pwc-archive/papers-with-abstracts huggingface dataset:
Root Identification in Minirhizotron Imagery with Multiple Instance Learning
Authors: Guohao Yu, Alina Zare, Hudanyun Sheng, Roser Matamala, Joel Reyes-Cabrera, Felix B. Fritschi, Thomas E. Juenger
Date: 2019-03-07 00:00:00
Tasks: Multiple Instance Learning, Segmentation

In this paper, multiple instance learning (MIL) algorithms to automatically perform root detection and segmentation in minirhizotron imagery using only image-level labels are proposed. Root and soil characteristics vary from location to location, thus, supervised machine learning approaches that are trained with local data provide the best ability to identify and segment roots in minirhizotron imagery. However, labeling roots for training data (or otherwise) is an extremely tedious and time-consuming task. This paper aims to address this problem by labeling data at the image level (rather than the individual root or root pixel level) and train algorithms to perform individual root pixel level segmentation using MIL strategies. Three MIL methods (multiple instance adaptive cosine coherence estimator, multiple instance support vector machine, multiple instance learning with randomized trees) were applied to root detection and compared to non-MIL approches. The results show that MIL methods improve root segmentation in challenging minirhizotron imagery and reduce the labeling burden. In our results, multiple instance support vector machine outperformed other methods. The multiple instance adaptive cosine coherence estimator algorithm was a close second with an added advantage that it learned an interpretable root signature which identified the traits used to distinguish roots from soil and did not require parameter selection.
Three-Way Decisions-Based Conflict Analysis Models
Authors: Guangming Lang
Date: 2019-03-07 00:00:00
Tasks: Decision Making

Three-way decision theory, which trisects the universe with less risks or
costs, is considered as a powerful mathematical tool for handling uncertainty
in incomplete and imprecise information tables, and provides an effective tool
for conflict analysis decision making in real-time situations. In this paper,
we propose the concepts of the agreement, disagreement and neutral subsets of a
strategy with two evaluation functions, which establish the three-way
decisions-based conflict analysis models(TWDCAMs) for trisecting the universe
of agents, and employ a pair of two-way decisions models to interpret the
mechanism of the three-way decision rules for an agent. Subsequently, we
develop the concepts of the agreement, disagreement and neutral strategies of
an agent group with two evaluation functions, which build the TWDCAMs for
trisecting the universe of issues, and take a couple of two-way decisions
models to explain the mechanism of the three-way decision rules for an issue.
Finally, we reconstruct Fan, Qi and Wei's conflict analysis models(FQWCAMs) and
Sun, Ma and Zhao's conflict analysis models(SMZCAMs) with two evaluation
functions, and interpret FQWCAMs and SMZCAMs with a pair of two-day decisions
models, which illustrates that FQWCAMs and SMZCAMs are special cases of
TWDCAMs.
Explicit-risk-aware Path Planning with Reward Maximization
Authors: Xuesu Xiao, Jan Dufek, Robin Murphy
Date: 2019-03-07 00:00:00

This paper develops a path planner that minimizes risk (e.g. motion
execution) while maximizing accumulated reward (e.g., quality of sensor
viewpoint) motivated by visual assistance or tracking scenarios in unstructured
or confined environments. In these scenarios, the robot should maintain the
best viewpoint as it moves to the goal. However, in unstructured or confined
environments, some paths may increase the risk of collision; therefore there is
a tradeoff between risk and reward. Conventional state-dependent risk or
probabilistic uncertainty modeling do not consider path-level risk or is
difficult to acquire. This risk-reward planner explicitly represents risk as a
function of motion plans, i.e., paths. Without manual assignment of the
negative impact to the planner caused by risk, this planner takes in a
pre-established viewpoint quality map and plans target location and path
leading to it simultaneously, in order to maximize overall reward along the
entire path while minimizing risk. Exact and approximate algorithms are
presented, whose solution is further demonstrated on a physical tethered aerial
vehicle. Other than the visual assistance problem, the proposed framework also
provides a new planning paradigm to address minimum-risk planning under
dynamical risk and absence of substructure optimality and to balance the
trade-off between reward and risk.
ENIGMA-NG: Efficient Neural and Gradient-Boosted Inference Guidance for E
Authors: Karel Chvalovský, Jan Jakubův, Martin Suda, Josef Urban
Date: 2019-03-07 00:00:00
Tasks: Automated Theorem Proving

We describe an efficient implementation of clause guidance in
saturation-based automated theorem provers extending the ENIGMA approach.
Unlike in the first ENIGMA implementation where fast linear classifier is
trained and used together with manually engineered features, we have started to
experiment with more sophisticated state-of-the-art machine learning methods
such as gradient boosted trees and recursive neural networks. In particular the
latter approach poses challenges in terms of efficiency of clause evaluation,
however, we show that deep integration of the neural evaluation with the ATP
data-structures can largely amortize this cost and lead to competitive
real-time results. Both methods are evaluated on a large dataset of theorem
proving problems and compared with the previous approaches. The resulting
methods improve on the manually designed clause guidance, providing the first
practically convincing application of gradient-boosted and neural clause
guidance in saturation-style automated theorem provers.
Fast Video Retargeting Based on Seam Carving with Parental Labeling
Authors: Zhu Chuning
Date: 2019-03-07 00:00:00

Seam carving is a state-of-the-art content-aware image resizing technique
that effectively preserves the salient areas of an image. However, when applied
to video retargeting, not only is it time intensive, but it also creates highly
visible frame-wise discontinuities. In this paper, we propose a novel video
retargeting method based on seam carving. First, for a single frame, we locate
and remove several seams instead of one seam at once. Second, we use a dynamic
spatiotemporal buffer of energy maps and a standard deviation operator to carve
out the same seams in a temporal cube of frames with low variation in energy.
Last but not least, an improved energy function that considers motions detected
through difference method is employed. During testing, these enhancements
result in a 93 percent reduction in processing time and a higher frame-wise
consistency, thus showing superior performance compared to existing video
retargeting methods.
Transfer Learning Using Ensemble Neural Networks for Organic Solar Cell Screening
Authors: Arindam Paul, Dipendra Jha, Reda Al-Bahrani, Wei-keng Liao, Alok Choudhary, Ankit Agrawal
Date: 2019-03-07 00:00:00
Tasks: BIG-bench Machine Learning, Transfer Learning

Organic Solar Cells are a promising technology for solving the clean energy crisis in the world. However, generating candidate chemical compounds for solar cells is a time-consuming process requiring thousands of hours of laboratory analysis. For a solar cell, the most important property is the power conversion efficiency which is dependent on the highest occupied molecular orbitals (HOMO) values of the donor molecules. Recently, machine learning techniques have proved to be very useful in building predictive models for HOMO values of donor structures of Organic Photovoltaic Cells (OPVs). Since experimental datasets are limited in size, current machine learning models are trained on data derived from calculations based on density functional theory (DFT). Molecular line notations such as SMILES or InChI are popular input representations for describing the molecular structure of donor molecules. The two types of line representations encode different information, such as SMILES defines the bond types while InChi defines protonation. In this work, we present an ensemble deep neural network architecture, called SINet, which harnesses both the SMILES and InChI molecular representations to predict HOMO values and leverage the potential of transfer learning from a sizeable DFT-computed dataset- Harvard CEP to build more robust predictive models for relatively smaller HOPV datasets. Harvard CEP dataset contains molecular structures and properties for 2.3 million candidate donor structures for OPV while HOPV contains DFT-computed and experimental values of 350 and 243 molecules respectively. Our results demonstrate significant performance improvement from the use of transfer learning and leveraging both molecular representations.
MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments
Authors: Kenny Young, Tian Tian
Date: 2019-03-07 00:00:00
Tasks: Atari Games, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

The Arcade Learning Environment (ALE) is a popular platform for evaluating reinforcement learning agents. Much of the appeal comes from the fact that Atari games demonstrate aspects of competency we expect from an intelligent agent and are not biased toward any particular solution approach. The challenge of the ALE includes (1) the representation learning problem of extracting pertinent information from raw pixels, and (2) the behavioural learning problem of leveraging complex, delayed associations between actions and rewards. Often, the research questions we are interested in pertain more to the latter, but the representation learning problem adds significant computational expense. We introduce MinAtar, short for miniature Atari, a new set of environments that capture the general mechanics of specific Atari games while simplifying the representational complexity to focus more on the behavioural challenges. MinAtar consists of analogues of five Atari games: Seaquest, Breakout, Asterix, Freeway and Space Invaders. Each MinAtar environment provides the agent with a 10x10xn binary state representation. Each game plays out on a 10x10 grid with n channels corresponding to game-specific objects, such as ball, paddle and brick in the game Breakout. To investigate the behavioural challenges posed by MinAtar, we evaluated a smaller version of the DQN architecture as well as online actor-critic with eligibility traces. With the representation learning problem simplified, we can perform experiments with significantly less computational expense. In our experiments, we use the saved compute time to perform step-size parameter sweeps and more runs than is typical for the ALE. Experiments like this improve reproducibility, and allow us to draw more confident conclusions. We hope that MinAtar can allow researchers to thoroughly investigate behavioural challenges similar to those inherent in the ALE.
CLEVR-Dialog: A Diagnostic Dataset for Multi-Round Reasoning in Visual Dialog
Authors: Satwik Kottur, José M. F. Moura, Devi Parikh, Dhruv Batra, Marcus Rohrbach
Date: 2019-03-07 00:00:00
Proceeding: NAACL 2019 6
Tasks: coreference-resolution, Coreference Resolution, Diagnostic, Visual Dialog

Visual Dialog is a multimodal task of answering a sequence of questions grounded in an image, using the conversation history as context. It entails challenges in vision, language, reasoning, and grounding. However, studying these subtasks in isolation on large, real datasets is infeasible as it requires prohibitively-expensive complete annotation of the 'state' of all images and dialogs. We develop CLEVR-Dialog, a large diagnostic dataset for studying multi-round reasoning in visual dialog. Specifically, we construct a dialog grammar that is grounded in the scene graphs of the images from the CLEVR dataset. This combination results in a dataset where all aspects of the visual dialog are fully annotated. In total, CLEVR-Dialog contains 5 instances of 10-round dialogs for about 85k CLEVR images, totaling to 4.25M question-answer pairs. We use CLEVR-Dialog to benchmark performance of standard visual dialog models; in particular, on visual coreference resolution (as a function of the coreference distance). This is the first analysis of its kind for visual dialog models that was not possible without this dataset. We hope the findings from CLEVR-Dialog will help inform the development of future models for visual dialog. Our dataset and code are publicly available.
Deep CNN-based Multi-task Learning for Open-Set Recognition
Authors: Poojan Oza, Vishal M. Patel
Date: 2019-03-07 00:00:00
Tasks: Decoder, General Classification, image-classification, Image Classification, Multi-Task Learning, Open Set Learning

We propose a novel deep convolutional neural network (CNN) based multi-task
learning approach for open-set visual recognition. We combine a classifier
network and a decoder network with a shared feature extractor network within a
multi-task learning framework. We show that this approach results in better
open-set recognition accuracy. In our approach, reconstruction errors from the
decoder network are utilized for open-set rejection. In addition, we model the
tail of the reconstruction error distribution from the known classes using the
statistical Extreme Value Theory to improve the overall performance.
Experiments on multiple image classification datasets are performed and it is
shown that this method can perform significantly better than many competitive
open set recognition algorithms available in the literature. The code will be
made available at: github.com/otkupjnoz/mlosr.
Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation
Authors: Adrian V. Dalca, John Guttag, Mert R. Sabuncu
Date: 2019-03-07 00:00:00
Proceeding: CVPR 2018 6
Tasks: MRI segmentation, Segmentation

We consider the problem of segmenting a biomedical image into anatomical
regions of interest. We specifically address the frequent scenario where we
have no paired training data that contains images and their manual
segmentations. Instead, we employ unpaired segmentation images to build an
anatomical prior. Critically these segmentations can be derived from imaging
data from a different dataset and imaging modality than the current task. We
introduce a generative probabilistic model that employs the learned prior
through a convolutional neural network to compute segmentations in an
unsupervised setting. We conducted an empirical analysis of the proposed
approach in the context of structural brain MRI segmentation, using a
multi-study dataset of more than 14,000 scans. Our results show that an
anatomical prior can enable fast unsupervised segmentation which is typically
not possible using standard convolutional networks. The integration of
anatomical priors can facilitate CNN-based anatomical segmentation in a range
of novel clinical problems, where few or no annotations are available and thus
standard networks are not trainable. The code is freely available at
http://github.com/adalca/neuron.
A Learnable ScatterNet: Locally Invariant Convolutional Layers
Authors: Fergal Cotter, Nick Kingsbury
Date: 2019-03-07 00:00:00

In this paper we explore tying together the ideas from Scattering Transforms
and Convolutional Neural Networks (CNN) for Image Analysis by proposing a
learnable ScatterNet. Previous attempts at tying them together in hybrid
networks have tended to keep the two parts separate, with the ScatterNet
forming a fixed front end and a CNN forming a learned backend. We instead look
at adding learning between scattering orders, as well as adding learned layers
before the ScatterNet. We do this by breaking down the scattering orders into
single convolutional-like layers we call 'locally invariant' layers, and adding
a learned mixing term to this layer. Our experiments show that these locally
invariant layers can improve accuracy when added to either a CNN or a
ScatterNet. We also discover some surprising results in that the ScatterNet may
be best positioned after one or more layers of learning rather than at the
front of a neural network.
Dynamic Anonymized Evaluation for Behavioral Continuous Authentication
Authors: Rasana Manandhar, Shaya Wolf, Mike Borowczak
Date: 2019-03-07 00:00:00

Emerging technology demands reliable authentication mechanisms, particularly
in interconnected systems. Current systems rely on a single moment of
authentication, however continuous authentication systems assess a users
identity utilizing a constant biometric analysis. Spy Hunter, a continuous
authentication mechanism uses keystroke dynamics to validate users over blocks
of data. This easily-incorporated periodic biometric authentication system
validates genuine users and detects intruders quickly. Because it verifies
users in the background, Spy Hunter is not constrained to a password box.
Instead, it is flexible and can be layered with other mechanisms to provide
high-level security. Where other continuous authentication techniques rely on
scripted typing, Spy Hunter validates over free text in authentic environments.
This is accomplished in two phases, one where the user is provided a prompt and
another where the user is allowed free access to their computer. Additionally,
Spy Hunter focuses on the timing of different keystrokes rather than the
specific key being pressed. This allows for anonymous data to authenticate
users and avoids holding personal data. Utilizing a couple K-fold
cross-validation techniques, Spy Hunter is assessed based on how often the
system falsely accepts an intruder, how often the system falsely rejects a
genuine user, and the time it takes to validate a users identity. Spy Hunter
maintains error rates below 6% and identifies users in minimal numbers of
keystrokes. Continuous authentication provides higher level security than
one-time verification processes and Spy Hunter expands on the possibilities for
behavioral analysis based on keystroke dynamics.
SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems
Authors: Beidi Chen, Tharun Medini, James Farwell, Sameh Gobriel, Charlie Tai, Anshumali Shrivastava
Date: 2019-03-07 00:00:00
Tasks: CPU, GPU

Deep Learning (DL) algorithms are the central focus of modern machine learning systems. As data volumes keep growing, it has become customary to train large neural networks with hundreds of millions of parameters to maintain enough capacity to memorize these volumes and obtain state-of-the-art accuracy. To get around the costly computations associated with large models and data, the community is increasingly investing in specialized hardware for model training. However, specialized hardware is expensive and hard to generalize to a multitude of tasks. The progress on the algorithmic front has failed to demonstrate a direct advantage over powerful hardware such as NVIDIA-V100 GPUs. This paper provides an exception. We propose SLIDE (Sub-LInear Deep learning Engine) that uniquely blends smart randomized algorithms, with multi-core parallelism and workload optimization. Using just a CPU, SLIDE drastically reduces the computations during both training and inference outperforming an optimized implementation of Tensorflow (TF) on the best available GPU. Our evaluations on industry-scale recommendation datasets, with large fully connected architectures, show that training with SLIDE on a 44 core CPU is more than 3.5 times (1 hour vs. 3.5 hours) faster than the same network trained using TF on Tesla V100 at any given accuracy level. On the same CPU hardware, SLIDE is over 10x faster than TF. We provide codes and scripts for reproducibility.
Phase-aware Speech Enhancement with Deep Complex U-Net
Authors: Hyeong-Seok Choi, Jang-Hyun Kim, Jaesung Huh, Adrian Kim, Jung-Woo Ha, Kyogu Lee
Date: 2019-03-07 00:00:00
Proceeding: ICLR 2019 5
Tasks: Speech Enhancement, valid

Most deep learning-based models for speech enhancement have mainly focused on
estimating the magnitude of spectrogram while reusing the phase from noisy
speech for reconstruction. This is due to the difficulty of estimating the
phase of clean speech. To improve speech enhancement performance, we tackle the
phase estimation problem in three ways. First, we propose Deep Complex U-Net,
an advanced U-Net structured model incorporating well-defined complex-valued
building blocks to deal with complex-valued spectrograms. Second, we propose a
polar coordinate-wise complex-valued masking method to reflect the distribution
of complex ideal ratio masks. Third, we define a novel loss function, weighted
source-to-distortion ratio (wSDR) loss, which is designed to directly correlate
with a quantitative evaluation measure. Our model was evaluated on a mixture of
the Voice Bank corpus and DEMAND database, which has been widely used by many
deep learning models for speech enhancement. Ablation experiments were
conducted on the mixed dataset showing that all three proposed approaches are
empirically valid. Experimental results show that the proposed method achieves
state-of-the-art performance in all metrics, outperforming previous approaches
by a large margin.
Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders
Authors: Hongyu Shen, Daniel George, E. A. Huerta, Zhizhen Zhao
Date: 2019-03-06 00:00:00
Tasks: Denoising, Time Series, Time Series Analysis, Translation

Denoising of time domain data is a crucial task for many applications such as
communication, translation, virtual assistants etc. For this task, a
combination of a recurrent neural net (RNNs) with a Denoising Auto-Encoder
(DAEs) has shown promising results. However, this combined model is challenged
when operating with low signal-to-noise ratio (SNR) data embedded in
non-Gaussian and non-stationary noise. To address this issue, we design a novel
model, referred to as 'Enhanced Deep Recurrent Denoising Auto-Encoder'
(EDRDAE), that incorporates a signal amplifier layer, and applies curriculum
learning by first denoising high SNR signals, before gradually decreasing the
SNR until the signals become noise dominated. We showcase the performance of
EDRDAE using time-series data that describes gravitational waves embedded in
very noisy backgrounds. In addition, we show that EDRDAE can accurately denoise
signals whose topology is significantly more complex than those used for
training, demonstrating that our model generalizes to new classes of
gravitational waves that are beyond the scope of established denoising
algorithms.
Realizing Continual Learning through Modeling a Learning System as a Fiber Bundle
Authors: Zhenfeng Cao
Date: 2019-02-16 00:00:00
Tasks: Continual Learning

A human brain is capable of continual learning by nature; however the current
mainstream deep neural networks suffer from a phenomenon named catastrophic
forgetting (i.e., learning a new set of patterns suddenly and completely would
result in fully forgetting what has already been learned). In this paper we
propose a generic learning model, which regards a learning system as a fiber
bundle. By comparing the learning performance of our model with conventional
ones whose neural networks are multilayer perceptrons through a variety of
machine-learning experiments, we found our proposed model not only enjoys a
distinguished capability of continual learning but also bears a high
information capacity. In addition, we found in some learning scenarios the
learning performance can be further enhanced by making the learning time-aware
to mimic the episodic memory in human brain. Last but not least, we found that
the properties of forgetting in our model correspond well to those of human
memory. This work may shed light on how a human brain learns.