pwc-archive/papers-with-abstracts huggingface dataset:
Hybrid Active Inference
Authors: André Ofner, Sebastian Stober
Date: 2018-10-05 00:00:00
Tasks: BIG-bench Machine Learning, Probabilistic Deep Learning, Representation Learning

We describe a framework of hybrid cognition by formulating a hybrid cognitive
agent that performs hierarchical active inference across a human and a machine
part. We suggest that, in addition to enhancing human cognitive functions with
an intelligent and adaptive interface, integrated cognitive processing could
accelerate emergent properties within artificial intelligence. To establish
this, a machine learning part learns to integrate into human cognition by
explaining away multi-modal sensory measurements from the environment and
physiology simultaneously with the brain signal. With ongoing training, the
amount of predictable brain signal increases. This lends the agent the ability
to self-supervise on increasingly high levels of cognitive processing in order
to further minimize surprise in predicting the brain signal. Furthermore, with
increasing level of integration, the access to sensory information about
environment and physiology is substituted with access to their representation
in the brain. While integrating into a joint embodiment of human and machine,
human action and perception are treated as the machine's own. The framework can
be implemented with invasive as well as non-invasive sensors for environment,
body and brain interfacing. Online and offline training with different machine
learning approaches are thinkable. Building on previous research on shared
representation learning, we suggest a first implementation leading towards
hybrid active inference with non-invasive brain interfacing and state of the
art probabilistic deep learning methods. We further discuss how implementation
might have effect on the meta-cognitive abilities of the described agent and
suggest that with adequate implementation the machine part can continue to
execute and build upon the learned cognitive processes autonomously.
SLIC Based Digital Image Enlargement
Authors: M. Z. F. Amara, R. Bandara, Thushari Silva
Date: 2018-10-05 00:00:00
Tasks: Clustering, Image Enhancement

Low resolution image enhancement is a classical computer vision problem.
Selecting the best method to reconstruct an image to a higher resolution with
the limited data available in the low-resolution image is quite a challenge. A
major drawback from the existing enlargement techniques is the introduction of
color bleeding while interpolating pixels over the edges that separate distinct
colors in an image. The color bleeding causes to accentuate the edges with new
colors as a result of blending multiple colors over adjacent regions. This
paper proposes a novel approach to mitigate the color bleeding by segmenting
the homogeneous color regions of the image using Simple Linear Iterative
Clustering (SLIC) and applying a higher order interpolation technique
separately on the isolated segments. The interpolation at the boundaries of
each of the isolated segments is handled by using a morphological operation.
The approach is evaluated by comparing against several frequently used image
enlargement methods such as bilinear and bicubic interpolation by means of Peak
Signal-to-Noise-Ratio (PSNR) value. The results obtained exhibit that the
proposed method outperforms the baseline methods by means of PSNR and also
mitigates the color bleeding at the edges which improves the overall
appearance.
Integrating Weakly Supervised Word Sense Disambiguation into Neural Machine Translation
Authors: Xiao Pu, Nikolaos Pappas, James Henderson, Andrei Popescu-Belis
Date: 2018-10-05 00:00:00
Proceeding: TACL 2018 1
Tasks: Clustering, Machine Translation, NMT, Translation, Word Sense Disambiguation

This paper demonstrates that word sense disambiguation (WSD) can improve
neural machine translation (NMT) by widening the source context considered when
modeling the senses of potentially ambiguous words. We first introduce three
adaptive clustering algorithms for WSD, based on k-means, Chinese restaurant
processes, and random walks, which are then applied to large word contexts
represented in a low-rank space and evaluated on SemEval shared-task data. We
then learn word vectors jointly with sense vectors defined by our best WSD
method, within a state-of-the-art NMT system. We show that the concatenation of
these vectors, and the use of a sense selection mechanism based on the weighted
average of sense vectors, outperforms several baselines including sense-aware
ones. This is demonstrated by translation on five language pairs. The
improvements are above one BLEU point over strong NMT baselines, +4% accuracy
over all ambiguous nouns and verbs, or +20% when scored manually over several
challenging words.
Accelerated Labeling of Discrete Abstractions for Autonomous Driving Subject to LTL Specifications
Authors: Brian Paden, Peng Liu, Schuyler Cullen
Date: 2018-10-05 00:00:00
Tasks: Autonomous Driving, Decision Making, GPU, Motion Planning, Task and Motion Planning

Linear temporal logic and automaton-based run-time verification provide a
powerful framework for designing task and motion planning algorithms for
autonomous agents. The drawback to this approach is the computational cost of
operating on high resolution discrete abstractions of continuous dynamical
systems. In particular, the computational bottleneck that arises is converting
perceived environment variables into a labeling function on the states of a
Kripke structure or analogously the transitions of a labeled transition system.
This paper presents the design and empirical evaluation of an approach to
constructing the labeling function that exposes a large degree of parallelism
in the operation as well as efficient memory access patterns. The approach is
implemented on a commodity GPU and empirical results demonstrate the efficacy
of the labeling technique for real-time planning and decision-making.
Spatially-weighted Anomaly Detection
Authors: Minori Narita, Daiki Kimura, Ryuki Tachibana
Date: 2018-10-05 00:00:00
Tasks: Anomaly Detection

Many types of anomaly detection methods have been proposed recently, and
applied to a wide variety of fields including medical screening and production
quality checking. Some methods have utilized images, and, in some cases, a part
of the anomaly images is known beforehand. However, this kind of information is
dismissed by previous methods, because the methods can only utilize a normal
pattern. Moreover, the previous methods suffer a decrease in accuracy due to
negative effects from surrounding noises. In this study, we propose a
spatially-weighted anomaly detection method (SPADE) that utilizes all of the
known patterns and lessens the vulnerability to ambient noises by applying
Grad-CAM, which is the visualization method of a CNN. We evaluated our method
quantitatively using two datasets, the MNIST dataset with noise and a dataset
based on a brief screening test for dementia.
AIRNet: Self-Supervised Affine Registration for 3D Medical Images using Neural Networks
Authors: Evelyn Chee, Zhenzhou Wu
Date: 2018-10-05 00:00:00
Tasks: Affine Image Registration, Image Registration, Self-Supervised Learning

In this work, we propose a self-supervised learning method for affine image
registration on 3D medical images. Unlike optimisation-based methods, our
affine image registration network (AIRNet) is designed to directly estimate the
transformation parameters between two input images without using any metric,
which represents the quality of the registration, as the optimising function.
But since it is costly to manually identify the transformation parameters
between any two images, we leverage the abundance of cheap unlabelled data to
generate a synthetic dataset for the training of the model. Additionally, the
structure of AIRNet enables us to learn the discriminative features of the
images which are useful for registration purpose. Our proposed method was
evaluated on magnetic resonance images of the axial view of human brain and
compared with the performance of a conventional image registration method.
Experiments demonstrate that our approach achieves better overall performance
on registration of images from different patients and modalities with 100x
speed-up in execution time.
Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime
Authors: Dengxin Dai, Luc van Gool
Date: 2018-10-05 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

This work addresses the problem of semantic image segmentation of nighttime
scenes. Although considerable progress has been made in semantic image
segmentation, it is mainly related to daytime scenarios. This paper proposes a
novel method to progressive adapt the semantic models trained on daytime
scenes, along with large-scale annotations therein, to nighttime scenes via the
bridge of twilight time -- the time between dawn and sunrise, or between sunset
and dusk. The goal of the method is to alleviate the cost of human annotation
for nighttime images by transferring knowledge from standard daytime
conditions. In addition to the method, a new dataset of road scenes is
compiled; it consists of 35,000 images ranging from daytime to twilight time
and to nighttime. Also, a subset of the nighttime images are densely annotated
for method evaluation. Our experiments show that our method is effective for
model adaptation from daytime scenes to nighttime scenes, without using extra
human annotation.
Improving Question Answering by Commonsense-Based Pre-Training
Authors: Wanjun Zhong, Duyu Tang, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin
Date: 2018-09-05 00:00:00
Tasks: Question Answering

Although neural network approaches achieve remarkable success on a variety of
NLP tasks, many of them struggle to answer questions that require commonsense
knowledge. We believe the main reason is the lack of commonsense
\mbox{connections} between concepts. To remedy this, we provide a simple and
effective method that leverages external commonsense knowledge base such as
ConceptNet. We pre-train direct and indirect relational functions between
concepts, and show that these pre-trained functions could be easily added to
existing neural network models. Results show that incorporating
commonsense-based function improves the baseline on three question answering
tasks that require commonsense reasoning. Further analysis shows that our
system \mbox{discovers} and leverages useful evidence from an external
commonsense knowledge base, which is missing in existing neural network models
and help derive the correct answer.
End-to-end Networks for Supervised Single-channel Speech Separation
Authors: Shrikant Venkataramani, Paris Smaragdis
Date: 2018-10-05 00:00:00
Tasks: Speech Separation

The performance of single channel source separation algorithms has improved
greatly in recent times with the development and deployment of neural networks.
However, many such networks continue to operate on the magnitude spectrogram of
a mixture, and produce an estimate of source magnitude spectrograms, to perform
source separation. In this paper, we interpret these steps as additional neural
network layers and propose an end-to-end source separation network that allows
us to estimate the separated speech waveform by operating directly on the raw
waveform of the mixture. Furthermore, we also propose the use of masking based
end-to-end separation networks that jointly optimize the mask and the latent
representations of the mixture waveforms. These networks show a significant
improvement in separation performance compared to existing architectures in our
experiments. To train these end-to-end models, we investigate the use of
composite cost functions that are derived from objective evaluation metrics as
measured on waveforms. We present subjective listening test results that
demonstrate the improvement attained by using masking based end-to-end networks
and also reveal insights into the performance of these cost functions for
end-to-end source separation.
Online Learning to Rank with Features
Authors: Shuai Li, Tor Lattimore, Csaba Szepesvári
Date: 2018-10-05 00:00:00
Tasks: Learning-To-Rank

We introduce a new model for online ranking in which the click probability factors into an examination and attractiveness function and the attractiveness function is a linear function of a feature vector and an unknown parameter. Only relatively mild assumptions are made on the examination function. A novel algorithm for this setup is analysed, showing that the dependence on the number of items is replaced by a dependence on the dimension, allowing the new algorithm to handle a large number of items. When reduced to the orthogonal case, the regret of the algorithm improves on the state-of-the-art.
Continuous-time Models for Stochastic Optimization Algorithms
Authors: Antonio Orvieto, Aurelien Lucchi
Date: 2018-10-05 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Stochastic Optimization

We propose new continuous-time formulations for first-order stochastic optimization algorithms such as mini-batch gradient descent and variance-reduced methods. We exploit these continuous-time models, together with simple Lyapunov analysis as well as tools from stochastic calculus, in order to derive convergence bounds for various types of non-convex functions. Guided by such analysis, we show that the same Lyapunov arguments hold in discrete-time, leading to matching rates. In addition, we use these models and Ito calculus to infer novel insights on the dynamics of SGD, proving that a decreasing learning rate acts as time warping or, equivalently, as landscape stretching.
GPdoemd: a Python package for design of experiments for model discrimination
Authors: Simon Olofsson, Lukas Hebing, Sebastian Niedenführ, Marc Peter Deisenroth, Ruth Misener
Date: 2018-10-05 00:00:00

Model discrimination identifies a mathematical model that usefully explains
and predicts a given system's behaviour. Researchers will often have several
models, i.e. hypotheses, about an underlying system mechanism, but insufficient
experimental data to discriminate between the models, i.e. discard inaccurate
models. Given rival mathematical models and an initial experimental data set,
optimal design of experiments suggests maximally informative experimental
observations that maximise a design criterion weighted by prediction
uncertainty. The model uncertainty requires gradients, which may not be readily
available for black-box models. This paper (i) proposes a new design criterion
using the Jensen-R\'enyi divergence, and (ii) develops a novel method replacing
black-box models with Gaussian process surrogates. Using the surrogates, we
marginalise out the model parameters with approximate inference. Results show
these contributions working well for both classical and new test instances. We
also (iii) introduce and discuss GPdoemd, the open-source implementation of the
Gaussian process surrogate method.
Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference
Authors: Mike Wu, Noah Goodman, Stefano Ermon
Date: 2018-10-05 00:00:00
Tasks: Stochastic Optimization, Variational Inference

Stochastic optimization techniques are standard in variational inference algorithms. These methods estimate gradients by approximating expectations with independent Monte Carlo samples. In this paper, we explore a technique that uses correlated, but more representative , samples to reduce estimator variance. Specifically, we show how to generate antithetic samples that match sample moments with the true moments of an underlying importance distribution. Combining a differentiable antithetic sampler with modern stochastic variational inference, we showcase the effectiveness of this approach for learning a deep generative model.
PPO-CMA: Proximal Policy Optimization with Covariance Matrix Adaptation
Authors: Perttu Hämäläinen, Amin Babadi, Xiaoxiao Ma, Jaakko Lehtinen
Date: 2018-10-05 00:00:00
Tasks: continuous-control, Continuous Control, Reinforcement Learning, Reinforcement Learning (RL)

Proximal Policy Optimization (PPO) is a highly popular model-free reinforcement learning (RL) approach. However, we observe that in a continuous action space, PPO can prematurely shrink the exploration variance, which leads to slow progress and may make the algorithm prone to getting stuck in local optima. Drawing inspiration from CMA-ES, a black-box evolutionary optimization method designed for robustness in similar situations, we propose PPO-CMA, a proximal policy optimization approach that adaptively expands the exploration variance to speed up progress. With only minor changes to PPO, our algorithm considerably improves performance in Roboschool continuous control benchmarks. Our results also show that PPO-CMA, as opposed to PPO, is significantly less sensitive to the choice of hyperparameters, allowing one to use it in complex movement optimization tasks without requiring tedious tuning.
Fast Super-Paramagnetic Clustering
Authors: Lionel Yelibi, Tim Gebbie
Date: 2018-10-05 00:00:00
Tasks: Clustering

We map stock market interactions to spin models to recover their hierarchical structure using a simulated annealing based Super-Paramagnetic Clustering (SPC) algorithm. This is directly compared to a modified implementation of a maximum likelihood approach we call Fast Super-Paramagnetic Clustering (f-SPC). The methods are first applied standard toy test-case problems, and then to a data-set of 447 stocks traded on the New York Stock Exchange (NYSE) over 1249 days. The signal to noise ratio of stock market correlation matrices is briefly considered. Our result recover approximately clusters representative of standard economic sectors and mixed ones whose dynamics shine light on the adaptive nature of financial markets and raise concerns relating to the effectiveness of industry based static financial market classification in the world of real-time data analytics. A key result is that we show that f-SPC maximum likelihood solutions converge to ones found within the Super-Paramagnetic Phase where the entropy is maximum, and those solutions are qualitatively better for high dimensionality data-sets.
Local Stability and Performance of Simple Gradient Penalty mu-Wasserstein GAN
Authors: Cheolhyeong Kim, Seungtae Park, Hyung Ju Hwang
Date: 2018-10-05 00:00:00

Wasserstein GAN(WGAN) is a model that minimizes the Wasserstein distance
between a data distribution and sample distribution. Recent studies have
proposed stabilizing the training process for the WGAN and implementing the
Lipschitz constraint. In this study, we prove the local stability of optimizing
the simple gradient penalty $\mu$-WGAN(SGP $\mu$-WGAN) under suitable
assumptions regarding the equilibrium and penalty measure $\mu$. The measure
valued differentiation concept is employed to deal with the derivative of the
penalty terms, which is helpful for handling abstract singular measures with
lower dimensional support. Based on this analysis, we claim that penalizing the
data manifold or sample manifold is the key to regularizing the original WGAN
with a gradient penalty. Experimental results obtained with unintuitive penalty
measures that satisfy our assumptions are also provided to support our
theoretical results.
Where Did My Optimum Go?: An Empirical Analysis of Gradient Descent Optimization in Policy Gradient Methods
Authors: Peter Henderson, Joshua Romoff, Joelle Pineau
Date: 2018-10-05 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, Policy Gradient Methods, Reinforcement Learning

Recent analyses of certain gradient descent optimization methods have shown
that performance can degrade in some settings - such as with stochasticity or
implicit momentum. In deep reinforcement learning (Deep RL), such optimization
methods are often used for training neural networks via the temporal difference
error or policy gradient. As an agent improves over time, the optimization
target changes and thus the loss landscape (and local optima) change. Due to
the failure modes of those methods, the ideal choice of optimizer for Deep RL
remains unclear. As such, we provide an empirical analysis of the effects that
a wide range of gradient descent optimizers and their hyperparameters have on
policy gradient methods, a subset of Deep RL algorithms, for benchmark
continuous control tasks. We find that adaptive optimizers have a narrow window
of effective learning rates, diverging in other cases, and that the
effectiveness of momentum varies depending on the properties of the
environment. Our analysis suggests that there is significant interplay between
the dynamics of the environment and Deep RL algorithm properties which aren't
necessarily accounted for by traditional adaptive gradient methods. We provide
suggestions for optimal settings of current methods and further lines of
research based on our findings.
Social Choice Random Utility Models of Intransitive Pairwise Comparisons
Authors: Rahul Makhijani, Johan Ugander
Date: 2018-10-05 00:00:00
Tasks: Discrete Choice Models, Recommendation Systems, Triplet

There is a growing need for discrete choice models that account for the
complex nature of human choices, escaping traditional behavioral assumptions
such as the transitivity of pairwise preferences. Recently, several parametric
models of intransitive comparisons have been proposed, but in all cases the
maximum likelihood problem is non-concave, making inference difficult. In this
work we generalize this trend, showing that there cannot exist any parametric
model with a concave log-likelihood function that can exhibit intransitive
preferences. Given this result, we motivate a new model for analyzing
intransitivity in pairwise comparisons, taking inspiration from the Condorcet
method (majority vote) in social choice theory. The Majority Vote model we
analyze is defined as a voting process over independent Random Utility Models
(RUMs). We infer a multidimensional embedding of each object or player, in
contrast to the traditional one-dimensional embedding used by models such as
the Thurstone or Bradley-Terry-Luce (BTL) models. We show that a
three-dimensional majority vote model is capable of modeling arbitrarily strong
and long intransitive cycles, and can also represent arbitrary pairwise
comparison probabilities on any triplet. We provide experimental results that
substantiate our claims regarding the effectiveness of our model in capturing
intransitivity for various pairwise choice tasks such as predicting choices in
recommendation systems, winners in online video games, and elections.
Learning To Simulate
Authors: Nataniel Ruiz, Samuel Schulter, Manmohan Chandraker
Date: 2018-10-05 00:00:00
Proceeding: ICLR 2019 5
Tasks: Reinforcement Learning

Simulation is a useful tool in situations where training data for machine learning models is costly to annotate or even hard to acquire. In this work, we propose a reinforcement learning-based method for automatically adjusting the parameters of any (non-differentiable) simulator, thereby controlling the distribution of synthesized data in order to maximize the accuracy of a model trained on that data. In contrast to prior art that hand-crafts these simulation parameters or adjusts only parts of the available parameters, our approach fully controls the simulator with the actual underlying goal of maximizing accuracy, rather than mimicking the real data distribution or randomly generating a large volume of data. We find that our approach (i) quickly converges to the optimal simulation parameters in controlled experiments and (ii) can indeed discover good sets of parameters for an image rendering simulator in actual computer vision applications.