pwc-archive/papers-with-abstracts huggingface dataset:
Recent advances and opportunities in scene classification of aerial images with deep models
Authors: Fan Hu, Gui-Song Xia, Wen Yang, Liangpei Zhang
Date: 2018-06-04 00:00:00
Tasks: Classification, Diversity, General Classification, Scene Classification

Scene classification is a fundamental task in interpretation of remote
sensing images, and has become an active research topic in remote sensing
community due to its important role in a wide range of applications. Over the
past years, tremendous efforts have been made for developing powerful
approaches for scene classification of remote sensing images, evolving from the
traditional bag-of-visual-words model to the new generation deep convolutional
neural networks (CNNs). The deep CNN based methods have exhibited remarkable
breakthrough on performance, dramatically outperforming previous methods which
strongly rely on hand-crafted features. However, performance with deep CNNs has
gradually plateaued on existing public scene datasets, due to the notable
drawbacks of these datasets, such as the small scale and low-diversity of
training samples. Therefore, to promote the development of new methods and move
the scene classification task a step further, we deeply discuss the existing
problems in scene classification task, and accordingly present three open
directions. We believe these potential directions will be instructive for the
researchers in this field.
Techniques for proving Asynchronous Convergence results for Markov Chain Monte Carlo methods
Authors: Alexander Terenin, Eric P. Xing
Date: 2017-11-17 00:00:00

Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling are finding
widespread use in applied statistics and machine learning. These often lead to
difficult computational problems, which are increasingly being solved on
parallel and distributed systems such as compute clusters. Recent work has
proposed running iterative algorithms such as gradient descent and MCMC in
parallel asynchronously for increased performance, with good empirical results
in certain problems. Unfortunately, for MCMC this parallelization technique
requires new convergence theory, as it has been explicitly demonstrated to lead
to divergence on some examples. Recent theory on Asynchronous Gibbs sampling
describes why these algorithms can fail, and provides a way to alter them to
make them converge. In this article, we describe how to apply this theory in a
generic setting, to understand the asynchronous behavior of any MCMC algorithm,
including those implemented using parameter servers, and those not based on
Gibbs sampling.
Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning
Authors: Barak Oshri, Annie Hu, Peter Adelson, Xiao Chen, Pascaline Dupas, Jeremy Weinstein, Marshall Burke, David Lobell, Stefano Ermon
Date: 2018-06-03 00:00:00
Tasks: Spatial Interpolation

The UN Sustainable Development Goals allude to the importance of
infrastructure quality in three of its seventeen goals. However, monitoring
infrastructure quality in developing regions remains prohibitively expensive
and impedes efforts to measure progress toward these goals. To this end, we
investigate the use of widely available remote sensing data for the prediction
of infrastructure quality in Africa. We train a convolutional neural network to
predict ground truth labels from the Afrobarometer Round 6 survey using Landsat
8 and Sentinel 1 satellite imagery.
  Our best models predict infrastructure quality with AUROC scores of 0.881 on
Electricity, 0.862 on Sewerage, 0.739 on Piped Water, and 0.786 on Roads using
Landsat 8. These performances are significantly better than models that
leverage OpenStreetMap or nighttime light intensity on the same tasks. We also
demonstrate that our trained model can accurately make predictions in an unseen
country after fine-tuning on a small sample of images. Furthermore, the model
can be deployed in regions with limited samples to predict infrastructure
outcomes with higher performance than nearest neighbor spatial interpolation.
Conservative Exploration using Interleaving
Authors: Sumeet Katariya, Branislav Kveton, Zheng Wen, Vamsi K. Potluru
Date: 2018-06-03 00:00:00
Tasks: Diversity

In many practical problems, a learning agent may want to learn the best
action in hindsight without ever taking a bad action, which is significantly
worse than the default production action. In general, this is impossible
because the agent has to explore unknown actions, some of which can be bad, to
learn better actions. However, when the actions are combinatorial, this may be
possible if the unknown action can be evaluated by interleaving it with the
production action. We formalize this concept as learning in stochastic
combinatorial semi-bandits with exchangeable actions. We design efficient
learning algorithms for this problem, bound their n-step regret, and evaluate
them on both synthetic and real-world problems. Our real-world experiments show
that our algorithms can learn to recommend K most attractive movies without
ever violating a strict production constraint, both overall and subject to a
diversity constraint.
On the Limitations of First-Order Approximation in GAN Dynamics
Authors: Jerry Li, Aleksander Madry, John Peebles, Ludwig Schmidt
Date: 2017-06-29 00:00:00
Proceeding: ICML 2018 7

While Generative Adversarial Networks (GANs) have demonstrated promising
performance on multiple vision tasks, their learning dynamics are not yet well
understood, both in theory and in practice. To address this issue, we study GAN
dynamics in a simple yet rich parametric model that exhibits several of the
common problematic convergence behaviors such as vanishing gradients, mode
collapse, and diverging or oscillatory behavior. In spite of the non-convex
nature of our model, we are able to perform a rigorous theoretical analysis of
its convergence behavior. Our analysis reveals an interesting dichotomy: a GAN
with an optimal discriminator provably converges, while first order
approximations of the discriminator steps lead to unstable GAN dynamics and
mode collapse. Our result suggests that using first order discriminator steps
(the de-facto standard in most existing GAN setups) might be one of the factors
that makes GAN training challenging in practice.
Soccer on Your Tabletop
Authors: Konstantinos Rematas, Ira Kemelmacher-Shlizerman, Brian Curless, Steve Seitz
Date: 2018-06-03 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Reconstruction, Depth Estimation

We present a system that transforms a monocular video of a soccer game into a
moving 3D reconstruction, in which the players and field can be rendered
interactively with a 3D viewer or through an Augmented Reality device. At the
heart of our paper is an approach to estimate the depth map of each player,
using a CNN that is trained on 3D player data extracted from soccer video
games. We compare with state of the art body pose and depth estimation
techniques, and show results on both synthetic ground truth benchmarks, and
real YouTube soccer footage.
Structural Learning of Multivariate Regression Chain Graphs via Decomposition
Authors: Mohammad Ali Javidian, Marco Valtorta
Date: 2018-06-03 00:00:00
Tasks: Model Selection, regression

We extend the decomposition approach for learning Bayesian networks (BNs) proposed by (Xie et. al.) to learning multivariate regression chain graphs (MVR CGs), which include BNs as a special case. The same advantages of this decomposition approach hold in the more general setting: reduced complexity and increased power of computational independence tests. Moreover, latent (hidden) variables can be represented in MVR CGs by using bidirected edges, and our algorithm correctly recovers any independence structure that is faithful to an MVR CG, thus greatly extending the range of applications of decomposition-based model selection techniques. Simulations under a variety of settings demonstrate the competitive performance of our method in comparison with the PC-like algorithm (Sonntag and Pena). In fact, the decomposition-based algorithm usually outperforms the PC-like algorithm except in running time. The performance of both algorithms is much better when the underlying graph is sparse.
Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior
Authors: Peng Liu, Ruogu Fang
Date: 2017-07-17 00:00:00
Tasks: Denoising, Image Denoising

We explore an innovative strategy for image denoising by using convolutional
neural networks (CNN) to learn similar pixel-distribution features from noisy
images. Many types of image noise follow a certain pixel-distribution in
common, such as additive white Gaussian noise (AWGN). By increasing CNN's width
with larger reception fields and more channels in each layer, CNNs can reveal
the ability to extract more accurate pixel-distribution features. The key to
our approach is a discovery that wider CNNs with more convolutions tend to
learn the similar pixel-distribution features, which reveals a new strategy to
solve low-level vision problems effectively that the inference mapping
primarily relies on the priors behind the noise property instead of deeper CNNs
with more stacked nonlinear layers. We evaluate our work, Wide inference
Networks (WIN), on AWGN and demonstrate that by learning pixel-distribution
features from images, WIN-based network consistently achieves significantly
better performance than current state-of-the-art deep CNN-based methods in both
quantitative and visual evaluations. \textit{Code and models are available at
\url{https://github.com/cswin/WIN}}.
Disconnected Manifold Learning for Generative Adversarial Networks
Authors: Mahyar Khayatkhoei, Ahmed Elgammal, Maneesh Singh
Date: 2018-06-03 00:00:00
Proceeding: NeurIPS 2018 12

Natural images may lie on a union of disjoint manifolds rather than one
globally connected manifold, and this can cause several difficulties for the
training of common Generative Adversarial Networks (GANs). In this work, we
first show that single generator GANs are unable to correctly model a
distribution supported on a disconnected manifold, and investigate how sample
quality, mode dropping and local convergence are affected by this. Next, we
show how using a collection of generators can address this problem, providing
new insights into the success of such multi-generator GANs. Finally, we explain
the serious issues caused by considering a fixed prior over the collection of
generators and propose a novel approach for learning the prior and inferring
the necessary number of generators without any supervision. Our proposed
modifications can be applied on top of any other GAN model to enable learning
of distributions supported on disconnected manifolds. We conduct several
experiments to illustrate the aforementioned shortcoming of GANs, its
consequences in practice, and the effectiveness of our proposed modifications
in alleviating these issues.
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization
Authors: Hoi-To Wai, Zhuoran Yang, Zhaoran Wang, Mingyi Hong
Date: 2018-06-03 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Despite the success of single-agent reinforcement learning, multi-agent
reinforcement learning (MARL) remains challenging due to complex interactions
between agents. Motivated by decentralized applications such as sensor
networks, swarm robotics, and power grids, we study policy evaluation in MARL,
where agents with jointly observed state-action pairs and private local rewards
collaborate to learn the value of a given policy. In this paper, we propose a
double averaging scheme, where each agent iteratively performs averaging over
both space and time to incorporate neighboring gradient information and local
reward information, respectively. We prove that the proposed algorithm
converges to the optimal solution at a global geometric rate. In particular,
such an algorithm is built upon a primal-dual reformulation of the mean squared
projected Bellman error minimization problem, which gives rise to a
decentralized convex-concave saddle-point problem. To the best of our
knowledge, the proposed double averaging primal-dual optimization algorithm is
the first to achieve fast finite-time convergence on decentralized
convex-concave saddle-point problems.
Deploying Customized Data Representation and Approximate Computing in Machine Learning Applications
Authors: Mahdi Nazemi, Massoud Pedram
Date: 2018-06-03 00:00:00
Tasks: BIG-bench Machine Learning

Major advancements in building general-purpose and customized hardware have
been one of the key enablers of versatility and pervasiveness of machine
learning models such as deep neural networks. To sustain this ubiquitous
deployment of machine learning models and cope with their computational and
storage complexity, several solutions such as low-precision representation of
model parameters using fixed-point representation and deploying approximate
arithmetic operations have been employed. Studying the potency of such
solutions in different applications requires integrating them into existing
machine learning frameworks for high-level simulations as well as implementing
them in hardware to analyze their effects on power/energy dissipation,
throughput, and chip area. Lop is a library for design space exploration that
bridges the gap between machine learning and efficient hardware realization. It
comprises a Python module, which can be integrated with some of the existing
machine learning frameworks and implements various customizable data
representations including fixed-point and floating-point as well as approximate
arithmetic operations.Furthermore, it includes a highly-parameterized Scala
module, which allows synthesizing hardware based on the said data
representations and arithmetic operations. Lop allows researchers and designers
to quickly compare quality of their models using various data representations
and arithmetic operations in Python and contrast the hardware cost of viable
representations by synthesizing them on their target platforms (e.g., FPGA or
ASIC). To the best of our knowledge, Lop is the first library that allows both
software simulation and hardware realization using customized data
representations and approximate computing techniques.
Patch-Based Image Hallucination for Super Resolution with Detail Reconstruction from Similar Sample Images
Authors: Chieh-Chi Kao, Yu-Xiang Wang, Jonathan Waltman, Pradeep Sen
Date: 2018-06-03 00:00:00
Tasks: Hallucination, Super-Resolution

Image hallucination and super-resolution have been studied for decades, and
many approaches have been proposed to upsample low-resolution images using
information from the images themselves, multiple example images, or large image
databases. However, most of this work has focused exclusively on small
magnification levels because the algorithms simply sharpen the blurry edges in
the upsampled images - no actual new detail is typically reconstructed in the
final result. In this paper, we present a patch-based algorithm for image
hallucination which, for the first time, properly synthesizes novel high
frequency detail. To do this, we pose the synthesis problem as a patch-based
optimization which inserts coherent, high-frequency detail from
contextually-similar images of the same physical scene/subject provided from
either a personal image collection or a large online database. The resulting
image is visually plausible and contains coherent high frequency information.
We demonstrate the robustness of our algorithm by testing it on a large number
of images and show that its performance is considerably superior to all
state-of-the-art approaches, a result that is verified to be statistically
significant through a randomized user study.
A Comprehensive Comparison between Neural Style Transfer and Universal Style Transfer
Authors: Somshubra Majumdar, Amlaan Bhoi, Ganesh Jagadeesan
Date: 2018-06-03 00:00:00
Tasks: Style Transfer

Style transfer aims to transfer arbitrary visual styles to content images. We
explore algorithms adapted from two papers that try to solve the problem of
style transfer while generalizing on unseen styles or compromised visual
quality. Majority of the improvements made focus on optimizing the algorithm
for real-time style transfer while adapting to new styles with considerably
less resources and constraints. We compare these strategies and compare how
they measure up to produce visually appealing images. We explore two approaches
to style transfer: neural style transfer with improvements and universal style
transfer. We also make a comparison between the different images produced and
how they can be qualitatively measured.
Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration
Authors: Seyed Sadegh Mohseni Salehi, Shadab Khan, Deniz Erdogmus, Ali Gholipour
Date: 2018-03-15 00:00:00
Tasks: 3D Pose Estimation, Anatomy, Generative Adversarial Network, Pose Estimation, regression

With an aim to increase the capture range and accelerate the performance of
state-of-the-art inter-subject and subject-to-template 3D registration, we
propose deep learning-based methods that are trained to find the 3D position of
arbitrarily oriented subjects or anatomy based on slices or volumes of medical
images. For this, we propose regression CNNs that learn to predict the
angle-axis representation of 3D rotations and translations using image
features. We use and compare mean square error and geodesic loss to train
regression CNNs for 3D pose estimation used in two different scenarios:
slice-to-volume registration and volume-to-volume registration. Our results
show that in such registration applications that are amendable to learning, the
proposed deep learning methods with geodesic loss minimization can achieve
accurate results with a wide capture range in real-time (<100ms). We also
tested the generalization capability of the trained CNNs on an expanded age
range and on images of newborn subjects with similar and different MR image
contrasts. We trained our models on T2-weighted fetal brain MRI scans and used
them to predict the 3D pose of newborn brains based on T1-weighted MRI scans.
We showed that the trained models generalized well for the new domain when we
performed image contrast transfer through a conditional generative adversarial
network. This indicates that the domain of application of the trained deep
regression CNNs can be further expanded to image modalities and contrasts other
than those used in training. A combination of our proposed methods with
accelerated optimization-based registration algorithms can dramatically enhance
the performance of automatic imaging devices and image processing methods of
the future.
Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering
Authors: Pan Li, Olgica Milenkovic
Date: 2018-03-10 00:00:00
Proceeding: ICML 2018 7
Tasks: Clustering

We introduce submodular hypergraphs, a family of hypergraphs that have
different submodular weights associated with different cuts of hyperedges.
Submodular hypergraphs arise in clustering applications in which higher-order
structures carry relevant information. For such hypergraphs, we define the
notion of p-Laplacians and derive corresponding nodal domain theorems and k-way
Cheeger inequalities. We conclude with the description of algorithms for
computing the spectra of 1- and 2-Laplacians that constitute the basis of new
spectral hypergraph clustering methods.
On the Flip Side: Identifying Counterexamples in Visual Question Answering
Authors: Gabriel Grand, Aron Szanto, Yoon Kim, Alexander Rush
Date: 2018-06-03 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Visual question answering (VQA) models respond to open-ended natural language
questions about images. While VQA is an increasingly popular area of research,
it is unclear to what extent current VQA architectures learn key semantic
distinctions between visually-similar images. To investigate this question, we
explore a reformulation of the VQA task that challenges models to identify
counterexamples: images that result in a different answer to the original
question. We introduce two methods for evaluating existing VQA models against a
supervised counterexample prediction task, VQA-CX. While our models surpass
existing benchmarks on VQA-CX, we find that the multimodal representations
learned by an existing state-of-the-art VQA model do not meaningfully
contribute to performance on this task. These results call into question the
assumption that successful performance on the VQA benchmark is indicative of
general visual-semantic reasoning abilities.
Intentional Control of Type I Error over Unconscious Data Distortion: a Neyman-Pearson Approach to Text Classification
Authors: Lucy Xia, Richard Zhao, Yanhui Wu, Xin Tong
Date: 2018-02-07 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

This paper addresses the challenges in classifying textual data obtained from open online platforms, which are vulnerable to distortion. Most existing classification methods minimize the overall classification error and may yield an undesirably large type I error (relevant textual messages are classified as irrelevant), particularly when available data exhibit an asymmetry between relevant and irrelevant information. Data distortion exacerbates this situation and often leads to fallacious prediction. To deal with inestimable data distortion, we propose the use of the Neyman-Pearson (NP) classification paradigm, which minimizes type II error under a user-specified type I error constraint. Theoretically, we show that the NP oracle is unaffected by data distortion when the class conditional distributions remain the same. Empirically, we study a case of classifying posts about worker strikes obtained from a leading Chinese microblogging platform, which are frequently prone to extensive, unpredictable and inestimable censorship. We demonstrate that, even though the training and test data are susceptible to different distortion and therefore potentially follow different distributions, our proposed NP methods control the type I error on test data at the targeted level. The methods and implementation pipeline proposed in our case study are applicable to many other problems involving data distortion.
On the Importance of Attention in Meta-Learning for Few-Shot Text Classification
Authors: Xiang Jiang, Mohammad Havaei, Gabriel Chartrand, Hassan Chouaib, Thomas Vincent, Andrew Jesson, Nicolas Chapados, Stan Matwin
Date: 2018-06-03 00:00:00
Tasks: Classification, Few-Shot Text Classification, General Classification, Inductive Bias, Meta-Learning, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Natural Language Understanding, Representation Learning, text-classification, Text Classification

Current deep learning based text classification methods are limited by their
ability to achieve fast learning and generalization when the data is scarce. We
address this problem by integrating a meta-learning procedure that uses the
knowledge learned across many tasks as an inductive bias towards better natural
language understanding. Based on the Model-Agnostic Meta-Learning framework
(MAML), we introduce the Attentive Task-Agnostic Meta-Learning (ATAML)
algorithm for text classification. The essential difference between MAML and
ATAML is in the separation of task-agnostic representation learning and
task-specific attentive adaptation. The proposed ATAML is designed to encourage
task-agnostic representation learning by way of task-agnostic parameterization
and facilitate task-specific adaptation via attention mechanisms. We provide
evidence to show that the attention mechanism in ATAML has a synergistic effect
on learning performance. In comparisons with models trained from random
initialization, pretrained models and meta trained MAML, our proposed ATAML
method generalizes better on single-label and multi-label classification tasks
in miniRCV1 and miniReuters-21578 datasets.