pwc-archive/papers-with-abstracts huggingface dataset:
Machine learning in policy evaluation: new tools for causal inference
Authors: Noemi Kreif, Karla DiazOrdaz
Date: 2019-03-01 00:00:00
Tasks: BIG-bench Machine Learning, Causal Inference, counterfactual, Model Selection, Variable Selection

While machine learning (ML) methods have received a lot of attention in
recent years, these methods are primarily for prediction. Empirical researchers
conducting policy evaluations are, on the other hand, pre-occupied with causal
problems, trying to answer counterfactual questions: what would have happened
in the absence of a policy? Because these counterfactuals can never be directly
observed (described as the "fundamental problem of causal inference")
prediction tools from the ML literature cannot be readily used for causal
inference. In the last decade, major innovations have taken place incorporating
supervised ML tools into estimators for causal parameters such as the average
treatment effect (ATE). This holds the promise of attenuating model
misspecification issues, and increasing of transparency in model selection. One
particularly mature strand of the literature include approaches that
incorporate supervised ML approaches in the estimation of the ATE of a binary
treatment, under the \textit{unconfoundedness} and positivity assumptions (also
known as exchangeability and overlap assumptions).
  This article reviews popular supervised machine learning algorithms,
including the Super Learner. Then, some specific uses of machine learning for
treatment effect estimation are introduced and illustrated, namely (1) to
create balance among treated and control groups, (2) to estimate so-called
nuisance models (e.g. the propensity score, or conditional expectations of the
outcome) in semi-parametric estimators that target causal parameters (e.g.
targeted maximum likelihood estimation or the double ML estimator), and (3) the
use of machine learning for variable selection in situations with a high number
of covariates.
Learning To Follow Directions in Street View
Authors: Karl Moritz Hermann, Mateusz Malinowski, Piotr Mirowski, Andras Banki-Horvath, Keith Anderson, Raia Hadsell
Date: 2019-03-01 00:00:00
Tasks: Deep Reinforcement Learning, Instruction Following, Navigate, Reinforcement Learning, Reinforcement Learning (RL)

Navigating and understanding the real world remains a key challenge in machine learning and inspires a great variety of research in areas such as language grounding, planning, navigation and computer vision. We propose an instruction-following task that requires all of the above, and which combines the practicality of simulated environments with the challenges of ambiguous, noisy real world data. StreetNav is built on top of Google Street View and provides visually accurate environments representing real places. Agents are given driving instructions which they must learn to interpret in order to successfully navigate in this environment. Since humans equipped with driving instructions can readily navigate in previously unseen cities, we set a high bar and test our trained agents for similar cognitive capabilities. Although deep reinforcement learning (RL) methods are frequently evaluated only on data that closely follow the training distribution, our dataset extends to multiple cities and has a clean train/test separation. This allows for thorough testing of generalisation ability. This paper presents the StreetNav environment and tasks, models that establish strong baselines, and extensive analysis of the task and the trained agents.
Single Image Haze Removal Using Conditional Wasserstein Generative Adversarial Networks
Authors: Joshua Peter Ebenezer, Bijaylaxmi Das, Sudipta Mukhopadhyay
Date: 2019-03-01 00:00:00
Tasks: Generative Adversarial Network, Image Dehazing, Single Image Haze Removal

We present a method to restore a clear image from a haze-affected image using
a Wasserstein generative adversarial network. As the problem is
ill-conditioned, previous methods have required a prior on natural images or
multiple images of the same scene. We train a generative adversarial network to
learn the probability distribution of clear images conditioned on the
haze-affected images using the Wasserstein loss function, using a gradient
penalty to enforce the Lipschitz constraint. The method is data-adaptive,
end-to-end, and requires no further processing or tuning of parameters. We also
incorporate the use of a texture-based loss metric and the L1 loss to improve
results, and show that our results are better than the current
state-of-the-art.
Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets
Authors: Viktor Varkarakis, Shabab Bazrafkan, Peter Corcoran
Date: 2019-03-01 00:00:00
Tasks: Data Augmentation, Iris Segmentation, Mixed Reality, Segmentation

A data augmentation methodology is presented and applied to generate a large
dataset of off-axis iris regions and train a low-complexity deep neural
network. Although of low complexity the resulting network achieves a high level
of accuracy in iris region segmentation for challenging off-axis eye-patches.
Interestingly, this network is also shown to achieve high levels of performance
for regular, frontal, segmentation of iris regions, comparing favorably with
state-of-the-art techniques of significantly higher complexity. Due to its
lower complexity, this network is well suited for deployment in embedded
applications such as augmented and mixed reality headsets.
Automatic microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression
Authors: Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel, Hua Li, Mark Anastasio
Date: 2019-03-01 00:00:00
Tasks: Automatic Cell Counting, Domain Adaptation, regression

Accurate cell counting in microscopic images is important for medical
diagnoses and biological studies. However, manual cell counting is very
time-consuming, tedious, and prone to subjective errors. We propose a new
density regression-based method for automatic cell counting that reduces the
need to manually annotate experimental images. A supervised learning-based
density regression model (DRM) is trained with annotated synthetic images (the
source domain) and their corresponding ground truth density maps. A domain
adaptation model (DAM) is built to map experimental images (the target domain)
to the feature space of the source domain. By use of the unsupervised
learning-based DAM and supervised learning-based DRM, a cell density map of a
given target image can be estimated, from which the number of cells can be
counted. Results from experimental immunofluorescent microscopic images of
human embryonic stem cells demonstrate the promising performance of the
proposed counting method.
On the complexity of logistic regression models
Authors: Nicola Bulso, Matteo Marsili, Yasser Roudi
Date: 2019-03-01 00:00:00
Tasks: Model Selection, regression

We investigate the complexity of logistic regression models which is defined
by counting the number of indistinguishable distributions that the model can
represent (Balasubramanian, 1997). We find that the complexity of logistic
models with binary inputs does not only depend on the number of parameters but
also on the distribution of inputs in a non-trivial way which standard
treatments of complexity do not address. In particular, we observe that
correlations among inputs induce effective dependencies among parameters thus
constraining the model and, consequently, reducing its complexity. We derive
simple relations for the upper and lower bounds of the complexity. Furthermore,
we show analytically that, defining the model parameters on a finite support
rather than the entire axis, decreases the complexity in a manner that
critically depends on the size of the domain. Based on our findings, we propose
a novel model selection criterion which takes into account the entropy of the
input distribution. We test our proposal on the problem of selecting the input
variables of a logistic regression model in a Bayesian Model Selection
framework. In our numerical tests, we find that, while the reconstruction
errors of standard model selection approaches (AIC, BIC, $\ell_1$
regularization) strongly depend on the sparsity of the ground truth, the
reconstruction error of our method is always close to the minimum in all
conditions of sparsity, data size and strength of input correlations. Finally,
we observe that, when considering categorical instead of binary inputs, in a
simple and mathematically tractable case, the contribution of the alphabet size
to the complexity is very small compared to that of parameter space dimension.
We further explore the issue by analysing the dataset of the "13 keys to the
White House" which is a method for forecasting the outcomes of US presidential
elections.
Data-driven Approach for Quality Evaluation on Knowledge Sharing Platform
Authors: Lu Xu, Jinhai Xiang, Yating Wang, Fuchuan Ni
Date: 2019-03-01 00:00:00
Tasks: Question Answering

In recent years, voice knowledge sharing and question answering (Q&A)
platforms have attracted much attention, which greatly facilitate the knowledge
acquisition for people. However, little research has evaluated on the quality
evaluation on voice knowledge sharing. This paper presents a data-driven
approach to automatically evaluate the quality of a specific Q&A platform
(Zhihu Live). Extensive experiments demonstrate the effectiveness of the
proposed method. Furthermore, we introduce a dataset of Zhihu Live as an open
resource for researchers in related areas. This dataset will facilitate the
development of new methods on knowledge sharing services quality evaluation.
Model-Based Reinforcement Learning for Atari
Authors: Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H. Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski
Date: 2019-03-01 00:00:00
Tasks: Atari Games, Atari Games 100k, model, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Video Prediction

Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari games, even from image observations. However, this typically requires very large amounts of interaction -- substantially more, in fact, than a human would need to learn the same games. How can people learn so quickly? Part of the answer may be that people can learn how the game works and predict which actions will lead to desirable outcomes. In this paper, we explore how video prediction models can similarly enable agents to solve Atari games with fewer interactions than model-free methods. We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting. Our experiments evaluate SimPLe on a range of Atari games in low data regime of 100k interactions between the agent and the environment, which corresponds to two hours of real-time play. In most games SimPLe outperforms state-of-the-art model-free algorithms, in some games by over an order of magnitude.
Answer Them All! Toward Universal Visual Question Answering Models
Authors: Robik Shrestha, Kushal Kafle, Christopher Kanan
Date: 2019-03-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: All, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Visual Question Answering (VQA) research is split into two camps: the first
focuses on VQA datasets that require natural image understanding and the second
focuses on synthetic datasets that test reasoning. A good VQA algorithm should
be capable of both, but only a few VQA algorithms are tested in this manner. We
compare five state-of-the-art VQA algorithms across eight VQA datasets covering
both domains. To make the comparison fair, all of the models are standardized
as much as possible, e.g., they use the same visual features, answer
vocabularies, etc. We find that methods do not generalize across the two
domains. To address this problem, we propose a new VQA algorithm that rivals or
exceeds the state-of-the-art for both domains.
TamperNN: Efficient Tampering Detection of Deployed Neural Nets
Authors: Erwan Le Merrer, Gilles Tredan
Date: 2019-03-01 00:00:00
Tasks: Quantization, Self-Driving Cars

Neural networks are powering the deployment of embedded devices and Internet of Things. Applications range from personal assistants to critical ones such as self-driving cars. It has been shown recently that models obtained from neural nets can be trojaned ; an attacker can then trigger an arbitrary model behavior facing crafted inputs. This has a critical impact on the security and reliability of those deployed devices. We introduce novel algorithms to detect the tampering with deployed models, classifiers in particular. In the remote interaction setup we consider, the proposed strategy is to identify markers of the model input space that are likely to change class if the model is attacked, allowing a user to detect a possible tampering. This setup makes our proposal compatible with a wide range of scenarios, such as embedded models, or models exposed through prediction APIs. We experiment those tampering detection algorithms on the canonical MNIST dataset, over three different types of neural nets, and facing five different attacks (trojaning, quantization, fine-tuning, compression and watermarking). We then validate over five large models (VGG16, VGG19, ResNet, MobileNet, DenseNet) with a state of the art dataset (VGGFace2), and report results demonstrating the possibility of an efficient detection of model tampering.
Progress Regression RNN for Online Spatial-Temporal Action Localization in Unconstrained Videos
Authors: Bo Hu, Jianfei Cai, Tat-Jen Cham, Junsong Yuan
Date: 2019-03-01 00:00:00
Tasks: Action Localization, object-detection, Object Detection, Position, regression, Temporal Action Localization

Previous spatial-temporal action localization methods commonly follow the
pipeline of object detection to estimate bounding boxes and labels of actions.
However, the temporal relation of an action has not been fully explored. In
this paper, we propose an end-to-end Progress Regression Recurrent Neural
Network (PR-RNN) for online spatial-temporal action localization, which learns
to infer the action by temporal progress regression. Two new action attributes,
called progression and progress rate, are introduced to describe the temporal
engagement and relative temporal position of an action. In our method,
frame-level features are first extracted by a Fully Convolutional Network
(FCN). Subsequently, detection results and action progress attributes are
regressed by the Convolutional Gated Recurrent Unit (ConvGRU) based on all the
observed frames instead of a single frame or a short clip. Finally, a novel
online linking method is designed to connect single-frame results to
spatial-temporal tubes with the help of the estimated action progress
attributes. Extensive experiments demonstrate that the progress attributes
improve the localization accuracy by providing more precise temporal position
of an action in unconstrained videos. Our proposed PR-RNN achieves the
stateof-the-art performance for most of the IoU thresholds on two benchmark
datasets.
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
Authors: Henggang Cui, Vladan Radosavljevic, Fang-Chieh Chou, Tsung-Han Lin, Thi Nguyen, Tzu-Kuo Huang, Jeff Schneider, Nemanja Djuric
Date: 2018-09-18 00:00:00
Tasks: Autonomous Driving, Motion Planning, Trajectory Prediction

Autonomous driving presents one of the largest problems that the robotics and
artificial intelligence communities are facing at the moment, both in terms of
difficulty and potential societal impact. Self-driving vehicles (SDVs) are
expected to prevent road accidents and save millions of lives while improving
the livelihood and life quality of many more. However, despite large interest
and a number of industry players working in the autonomous domain, there still
remains more to be done in order to develop a system capable of operating at a
level comparable to best human drivers. One reason for this is high uncertainty
of traffic behavior and large number of situations that an SDV may encounter on
the roads, making it very difficult to create a fully generalizable system. To
ensure safe and efficient operations, an autonomous vehicle is required to
account for this uncertainty and to anticipate a multitude of possible
behaviors of traffic actors in its surrounding. We address this critical
problem and present a method to predict multiple possible trajectories of
actors while also estimating their probabilities. The method encodes each
actor's surrounding context into a raster image, used as input by deep
convolutional networks to automatically derive relevant features for the task.
Following extensive offline evaluation and comparison to state-of-the-art
baselines, the method was successfully tested on SDVs in closed-course tests.
Provably scale-covariant networks from oriented quasi quadrature measures in cascade
Authors: Tony Lindeberg
Date: 2019-03-01 00:00:00
Tasks: Texture Classification

This article presents a continuous model for hierarchical networks based on a combination of mathematically derived models of receptive fields and biologically inspired computations. Based on a functional model of complex cells in terms of an oriented quasi quadrature combination of first- and second-order directional Gaussian derivatives, we couple such primitive computations in cascade over combinatorial expansions over image orientations. Scale-space properties of the computational primitives are analysed and it is shown that the resulting representation allows for provable scale and rotation covariance. A prototype application to texture analysis is developed and it is demonstrated that a simplified mean-reduced representation of the resulting QuasiQuadNet leads to promising experimental results on three texture datasets.
Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment
Authors: Cedric Renggli, Bojan Karlaš, Bolin Ding, Feng Liu, Kevin Schawinski, Wentao Wu, Ce Zhang
Date: 2019-03-01 00:00:00
Tasks: 2k, BIG-bench Machine Learning

Continuous integration is an indispensable step of modern software
engineering practices to systematically manage the life cycles of system
development. Developing a machine learning model is no difference - it is an
engineering process with a life cycle, including design, implementation,
tuning, testing, and deployment. However, most, if not all, existing continuous
integration engines do not support machine learning as first-class citizens.
  In this paper, we present ease.ml/ci, to our best knowledge, the first
continuous integration system for machine learning. The challenge of building
ease.ml/ci is to provide rigorous guarantees, e.g., single accuracy point error
tolerance with 0.999 reliability, with a practical amount of labeling effort,
e.g., 2K labels per test. We design a domain specific language that allows
users to specify integration conditions with reliability constraints, and
develop simple novel optimizations that can lower the number of labels required
by up to two orders of magnitude for test conditions popularly used in real
production systems.
Adversarial Generation of Handwritten Text Images Conditioned on Sequences
Authors: Eloi Alonso, Bastien Moysset, Ronaldo Messina
Date: 2019-03-01 00:00:00

State-of-the-art offline handwriting text recognition systems tend to use
neural networks and therefore require a large amount of annotated data to be
trained. In order to partially satisfy this requirement, we propose a system
based on Generative Adversarial Networks (GAN) to produce synthetic images of
handwritten words. We use bidirectional LSTM recurrent layers to get an
embedding of the word to be rendered, and we feed it to the generator network.
We also modify the standard GAN by adding an auxiliary network for text
recognition. The system is then trained with a balanced combination of an
adversarial loss and a CTC loss. Together, these extensions to GAN enable to
control the textual content of the generated word images. We obtain realistic
images on both French and Arabic datasets, and we show that integrating these
synthetic images into the existing training data of a text recognition system
can slightly enhance its performance.
Frequency Domain Transformer Networks for Video Prediction
Authors: Hafez Farazi, Sven Behnke
Date: 2019-03-01 00:00:00
Tasks: Prediction, Video Prediction

The task of video prediction is forecasting the next frames given some
previous frames. Despite much recent progress, this task is still challenging
mainly due to high nonlinearity in the spatial domain. To address this issue,
we propose a novel architecture, Frequency Domain Transformer Network (FDTN),
which is an end-to-end learnable model that estimates and uses the
transformations of the signal in the frequency domain. Experimental evaluations
show that this approach can outperform some widely used video prediction
methods like Video Ladder Network (VLN) and Predictive Gated Pyramids (PGP).
Crowding in humans is unlike that in convolutional neural networks
Authors: Ben Lonnqvist, Alasdair D. F. Clarke, Ramakrishna Chakravarthi
Date: 2019-03-01 00:00:00
Tasks: Object, Object Recognition

Object recognition is a primary function of the human visual system. It has recently been claimed that the highly successful ability to recognise objects in a set of emergent computer vision systems---Deep Convolutional Neural Networks (DCNNs)---can form a useful guide to recognition in humans. To test this assertion, we systematically evaluated visual crowding, a dramatic breakdown of recognition in clutter, in DCNNs and compared their performance to extant research in humans. We examined crowding in three architectures of DCNNs with the same methodology as that used among humans. We manipulated multiple stimulus factors including inter-letter spacing, letter colour, size, and flanker location to assess the extent and shape of crowding in DCNNs. We found that crowding followed a predictable pattern across architectures that was different from that in humans. Some characteristic hallmarks of human crowding, such as invariance to size, the effect of target-flanker similarity, and confusions between target and flanker identities, were completely missing, minimised or even reversed. These data show that DCNNs, while proficient in object recognition, likely achieve this competence through a set of mechanisms that are distinct from those in humans. They are not necessarily equivalent models of human or primate object recognition and caution must be exercised when inferring mechanisms derived from their operation.