pwc-archive/papers-with-abstracts huggingface dataset:
Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Features
Authors: Xue Feng, Nicholas Tustison, Craig Meyer
Date: 2018-12-03 00:00:00
Tasks: Brain Segmentation, Brain Tumor Segmentation, Image Segmentation, Medical Image Segmentation, Prognosis, Segmentation, Semantic Segmentation, Survival Prediction, Tumor Segmentation

Accurate segmentation of different sub-regions of gliomas including
peritumoral edema, necrotic core, enhancing and non-enhancing tumor core from
multimodal MRI scans has important clinical relevance in diagnosis, prognosis
and treatment of brain tumors. However, due to the highly heterogeneous
appearance and shape, segmentation of the sub-regions is very challenging.
Recent development using deep learning models has proved its effectiveness in
the past several brain segmentation challenges as well as other semantic and
medical image segmentation problems. Most models in brain tumor segmentation
use a 2D/3D patch to predict the class label for the center voxel and variant
patch sizes and scales are used to improve the model performance. However, it
has low computation efficiency and also has limited receptive field. U-Net is a
widely used network structure for end-to-end segmentation and can be used on
the entire image or extracted patches to provide classification labels over the
entire input voxels so that it is more efficient and expect to yield better
performance with larger input size. Furthermore, instead of picking the best
network structure, an ensemble of multiple models, trained on different dataset
or different hyper-parameters, can generally improve the segmentation
performance. In this study we propose to use an ensemble of 3D U-Nets with
different hyper-parameters for brain tumor segmentation. Preliminary results
showed effectiveness of this model. In addition, we developed a linear model
for survival prediction using extracted imaging and non-imaging features,
which, despite the simplicity, can effectively reduce overfitting and
regression errors.
Identification and Recognition of Rice Diseases and Pests Using Convolutional Neural Networks
Authors: Chowdhury Rafeed Rahman, Preetom Saha Arko, Mohammed Eunus Ali, Mohammad Ashik Iqbal Khan, Sajid Hasan Apon, Farzana Nowrin, Abu Wasif
Date: 2018-12-03 00:00:00
Tasks: General Classification, image-classification, Image Classification

An accurate and timely detection of diseases and pests in rice plants can help farmers in applying timely treatment on the plants and thereby can reduce the economic losses substantially. Recent developments in deep learning based convolutional neural networks (CNN) have greatly improved the image classification accuracy. Being motivated by the success of CNNs in image classification, deep learning based approaches have been developed in this paper for detecting diseases and pests from rice plant images. The contribution of this paper is two fold: (i) State-of-the-art large scale architectures such as VGG16 and InceptionV3 have been adopted and fine tuned for detecting and recognizing rice diseases and pests. Experimental results show the effectiveness of these models with real datasets. (ii) Since large scale architectures are not suitable for mobile devices, a two-stage small CNN architecture has been proposed, and compared with the state-of-the-art memory efficient CNN architectures such as MobileNet, NasNet Mobile and SqueezeNet. Experimental results show that the proposed architecture can achieve the desired accuracy of 93.3\% with a significantly reduced model size (e.g., 99\% less size compared to that of VGG16).
TwoStreamVAN: Improving Motion Modeling in Video Generation
Authors: Ximeng Sun, Huijuan Xu, Kate Saenko
Date: 2018-12-03 00:00:00
Tasks: Motion Generation, Video Generation

Video generation is an inherently challenging task, as it requires modeling realistic temporal dynamics as well as spatial content. Existing methods entangle the two intrinsically different tasks of motion and content creation in a single generator network, but this approach struggles to simultaneously generate plausible motion and content. To im-prove motion modeling in video generation tasks, we propose a two-stream model that disentangles motion generation from content generation, called a Two-Stream Variational Adversarial Network (TwoStreamVAN). Given an action label and a noise vector, our model is able to create clear and consistent motion, and thus yields photorealistic videos. The key idea is to progressively generate and fuse multi-scale motion with its corresponding spatial content. Our model significantly outperforms existing methods on the standard Weizmann Human Action, MUG Facial Expression, and VoxCeleb datasets, as well as our new dataset of diverse human actions with challenging and complex motion. Our code is available at https://github.com/sunxm2357/TwoStreamVAN/.
Designing quantum experiments with a genetic algorithm
Authors: Rosanna Nichols, Lana Mineh, Jesús Rubio, Jonathan C. F. Matthews, Paul A. Knott
Date: 2018-12-03 00:00:00

We introduce a genetic algorithm that designs quantum optics experiments for engineering quantum states with specific properties. Our algorithm is powerful and flexible, and can easily be modified to find methods of engineering states for a range of applications. Here we focus on quantum metrology. First, we consider the noise-free case, and use the algorithm to find quantum states with a large quantum Fisher information (QFI). We find methods, which only involve experimental elements that are available with current or near-future technology, for engineering quantum states with up to a 100-fold improvement over the best classical state, and a 20-fold improvement over the optimal Gaussian state. Such states are a superposition of the vacuum with a large number of photons (around $80$), and can hence be seen as Schr\"odinger-cat-like states. We then apply the two most dominant noise sources in our setting -- photon loss and imperfect heralding -- and use the algorithm to find quantum states that still improve over the optimal Gaussian state with realistic levels of noise. This will open up experimental and technological work in using exotic non-Gaussian states for quantum-enhanced phase measurements. Finally, we use the Bayesian mean square error to look beyond the regime of validity of the QFI, finding quantum states with precision enhancements over the alternatives even when the experiment operates in the regime of limited data.
Sensitivity based Neural Networks Explanations
Authors: Enguerrand Horel, Virgile Mison, Tao Xiong, Kay Giesecke, Lidia Mangu
Date: 2018-12-03 00:00:00
Tasks: Sensitivity

Although neural networks can achieve very high predictive performance on
various different tasks such as image recognition or natural language
processing, they are often considered as opaque "black boxes". The difficulty
of interpreting the predictions of a neural network often prevents its use in
fields where explainability is important, such as the financial industry where
regulators and auditors often insist on this aspect. In this paper, we present
a way to assess the relative input features importance of a neural network
based on the sensitivity of the model output with respect to its input. This
method has the advantage of being fast to compute, it can provide both global
and local levels of explanations and is applicable for many types of neural
network architectures. We illustrate the performance of this method on both
synthetic and real data and compare it with other interpretation techniques.
This method is implemented into an open-source Python package that allows its
users to easily generate and visualize explanations for their neural networks.
DeepVoxels: Learning Persistent 3D Feature Embeddings
Authors: Vincent Sitzmann, Justus Thies, Felix Heide, Matthias Nießner, Gordon Wetzstein, Michael Zollhöfer
Date: 2018-12-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Reconstruction, Novel View Synthesis

In this work, we address the lack of 3D understanding of generative neural
networks by introducing a persistent 3D feature embedding for view synthesis.
To this end, we propose DeepVoxels, a learned representation that encodes the
view-dependent appearance of a 3D scene without having to explicitly model its
geometry. At its core, our approach is based on a Cartesian 3D grid of
persistent embedded features that learn to make use of the underlying 3D scene
structure. Our approach combines insights from 3D geometric computer vision
with recent advances in learning image-to-image mappings based on adversarial
loss functions. DeepVoxels is supervised, without requiring a 3D reconstruction
of the scene, using a 2D re-rendering loss and enforces perspective and
multi-view geometry in a principled manner. We apply our persistent 3D scene
representation to the problem of novel view synthesis demonstrating
high-quality results for a variety of challenging scenes.
Protection Against Reconstruction and Its Applications in Private Federated Learning
Authors: Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, Ryan Rogers
Date: 2018-12-03 00:00:00
Tasks: Federated Learning, image-classification, Image Classification

In large-scale statistical learning, data collection and model fitting are moving increasingly toward peripheral devices---phones, watches, fitness trackers---away from centralized data collection. Concomitant with this rise in decentralized data are increasing challenges of maintaining privacy while allowing enough information to fit accurate, useful statistical models. This motivates local notions of privacy---most significantly, local differential privacy, which provides strong protections against sensitive data disclosures---where data is obfuscated before a statistician or learner can even observe it, providing strong protections to individuals' data. Yet local privacy as traditionally employed may prove too stringent for practical use, especially in modern high-dimensional statistical and machine learning problems. Consequently, we revisit the types of disclosures and adversaries against which we provide protections, considering adversaries with limited prior information and ensuring that with high probability, ensuring they cannot reconstruct an individual's data within useful tolerances. By reconceptualizing these protections, we allow more useful data release---large privacy parameters in local differential privacy---and we design new (minimax) optimal locally differentially private mechanisms for statistical learning problems for \emph{all} privacy levels. We thus present practicable approaches to large-scale locally private model training that were previously impossible, showing theoretically and empirically that we can fit large-scale image classification and language models with little degradation in utility.
Deep Reinforcement Learning for Intelligent Transportation Systems
Authors: Xiao-Yang Liu, Zihan Ding, Sem Borst, Anwar Walid
Date: 2018-12-03 00:00:00
Tasks: Deep Reinforcement Learning, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Intelligent Transportation Systems (ITSs) are envisioned to play a critical
role in improving traffic flow and reducing congestion, which is a pervasive
issue impacting urban areas around the globe. Rapidly advancing vehicular
communication and edge cloud computation technologies provide key enablers for
smart traffic management. However, operating viable real-time actuation
mechanisms on a practically relevant scale involves formidable challenges,
e.g., policy iteration and conventional Reinforcement Learning (RL) techniques
suffer from poor scalability due to state space explosion. Motivated by these
issues, we explore the potential for Deep Q-Networks (DQN) to optimize traffic
light control policies. As an initial benchmark, we establish that the DQN
algorithms yield the "thresholding" policy in a single-intersection. Next, we
examine the scalability properties of DQN algorithms and their performance in a
linear network topology with several intersections along a main artery. We
demonstrate that DQN algorithms produce intelligent behavior, such as the
emergence of "greenwave" patterns, reflecting their ability to learn favorable
traffic light actuations.
A Survey on Semantic Parsing
Authors: Aishwarya Kamath, Rajarshi Das
Date: 2018-12-03 00:00:00
Proceeding: AKBC 2019
Tasks: Program Synthesis, Semantic Parsing, Survey

A significant amount of information in today's world is stored in structured and semi-structured knowledge bases. Efficient and simple methods to query them are essential and must not be restricted to only those who have expertise in formal query languages. The field of semantic parsing deals with converting natural language utterances to logical forms that can be easily executed on a knowledge base. In this survey, we examine the various components of a semantic parsing system and discuss prominent work ranging from the initial rule based methods to the current neural approaches to program synthesis. We also discuss methods that operate using varying levels of supervision and highlight the key challenges involved in the learning of such systems.
Structure Learning Using Forced Pruning
Authors: Ahmed Abdelatty, Pracheta Sahoo, Chiradeep Roy
Date: 2018-12-03 00:00:00
Tasks: BIG-bench Machine Learning

Markov networks are widely used in many Machine Learning applications
including natural language processing, computer vision, and bioinformatics .
Learning Markov networks have many complications ranging from intractable
computations involved to the possibility of learning a model with a huge number
of parameters. In this report, we provide a computationally tractable greedy
heuristic for learning Markov networks structure. The proposed heuristic
results in a model with a limited predefined number of parameters. We ran our
method on 3 fully-observed real datasets, and we observed that our method is
doing comparably good to the state of the art methods.
Online Graph-Adaptive Learning with Scalability and Privacy
Authors: Yanning Shen, Geert Leus, Georgios B. Giannakis
Date: 2018-12-03 00:00:00

Graphs are widely adopted for modeling complex systems, including financial,
biological, and social networks. Nodes in networks usually entail attributes,
such as the age or gender of users in a social network. However, real-world
networks can have very large size, and nodal attributes can be unavailable to a
number of nodes, e.g., due to privacy concerns. Moreover, new nodes can emerge
over time, which can necessitate real-time evaluation of their nodal
attributes. In this context, the present paper deals with scalable learning of
nodal attributes by estimating a nodal function based on noisy observations at
a subset of nodes. A multikernel-based approach is developed which is scalable
to large-size networks. Unlike most existing methods that re-solve the function
estimation problem over all existing nodes whenever a new node joins the
network, the novel method is capable of providing real-time evaluation of the
function values on newly-joining nodes without resorting to a batch solver.
Interestingly, the novel scheme only relies on an encrypted version of each
node's connectivity in order to learn the nodal attributes, which promotes
privacy. Experiments on both synthetic and real datasets corroborate the
effectiveness of the proposed methods.
Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning
Authors: Mitchell Wortsman, Kiana Ehsani, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi
Date: 2018-12-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: Meta-Learning, Meta Reinforcement Learning, Reinforcement Learning, Visual Navigation

Learning is an inherently continuous phenomenon. When humans learn a new task
there is no explicit distinction between training and inference. As we learn a
task, we keep learning about it while performing the task. What we learn and
how we learn it varies during different stages of learning. Learning how to
learn and adapt is a key property that enables us to generalize effortlessly to
new settings. This is in contrast with conventional settings in machine
learning where a trained model is frozen during inference. In this paper we
study the problem of learning to learn at both training and test time in the
context of visual navigation. A fundamental challenge in navigation is
generalization to unseen scenes. In this paper we propose a self-adaptive
visual navigation method (SAVN) which learns to adapt to new environments
without any explicit supervision. Our solution is a meta-reinforcement learning
approach where an agent learns a self-supervised interaction loss that
encourages effective navigation. Our experiments, performed in the AI2-THOR
framework, show major improvements in both success rate and SPL for visual
navigation in novel scenes. Our code and data are available at:
https://github.com/allenai/savn .
FoldingZero: Protein Folding from Scratch in Hydrophobic-Polar Model
Authors: Yanjun Li, Hengtong Kang, Ketian Ye, Shuyu Yin, Xiaolin Li
Date: 2018-12-03 00:00:00
Tasks: Deep Reinforcement Learning, Protein Folding, Protein Structure Prediction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

De novo protein structure prediction from amino acid sequence is one of the
most challenging problems in computational biology. As one of the extensively
explored mathematical models for protein folding, Hydrophobic-Polar (HP) model
enables thorough investigation of protein structure formation and evolution.
Although HP model discretizes the conformational space and simplifies the
folding energy function, it has been proven to be an NP-complete problem. In
this paper, we propose a novel protein folding framework FoldingZero,
self-folding a de novo protein 2D HP structure from scratch based on deep
reinforcement learning. FoldingZero features the coupled approach of a two-head
(policy and value heads) deep convolutional neural network (HPNet) and a
regularized Upper Confidence Bounds for Trees (R-UCT). It is trained solely by
a reinforcement learning algorithm, which improves HPNet and R-UCT iteratively
through iterative policy optimization. Without any supervision and domain
knowledge, FoldingZero not only achieves comparable results, but also learns
the latent folding knowledge to stabilize the structure. Without exponential
computation, FoldingZero shows promising potential to be adopted for real-world
protein properties prediction.
Analysing the Robustness of Evolutionary Algorithms to Noise: Refined Runtime Bounds and an Example Where Noise is Beneficial
Authors: Dirk Sudholt
Date: 2018-12-03 00:00:00
Tasks: Evolutionary Algorithms

We analyse the performance of well-known evolutionary algorithms (1+1)EA and
(1+$\lambda$)EA in the prior noise model, where in each fitness evaluation the
search point is altered before evaluation with probability $p$. We present
refined results for the expected optimisation time of the (1+1)EA and the
(1+$\lambda$)EA on the function LeadingOnes, where bits have to be optimised in
sequence. Previous work showed that the (1+1)EA on LeadingOnes runs in
polynomial expected time if $p = O((\log n)/n^2)$ and needs superpolynomial
expected time if $p = \omega((\log n)/n)$, leaving a huge gap for which no
results were known. We close this gap by showing that the expected optimisation
time is $\Theta(n^2) \cdot \exp(\Theta(\min\{pn^2, n\}))$ for all $p \le 1/2$,
allowing for the first time to locate the threshold between polynomial and
superpolynomial expected times at $p = \Theta((\log n)/n^2)$. Hence the (1+1)EA
on LeadingOnes is much more sensitive to noise than previously thought. We also
show that offspring populations of size $\lambda \ge 3.42\log n$ can
effectively deal with much higher noise than known before.
  Finally, we present an example of a rugged landscape where prior noise can
help to escape from local optima by blurring the landscape and allowing a hill
climber to see the underlying gradient. We prove that in this particular
setting noise can have a highly beneficial effect on performance.
Context Encoding Chest X-rays
Authors: Davide Belli, Shi Hu, Ecem Sogancioglu, Bram van Ginneken
Date: 2018-12-03 00:00:00
Tasks: Anomaly Detection, Image Inpainting, Medical Diagnosis

Chest X-rays are one of the most commonly used technologies for medical
diagnosis. Many deep learning models have been proposed to improve and automate
the abnormality detection task on this type of data. In this paper, we propose
a different approach based on image inpainting under adversarial training first
introduced by Goodfellow et al. We configure the context encoder model for this
task and train it over 1.1M 128x128 images from healthy X-rays. The goal of our
model is to reconstruct the missing central 64x64 patch. Once the model has
learned how to inpaint healthy tissue, we test its performance on images with
and without abnormalities. We discuss and motivate our results considering
PSNR, MSE and SSIM scores as evaluation metrics. In addition, we conduct a 2AFC
observer study showing that in half of the times an expert is unable to
distinguish real images from the ones reconstructed using our model. By
computing and visualizing the pixel-wise difference between the source and the
reconstructed images, we can highlight abnormalities to simplify further
detection and classification tasks.
Generative Adversarial Self-Imitation Learning
Authors: Yijie Guo, Junhyuk Oh, Satinder Singh, Honglak Lee
Date: 2018-12-03 00:00:00
Proceeding: ICLR 2019 5
Tasks: Imitation Learning, MuJoCo, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper explores a simple regularizer for reinforcement learning by
proposing Generative Adversarial Self-Imitation Learning (GASIL), which
encourages the agent to imitate past good trajectories via generative
adversarial imitation learning framework. Instead of directly maximizing
rewards, GASIL focuses on reproducing past good trajectories, which can
potentially make long-term credit assignment easier when rewards are sparse and
delayed. GASIL can be easily combined with any policy gradient objective by
using GASIL as a learned shaped reward function. Our experimental results show
that GASIL improves the performance of proximal policy optimization on 2D Point
Mass and MuJoCo environments with delayed reward and stochastic dynamics.
Visual Memory for Robust Path Following
Authors: Ashish Kumar, Saurabh Gupta, David Fouhey, Sergey Levine, Jitendra Malik
Date: 2018-12-03 00:00:00
Proceeding: NeurIPS 2018 12

Humans routinely retrace paths in a novel environment both forwards and
backwards despite uncertainty in their motion. This paper presents an approach
for doing so. Given a demonstration of a path, a first network generates a path
abstraction. Equipped with this abstraction, a second network observes the
world and decides how to act to retrace the path under noisy actuation and a
changing environment. The two networks are optimized end-to-end at training
time. We evaluate the method in two realistic simulators, performing path
following and homing under actuation noise and environmental changes. Our
experiments show that our approach outperforms classical approaches and other
learning based baselines.