pwc-archive/papers-with-abstracts huggingface dataset:
Relational inductive bias for physical construction in humans and machines
Authors: Jessica B. Hamrick, Kelsey R. Allen, Victor Bapst, Tina Zhu, Kevin R. McKee, Joshua B. Tenenbaum, Peter W. Battaglia
Date: 2018-06-04 00:00:00
Tasks: Deep Reinforcement Learning, Inductive Bias, Object, Reinforcement Learning

While current deep learning systems excel at tasks such as object
classification, language processing, and gameplay, few can construct or modify
a complex system such as a tower of blocks. We hypothesize that what these
systems lack is a "relational inductive bias": a capacity for reasoning about
inter-object relations and making choices over a structured description of a
scene. To test this hypothesis, we focus on a task that involves gluing pairs
of blocks together to stabilize a tower, and quantify how well humans perform.
We then introduce a deep reinforcement learning agent which uses object- and
relation-centric scene and policy representations and apply it to the task. Our
results show that these structured representations allow the agent to
outperform both humans and more naive approaches, suggesting that relational
inductive bias is an important component in solving structured reasoning
problems and for building more intelligent, flexible machines.
Face Synthesis for Eyeglass-Robust Face Recognition
Authors: Jianzhu Guo, Xiangyu Zhu, Zhen Lei, Stan Z. Li
Date: 2018-06-04 00:00:00
Tasks: Deep Learning, Face Generation, Face Model, Face Recognition, Robust Face Recognition

In the application of face recognition, eyeglasses could significantly degrade the recognition accuracy. A feasible method is to collect large-scale face images with eyeglasses for training deep learning methods. However, it is difficult to collect the images with and without glasses of the same identity, so that it is difficult to optimize the intra-variations caused by eyeglasses. In this paper, we propose to address this problem in a virtual synthesis manner. The high-fidelity face images with eyeglasses are synthesized based on 3D face model and 3D eyeglasses. Models based on deep learning methods are then trained on the synthesized eyeglass face dataset, achieving better performance than previous ones. Experiments on the real face database validate the effectiveness of our synthesized data for improving eyeglass face recognition performance.
Penalizing side effects using stepwise relative reachability
Authors: Victoria Krakovna, Laurent Orseau, Ramana Kumar, Miljan Martic, Shane Legg
Date: 2018-06-04 00:00:00
Tasks: Reinforcement Learning, Safe Reinforcement Learning

How can we design safe reinforcement learning agents that avoid unnecessary
disruptions to their environment? We show that current approaches to penalizing
side effects can introduce bad incentives, e.g. to prevent any irreversible
changes in the environment, including the actions of other agents. To isolate
the source of such undesirable incentives, we break down side effects penalties
into two components: a baseline state and a measure of deviation from this
baseline state. We argue that some of these incentives arise from the choice of
baseline, and others arise from the choice of deviation measure. We introduce a
new variant of the stepwise inaction baseline and a new deviation measure based
on relative reachability of states. The combination of these design choices
avoids the given undesirable incentives, while simpler baselines and the
unreachability measure fail. We demonstrate this empirically by comparing
different combinations of baseline and deviation measure choices on a set of
gridworld experiments designed to illustrate possible bad incentives.
An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks
Authors: Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Timo Bremer
Date: 2018-05-18 00:00:00
Tasks: blind source separation, Deblurring, Image Deblurring, Image Inpainting

Solving inverse problems continues to be a challenge in a wide array of
applications ranging from deblurring, image inpainting, source separation etc.
Most existing techniques solve such inverse problems by either explicitly or
implicitly finding the inverse of the model. The former class of techniques
require explicit knowledge of the measurement process which can be unrealistic,
and rely on strong analytical regularizers to constrain the solution space,
which often do not generalize well. The latter approaches have had remarkable
success in part due to deep learning, but require a large collection of
source-observation pairs, which can be prohibitively expensive. In this paper,
we propose an unsupervised technique to solve inverse problems with generative
adversarial networks (GANs). Using a pre-trained GAN in the space of source
signals, we show that one can reliably recover solutions to under determined
problems in a `blind' fashion, i.e., without knowledge of the measurement
process. We solve this by making successive estimates on the model and the
solution in an iterative fashion. We show promising results in three
challenging applications -- blind source separation, image deblurring, and
recovering an image from its edge map, and perform better than several
baselines.
History Playground: A Tool for Discovering Temporal Trends in Massive Textual Corpora
Authors: Thomas Lansdall-Welfare, Nello Cristianini
Date: 2018-06-04 00:00:00
Tasks: Change Point Detection, Time Series, Time Series Analysis

Recent studies have shown that macroscopic patterns of continuity and change
over the course of centuries can be detected through the analysis of time
series extracted from massive textual corpora. Similar data-driven approaches
have already revolutionised the natural sciences, and are widely believed to
hold similar potential for the humanities and social sciences, driven by the
mass-digitisation projects that are currently under way, and coupled with the
ever-increasing number of documents which are "born digital". As such, new
interactive tools are required to discover and extract macroscopic patterns
from these vast quantities of textual data. Here we present History Playground,
an interactive web-based tool for discovering trends in massive textual
corpora. The tool makes use of scalable algorithms to first extract trends from
textual corpora, before making them available for real-time search and
discovery, presenting users with an interface to explore the data. Included in
the tool are algorithms for standardization, regression, change-point detection
in the relative frequencies of ngrams, multi-term indices and comparison of
trends across different corpora.
Gradient-based Filter Design for the Dual-tree Wavelet Transform
Authors: Daniel Recoskie, Richard Mann
Date: 2018-06-04 00:00:00

The wavelet transform has seen success when incorporated into neural network
architectures, such as in wavelet scattering networks. More recently, it has
been shown that the dual-tree complex wavelet transform can provide better
representations than the standard transform. With this in mind, we extend our
previous method for learning filters for the 1D and 2D wavelet transforms into
the dual-tree domain. We show that with few modifications to our original
model, we can learn directional filters that leverage the properties of the
dual-tree wavelet transform.
Experimental Tests of Spirituality
Authors: Abraham Loeb
Date: 2018-06-04 00:00:00

We currently harness technologies that could shed new light on old
philosophical questions, such as whether our mind entails anything beyond our
body or whether our moral values reflect universal truth.
Deep Continuous Conditional Random Fields with Asymmetric Inter-object Constraints for Online Multi-object Tracking
Authors: Hui Zhou, Wanli Ouyang, Jian Cheng, Xiaogang Wang, Hongsheng Li
Date: 2018-06-04 00:00:00
Tasks: Autonomous Driving, Multi-Object Tracking, Object, Object Tracking, Online Multi-Object Tracking, Robot Navigation

Online Multi-Object Tracking (MOT) is a challenging problem and has many
important applications including intelligence surveillance, robot navigation
and autonomous driving. In existing MOT methods, individual object's movements
and inter-object relations are mostly modeled separately and relations between
them are still manually tuned. In addition, inter-object relations are mostly
modeled in a symmetric way, which we argue is not an optimal setting. To tackle
those difficulties, in this paper, we propose a Deep Continuous Conditional
Random Field (DCCRF) for solving the online MOT problem in a track-by-detection
framework. The DCCRF consists of unary and pairwise terms. The unary terms
estimate tracked objects' displacements across time based on visual appearance
information. They are modeled as deep Convolution Neural Networks, which are
able to learn discriminative visual features for tracklet association. The
asymmetric pairwise terms model inter-object relations in an asymmetric way,
which encourages high-confidence tracklets to help correct errors of
low-confidence tracklets and not to be affected by low-confidence ones much.
The DCCRF is trained in an end-to-end manner for better adapting the influences
of visual information as well as inter-object relations. Extensive experimental
comparisons with state-of-the-arts as well as detailed component analysis of
our proposed DCCRF on two public benchmarks demonstrate the effectiveness of
our proposed MOT framework.
Online Reciprocal Recommendation with Theoretical Performance Guarantees
Authors: Fabio Vitale, Nikos Parotsidis, Claudio Gentile
Date: 2018-06-04 00:00:00
Proceeding: NeurIPS 2018 12

A reciprocal recommendation problem is one where the goal of learning is not
just to predict a user's preference towards a passive item (e.g., a book), but
to recommend the targeted user on one side another user from the other side
such that a mutual interest between the two exists. The problem thus is sharply
different from the more traditional items-to-users recommendation, since a good
match requires meeting the preferences of both users. We initiate a rigorous
theoretical investigation of the reciprocal recommendation task in a specific
framework of sequential learning. We point out general limitations, formulate
reasonable assumptions enabling effective learning and, under these
assumptions, we design and analyze a computationally efficient algorithm that
uncovers mutual likes at a pace comparable to those achieved by a clearvoyant
algorithm knowing all user preferences in advance. Finally, we validate our
algorithm against synthetic and real-world datasets, showing improved empirical
performance over simple baselines.
The Anatomy of a Modular System for Media Content Analysis
Authors: Ilias Flaounas, Thomas Lansdall-Welfare, Panagiota Antonakaki, Nello Cristianini
Date: 2014-02-25 00:00:00
Tasks: Anatomy, Machine Translation, Topic Classification, Translation

Intelligent systems for the annotation of media content are increasingly
being used for the automation of parts of social science research. In this
domain the problem of integrating various Artificial Intelligence (AI)
algorithms into a single intelligent system arises spontaneously. As part of
our ongoing effort in automating media content analysis for the social
sciences, we have built a modular system by combining multiple AI modules into
a flexible framework in which they can cooperate in complex tasks. Our system
combines data gathering, machine translation, topic classification, extraction
and annotation of entities and social networks, as well as many other tasks
that have been perfected over the past years of AI research. Over the last few
years, it has allowed us to realise a series of scientific studies over a vast
range of applications including comparative studies between news outlets and
media content in different countries, modelling of user preferences, and
monitoring public mood. The framework is flexible and allows the design and
implementation of modular agents, where simple modules cooperate in the
annotation of a large dataset without central coordination.
TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning
Authors: Artemij Amiranashvili, Alexey Dosovitskiy, Vladlen Koltun, Thomas Brox
Date: 2018-06-04 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

Our understanding of reinforcement learning (RL) has been shaped by
theoretical and empirical results that were obtained decades ago using tabular
representations and linear function approximators. These results suggest that
RL methods that use temporal differencing (TD) are superior to direct Monte
Carlo estimation (MC). How do these results hold up in deep RL, which deals
with perceptually complex environments and deep nonlinear models? In this
paper, we re-examine the role of TD in modern deep RL, using specially designed
environments that control for specific factors that affect performance, such as
reward sparsity, reward delay, and the perceptual complexity of the task. When
comparing TD with infinite-horizon MC, we are able to reproduce classic results
in modern settings. Yet we also find that finite-horizon MC is not inferior to
TD, even when rewards are sparse or delayed. This makes MC a viable alternative
to TD in deep RL.
Do Neural Network Cross-Modal Mappings Really Bridge Modalities?
Authors: Guillem Collell, Marie-Francine Moens
Date: 2018-05-19 00:00:00
Proceeding: ACL 2018 7
Tasks: Diversity, Retrieval

Feed-forward networks are widely used in cross-modal applications to bridge
modalities by mapping distributed vectors of one modality to the other, or to a
shared space. The predicted vectors are then used to perform e.g., retrieval or
labeling. Thus, the success of the whole system relies on the ability of the
mapping to make the neighborhood structure (i.e., the pairwise similarities) of
the predicted vectors akin to that of the target vectors. However, whether this
is achieved has not been investigated yet. Here, we propose a new similarity
measure and two ad hoc experiments to shed light on this issue. In three
cross-modal benchmarks we learn a large number of language-to-vision and
vision-to-language neural network mappings (up to five layers) using a rich
diversity of image and text features and loss functions. Our results reveal
that, surprisingly, the neighborhood structure of the predicted vectors
consistently resembles more that of the input vectors than that of the target
vectors. In a second experiment, we further show that untrained nets do not
significantly disrupt the neighborhood (i.e., semantic) structure of the input
vectors.
Efficient Online Scalar Annotation with Bounded Support
Authors: Keisuke Sakaguchi, Benjamin Van Durme
Date: 2018-06-04 00:00:00
Proceeding: ACL 2018 7

We describe a novel method for efficiently eliciting scalar annotations for
dataset construction and system quality estimation by human judgments. We
contrast direct assessment (annotators assign scores to items directly), online
pairwise ranking aggregation (scores derive from annotator comparison of
items), and a hybrid approach (EASL: Efficient Annotation of Scalar Labels)
proposed here. Our proposal leads to increased correlation with ground truth,
at far greater annotator efficiency, suggesting this strategy as an improved
mechanism for dataset creation and manual system evaluation.
Probabilistic Model of Visual Segmentation
Authors: Jonathan Vacher, Pascal Mamassian, Ruben Coen-Cagli
Date: 2018-05-31 00:00:00
Tasks: model, Segmentation, Semantic Segmentation

Visual segmentation is a key perceptual function that partitions visual space
and allows for detection, recognition and discrimination of objects in complex
environments. The processes underlying human segmentation of natural images are
still poorly understood. In part, this is because we lack segmentation models
consistent with experimental and theoretical knowledge in visual neuroscience.
Biological sensory systems have been shown to approximate probabilistic
inference to interpret their inputs. This requires a generative model that
captures both the statistics of the sensory inputs and expectations about the
causes of those inputs. Following this hypothesis, we propose a probabilistic
generative model of visual segmentation that combines knowledge about 1) the
sensitivity of neurons in the visual cortex to statistical regularities in
natural images; and 2) the preference of humans to form contiguous partitions
of visual space. We develop an efficient algorithm for training and inference
based on expectation-maximization and validate it on synthetic data.
Importantly, with the appropriate choice of the prior, we derive an intuitive
closed--form update rule for assigning pixels to segments: at each iteration,
the pixel assignment probabilities to segments is the sum of the evidence (i.e.
local pixel statistics) and prior (i.e. the assignments of neighboring pixels)
weighted by their relative uncertainty. The model performs competitively on
natural images from the Berkeley Segmentation Dataset (BSD), and we illustrate
how the likelihood and prior components improve segmentation relative to
traditional mixture models. Furthermore, our model explains some variability
across human subjects as reflecting local uncertainty about the number of
segments. Our model thus provides a viable approach to probe human visual
segmentation.
Explaining Explanations: An Overview of Interpretability of Machine Learning
Authors: Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael Specter, Lalana Kagal
Date: 2018-05-31 00:00:00
Tasks: BIG-bench Machine Learning, Explainable Artificial Intelligence (XAI), Fairness

There has recently been a surge of work in explanatory artificial
intelligence (XAI). This research area tackles the important problem that
complex machines and algorithms often cannot provide insights into their
behavior and thought processes. XAI allows users and parts of the internal
system to be more transparent, providing explanations of their decisions in
some level of detail. These explanations are important to ensure algorithmic
fairness, identify potential bias/problems in the training data, and to ensure
that the algorithms perform as expected. However, explanations produced by
these systems is neither standardized nor systematically assessed. In an effort
to create best practices and identify open challenges, we provide our
definition of explainability and show how it can be used to classify existing
literature. We discuss why current approaches to explanatory methods especially
for deep neural networks are insufficient. Finally, based on our survey, we
conclude with suggested future research directions for explanatory artificial
intelligence.
Unleashing Linear Optimizers for Group-Fair Learning and Optimization
Authors: Daniel Alabi, Nicole Immorlica, Adam Tauman Kalai
Date: 2018-04-11 00:00:00
Tasks: Fairness

Most systems and learning algorithms optimize average performance or average
loss -- one reason being computational complexity. However, many objectives of
practical interest are more complex than simply average loss. This arises, for
example, when balancing performance or loss with fairness across people. We
prove that, from a computational perspective, optimizing arbitrary objectives
that take into account performance over a small number of groups is not
significantly harder to optimize than average performance. Our main result is a
polynomial-time reduction that uses a linear optimizer to optimize an arbitrary
(Lipschitz continuous) function of performance over a (constant) number of
possibly-overlapping groups. This includes fairness objectives over small
numbers of groups, and we further point out that other existing notions of
fairness such as individual fairness can be cast as convex optimization and
hence more standard convex techniques can be used. Beyond learning, our
approach applies to multi-objective optimization, more generally.
Efficient and Scalable Batch Bayesian Optimization Using K-Means
Authors: Matthew Groves, Edward O. Pyzer-Knapp
Date: 2018-06-04 00:00:00
Tasks: Bayesian Optimization, compressed sensing, Dimensionality Reduction, Drug Discovery

We present K-Means Batch Bayesian Optimization (KMBBO), a novel batch
sampling algorithm for Bayesian Optimization (BO). KMBBO uses unsupervised
learning to efficiently estimate peaks of the model acquisition function. We
show in empirical experiments that our method outperforms the current
state-of-the-art batch allocation algorithms on a variety of test problems
including tuning of algorithm hyper-parameters and a challenging drug discovery
problem. In order to accommodate the real-world problem of high dimensional
data, we propose a modification to KMBBO by combining it with compressed
sensing to project the optimization into a lower dimensional subspace. We
demonstrate empirically that this 2-step method outperforms algorithms where no
dimensionality reduction has taken place.
Shallow decision-making analysis in General Video Game Playing
Authors: Ivan Bravi, Jialin Liu, Diego Perez-Liebana, Simon Lucas
Date: 2018-06-04 00:00:00
Tasks: AI Agent, Decision Making, Descriptive

The General Video Game AI competitions have been the testing ground for
several techniques for game playing, such as evolutionary computation
techniques, tree search algorithms, hyper heuristic based or knowledge based
algorithms. So far the metrics used to evaluate the performance of agents have
been win ratio, game score and length of games. In this paper we provide a
wider set of metrics and a comparison method for evaluating and comparing
agents. The metrics and the comparison method give shallow introspection into
the agent's decision making process and they can be applied to any agent
regardless of its algorithmic nature. In this work, the metrics and the
comparison method are used to measure the impact of the terms that compose a
tree policy of an MCTS based agent, comparing with several baseline agents. The
results clearly show how promising such general approach is and how it can be
useful to understand the behaviour of an AI agent, in particular, how the
comparison with baseline agents can help understanding the shape of the agent
decision landscape. The presented metrics and comparison method represent a
step toward to more descriptive ways of logging and analysing agent's
behaviours.
Learning from Exemplars and Prototypes in Machine Learning and Psychology
Authors: Julian Zubek, Ludmila Kuncheva
Date: 2018-06-04 00:00:00
Tasks: BIG-bench Machine Learning, Prototype Selection

This paper draws a parallel between similarity-based categorisation models
developed in cognitive psychology and the nearest neighbour classifier (1-NN)
in machine learning. Conceived as a result of the historical rivalry between
prototype theories (abstraction) and exemplar theories (memorisation), recent
models of human categorisation seek a compromise in-between. Regarding the
stimuli (entities to be categorised) as points in a metric space, machine
learning offers a large collection of methods to select a small, representative
and discriminative point set. These methods are known under various names:
instance selection, data editing, prototype selection, prototype generation or
prototype replacement. The nearest neighbour classifier is used with the
selected reference set. Such a set can be interpreted as a data-driven
categorisation model. We juxtapose the models from the two fields to enable
cross-referencing. We believe that both machine learning and cognitive
psychology can draw inspiration from the comparison and enrich their repertoire
of similarity-based models.