying dynamic robotic control with
deep-learning recognition systems in Fog Robotics. Finally, we show that Fog
Robotics enables the self-balancing service robot to pick up a box
automatically from a person under unstructured environments.
AUEB at BioASQ 6: Document and Snippet Retrieval
Authors: Georgios-Ioannis Brokos, Polyvios Liosis, Ryan Mcdonald, Dimitris Pappas, Ion Androutsopoulos
Date: 2018-09-15 00:00:00
Proceeding: WS 2018 11
Tasks: Deep Learning, Retrieval

We present AUEB's submissions to the BioASQ 6 document and snippet retrieval
tasks (parts of Task 6b, Phase A). Our models use novel extensions to deep
learning architectures that operate solely over the text of the query and
candidate document/snippets. Our systems scored at the top or near the top for
all batches of the challenge, highlighting the effectiveness of deep learning
for these tasks.
Document Informed Neural Autoregressive Topic Models with Distributional Prior
Authors: Pankaj Gupta, Yatin Chaudhary, Florian Buettner, Hinrich Schütze
Date: 2018-09-15 00:00:00
Tasks: Language Modeling, Language Modelling, Retrieval, Topic Models

We address two challenges in topic models: (1) Context information around
words helps in determining their actual meaning, e.g., "networks" used in the
contexts "artificial neural networks" vs. "biological neuron networks".
Generative topic models infer topic-word distributions, taking no or only
little context into account. Here, we extend a neural autoregressive topic
model to exploit the full context information around words in a document in a
language modeling fashion. The proposed model is named as iDocNADE. (2) Due to
the small number of word occurrences (i.e., lack of context) in short text and
data sparsity in a corpus of few documents, the application of topic models is
challenging on such texts. Therefore, we propose a simple and efficient way of
incorporating external knowledge into neural autoregressive topic models: we
use embeddings as a distributional prior. The proposed variants are named as
DocNADEe and iDocNADEe.
  We present novel neural autoregressive topic model variants that consistently
outperform state-of-the-art generative topic models in terms of generalization,
interpretability (topic coherence) and applicability (retrieval and
classification) over 7 long-text and 8 short-text datasets from diverse
domains.
Adversarial Reinforcement Learning for Observer Design in Autonomous Systems under Cyber Attacks
Authors: Abhishek Gupta, Zhaoyuan Yang
Date: 2018-09-15 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Complex autonomous control systems are subjected to sensor failures,
cyber-attacks, sensor noise, communication channel failures, etc. that
introduce errors in the measurements. The corrupted information, if used for
making decisions, can lead to degraded performance. We develop a framework for
using adversarial deep reinforcement learning to design observer strategies
that are robust to adversarial errors in information channels. We further show
through simulation studies that the learned observation strategies perform
remarkably well when the adversary's injected errors are bounded in some sense.
We use neural network as function approximator in our studies with the
understanding that any other suitable function approximating class can be used
within our framework.
Visual Diagnostics for Deep Reinforcement Learning Policy Development
Authors: Jieliang Luo, Sam Green, Peter Feghali, George Legrady, Çetin Kaya Koç
Date: 2018-09-14 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Modern vision-based reinforcement learning techniques often use convolutional
neural networks (CNN) as universal function approximators to choose which
action to take for a given visual input. Until recently, CNNs have been treated
like black-box functions, but this mindset is especially dangerous when used
for control in safety-critical settings. In this paper, we present our
extensions of CNN visualization algorithms to the domain of vision-based
reinforcement learning. We use a simulated drone environment as an example
scenario. These visualization algorithms are an important tool for behavior
introspection and provide insight into the qualities and flaws of trained
policies when interacting with the physical world. A video may be seen at
https://sites.google.com/view/drlvisual .
DFTerNet: Towards 2-bit Dynamic Fusion Networks for Accurate Human Activity Recognition
Authors: Zhan Yang, Osolo Ian Raymond, ChengYuan Zhang, Ying Wan, Jun Long
Date: 2018-07-31 00:00:00
Tasks: Action Detection, Activity Detection, Activity Recognition, Human Activity Recognition, Quantization

Deep Convolutional Neural Networks (DCNNs) are currently popular in human
activity recognition applications. However, in the face of modern artificial
intelligence sensor-based games, many research achievements cannot be
practically applied on portable devices. DCNNs are typically resource-intensive
and too large to be deployed on portable devices, thus this limits the
practical application of complex activity detection. In addition, since
portable devices do not possess high-performance Graphic Processing Units
(GPUs), there is hardly any improvement in Action Game (ACT) experience.
Besides, in order to deal with multi-sensor collaboration, all previous human
activity recognition models typically treated the representations from
different sensor signal sources equally. However, distinct types of activities
should adopt different fusion strategies. In this paper, a novel scheme is
proposed. This scheme is used to train 2-bit Convolutional Neural Networks with
weights and activations constrained to {-0.5,0,0.5}. It takes into account the
correlation between different sensor signal sources and the activity types.
This model, which we refer to as DFTerNet, aims at producing a more reliable
inference and better trade-offs for practical applications. Our basic idea is
to exploit quantization of weights and activations directly in pre-trained
filter banks and adopt dynamic fusion strategies for different activity types.
Experiments demonstrate that by using dynamic fusion strategy can exceed the
baseline model performance by up to ~5% on activity recognition like
OPPORTUNITY and PAMAP2 datasets. Using the quantization method proposed, we
were able to achieve performances closer to that of full-precision counterpart.
These results were also verified using the UniMiB-SHAR dataset. In addition,
the proposed method can achieve ~9x acceleration on CPUs and ~11x memory
saving.