pwc-archive/papers-with-abstracts huggingface dataset:
Empathy in Bimatrix Games
Authors: Brian Powers, Michalis Smyrnakis, Hamidou Tembine
Date: 2017-08-06 00:00:00
Tasks: Decision Making

Although the definition of what empathetic preferences exactly are is still
evolving, there is a general consensus in the psychology, science and
engineering communities that the evolution toward players' behaviors in
interactive decision-making problems will be accompanied by the exploitation of
their empathy, sympathy, compassion, antipathy, spitefulness, selfishness,
altruism, and self-abnegating states in the payoffs. In this article, we study
one-shot bimatrix games from a psychological game theory viewpoint. A new
empathetic payoff model is calculated to fit empirical observations and both
pure and mixed equilibria are investigated. For a realized empathy structure,
the bimatrix game is categorized among four generic class of games. Number of
interesting results are derived. A notable level of involvement can be observed
in the empathetic one-shot game compared the non-empathetic one and this holds
even for games with dominated strategies. Partial altruism can help in breaking
symmetry, in reducing payoff-inequality and in selecting social welfare and
more efficient outcomes. By contrast, partial spite and self-abnegating may
worsen payoff equity. Empathetic evolutionary game dynamics are introduced to
capture the resulting empathetic evolutionarily stable strategies under wide
range of revision protocols including Brown-von Neumann-Nash, Smith, imitation,
replicator, and hybrid dynamics. Finally, mutual support and Berge solution are
investigated and their connection with empathetic preferences are established.
We show that pure altruism is logically inconsistent, only by balancing it with
some partial selfishness does it create a consistent psychology.
Image Quality Assessment Techniques Show Improved Training and Evaluation of Autoencoder Generative Adversarial Networks
Authors: Michael O. Vertolli, Jim Davies
Date: 2017-08-06 00:00:00
Tasks: Generative Adversarial Network, Image Quality Assessment

We propose a training and evaluation approach for autoencoder Generative
Adversarial Networks (GANs), specifically the Boundary Equilibrium Generative
Adversarial Network (BEGAN), based on methods from the image quality assessment
literature. Our approach explores a multidimensional evaluation criterion that
utilizes three distance functions: an $l_1$ score, the Gradient Magnitude
Similarity Mean (GMSM) score, and a chrominance score. We show that each of the
different distance functions captures a slightly different set of properties in
image space and, consequently, requires its own evaluation criterion to
properly assess whether the relevant property has been adequately learned. We
show that models using the new distance functions are able to produce better
images than the original BEGAN model in predicted ways.
One-Trial Correction of Legacy AI Systems and Stochastic Separation Theorems
Authors: Alexander N. Gorban, Ilya Romanenko, Richard Burton, Ivan Y. Tyukin
Date: 2016-10-03 00:00:00

We consider the problem of efficient "on the fly" tuning of existing, or {\it
legacy}, Artificial Intelligence (AI) systems. The legacy AI systems are
allowed to be of arbitrary class, albeit the data they are using for computing
interim or final decision responses should posses an underlying structure of a
high-dimensional topological real vector space. The tuning method that we
propose enables dealing with errors without the need to re-train the system.
Instead of re-training a simple cascade of perceptron nodes is added to the
legacy system. The added cascade modulates the AI legacy system's decisions. If
applied repeatedly, the process results in a network of modulating rules
"dressing up" and improving performance of existing AI systems. Mathematical
rationale behind the method is based on the fundamental property of measure
concentration in high dimensional spaces. The method is illustrated with an
example of fine-tuning a deep convolutional network that has been pre-trained
to detect pedestrians in images.
Spatio-temporal Human Action Localisation and Instance Segmentation in Temporally Untrimmed Videos
Authors: Suman Saha, Gurkirt Singh, Michael Sapienza, Philip H. S. Torr, Fabio Cuzzolin
Date: 2017-07-22 00:00:00
Tasks: Action Recognition, Instance Segmentation, Semantic Segmentation, Temporal Action Localization

Current state-of-the-art human action recognition is focused on the
classification of temporally trimmed videos in which only one action occurs per
frame. In this work we address the problem of action localisation and instance
segmentation in which multiple concurrent actions of the same class may be
segmented out of an image sequence. We cast the action tube extraction as an
energy maximisation problem in which configurations of region proposals in each
frame are assigned a cost and the best action tubes are selected via two passes
of dynamic programming. One pass associates region proposals in space and time
for each action category, and another pass is used to solve for the tube's
temporal extent and to enforce a smooth label sequence through the video. In
addition, by taking advantage of recent work on action foreground-background
segmentation, we are able to associate each tube with class-specific
segmentations. We demonstrate the performance of our algorithm on the
challenging LIRIS-HARL dataset and achieve a new state-of-the-art result which
is 14.3 times better than previous methods.
Machine learning in sentiment reconstruction of the simulated stock market
Authors: Mikhail Goykhman, Ali Teimouri
Date: 2017-08-06 00:00:00
Tasks: BIG-bench Machine Learning

In this paper we continue the study of the simulated stock market framework
defined by the driving sentiment processes. We focus on the market environment
driven by the buy/sell trading sentiment process of the Markov chain type. We
apply the methodology of the Hidden Markov Models and the Recurrent Neural
Networks to reconstruct the transition probabilities matrix of the Markov
sentiment process and recover the underlying sentiment states from the observed
stock price behavior.
AMTnet: Action-Micro-Tube Regression by End-to-end Trainable Deep Architecture
Authors: Suman Saha, Gurkirt Singh, Fabio Cuzzolin
Date: 2017-04-17 00:00:00
Proceeding: ICCV 2017 10
Tasks: Action Detection, Region Proposal, regression

Dominant approaches to action detection can only provide sub-optimal
solutions to the problem, as they rely on seeking frame-level detections, to
later compose them into "action tubes" in a post-processing step. With this
paper we radically depart from current practice, and take a first step towards
the design and implementation of a deep network architecture able to classify
and regress whole video subsets, so providing a truly optimal solution of the
action detection problem. In this work, in particular, we propose a novel deep
net framework able to regress and classify 3D region proposals spanning two
successive video frames, whose core is an evolution of classical region
proposal networks (RPNs). As such, our 3D-RPN net is able to effectively encode
the temporal aspect of actions by purely exploiting appearance, as opposed to
methods which heavily rely on expensive flow maps. The proposed model is
end-to-end trainable and can be jointly optimised for action localisation and
classification in a single step. At test time the network predicts
"micro-tubes" encompassing two successive frames, which are linked up into
complete action tubes via a new algorithm which exploits the temporal encoding
learned by the network and cuts computation time by 50%. Promising results on
the J-HMDB-21 and UCF-101 action detection datasets show that our model does
outperform the state-of-the-art when relying purely on appearance.
End-to-end learning potentials for structured attribute prediction
Authors: Kota Yamaguchi, Takayuki Okatani, Takayuki Umeda, Kazuhiko Murasaki, Kyoko Sudo
Date: 2017-08-06 00:00:00
Tasks: Attribute, Prediction

We present a structured inference approach in deep neural networks for
multiple attribute prediction. In attribute prediction, a common approach is to
learn independent classifiers on top of a good feature representation. However,
such classifiers assume conditional independence on features and do not
explicitly consider the dependency between attributes in the inference process.
We propose to formulate attribute prediction in terms of marginal inference in
the conditional random field. We model potential functions by deep neural
networks and apply the sum-product algorithm to solve for the approximate
marginal distribution in feed-forward networks. Our message passing layer
implements sparse pairwise potentials by a softplus-linear function that is
equivalent to a higher-order classifier, and learns all the model parameters by
end-to-end back propagation. The experimental results using SUN attributes and
CelebA datasets suggest that the structured inference improves the attribute
prediction performance, and possibly uncovers the hidden relationship between
attributes.
Probabilistic Generative Adversarial Networks
Authors: Hamid Eghbal-zadeh, Gerhard Widmer
Date: 2017-08-06 00:00:00
Tasks: Generative Adversarial Network

We introduce the Probabilistic Generative Adversarial Network (PGAN), a new
GAN variant based on a new kind of objective function. The central idea is to
integrate a probabilistic model (a Gaussian Mixture Model, in our case) into
the GAN framework which supports a new kind of loss function (based on
likelihood rather than classification loss), and at the same time gives a
meaningful measure of the quality of the outputs generated by the network.
Experiments with MNIST show that the model learns to generate realistic images,
and at the same time computes likelihoods that are correlated with the quality
of the generated images. We show that PGAN is better able to cope with
instability problems that are usually observed in the GAN training procedure.
We investigate this from three aspects: the probability landscape of the
discriminator, gradients of the generator, and the perfect discriminator
problem.
Long Short-Term Memory Kalman Filters:Recurrent Neural Estimators for Pose Regularization
Authors: Huseyin Coskun, Felix Achilles, Robert DiPietro, Nassir Navab, Federico Tombari
Date: 2017-08-06 00:00:00
Tasks: Camera Pose Estimation, Object Tracking, Pose Estimation

One-shot pose estimation for tasks such as body joint localization, camera
pose estimation, and object tracking are generally noisy, and temporal filters
have been extensively used for regularization. One of the most widely-used
methods is the Kalman filter, which is both extremely simple and general.
However, Kalman filters require a motion model and measurement model to be
specified a priori, which burdens the modeler and simultaneously demands that
we use explicit models that are often only crude approximations of reality. For
example, in the pose-estimation tasks mentioned above, it is common to use
motion models that assume constant velocity or constant acceleration, and we
believe that these simplified representations are severely inhibitive. In this
work, we propose to instead learn rich, dynamic representations of the motion
and noise models. In particular, we propose learning these models from data
using long short term memory, which allows representations that depend on all
previous observations and all previous states. We evaluate our method using
three of the most popular pose estimation tasks in computer vision, and in all
cases we obtain state-of-the-art performance.
Unsupervised learning of object landmarks by factorized spatial embeddings
Authors: James Thewlis, Hakan Bilen, Andrea Vedaldi
Date: 2017-05-05 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object, Unsupervised Facial Landmark Detection, Unsupervised Human Pose Estimation, Unsupervised Keypoints

Learning automatically the structure of object categories remains an
important open problem in computer vision. In this paper, we propose a novel
unsupervised approach that can discover and learn landmarks in object
categories, thus characterizing their structure. Our approach is based on
factorizing image deformations, as induced by a viewpoint change or an object
deformation, by learning a deep neural network that detects landmarks
consistently with such visual effects. Furthermore, we show that the learned
landmarks establish meaningful correspondences between different object
instances in a category without having to impose this requirement explicitly.
We assess the method qualitatively on a variety of object types, natural and
man-made. We also show that our unsupervised landmarks are highly predictive of
manually-annotated landmarks in face benchmark datasets, and can be used to
regress these with a high degree of accuracy.
Information Potential Auto-Encoders
Authors: Yan Zhang, Mete Ozay, Zhun Sun, Takayuki Okatani
Date: 2017-06-14 00:00:00
Tasks: Representation Learning

In this paper, we suggest a framework to make use of mutual information as a
regularization criterion to train Auto-Encoders (AEs). In the proposed
framework, AEs are regularized by minimization of the mutual information
between input and encoding variables of AEs during the training phase. In order
to estimate the entropy of the encoding variables and the mutual information,
we propose a non-parametric method. We also give an information theoretic view
of Variational AEs (VAEs), which suggests that VAEs can be considered as
parametric methods that estimate entropy. Experimental results show that the
proposed non-parametric models have more degree of freedom in terms of
representation learning of features drawn from complex distributions such as
Mixture of Gaussians, compared to methods which estimate entropy using
parametric approaches, such as Variational AEs.
SVDNet for Pedestrian Retrieval
Authors: Yifan Sun, Liang Zheng, Weijian Deng, Shengjin Wang
Date: 2017-03-16 00:00:00
Proceeding: ICCV 2017 10
Tasks: Person Re-Identification, Representation Learning, Retrieval

This paper proposes the SVDNet for retrieval problems, with focus on the
application of person re-identification (re-ID). We view each weight vector
within a fully connected (FC) layer in a convolutional neuron network (CNN) as
a projection basis. It is observed that the weight vectors are usually highly
correlated. This problem leads to correlations among entries of the FC
descriptor, and compromises the retrieval performance based on the Euclidean
distance. To address the problem, this paper proposes to optimize the deep
representation learning process with Singular Vector Decomposition (SVD).
Specifically, with the restraint and relaxation iteration (RRI) training
scheme, we are able to iteratively integrate the orthogonality constraint in
CNN training, yielding the so-called SVDNet. We conduct experiments on the
Market-1501, CUHK03, and Duke datasets, and show that RRI effectively reduces
the correlation among the projection vectors, produces more discriminative FC
descriptors, and significantly improves the re-ID accuracy. On the Market-1501
dataset, for instance, rank-1 accuracy is improved from 55.3% to 80.5% for
CaffeNet, and from 73.8% to 82.3% for ResNet-50.
Manifold Constrained Low-Rank Decomposition
Authors: Chen Chen, Baochang Zhang, Alessio Del Bue, Vittorio Murino
Date: 2017-08-06 00:00:00

Low-rank decomposition (LRD) is a state-of-the-art method for visual data
reconstruction and modelling. However, it is a very challenging problem when
the image data contains significant occlusion, noise, illumination variation,
and misalignment from rotation or viewpoint changes. We leverage the specific
structure of data in order to improve the performance of LRD when the data are
not ideal. To this end, we propose a new framework that embeds manifold priors
into LRD. To implement the framework, we design an alternating direction method
of multipliers (ADMM) method which efficiently integrates the manifold
constraints during the optimization process. The proposed approach is
successfully used to calculate low-rank models from face images, hand-written
digits and planar surface images. The results show a consistent increase of
performance when compared to the state-of-the-art over a wide range of
realistic image misalignments and corruptions.
Weakly Supervised Semantic Segmentation Based on Web Image Co-segmentation
Authors: Tong Shen, Guosheng Lin, Lingqiao Liu, Chunhua Shen, Ian Reid
Date: 2017-05-25 00:00:00
Tasks: Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Training a Fully Convolutional Network (FCN) for semantic segmentation
requires a large number of masks with pixel level labelling, which involves a
large amount of human labour and time for annotation. In contrast, web images
and their image-level labels are much easier and cheaper to obtain. In this
work, we propose a novel method for weakly supervised semantic segmentation
with only image-level labels. The method utilizes the internet to retrieve a
large number of images and uses a large scale co-segmentation framework to
generate masks for the retrieved images. We first retrieve images from search
engines, e.g. Flickr and Google, using semantic class names as queries, e.g.
class names in the dataset PASCAL VOC 2012. We then use high quality masks
produced by co-segmentation on the retrieved images as well as the target
dataset images with image level labels to train segmentation networks. We
obtain an IoU score of 56.9 on test set of PASCAL VOC 2012, which reaches the
state-of-the-art performance.
Learning Visual N-Grams from Web Data
Authors: Ang Li, Allan Jabri, Armand Joulin, Laurens van der Maaten
Date: 2016-12-29 00:00:00
Proceeding: ICCV 2017 10
Tasks: Language Modeling, Language Modelling, Representation Learning, Retrieval, Zero-Shot Transfer Image Classification

Real-world image recognition systems need to recognize tens of thousands of
classes that constitute a plethora of visual concepts. The traditional approach
of annotating thousands of images per class for training is infeasible in such
a scenario, prompting the use of webly supervised data. This paper explores the
training of image-recognition systems on large numbers of images and associated
user comments. In particular, we develop visual n-gram models that can predict
arbitrary phrases that are relevant to the content of an image. Our visual
n-gram models are feed-forward convolutional networks trained using new loss
functions that are inspired by n-gram models commonly used in language
modeling. We demonstrate the merits of our models in phrase prediction,
phrase-based image retrieval, relating images and captions, and zero-shot
transfer.
Fairness in Reinforcement Learning
Authors: Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Aaron Roth
Date: 2016-11-09 00:00:00
Proceeding: ICML 2017 8
Tasks: Fairness, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We initiate the study of fairness in reinforcement learning, where the
actions of a learning algorithm may affect its environment and future rewards.
Our fairness constraint requires that an algorithm never prefers one action
over another if the long-term (discounted) reward of choosing the latter action
is higher. Our first result is negative: despite the fact that fairness is
consistent with the optimal policy, any learning algorithm satisfying fairness
must take time exponential in the number of states to achieve non-trivial
approximation to the optimal policy. We then provide a provably fair polynomial
time algorithm under an approximate notion of fairness, thus establishing an
exponential gap between exact and approximate fairness
Categorical Reparameterization with Gumbel-Softmax
Authors: Eric Jang, Shixiang Gu, Ben Poole
Date: 2016-11-03 00:00:00
Tasks: General Classification

Categorical variables are a natural choice for representing discrete
structure in the world. However, stochastic neural networks rarely use
categorical latent variables due to the inability to backpropagate through
samples. In this work, we present an efficient gradient estimator that replaces
the non-differentiable sample from a categorical distribution with a
differentiable sample from a novel Gumbel-Softmax distribution. This
distribution has the essential property that it can be smoothly annealed into a
categorical distribution. We show that our Gumbel-Softmax estimator outperforms
state-of-the-art gradient estimators on structured output prediction and
unsupervised generative modeling tasks with categorical latent variables, and
enables large speedups on semi-supervised classification.
Attributes2Classname: A discriminative model for attribute-based unsupervised zero-shot learning
Authors: Berkan Demirel, Ramazan Gokberk Cinbis, Nazli Ikizler-Cinbis
Date: 2017-05-04 00:00:00
Proceeding: ICCV 2017 10
Tasks: Attribute, Zero-Shot Learning

We propose a novel approach for unsupervised zero-shot learning (ZSL) of
classes based on their names. Most existing unsupervised ZSL methods aim to
learn a model for directly comparing image features and class names. However,
this proves to be a difficult task due to dominance of non-visual semantics in
underlying vector-space embeddings of class names. To address this issue, we
discriminatively learn a word representation such that the similarities between
class and combination of attribute names fall in line with the visual
similarity. Contrary to the traditional zero-shot learning approaches that are
built upon attribute presence, our approach bypasses the laborious
attribute-class relation annotations for unseen classes. In addition, our
proposed approach renders text-only training possible, hence, the training can
be augmented without the need to collect additional image data. The
experimental results show that our method yields state-of-the-art results for
unsupervised ZSL in three benchmark datasets.
Parametrization and generation of geological models with generative adversarial networks
Authors: Shing Chan, Ahmed H. Elsheikh
Date: 2017-08-05 00:00:00

One of the main challenges in the parametrization of geological models is the
ability to capture complex geological structures often observed in the
subsurface. In recent years, generative adversarial networks (GAN) were
proposed as an efficient method for the generation and parametrization of
complex data, showing state-of-the-art performances in challenging computer
vision tasks such as reproducing natural images (handwritten digits, human
faces, etc.). In this work, we study the application of Wasserstein GAN for the
parametrization of geological models. The effectiveness of the method is
assessed for uncertainty propagation tasks using several test cases involving
different permeability patterns and subsurface flow problems. Results show that
GANs are able to generate samples that preserve the multipoint statistical
features of the geological models both visually and quantitatively. The
generated samples reproduce both the geological structures and the flow
statistics of the reference geology.
A Comparison of Neural Models for Word Ordering
Authors: Eva Hasler, Felix Stahlberg, Marcus Tomalin, Adri`a de Gispert, Bill Byrne
Date: 2017-08-05 00:00:00
Proceeding: WS 2017 9

We compare several language models for the word-ordering task and propose a
new bag-to-sequence neural model based on attention-based sequence-to-sequence
models. We evaluate the model on a large German WMT data set where it
significantly outperforms existing models. We also describe a novel search
strategy for LM-based word ordering and report results on the English Penn
Treebank. Our best model setup outperforms prior work both in terms of speed
and quality.