pwc-archive/papers-with-abstracts huggingface dataset:
Target Driven Visual Navigation with Hybrid Asynchronous Universal Successor Representations
Authors: Shamane Siriwardhana, Rivindu Weerasekera, Suranga Nanayakkara
Date: 2018-11-27 00:00:00
Tasks: Navigate, Visual Navigation

Being able to navigate to a target with minimal supervision and prior
knowledge is critical to creating human-like assistive agents. Prior work on
map-based and map-less approaches have limited generalizability. In this paper,
we present a novel approach, Hybrid Asynchronous Universal Successor
Representations (HAUSR), which overcomes the problem of generalizability to new
goals by adapting recent work on Universal Successor Representations with
Asynchronous Actor-Critic Agents. We show that the agent was able to
successfully reach novel goals and we were able to quickly fine-tune the
network for adapting to new scenes. This opens up novel application scenarios
where intelligent agents could learn from and adapt to a wide range of
environments with minimal human input.
Using Attribution to Decode Dataset Bias in Neural Network Models for Chemistry
Authors: Kevin McCloskey, Ankur Taly, Federico Monti, Michael P. Brenner, Lucy Colwell
Date: 2018-11-27 00:00:00

Deep neural networks have achieved state of the art accuracy at classifying molecules with respect to whether they bind to specific protein targets. A key breakthrough would occur if these models could reveal the fragment pharmacophores that are causally involved in binding. Extracting chemical details of binding from the networks could potentially lead to scientific discoveries about the mechanisms of drug actions. But doing so requires shining light into the black box that is the trained neural network model, a task that has proved difficult across many domains. Here we show how the binding mechanism learned by deep neural network models can be interrogated, using a recently described attribution method. We first work with carefully constructed synthetic datasets, in which the 'fragment logic' of binding is fully known. We find that networks that achieve perfect accuracy on held out test datasets still learn spurious correlations due to biases in the datasets, and we are able to exploit this non-robustness to construct adversarial examples that fool the model. The dataset bias makes these models unreliable for accurately revealing information about the mechanisms of protein-ligand binding. In light of our findings, we prescribe a test that checks for dataset bias given a hypothesis. If the test fails, it indicates that either the model must be simplified or regularized and/or that the training dataset requires augmentation.
Improved Speech Enhancement with the Wave-U-Net
Authors: Craig Macartney, Tillman Weyde
Date: 2018-11-27 00:00:00
Tasks: Audio Source Separation, Speech Enhancement, speech-recognition, Speech Recognition

We study the use of the Wave-U-Net architecture for speech enhancement, a
model introduced by Stoller et al for the separation of music vocals and
accompaniment. This end-to-end learning method for audio source separation
operates directly in the time domain, permitting the integrated modelling of
phase information and being able to take large temporal contexts into account.
Our experiments show that the proposed method improves several metrics, namely
PESQ, CSIG, CBAK, COVL and SSNR, over the state-of-the-art with respect to the
speech enhancement task on the Voice Bank corpus (VCTK) dataset. We find that a
reduced number of hidden layers is sufficient for speech enhancement in
comparison to the original system designed for singing voice separation in
music. We see this initial result as an encouraging signal to further explore
speech enhancement in the time-domain, both as an end in itself and as a
pre-processing step to speech recognition systems.
Universal Adversarial Training
Authors: Ali Shafahi, Mahyar Najibi, Zheng Xu, John Dickerson, Larry S. Davis, Tom Goldstein
Date: 2018-11-27 00:00:00

Standard adversarial attacks change the predicted class label of a selected image by adding specially tailored small perturbations to its pixels. In contrast, a universal perturbation is an update that can be added to any image in a broad class of images, while still changing the predicted class label. We study the efficient generation of universal adversarial perturbations, and also efficient methods for hardening networks to these attacks. We propose a simple optimization-based universal attack that reduces the top-1 accuracy of various network architectures on ImageNet to less than 20%, while learning the universal perturbation 13X faster than the standard method. To defend against these perturbations, we propose universal adversarial training, which models the problem of robust classifier generation as a two-player min-max game, and produces robust models with only 2X the cost of natural training. We also propose a simultaneous stochastic gradient method that is almost free of extra computation, which allows us to do universal adversarial training on ImageNet.
Exploring Restart Distributions
Authors: Arash Tavakoli, Vitaly Levdik, Riashat Islam, Christopher M. Smith, Petar Kormushev
Date: 2018-11-27 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning

We consider the generic approach of using an experience memory to help exploration by adapting a restart distribution. That is, given the capacity to reset the state with those corresponding to the agent's past observations, we help exploration by promoting faster state-space coverage via restarting the agent from a more diverse set of initial states, as well as allowing it to restart in states associated with significant past experiences. This approach is compatible with both on-policy and off-policy methods. However, a caveat is that altering the distribution of initial states could change the optimal policies when searching within a restricted class of policies. To reduce this unsought learning bias, we evaluate our approach in deep reinforcement learning which benefits from the high representational capacity of deep neural networks. We instantiate three variants of our approach, each inspired by an idea in the context of experience replay. Using these variants, we show that performance gains can be achieved, especially in hard exploration problems.
Taking Control of Intra-class Variation in Conditional GANs Under Weak Supervision
Authors: Richard T. Marriott, Sami Romdhani, Liming Chen
Date: 2018-11-27 00:00:00
Tasks: Attribute, Continuous Control

Generative Adversarial Networks (GANs) are able to learn mappings between simple, relatively low-dimensional, random distributions and points on the manifold of realistic images in image-space. The semantics of this mapping, however, are typically entangled such that meaningful image properties cannot be controlled independently of one another. Conditional GANs (cGANs) provide a potential solution to this problem, allowing specific semantics to be enforced during training. This solution, however, depends on the availability of precise labels, which are sometimes difficult or near impossible to obtain, e.g. labels representing lighting conditions or describing the background. In this paper we introduce a new formulation of the cGAN that is able to learn disentangled, multivariate models of semantically meaningful variation and which has the advantage of requiring only the weak supervision of binary attribute labels. For example, given only labels of ambient / non-ambient lighting, our method is able to learn multivariate lighting models disentangled from other factors such as the identity and pose. We coin the method intra-class variation isolation (IVI) and the resulting network the IVI-GAN. We evaluate IVI-GAN on the CelebA dataset and on synthetic 3D morphable model data, learning to disentangle attributes such as lighting, pose, expression, and even the background.
Questioning the assumptions behind fairness solutions
Authors: Rebekah Overdorf, Bogdan Kulynych, Ero Balsa, Carmela Troncoso, Seda Gürses
Date: 2018-11-27 00:00:00
Tasks: Decision Making, Fairness, Recommendation Systems

In addition to their benefits, optimization systems can have negative
economic, moral, social, and political effects on populations as well as their
environments. Frameworks like fairness have been proposed to aid service
providers in addressing subsequent bias and discrimination during data
collection and algorithm design. However, recent reports of neglect,
unresponsiveness, and malevolence cast doubt on whether service providers can
effectively implement fairness solutions. These reports invite us to revisit
assumptions made about the service providers in fairness solutions. Namely,
that service providers have (i) the incentives or (ii) the means to mitigate
optimization externalities. Moreover, the environmental impact of these systems
suggests that we need (iii) novel frameworks that consider systems other than
algorithmic decision-making and recommender systems, and (iv) solutions that go
beyond removing related algorithmic biases. Going forward, we propose
Protective Optimization Technologies that enable optimization subjects to
defend against negative consequences of optimization systems.
Lagged correlation-based deep learning for directional trend change prediction in financial time series
Authors: Ben Moews, J. Michael Herrmann, Gbenga Ibikunle
Date: 2018-11-27 00:00:00
Tasks: Feature Engineering, Time Series, Time Series Analysis

Trend change prediction in complex systems with a large number of noisy time
series is a problem with many applications for real-world phenomena, with stock
markets as a notoriously difficult to predict example of such systems. We
approach predictions of directional trend changes via complex lagged
correlations between them, excluding any information about the target series
from the respective inputs to achieve predictions purely based on such
correlations with other series. We propose the use of deep neural networks that
employ step-wise linear regressions with exponential smoothing in the
preparatory feature engineering for this task, with regression slopes as trend
strength indicators for a given time interval. We apply this method to
historical stock market data from 2011 to 2016 as a use case example of lagged
correlations between large numbers of time series that are heavily influenced
by externally arising new information as a random factor. The results
demonstrate the viability of the proposed approach, with state-of-the-art
accuracies and accounting for the statistical significance of the results for
additional validation, as well as important implications for modern financial
economics.
Patch-based Progressive 3D Point Set Upsampling
Authors: Wang Yifan, Shihao Wu, Hui Huang, Daniel Cohen-Or, Olga Sorkine-Hornung
Date: 2018-11-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: Point Cloud Super Resolution, Point Set Upsampling, Super-Resolution, Surface Reconstruction

We present a detail-driven deep neural network for point set upsampling. A
high-resolution point set is essential for point-based rendering and surface
reconstruction. Inspired by the recent success of neural image super-resolution
techniques, we progressively train a cascade of patch-based upsampling networks
on different levels of detail end-to-end. We propose a series of architectural
design contributions that lead to a substantial performance boost. The effect
of each technical contribution is demonstrated in an ablation study.
Qualitative and quantitative experiments show that our method significantly
outperforms the state-of-the-art learning-based and optimazation-based
approaches, both in terms of handling low-resolution inputs and revealing
high-fidelity details.
A Compact Embedding for Facial Expression Similarity
Authors: Raviteja Vemulapalli, Aseem Agarwala
Date: 2018-11-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Unit Detection, Emotion Recognition, Facial Expression Recognition (FER), Retrieval

Most of the existing work on automatic facial expression analysis focuses on
discrete emotion recognition, or facial action unit detection. However, facial
expressions do not always fall neatly into pre-defined semantic categories.
Also, the similarity between expressions measured in the action unit space need
not correspond to how humans perceive expression similarity. Different from
previous work, our goal is to describe facial expressions in a continuous
fashion using a compact embedding space that mimics human visual preferences.
To achieve this goal, we collect a large-scale faces-in-the-wild dataset with
human annotations in the form: Expressions A and B are visually more similar
when compared to expression C, and use this dataset to train a neural network
that produces a compact (16-dimensional) expression embedding. We
experimentally demonstrate that the learned embedding can be successfully used
for various applications such as expression retrieval, photo album
summarization, and emotion recognition. We also show that the embedding learned
using the proposed dataset performs better than several other embeddings
learned using existing emotion or action unit datasets.
Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving
Authors: Junyao Guo, Unmesh Kurup, Mohak Shah
Date: 2018-11-27 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles

With recent advances in learning algorithms and hardware development,
autonomous cars have shown promise when operating in structured environments
under good driving conditions. However, for complex, cluttered and unseen
environments with high uncertainty, autonomous driving systems still frequently
demonstrate erroneous or unexpected behaviors, that could lead to catastrophic
outcomes. Autonomous vehicles should ideally adapt to driving conditions; while
this can be achieved through multiple routes, it would be beneficial as a first
step to be able to characterize Driveability in some quantified form. To this
end, this paper aims to create a framework for investigating different factors
that can impact driveability. Also, one of the main mechanisms to adapt
autonomous driving systems to any driving condition is to be able to learn and
generalize from representative scenarios. The machine learning algorithms that
currently do so learn predominantly in a supervised manner and consequently
need sufficient data for robust and efficient learning. Therefore, we also
perform a comparative overview of 45 public driving datasets that enable
learning and publish this dataset index at
https://sites.google.com/view/driveability-survey-datasets. Specifically, we
categorize the datasets according to use cases, and highlight the datasets that
capture complicated and hazardous driving conditions which can be better used
for training robust driving models. Furthermore, by discussions of what driving
scenarios are not covered by existing public datasets and what driveability
factors need more investigation and data acquisition, this paper aims to
encourage both targeted dataset collection and the proposal of novel
driveability metrics that enhance the robustness of autonomous cars in adverse
environments.
Clustering Player Strategies from Variable-Length Game Logs in Dominion
Authors: Henry Bendekgey
Date: 2018-11-27 00:00:00
Tasks: Clustering

We present a method for encoding game logs as numeric features in the card
game Dominion. We then run the manifold learning algorithm t-SNE on these
encodings to visualize the landscape of player strategies. By quantifying game
states as the relative prevalence of cards in a player's deck, we create
visualizations that capture qualitative differences in player strategies.
Different ways of deviating from the starting game state appear as different
rays in the visualization, giving it an intuitive explanation. This is a
promising new direction for understanding player strategies across games that
vary in length.
Generalizing semi-supervised generative adversarial networks to regression using feature contrasting
Authors: Greg Olmschenk, Zhigang Zhu, Hao Tang
Date: 2018-11-27 00:00:00
Tasks: Age Estimation, Crowd Counting, General Classification, regression

In this work, we generalize semi-supervised generative adversarial networks (GANs) from classification problems to regression problems. In the last few years, the importance of improving the training of neural networks using semi-supervised training has been demonstrated for classification problems. We present a novel loss function, called feature contrasting, resulting in a discriminator which can distinguish between fake and real data based on feature statistics. This method avoids potential biases and limitations of alternative approaches. The generalization of semi-supervised GANs to the regime of regression problems of opens their use to countless applications as well as providing an avenue for a deeper understanding of how GANs function. We first demonstrate the capabilities of semi-supervised regression GANs on a toy dataset which allows for a detailed understanding of how they operate in various circumstances. This toy dataset is used to provide a theoretical basis of the semi-supervised regression GAN. We then apply the semi-supervised regression GANs to a number of real-world computer vision applications: age estimation, driving steering angle prediction, and crowd counting from single images. We perform extensive tests of what accuracy can be achieved with significantly reduced annotated data. Through the combination of the theoretical example and real-world scenarios, we demonstrate how semi-supervised GANs can be generalized to regression problems.
Synthesizing Tabular Data using Generative Adversarial Networks
Authors: Lei Xu, Kalyan Veeramachaneni
Date: 2018-11-27 00:00:00
Tasks: Generative Adversarial Network

Generative adversarial networks (GANs) implicitly learn the probability
distribution of a dataset and can draw samples from the distribution. This
paper presents, Tabular GAN (TGAN), a generative adversarial network which can
generate tabular data like medical or educational records. Using the power of
deep neural networks, TGAN generates high-quality and fully synthetic tables
while simultaneously generating discrete and continuous variables. When we
evaluate our model on three datasets, we find that TGAN outperforms
conventional statistical generative models in both capturing the correlation
between columns and scaling up for large datasets.
Scaling Configuration of Energy Harvesting Sensors with Reinforcement Learning
Authors: Francesco Fraternali, Bharathan Balaji, Rajesh Gupta
Date: 2018-11-27 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

With the advent of the Internet of Things (IoT), an increasing number of
energy harvesting methods are being used to supplement or supplant battery
based sensors. Energy harvesting sensors need to be configured according to the
application, hardware, and environmental conditions to maximize their
usefulness. As of today, the configuration of sensors is either manual or
heuristics based, requiring valuable domain expertise. Reinforcement learning
(RL) is a promising approach to automate configuration and efficiently scale
IoT deployments, but it is not yet adopted in practice. We propose solutions to
bridge this gap: reduce the training phase of RL so that nodes are operational
within a short time after deployment and reduce the computational requirements
to scale to large deployments. We focus on configuration of the sampling rate
of indoor solar panel based energy harvesting sensors. We created a simulator
based on 3 months of data collected from 5 sensor nodes subject to different
lighting conditions. Our simulation results show that RL can effectively learn
energy availability patterns and configure the sampling rate of the sensor
nodes to maximize the sensing data while ensuring that energy storage is not
depleted. The nodes can be operational within the first day by using our
methods. We show that it is possible to reduce the number of RL policies by
using a single policy for nodes that share similar lighting conditions.
ShelfNet for Fast Semantic Segmentation
Authors: Juntang Zhuang, Junlin Yang, Lin Gu, Nicha Dvornek
Date: 2018-11-27 00:00:00
Tasks: Autonomous Driving, Decoder, Real-Time Semantic Segmentation, Scene Understanding, Segmentation, Semantic Segmentation

In this paper, we present ShelfNet, a novel architecture for accurate fast semantic segmentation. Different from the single encoder-decoder structure, ShelfNet has multiple encoder-decoder branch pairs with skip connections at each spatial level, which looks like a shelf with multiple columns. The shelf-shaped structure can be viewed as an ensemble of multiple deep and shallow paths, thus improving accuracy. We significantly reduce computation burden by reducing channel number, at the same time achieving high accuracy with this unique structure. In addition, we propose a shared-weight strategy in the residual block which reduces parameter number without sacrificing performance. Compared with popular non real-time methods such as PSPNet, our ShelfNet achieves 4$\times$ faster inference speed with similar accuracy on PASCAL VOC dataset. Compared with real-time segmentation models such as BiSeNet, our model achieves higher accuracy at comparable speed on the Cityscapes Dataset, enabling the application in speed-demanding tasks such as street-scene understanding for autonomous driving. Furthermore, our ShelfNet achieves 79.0\% mIoU on Cityscapes Dataset with ResNet34 backbone, outperforming PSPNet and BiSeNet with large backbones such as ResNet101. Through extensive experiments, we validated the superior performance of ShelfNet. We provide link to the implementation \url{https://github.com/juntang-zhuang/ShelfNet-lw-cityscapes}.
Multiview Supervision By Registration
Authors: Yilun Zhang, Hyun Soo Park
Date: 2018-11-27 00:00:00
Tasks: 3D Reconstruction, Keypoint Detection, Optical Flow Estimation, Visual Tracking

This paper presents a semi-supervised learning framework to train a keypoint
detector using multiview image streams given the limited labeled data
(typically $<$4\%). We leverage the complementary relationship between
multiview geometry and visual tracking to provide three types of supervisionary
signals to utilize the unlabeled data: (1) keypoint detection in one view can
be supervised by other views via the epipolar geometry; (2) a keypoint moves
smoothly over time where its optical flow can be used to temporally supervise
consecutive image frames to each other; (3) visible keypoint in one view is
likely to be visible in the adjacent view. We integrate these three signals in
a differentiable fashion to design a new end-to-end neural network composed of
three pathways. This design allows us to extensively use the unlabeled data to
train the keypoint detector. We show that our approach outperforms existing
detectors including DeepLabCut tailored to the keypoint detection of non-human
species such as monkeys, dogs, and mice.