pwc-archive/papers-with-abstracts huggingface dataset:
Relative Entropy Regularized Policy Iteration
Authors: Abbas Abdolmaleki, Jost Tobias Springenberg, Jonas Degrave, Steven Bohez, Yuval Tassa, Dan Belov, Nicolas Heess, Martin Riedmiller
Date: 2018-12-05 00:00:00
Tasks: continuous-control, Continuous Control, OpenAI Gym, Reinforcement Learning, Reinforcement Learning (RL)

We present an off-policy actor-critic algorithm for Reinforcement Learning
(RL) that combines ideas from gradient-free optimization via stochastic search
with learned action-value function. The result is a simple procedure consisting
of three steps: i) policy evaluation by estimating a parametric action-value
function; ii) policy improvement via the estimation of a local non-parametric
policy; and iii) generalization by fitting a parametric policy. Each step can
be implemented in different ways, giving rise to several algorithm variants.
Our algorithm draws on connections to existing literature on black-box
optimization and 'RL as an inference' and it can be seen either as an extension
of the Maximum a Posteriori Policy Optimisation algorithm (MPO) [Abdolmaleki et
al., 2018a], or as an extension of Trust Region Covariance Matrix Adaptation
Evolutionary Strategy (CMA-ES) [Abdolmaleki et al., 2017b; Hansen et al., 1997]
to a policy iteration scheme. Our comparison on 31 continuous control tasks
from parkour suite [Heess et al., 2017], DeepMind control suite [Tassa et al.,
2018] and OpenAI Gym [Brockman et al., 2016] with diverse properties, limited
amount of compute and a single set of hyperparameters, demonstrate the
effectiveness of our method and the state of art results. Videos, summarizing
results, can be found at goo.gl/HtvJKR .
Weighted Global Normalization for Multiple Choice Reading Comprehension over Long Documents
Authors: Aditi Chaudhary, Bhargavi Paranjape, Michiel de Jong
Date: 2018-12-05 00:00:00
Tasks: Answer Selection, Multiple-choice, Prediction, Reading Comprehension

Motivated by recent evidence pointing out the fragility of high-performing span prediction models, we direct our attention to multiple choice reading comprehension. In particular, this work introduces a novel method for improving answer selection on long documents through weighted global normalization of predictions over portions of the documents. We show that applying our method to a span prediction model adapted for answer selection helps model performance on long summaries from NarrativeQA, a challenging reading comprehension dataset with an answer selection task, and we strongly improve on the task baseline performance by +36.2 Mean Reciprocal Rank.
Photo Wake-Up: 3D Character Animation from a Single Photo
Authors: Chung-Yi Weng, Brian Curless, Ira Kemelmacher-Shlizerman
Date: 2018-12-05 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Character Animation From A Single Photo

We present a method and application for animating a human subject from a
single photo. E.g., the character can walk out, run, sit, or jump in 3D. The
key contributions of this paper are: 1) an application of viewing and animating
humans in single photos in 3D, 2) a novel 2D warping method to deform a posable
template body model to fit the person's complex silhouette to create an
animatable mesh, and 3) a method for handling partial self occlusions. We
compare to state-of-the-art related methods and evaluate results with human
studies. Further, we present an interactive interface that allows re-posing the
person in 3D, and an augmented reality setup where the animated 3D person can
emerge from the photo into the real world. We demonstrate the method on photos,
posters, and art.
Efficient and Robust Machine Learning for Real-World Systems
Authors: Franz Pernkopf, Wolfgang Roth, Matthias Zoehrer, Lukas Pfeifenberger, Guenther Schindler, Holger Froening, Sebastian Tschiatschek, Robert Peharz, Matthew Mattina, Zoubin Ghahramani
Date: 2018-12-05 00:00:00
Tasks: Autonomous Navigation, BIG-bench Machine Learning

While machine learning is traditionally a resource intensive task, embedded
systems, autonomous navigation and the vision of the Internet-of-Things fuel
the interest in resource efficient approaches. These approaches require a
carefully chosen trade-off between performance and resource consumption in
terms of computation and energy. On top of this, it is crucial to treat
uncertainty in a consistent manner in all but the simplest applications of
machine learning systems. In particular, a desideratum for any real-world
system is to be robust in the presence of outliers and corrupted data, as well
as being `aware' of its limits, i.e.\ the system should maintain and provide an
uncertainty estimate over its own predictions. These complex demands are among
the major challenges in current machine learning research and key to ensure a
smooth transition of machine learning technology into every day's applications.
In this article, we provide an overview of the current state of the art of
machine learning techniques facilitating these real-world requirements. First
we provide a comprehensive review of resource-efficiency in deep neural
networks with focus on techniques for model size reduction, compression and
reduced precision. These techniques can be applied during training or as
post-processing and are widely used to reduce both computational complexity and
memory footprint. As most (practical) neural networks are limited in their ways
to treat uncertainty, we contrast them with probabilistic graphical models,
which readily serve these desiderata by means of probabilistic inference. In
that way, we provide an extensive overview of the current state-of-the-art of
robust and efficient machine learning for real-world systems.
Towards a Definition of Disentangled Representations
Authors: Irina Higgins, David Amos, David Pfau, Sebastien Racaniere, Loic Matthey, Danilo Rezende, Alexander Lerchner
Date: 2018-12-05 00:00:00
Tasks: Representation Learning

How can intelligent agents solve a diverse set of tasks in a data-efficient
manner? The disentangled representation learning approach posits that such an
agent would benefit from separating out (disentangling) the underlying
structure of the world into disjoint parts of its representation. However,
there is no generally agreed-upon definition of disentangling, not least
because it is unclear how to formalise the notion of world structure beyond toy
datasets with a known ground truth generative process. Here we propose that a
principled solution to characterising disentangled representations can be found
by focusing on the transformation properties of the world. In particular, we
suggest that those transformations that change only some properties of the
underlying world state, while leaving all other properties invariant, are what
gives exploitable structure to any kind of data. Similar ideas have already
been successfully applied in physics, where the study of symmetry
transformations has revolutionised the understanding of the world structure. By
connecting symmetry transformations to vector representations using the
formalism of group and representation theory we arrive at the first formal
definition of disentangled representations. Our new definition is in agreement
with many of the current intuitions about disentangling, while also providing
principled resolutions to a number of previous points of contention. While this
work focuses on formally defining disentangling - as opposed to solving the
learning problem - we believe that the shift in perspective to studying data
transformations can stimulate the development of better representation learning
algorithms.
Adapting Auxiliary Losses Using Gradient Similarity
Authors: Yunshu Du, Wojciech M. Czarnecki, Siddhant M. Jayakumar, Mehrdad Farajtabar, Razvan Pascanu, Balaji Lakshminarayanan
Date: 2018-12-05 00:00:00
Tasks: Atari Games, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

One approach to deal with the statistical inefficiency of neural networks is to rely on auxiliary losses that help to build useful representations. However, it is not always trivial to know if an auxiliary task will be helpful for the main task and when it could start hurting. We propose to use the cosine similarity between gradients of tasks as an adaptive weight to detect when an auxiliary loss is helpful to the main loss. We show that our approach is guaranteed to converge to critical points of the main task and demonstrate the practical usefulness of the proposed algorithm in a few domains: multi-task supervised learning on subsets of ImageNet, reinforcement learning on gridworld, and reinforcement learning on Atari games.
Predicting pregnancy using large-scale data from a women's health tracking mobile application
Authors: Bo Liu, Shuyang Shi, Yongshang Wu, Daniel Thomas, Laura Symul, Emma Pierson, Jure Leskovec
Date: 2018-12-05 00:00:00

Predicting pregnancy has been a fundamental problem in women's health for
more than 50 years. Previous datasets have been collected via carefully curated
medical studies, but the recent growth of women's health tracking mobile apps
offers potential for reaching a much broader population. However, the
feasibility of predicting pregnancy from mobile health tracking data is
unclear. Here we develop four models -- a logistic regression model, and 3 LSTM
models -- to predict a woman's probability of becoming pregnant using data from
a women's health tracking app, Clue by BioWink GmbH. Evaluating our models on a
dataset of 79 million logs from 65,276 women with ground truth pregnancy test
data, we show that our predicted pregnancy probabilities meaningfully stratify
women: women in the top 10% of predicted probabilities have a 89% chance of
becoming pregnant over 6 menstrual cycles, as compared to a 27% chance for
women in the bottom 10%. We develop a technique for extracting interpretable
time trends from our deep learning models, and show these trends are consistent
with previous fertility research. Our findings illustrate the potential that
women's health tracking data offers for predicting pregnancy on a broader
population; we conclude by discussing the steps needed to fulfill this
potential.
Truly Autonomous Machines Are Ethical
Authors: John Hooker
Date: 2018-12-05 00:00:00
Tasks: Ethics

While many see the prospect of autonomous machines as threatening, autonomy
may be exactly what we want in a superintelligent machine. There is a sense of
autonomy, deeply rooted in the ethical literature, in which an autonomous
machine is necessarily an ethical one. Development of the theory underlying
this idea not only reveals the advantages of autonomy, but it sheds light on a
number of issues in the ethics of artificial intelligence. It helps us to
understand what sort of obligations we owe to machines, and what obligations
they owe to us. It clears up the issue of assigning responsibility to machines
or their creators. More generally, a concept of autonomy that is adequate to
both human and artificial intelligence can lead to a more adequate ethical
theory for both.
Composing Entropic Policies using Divergence Correction
Authors: Jonathan J. Hunt, Andre Barreto, Timothy P. Lillicrap, Nicolas Heess
Date: 2018-12-05 00:00:00
Tasks: continuous-control, Continuous Control, Reinforcement Learning, Reinforcement Learning (RL)

Composing previously mastered skills to solve novel tasks promises dramatic improvements in the data efficiency of reinforcement learning. Here, we analyze two recent works composing behaviors represented in the form of action-value functions and show that they perform poorly in some situations. As part of this analysis, we extend an important generalization of policy improvement to the maximum entropy framework and introduce an algorithm for the practical implementation of successor features in continuous action spaces. Then we propose a novel approach which addresses the failure cases of prior work and, in principle, recovers the optimal policy during transfer. This method works by explicitly learning the (discounted, future) divergence between base policies. We study this approach in the tabular case and on non-trivial continuous control problems with compositional structure and show that it outperforms or matches existing methods across all tasks considered.
Consistency for 0-1 Programming
Authors: Danial Davarnia, J. N. Hooker
Date: 2018-12-05 00:00:00
Tasks: valid

Concepts of consistency have long played a key role in constraint programming
but never developed in integer programming (IP). Consistency nonetheless plays
a role in IP as well. For example, cutting planes can reduce backtracking by
achieving various forms of consistency as well as by tightening the linear
programming (LP) relaxation. We introduce a type of consistency that is
particularly suited for 0-1 programming and develop the associated theory. We
define a 0-1 constraint set as LP-consistent when any partial assignment that
is consistent with its linear programming relaxation is consistent with the
original 0-1 constraint set. We prove basic properties of LP-consistency,
including its relationship with Chvatal-Gomory cuts and the integer hull. We
show that a weak form of LP-consistency can reduce or eliminate backtracking in
a way analogous to k-consistency but is easier to achieve. In so doing, we
identify a class of valid inequalities that can be more effective than
traditional cutting planes at cutting off infeasible 0-1 partial assignments.
Better Trees: An empirical study on hyperparameter tuning of classification decision tree induction algorithms
Authors: Rafael Gomes Mantovani, Tomáš Horváth, André L. D. Rossi, Ricardo Cerri, Sylvio Barbon Junior, Joaquin Vanschoren, André Carlos Ponce de Leon Ferreira de Carvalho
Date: 2018-12-05 00:00:00
Tasks: General Classification

Machine learning algorithms often contain many hyperparameters (HPs) whose values affect the predictive performance of the induced models in intricate ways. Due to the high number of possibilities for these HP configurations and their complex interactions, it is common to use optimization techniques to find settings that lead to high predictive performance. However, insights into efficiently exploring this vast space of configurations and dealing with the trade-off between predictive and runtime performance remain challenging. Furthermore, there are cases where the default HPs fit the suitable configuration. Additionally, for many reasons, including model validation and attendance to new legislation, there is an increasing interest in interpretable models, such as those created by the Decision Tree (DT) induction algorithms. This paper provides a comprehensive approach for investigating the effects of hyperparameter tuning for the two DT induction algorithms most often used, CART and C4.5. DT induction algorithms present high predictive performance and interpretable classification models, though many HPs need to be adjusted. Experiments were carried out with different tuning strategies to induce models and to evaluate HPs' relevance using 94 classification datasets from OpenML. The experimental results point out that different HP profiles for the tuning of each algorithm provide statistically significant improvements in most of the datasets for CART, but only in one-third for C4.5. Although different algorithms may present different tuning scenarios, the tuning techniques generally required few evaluations to find accurate solutions. Furthermore, the best technique for all the algorithms was the IRACE. Finally, we found out that tuning a specific small subset of HPs is a good alternative for achieving optimal predictive performance.
Are you tough enough? Framework for Robustness Validation of Machine Comprehension Systems
Authors: Barbara Rychalska, Dominika Basaj, Przemyslaw Biecek
Date: 2018-12-05 00:00:00
Tasks: Question Answering, Reading Comprehension, Semantic Similarity, Semantic Textual Similarity, Word Embeddings

Deep Learning NLP domain lacks procedures for the analysis of model
robustness. In this paper we propose a framework which validates robustness of
any Question Answering model through model explainers. We propose that a robust
model should transgress the initial notion of semantic similarity induced by
word embeddings to learn a more human-like understanding of meaning. We test
this property by manipulating questions in two ways: swapping important
question word for 1) its semantically correct synonym and 2) for word vector
that is close in embedding space. We estimate importance of words in asked
questions with Locally Interpretable Model Agnostic Explanations method (LIME).
With these two steps we compare state-of-the-art Q&A models. We show that
although accuracy of state-of-the-art models is high, they are very fragile to
changes in the input. Moreover, we propose 2 adversarial training scenarios
which raise model sensitivity to true synonyms by up to 7% accuracy measure.
Our findings help to understand which models are more stable and how they can
be improved. In addition, we have created and published a new dataset that may
be used for validation of robustness of a Q&A model.
Perceptual reversals in binocular rivalry: improved detection from OKN
Authors: Stepan Aleshin, Gergo Ziman, Ilona Kovacs, Jochen Braun
Date: 2018-12-06 00:00:00

When binocular rivalry is induced by opponent motion displays, perceptual
reversals are often associated with changed oculomotor behaviour (Frassle et
al., 2014; Fujiwara et al., 2017). Specifically, the direction of smooth
pursuit phases in optokinetic nystagmus (OKN) typically corresponds to the
direction of motion that dominates perceptual appearance at any given time.
Here we report an improved analysis that continuously estimates perceived
motion in terms of `cumulative smooth pursuit'. In essence, smooth pursuit
segments are identified, interpolated where necessary, and joined
probabilistically into a continuous record of `cumulative smooth pursuit'
(i.e., a probability of eye position disregarding blinks, saccades, signal
losses, and artefacts). The analysis is fully automated and robust in healthy,
developmental, and patient populations. To validate reliability, we compare
volitional reports of perceptual reversals in rivalry displays, and of physical
reversals in non-rivalrous control displays. `Cumulative smooth pursuit'
detects physical reversals and estimates eye velocity more accurately than
existing methods do (Frassle et al., 2014). It also appears to distinguish
dominant and transitional perceptual states, detecting changes with a precision
of $\pm100\,\mathit{ms}$. We conclude that `cumulative smooth pursuit'
significantly improves the monitoring of binocular rivalry by means of
recording OKN.
Neural Matching Models for Question Retrieval and Next Question Prediction in Conversation
Authors: Liu Yang, Hamed Zamani, Yongfeng Zhang, Jiafeng Guo, W. Bruce Croft
Date: 2017-07-17 00:00:00
Tasks: Information Retrieval, Representation Learning, Retrieval

The recent boom of AI has seen the emergence of many human-computer
conversation systems such as Google Assistant, Microsoft Cortana, Amazon Echo
and Apple Siri. We introduce and formalize the task of predicting questions in
conversations, where the goal is to predict the new question that the user will
ask, given the past conversational context. This task can be modeled as a
"sequence matching" problem, where two sequences are given and the aim is to
learn a model that maps any pair of sequences to a matching probability. Neural
matching models, which adopt deep neural networks to learn sequence
representations and matching scores, have attracted immense research interests
of information retrieval and natural language processing communities. In this
paper, we first study neural matching models for the question retrieval task
that has been widely explored in the literature, whereas the effectiveness of
neural models for this task is relatively unstudied. We further evaluate the
neural matching models in the next question prediction task in conversations.
We have used the publicly available Quora data and Ubuntu chat logs in our
experiments. Our evaluations investigate the potential of neural matching
models with representation learning for question retrieval and next question
prediction in conversations. Experimental results show that neural matching
models perform well for both tasks.
Deep Learning for Classical Japanese Literature
Authors: Tarin Clanuwat, Mikel Bober-Irizar, Asanobu Kitamoto, Alex Lamb, Kazuaki Yamamoto, David Ha
Date: 2018-12-03 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Image Classification

Much of machine learning research focuses on producing models which perform
well on benchmark tasks, in turn improving our understanding of the challenges
associated with those tasks. From the perspective of ML researchers, the
content of the task itself is largely irrelevant, and thus there have
increasingly been calls for benchmark tasks to more heavily focus on problems
which are of social or cultural relevance. In this work, we introduce
Kuzushiji-MNIST, a dataset which focuses on Kuzushiji (cursive Japanese), as
well as two larger, more challenging datasets, Kuzushiji-49 and
Kuzushiji-Kanji. Through these datasets, we wish to engage the machine learning
community into the world of classical Japanese literature. Dataset available at
https://github.com/rois-codh/kmnist
Variational Saccading: Efficient Inference for Large Resolution Images
Authors: Jason Ramapuram, Maurits Diephuis, Frantzeska Lavda, Russ Webb, Alexandros Kalousis
Date: 2018-12-08 00:00:00
Tasks: General Classification, image-classification, Image Classification, Starcraft, Starcraft II

Image classification with deep neural networks is typically restricted to images of small dimensionality such as 224 x 244 in Resnet models [24]. This limitation excludes the 4000 x 3000 dimensional images that are taken by modern smartphone cameras and smart devices. In this work, we aim to mitigate the prohibitive inferential and memory costs of operating in such large dimensional spaces. To sample from the high-resolution original input distribution, we propose using a smaller proxy distribution to learn the co-ordinates that correspond to regions of interest in the high-dimensional space. We introduce a new principled variational lower bound that captures the relationship of the proxy distribution's posterior and the original image's co-ordinate space in a way that maximizes the conditional classification likelihood. We empirically demonstrate on one synthetic benchmark and one real world large resolution DSLR camera image dataset that our method produces comparable results with ~10x faster inference and lower memory consumption than a model that utilizes the entire original input distribution. Finally, we experiment with a more complex setting using mini-maps from Starcraft II [56] to infer the number of characters in a complex 3d-rendered scene. Even in such complicated scenes our model provides strong localization: a feature missing from traditional classification models.