pwc-archive/papers-with-abstracts huggingface dataset:
Overcoming Small Minirhizotron Datasets Using Transfer Learning
Authors: Weihuang Xu, Guohao Yu, Alina Zare, Brendan Zurweller, Diane Rowland, Joel Reyes-Cabrera, Felix B. Fritschi, Roser Matamala, Thomas E. Juenger
Date: 2019-03-22 00:00:00
Tasks: Segmentation, Transfer Learning

Minirhizotron technology is widely used for studying the development of roots. Such systems collect visible-wavelength color imagery of plant roots in-situ by scanning an imaging system within a clear tube driven into the soil. Automated analysis of root systems could facilitate new scientific discoveries that would be critical to address the world's pressing food, resource, and climate issues. A key component of automated analysis of plant roots from imagery is the automated pixel-level segmentation of roots from their surrounding soil. Supervised learning techniques appear to be an appropriate tool for the challenge due to varying local soil and root conditions, however, lack of enough annotated training data is a major limitation due to the error-prone and time-consuming manually labeling process. In this paper, we investigate the use of deep neural networks based on the U-net architecture for automated, precise pixel-wise root segmentation in minirhizotron imagery. We compiled two minirhizotron image datasets to accomplish this study: one with 17,550 peanut root images and another with 28 switchgrass root images. Both datasets were paired with manually labeled ground truth masks. We trained three neural networks with different architectures on the larger peanut root dataset to explore the effect of the neural network depth on segmentation performance. To tackle the more limited switchgrass root dataset, we showed that models initialized with features pre-trained on the peanut dataset and then fine-tuned on the switchgrass dataset can improve segmentation performance significantly. We obtained 99\% segmentation accuracy in switchgrass imagery using only 21 training images. We also observed that features pre-trained on a closely related but relatively moderate size dataset like our peanut dataset are more effective than features pre-trained on the large but unrelated ImageNet dataset.
The Binary Space Partitioning-Tree Process
Authors: Xuhui Fan, Bin Li, Scott Anthony Sisson
Date: 2019-03-22 00:00:00

The Mondrian process represents an elegant and powerful approach for space
partition modelling. However, as it restricts the partitions to be
axis-aligned, its modelling flexibility is limited. In this work, we propose a
self-consistent Binary Space Partitioning (BSP)-Tree process to generalize the
Mondrian process. The BSP-Tree process is an almost surely right continuous
Markov jump process that allows uniformly distributed oblique cuts in a
two-dimensional convex polygon. The BSP-Tree process can also be extended using
a non-uniform probability measure to generate direction differentiated cuts.
The process is also self-consistent, maintaining distributional invariance
under a restricted subdomain. We use Conditional-Sequential Monte Carlo for
inference using the tree structure as the high-dimensional variable. The
BSP-Tree process's performance on synthetic data partitioning and relational
modelling demonstrates clear inferential improvements over the standard
Mondrian process and other related methods.
Unsupervised Speech Enhancement Based on Multichannel NMF-Informed Beamforming for Noise-Robust Automatic Speech Recognition
Authors: Kazuki Shimada, Yoshiaki Bando, Masato Mimura, Katsutoshi Itoyama, Kazuyoshi Yoshii, Tatsuya Kawahara
Date: 2019-03-22 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition

This paper describes multichannel speech enhancement for improving automatic
speech recognition (ASR) in noisy environments. Recently, the minimum variance
distortionless response (MVDR) beamforming has widely been used because it
works well if the steering vector of speech and the spatial covariance matrix
(SCM) of noise are given. To estimating such spatial information, conventional
studies take a supervised approach that classifies each time-frequency (TF) bin
into noise or speech by training a deep neural network (DNN). The performance
of ASR, however, is degraded in an unknown noisy environment. To solve this
problem, we take an unsupervised approach that decomposes each TF bin into the
sum of speech and noise by using multichannel nonnegative matrix factorization
(MNMF). This enables us to accurately estimate the SCMs of speech and noise not
from observed noisy mixtures but from separated speech and noise components. In
this paper we propose online MVDR beamforming by effectively initializing and
incrementally updating the parameters of MNMF. Another main contribution is to
comprehensively investigate the performances of ASR obtained by various types
of spatial filters, i.e., time-invariant and variant versions of MVDR
beamformers and those of rank-1 and full-rank multichannel Wiener filters, in
combination with MNMF. The experimental results showed that the proposed method
outperformed the state-of-the-art DNN-based beamforming method in unknown
environments that did not match training data.
Pose Estimation of Periacetabular Osteotomy Fragments with Intraoperative X-Ray Navigation
Authors: Robert B. Grupp, Rachel A. Hegeman, Ryan J. Murphy, Clayton P. Alexander, Yoshito Otake, Benjamin A. McArthur, Mehran Armand, Russell H. Taylor
Date: 2019-03-22 00:00:00
Tasks: Pose Estimation

Objective: State of the art navigation systems for pelvic osteotomies use optical systems with external fiducials. We propose the use of X-Ray navigation for pose estimation of periacetabular fragments without fiducials. Methods: A 2D/3D registration pipeline was developed to recover fragment pose. This pipeline was tested through an extensive simulation study and 6 cadaveric surgeries. Using osteotomy boundaries in the fluoroscopic images, the preoperative plan is refined to more accurately match the intraoperative shape. Results: In simulation, average fragment pose errors were 1.3{\deg}/1.7 mm when the planned fragment matched the intraoperative fragment, 2.2{\deg}/2.1 mm when the plan was not updated to match the true shape, and 1.9{\deg}/2.0 mm when the fragment shape was intraoperatively estimated. In cadaver experiments, the average pose errors were 2.2{\deg}/2.2 mm, 3.8{\deg}/2.5 mm, and 3.5{\deg}/2.2 mm when registering with the actual fragment shape, a preoperative plan, and an intraoperatively refined plan, respectively. Average errors of the lateral center edge angle were less than 2{\deg} for all fragment shapes in simulation and cadaver experiments. Conclusion: The proposed pipeline is capable of accurately reporting femoral head coverage within a range clinically identified for long-term joint survivability. Significance: Human interpretation of fragment pose is challenging and usually restricted to rotation about a single anatomical axis. The proposed pipeline provides an intraoperative estimate of rigid pose with respect to all anatomical axes, is compatible with minimally invasive incisions, and has no dependence on external fiducials.
Optimization Methods for Interpretable Differentiable Decision Trees in Reinforcement Learning
Authors: Andrew Silva, Taylor Killian, Ivan Dario Jimenez Rodriguez, Sung-Hyun Son, Matthew Gombolay
Date: 2019-03-22 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Decision trees are ubiquitous in machine learning for their ease of use and interpretability. Yet, these models are not typically employed in reinforcement learning as they cannot be updated online via stochastic gradient descent. We overcome this limitation by allowing for a gradient update over the entire tree that improves sample complexity affords interpretable policy extraction. First, we include theoretical motivation on the need for policy-gradient learning by examining the properties of gradient descent over differentiable decision trees. Second, we demonstrate that our approach equals or outperforms a neural network on all domains and can learn discrete decision trees online with average rewards up to 7x higher than a batch-trained decision tree. Third, we conduct a user study to quantify the interpretability of a decision tree, rule list, and a neural network with statistically significant results ($p < 0.001$).
A Type-coherent, Expressive Representation as an Initial Step to Language Understanding
Authors: Gene Louis Kim, Lenhart Schubert
Date: 2019-03-22 00:00:00
Proceeding: WS 2019 5
Tasks: Semantic Parsing

A growing interest in tasks involving language understanding by the NLP
community has led to the need for effective semantic parsing and inference.
Modern NLP systems use semantic representations that do not quite fulfill the
nuanced needs for language understanding: adequately modeling language
semantics, enabling general inferences, and being accurately recoverable. This
document describes underspecified logical forms (ULF) for Episodic Logic (EL),
which is an initial form for a semantic representation that balances these
needs. ULFs fully resolve the semantic type structure while leaving issues such
as quantifier scope, word sense, and anaphora unresolved; they provide a
starting point for further resolution into EL, and enable certain structural
inferences without further resolution. This document also presents preliminary
results of creating a hand-annotated corpus of ULFs for the purpose of training
a precise ULF parser, showing a three-person pairwise interannotator agreement
of 0.88 on confident annotations. We hypothesize that a divide-and-conquer
approach to semantic parsing starting with derivation of ULFs will lead to
semantic analyses that do justice to subtle aspects of linguistic meaning, and
will enable construction of more accurate semantic parsers.
Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations
Authors: Chen Qin, Bibo Shi, Rui Liao, Tommaso Mansi, Daniel Rueckert, Ali Kamen
Date: 2019-03-22 00:00:00
Tasks: Image Registration, Image-to-Image Translation, Medical Image Analysis

We propose a fully unsupervised multi-modal deformable image registration
method (UMDIR), which does not require any ground truth deformation fields or
any aligned multi-modal image pairs during training. Multi-modal registration
is a key problem in many medical image analysis applications. It is very
challenging due to complicated and unknown relationships between different
modalities. In this paper, we propose an unsupervised learning approach to
reduce the multi-modal registration problem to a mono-modal one through image
disentangling. In particular, we decompose images of both modalities into a
common latent shape space and separate latent appearance spaces via an
unsupervised multi-modal image-to-image translation approach. The proposed
registration approach is then built on the factorized latent shape code, with
the assumption that the intrinsic shape deformation existing in original image
domain is preserved in this latent space. Specifically, two metrics have been
proposed for training the proposed network: a latent similarity metric defined
in the common shape space and a learningbased image similarity metric based on
an adversarial loss. We examined different variations of our proposed approach
and compared them with conventional state-of-the-art multi-modal registration
methods. Results show that our proposed methods achieve competitive performance
against other methods at substantially reduced computation time.
A resnet-based universal method for speckle reduction in optical coherence tomography images
Authors: Cai Ning, Shi Fei, Hu Dianlin, Chen Yang
Date: 2019-03-22 00:00:00

In this work we propose a ResNet-based universal method for speckle reduction
in optical coherence tomography (OCT) images. The proposed model contains 3
main modules: Convolution-BN-ReLU, Branch and Residual module. Unlike
traditional algorithms, the model can learn from training data instead of
selecting parameters manually such as noise level. Application of this proposed
method to the OCT images shows a more than 22 dB signal-to-noise ratio
improvement in speckle noise reduction with minimal structure blurring. The
proposed method provides strong generalization ability and can process noisy
other types of OCT images without retraining. It outperforms other filtering
methods in suppressing speckle noises and revealing subtle features.
Improving Safety in Reinforcement Learning Using Model-Based Architectures and Human Intervention
Authors: Bharat Prakash, Mohit Khatwani, Nicholas Waytowich, Tinoosh Mohsenin
Date: 2019-03-22 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent progress in AI and Reinforcement learning has shown great success in
solving complex problems with high dimensional state spaces. However, most of
these successes have been primarily in simulated environments where failure is
of little or no consequence. Most real-world applications, however, require
training solutions that are safe to operate as catastrophic failures are
inadmissible especially when there is human interaction involved. Currently,
Safe RL systems use human oversight during training and exploration in order to
make sure the RL agent does not go into a catastrophic state. These methods
require a large amount of human labor and it is very difficult to scale up. We
present a hybrid method for reducing the human intervention time by combining
model-based approaches and training a supervised learner to improve sample
efficiency while also ensuring safety. We evaluate these methods on various
grid-world environments using both standard and visual representations and show
that our approach achieves better performance in terms of sample efficiency,
number of catastrophic states reached as well as overall task performance
compared to traditional model-free approaches
A Novel Independent RNN Approach to Classification of Seizures against Non-seizures
Authors: Xinghua Yao, Qiang Cheng, Guo-Qiang Zhang
Date: 2019-03-22 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification

In current clinical practices, electroencephalograms (EEG) are reviewed and
analyzed by trained neurologists to provide supports for therapeutic decisions.
Manual reviews can be laborious and error prone. Automatic and accurate
seizure/non-seizure classification methods are desirable. A critical challenge
is that seizure morphologies exhibit considerable variabilities. In order to
capture essential seizure features, this paper leverages an emerging deep
learning model, the independently recurrent neural network (IndRNN), to
construct a new approach for the seizure/non-seizure classification. This new
approach gradually expands the time scales, thereby extracting temporal and
spatial features from the local time duration to the entire record. Evaluations
are conducted with cross-validation experiments across subjects over the noisy
data of CHB-MIT. Experimental results demonstrate that the proposed approach
outperforms the current state-of-the-art methods. In addition, we explore how
the segment length affects the classification performance. Thirteen different
segment lengths are assessed, showing that the classification performance
varies over the segment lengths, and the maximal fluctuating margin is more
than 4%. Thus, the segment length is an important factor influencing the
classification performance.
WONDER: Weighted one-shot distributed ridge regression in high dimensions
Authors: Edgar Dobriban, Yue Sheng
Date: 2019-03-22 00:00:00
Tasks: Distributed Computing, regression, Unity, Vocal Bursts Intensity Prediction

In many areas, practitioners need to analyze large datasets that challenge conventional single-machine computing. To scale up data analysis, distributed and parallel computing approaches are increasingly needed. Here we study a fundamental and highly important problem in this area: How to do ridge regression in a distributed computing environment? Ridge regression is an extremely popular method for supervised learning, and has several optimality properties, thus it is important to study. We study one-shot methods that construct weighted combinations of ridge regression estimators computed on each machine. By analyzing the mean squared error in a high dimensional random-effects model where each predictor has a small effect, we discover several new phenomena. 1. Infinite-worker limit: The distributed estimator works well for very large numbers of machines, a phenomenon we call "infinite-worker limit". 2. Optimal weights: The optimal weights for combining local estimators sum to more than unity, due to the downward bias of ridge. Thus, all averaging methods are suboptimal. We also propose a new Weighted ONe-shot DistributEd Ridge regression (WONDER) algorithm. We test WONDER in simulation studies and using the Million Song Dataset as an example. There it can save at least 100x in computation time, while nearly preserving test accuracy.
Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records
Authors: Li Huang, Dianbo Liu
Date: 2019-03-22 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

Electronic medical records (EMRs) supports the development of machine
learning algorithms for predicting disease incidence, patient response to
treatment, and other healthcare events. But insofar most algorithms have been
centralized, taking little account of the decentralized, non-identically
independently distributed (non-IID), and privacy-sensitive characteristics of
EMRs that can complicate data collection, sharing and learning. To address this
challenge, we introduced a community-based federated machine learning (CBFL)
algorithm and evaluated it on non-IID ICU EMRs. Our algorithm clustered the
distributed data into clinically meaningful communities that captured similar
diagnoses and geological locations, and learnt one model for each community.
Throughout the learning process, the data was kept local on hospitals, while
locally-computed results were aggregated on a server. Evaluation results show
that CBFL outperformed the baseline FL algorithm in terms of Area Under the
Receiver Operating Characteristic Curve (ROC AUC), Area Under the
Precision-Recall Curve (PR AUC), and communication cost between hospitals and
the server. Furthermore, communities' performance difference could be explained
by how dissimilar one community was to others.
DQN with model-based exploration: efficient learning on environments with sparse rewards
Authors: Stephen Zhen Gou, Yuyang Liu
Date: 2019-03-22 00:00:00
Tasks: Atari Games, OpenAI Gym, Reinforcement Learning, Reinforcement Learning (RL)

We propose Deep Q-Networks (DQN) with model-based exploration, an algorithm
combining both model-free and model-based approaches that explores better and
learns environments with sparse rewards more efficiently. DQN is a
general-purpose, model-free algorithm and has been proven to perform well in a
variety of tasks including Atari 2600 games since it's first proposed by Minh
et el. However, like many other reinforcement learning (RL) algorithms, DQN
suffers from poor sample efficiency when rewards are sparse in an environment.
As a result, most of the transitions stored in the replay memory have no
informative reward signal, and provide limited value to the convergence and
training of the Q-Network. However, one insight is that these transitions can
be used to learn the dynamics of the environment as a supervised learning
problem. The transitions also provide information of the distribution of
visited states. Our algorithm utilizes these two observations to perform a
one-step planning during exploration to pick an action that leads to states
least likely to be seen, thus improving the performance of exploration. We
demonstrate our agent's performance in two classic environments with sparse
rewards in OpenAI gym: Mountain Car and Lunar Lander.
Towards Optimal Structured CNN Pruning via Generative Adversarial Learning
Authors: Shaohui Lin, Rongrong Ji, Chenqian Yan, Baochang Zhang, Liujuan Cao, Qixiang Ye, Feiyue Huang, David Doermann
Date: 2019-03-22 00:00:00
Proceeding: CVPR 2019 6

Structured pruning of filters or neurons has received increased focus for
compressing convolutional neural networks. Most existing methods rely on
multi-stage optimizations in a layer-wise manner for iteratively pruning and
retraining which may not be optimal and may be computation intensive. Besides,
these methods are designed for pruning a specific structure, such as filter or
block structures without jointly pruning heterogeneous structures. In this
paper, we propose an effective structured pruning approach that jointly prunes
filters as well as other structures in an end-to-end manner. To accomplish
this, we first introduce a soft mask to scale the output of these structures by
defining a new objective function with sparsity regularization to align the
output of baseline and network with this mask. We then effectively solve the
optimization problem by generative adversarial learning (GAL), which learns a
sparse soft mask in a label-free and an end-to-end manner. By forcing more
scaling factors in the soft mask to zero, the fast iterative
shrinkage-thresholding algorithm (FISTA) can be leveraged to fast and reliably
remove the corresponding structures. Extensive experiments demonstrate the
effectiveness of GAL on different datasets, including MNIST, CIFAR-10 and
ImageNet ILSVRC 2012. For example, on ImageNet ILSVRC 2012, the pruned
ResNet-50 achieves 10.88\% Top-5 error and results in a factor of 3.7x speedup.
This significantly outperforms state-of-the-art methods.
Learning Mixtures of Separable Dictionaries for Tensor Data: Analysis and Algorithms
Authors: Mohsen Ghassemi, Zahra Shakeri, Anand D. Sarwate, Waheed U. Bajwa
Date: 2019-03-22 00:00:00
Tasks: Dictionary Learning

This work addresses the problem of learning sparse representations of tensor data using structured dictionary learning. It proposes learning a mixture of separable dictionaries to better capture the structure of tensor data by generalizing the separable dictionary learning model. Two different approaches for learning mixture of separable dictionaries are explored and sufficient conditions for local identifiability of the underlying dictionary are derived in each case. Moreover, computational algorithms are developed to solve the problem of learning mixture of separable dictionaries in both batch and online settings. Numerical experiments are used to show the usefulness of the proposed model and the efficacy of the developed algorithms.
Fast and accurate reconstruction of HARDI using a 1D encoder-decoder convolutional network
Authors: Shi Yin, Zhengqiang Zhang, Qinmu Peng, Xinge You
Date: 2019-03-21 00:00:00
Tasks: compressed sensing, Decoder

High angular resolution diffusion imaging (HARDI) demands a lager amount of
data measurements compared to diffusion tensor imaging, restricting its use in
practice. In this work, we explore a learning-based approach to reconstruct
HARDI from a smaller number of measurements in q-space. The approach aims to
directly learn the mapping relationship between the measured and HARDI signals
from the collecting HARDI acquisitions of other subjects. Specifically, the
mapping is represented as a 1D encoder-decoder convolutional neural network
under the guidance of the compressed sensing (CS) theory for HARDI
reconstruction. The proposed network architecture mainly consists of two parts:
an encoder network produces the sparse coefficients and a decoder network
yields a reconstruction result. Experiment results demonstrate we can robustly
reconstruct HARDI signals with the accurate results and fast speed.