pwc-archive/papers-with-abstracts huggingface dataset:
"How to rate a video game?" - A prediction system for video games based on multimodal information
Authors: Vishal Batchu, Varshit Battu, Murali Krishna Reddy, Radhika Mamidi
Date: 2018-05-29 00:00:00
Tasks: Recommendation Systems

Video games have become an integral part of most people's lives in recent
times. This led to an abundance of data related to video games being shared
online. However, this comes with issues such as incorrect ratings, reviews or
anything that is being shared. Recommendation systems are powerful tools that
help users by providing them with meaningful recommendations. A straightforward
approach would be to predict the scores of video games based on other
information related to the game. It could be used as a means to validate
user-submitted ratings as well as provide recommendations. This work provides a
method to predict the G-Score, that defines how good a video game is, from its
trailer (video) and summary (text). We first propose models to predict the
G-Score based on the trailer alone (unimodal). Later on, we show that
considering information from multiple modalities helps the models perform
better compared to using information from videos alone. Since we couldn't find
any suitable multimodal video game dataset, we created our own dataset named
VGD (Video Game Dataset) and provide it along with this work. The approach
mentioned here can be generalized to other multimodal datasets such as movie
trailers and summaries etc. Towards the end, we talk about the shortcomings of
the work and some methods to overcome them.
How to Blend a Robot within a Group of Zebrafish: Achieving Social Acceptance through Real-time Calibration of a Multi-level Behavioural Model
Authors: Leo Cazenille, Yohann Chemtob, Frank Bonnet, Alexey Gribovskiy, Francesco Mondada, Nicolas Bredeche, Jose Halloy
Date: 2018-05-29 00:00:00
Tasks: Evolutionary Algorithms

We have previously shown how to socially integrate a fish robot into a group
of zebrafish thanks to biomimetic behavioural models. The models have to be
calibrated on experimental data to present correct behavioural features. This
calibration is essential to enhance the social integration of the robot into
the group. When calibrated, the behavioural model of fish behaviour is
implemented to drive a robot with closed-loop control of social interactions
into a group of zebrafish. This approach can be useful to form mixed-groups,
and study animal individual and collective behaviour by using biomimetic
autonomous robots capable of responding to the animals in long-standing
experiments. Here, we show a methodology for continuous real-time calibration
and refinement of multi-level behavioural model. The real-time calibration, by
an evolutionary algorithm, is based on simulation of the model to correspond to
the observed fish behaviour in real-time. The calibrated model is updated on
the robot and tested during the experiments. This method allows to cope with
changes of dynamics in fish behaviour. Moreover, each fish presents individual
behavioural differences. Thus, each trial is done with naive fish groups that
display behavioural variability. This real-time calibration methodology can
optimise the robot behaviours during the experiments. Our implementation of
this methodology runs on three different computers that perform individual
tracking, data-analysis, multi-objective evolutionary algorithms, simulation of
the fish robot and adaptation of the robot behavioural models, all in
real-time.
Lovasz Convolutional Networks
Authors: Prateek Yadav, Madhav Nimishakavi, Naganand Yadati, Shikhar Vashishth, Arun Rajkumar, Partha Talukdar
Date: 2018-05-29 00:00:00
Tasks: Multi-class Classification

Semi-supervised learning on graph structured data has received significant
attention with the recent introduction of Graph Convolution Networks (GCN).
While traditional methods have focused on optimizing a loss augmented with
Laplacian regularization framework, GCNs perform an implicit Laplacian type
regularization to capture local graph structure. In this work, we propose
Lovasz Convolutional Network (LCNs) which are capable of incorporating global
graph properties. LCNs achieve this by utilizing Lovasz's orthonormal
embeddings of the nodes. We analyse local and global properties of graphs and
demonstrate settings where LCNs tend to work better than GCNs. We validate the
proposed method on standard random graph models such as stochastic block models
(SBM) and certain community structure based graphs where LCNs outperform GCNs
and learn more intuitive embeddings. We also perform extensive binary and
multi-class classification experiments on real world datasets to demonstrate
LCN's effectiveness. In addition to simple graphs, we also demonstrate the use
of LCNs on hyper-graphs by identifying settings where they are expected to work
better than GCNs.
Regularized Optimal Transport and the Rot Mover's Distance
Authors: Arnaud Dessein, Nicolas Papadakis, Jean-Luc Rouas
Date: 2016-10-20 00:00:00
Tasks: Scene Classification

This paper presents a unified framework for smooth convex regularization of
discrete optimal transport problems. In this context, the regularized optimal
transport turns out to be equivalent to a matrix nearness problem with respect
to Bregman divergences. Our framework thus naturally generalizes a previously
proposed regularization based on the Boltzmann-Shannon entropy related to the
Kullback-Leibler divergence, and solved with the Sinkhorn-Knopp algorithm. We
call the regularized optimal transport distance the rot mover's distance in
reference to the classical earth mover's distance. We develop two generic
schemes that we respectively call the alternate scaling algorithm and the
non-negative alternate scaling algorithm, to compute efficiently the
regularized optimal plans depending on whether the domain of the regularizer
lies within the non-negative orthant or not. These schemes are based on
Dykstra's algorithm with alternate Bregman projections, and further exploit the
Newton-Raphson method when applied to separable divergences. We enhance the
separable case with a sparse extension to deal with high data dimensions. We
also instantiate our proposed framework and discuss the inherent specificities
for well-known regularizers and statistical divergences in the machine learning
and information geometry communities. Finally, we demonstrate the merits of our
methods with experiments using synthetic data to illustrate the effect of
different regularizers and penalties on the solutions, as well as real-world
data for a pattern recognition application to audio scene classification.
Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information
Authors: Seonhoon Kim, Inho Kang, Nojun Kwak
Date: 2018-05-29 00:00:00
Tasks: Natural Language Inference, Paraphrase Identification, Question Answering, Sentence

Sentence matching is widely used in various natural language tasks such as
natural language inference, paraphrase identification, and question answering.
For these tasks, understanding logical and semantic relationship between two
sentences is required but it is yet challenging. Although attention mechanism
is useful to capture the semantic relationship and to properly align the
elements of two sentences, previous methods of attention mechanism simply use a
summation operation which does not retain original features enough. Inspired by
DenseNet, a densely connected convolutional network, we propose a
densely-connected co-attentive recurrent neural network, each layer of which
uses concatenated information of attentive features as well as hidden features
of all the preceding recurrent layers. It enables preserving the original and
the co-attentive feature information from the bottommost word embedding layer
to the uppermost recurrent layer. To alleviate the problem of an
ever-increasing size of feature vectors due to dense concatenation operations,
we also propose to use an autoencoder after dense concatenation. We evaluate
our proposed architecture on highly competitive benchmark datasets related to
sentence matching. Experimental results show that our architecture, which
retains recurrent and attentive features, achieves state-of-the-art
performances for most of the tasks.
CocoNet: A deep neural network for mapping pixel coordinates to color values
Authors: Paul Andrei Bricman, Radu Tudor Ionescu
Date: 2018-05-29 00:00:00
Tasks: Denoising, Image Compression, Image Denoising

In this paper, we propose a deep neural network approach for mapping the 2D
pixel coordinates in an image to the corresponding Red-Green-Blue (RGB) color
values. The neural network is termed CocoNet, i.e. coordinates-to-color
network. During the training process, the neural network learns to encode the
input image within its layers. More specifically, the network learns a
continuous function that approximates the discrete RGB values sampled over the
discrete 2D pixel locations. At test time, given a 2D pixel coordinate, the
neural network will output the approximate RGB values of the corresponding
pixel. By considering every 2D pixel location, the network can actually
reconstruct the entire learned image. It is important to note that we have to
train an individual neural network for each input image, i.e. one network
encodes a single image only. To the best of our knowledge, we are the first to
propose a neural approach for encoding images individually, by learning a
mapping from the 2D pixel coordinate space to the RGB color space. Our neural
image encoding approach has various low-level image processing applications
ranging from image encoding, image compression and image denoising to image
resampling and image completion. We conduct experiments that include both
quantitative and qualitative results, demonstrating the utility of our approach
and its superiority over standard baselines, e.g. bilateral filtering or
bicubic interpolation. Our code is available at
https://github.com/paubric/python-fuse-coconet.
A universal framework for learning the elliptical mixture model
Authors: Shengxi Li, Zeyang Yu, Danilo Mandic
Date: 2018-05-21 00:00:00

Mixture modelling using elliptical distributions promises enhanced robustness, flexibility and stability over the widely employed Gaussian mixture model (GMM). However, existing studies based on the elliptical mixture model (EMM) are restricted to several specific types of elliptical probability density functions, which are not supported by general solutions or systematic analysis frameworks; this significantly limits the rigour and the power of EMMs in applications. To this end, we propose a novel general framework for estimating and analysing the EMMs, achieved through Riemannian manifold optimisation. First, we investigate the relationships between Riemannian manifolds and elliptical distributions, and the so established connection between the original manifold and a reformulated one indicates a mismatch between those manifolds, the major cause of failure of the existing optimisation for solving general EMMs. We next propose a universal solver which is based on the optimisation of a re-designed cost and prove the existence of the same optimum as in the original problem; this is achieved in a simple, fast and stable way. We further calculate the influence functions of the EMM as theoretical bounds to quantify robustness to outliers. Comprehensive numerical results demonstrate the ability of the proposed framework to accommodate EMMs with different properties of individual functions in a stable way and with fast convergence speed. Finally, the enhanced robustness and flexibility of the proposed framework over the standard GMM are demonstrated both analytically and through comprehensive simulations.
Quantum-inspired Complex Word Embedding
Authors: Qiuchi Li, Sagar Uprety, Benyou Wang, Dawei Song
Date: 2018-05-29 00:00:00
Proceeding: WS 2018 7
Tasks: Sentence, Sentence Classification, Word Embeddings

A challenging task for word embeddings is to capture the emergent meaning or
polarity of a combination of individual words. For example, existing approaches
in word embeddings will assign high probabilities to the words "Penguin" and
"Fly" if they frequently co-occur, but it fails to capture the fact that they
occur in an opposite sense - Penguins do not fly. We hypothesize that humans do
not associate a single polarity or sentiment to each word. The word contributes
to the overall polarity of a combination of words depending upon which other
words it is combined with. This is analogous to the behavior of microscopic
particles which exist in all possible states at the same time and interfere
with each other to give rise to new states depending upon their relative
phases. We make use of the Hilbert Space representation of such particles in
Quantum Mechanics where we subscribe a relative phase to each word, which is a
complex number, and investigate two such quantum inspired models to derive the
meaning of a combination of words. The proposed models achieve better
performances than state-of-the-art non-quantum models on the binary sentence
classification task.
Higher-order Relation Schema Induction using Tensor Factorization with Back-off and Aggregation
Authors: Madhav Nimishakavi, Partha Talukdar
Date: 2017-07-06 00:00:00
Proceeding: ACL 2018 7
Tasks: Relation

Relation Schema Induction (RSI) is the problem of identifying type signatures
of arguments of relations from unlabeled text. Most of the previous work in
this area have focused only on binary RSI, i.e., inducing only the subject and
object type signatures per relation. However, in practice, many relations are
high-order, i.e., they have more than two arguments and inducing type
signatures of all arguments is necessary. For example, in the sports domain,
inducing a schema win(WinningPlayer, OpponentPlayer, Tournament, Location) is
more informative than inducing just win(WinningPlayer, OpponentPlayer). We
refer to this problem as Higher-order Relation Schema Induction (HRSI). In this
paper, we propose Tensor Factorization with Back-off and Aggregation (TFBA), a
novel framework for the HRSI problem. To the best of our knowledge, this is the
first attempt at inducing higher-order relation schemata from unlabeled text.
Using the experimental analysis on three real world datasets, we show how TFBA
helps in dealing with sparsity and induce higher order schemata.
Fully Statistical Neural Belief Tracking
Authors: Nikola Mrkšić, Ivan Vulić
Date: 2018-05-29 00:00:00
Tasks: Dialogue State Tracking

This paper proposes an improvement to the existing data-driven Neural Belief
Tracking (NBT) framework for Dialogue State Tracking (DST). The existing NBT
model uses a hand-crafted belief state update mechanism which involves an
expensive manual retuning step whenever the model is deployed to a new dialogue
domain. We show that this update mechanism can be learned jointly with the
semantic decoding and context modelling parts of the NBT model, eliminating the
last rule-based module from this DST framework. We propose two different
statistical update mechanisms and show that dialogue dynamics can be modelled
with a very small number of additional model parameters. In our DST evaluation
over three languages, we show that this model achieves competitive performance
and provides a robust framework for building resource-light DST models.
Uncertainty Gated Network for Land Cover Segmentation
Authors: Guillem Pascual, Santi Seguí, Jordi Vitrià
Date: 2018-05-29 00:00:00
Tasks: General Classification, Segmentation, Semantic Segmentation

The production of thematic maps depicting land cover is one of the most
common applications of remote sensing. To this end, several semantic
segmentation approaches, based on deep learning, have been proposed in the
literature, but land cover segmentation is still considered an open problem due
to some specific problems related to remote sensing imaging. In this paper we
propose a novel approach to deal with the problem of modelling multiscale
contexts surrounding pixels of different land cover categories. The approach
leverages the computation of a heteroscedastic measure of uncertainty when
classifying individual pixels in an image. This classification uncertainty
measure is used to define a set of memory gates between layers that allow a
principled method to select the optimal decision for each pixel.
Pointly-Supervised Action Localization
Authors: Pascal Mettes, Cees G. M. Snoek
Date: 2018-05-29 00:00:00
Tasks: Action Localization, Multiple Instance Learning, Temporal Localization

This paper strives for spatio-temporal localization of human actions in
videos. In the literature, the consensus is to achieve localization by training
on bounding box annotations provided for each frame of each training video. As
annotating boxes in video is expensive, cumbersome and error-prone, we propose
to bypass box-supervision. Instead, we introduce action localization based on
point-supervision. We start from unsupervised spatio-temporal proposals, which
provide a set of candidate regions in videos. While normally used exclusively
for inference, we show spatio-temporal proposals can also be leveraged during
training when guided by a sparse set of point annotations. We introduce an
overlap measure between points and spatio-temporal proposals and incorporate
them all into a new objective of a Multiple Instance Learning optimization.
During inference, we introduce pseudo-points, visual cues from videos, that
automatically guide the selection of spatio-temporal proposals. We outline five
spatial and one temporal pseudo-point, as well as a measure to best leverage
pseudo-points at test time. Experimental evaluation on three action
localization datasets shows our pointly-supervised approach (i) is as effective
as traditional box-supervision at a fraction of the annotation cost, (ii) is
robust to sparse and noisy point annotations, (iii) benefits from pseudo-points
during inference, and (iv) outperforms recent weakly-supervised alternatives.
This leads us to conclude that points provide a viable alternative to boxes for
action localization.
Training verified learners with learned verifiers
Authors: Krishnamurthy Dvijotham, Sven Gowal, Robert Stanforth, Relja Arandjelovic, Brendan O'Donoghue, Jonathan Uesato, Pushmeet Kohli
Date: 2018-05-25 00:00:00

This paper proposes a new algorithmic framework, predictor-verifier training,
to train neural networks that are verifiable, i.e., networks that provably
satisfy some desired input-output properties. The key idea is to simultaneously
train two networks: a predictor network that performs the task at hand,e.g.,
predicting labels given inputs, and a verifier network that computes a bound on
how well the predictor satisfies the properties being verified. Both networks
can be trained simultaneously to optimize a weighted combination of the
standard data-fitting loss and a term that bounds the maximum violation of the
property. Experiments show that not only is the predictor-verifier architecture
able to train networks to achieve state of the art verified robustness to
adversarial examples with much shorter training times (outperforming previous
algorithms on small datasets like MNIST and SVHN), but it can also be scaled to
produce the first known (to the best of our knowledge) verifiably robust
networks for CIFAR-10.
Hamiltonian Variational Auto-Encoder
Authors: Anthony L. Caterini, Arnaud Doucet, Dino Sejdinovic
Date: 2018-05-29 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Variational Inference

Variational Auto-Encoders (VAEs) have become very popular techniques to
perform inference and learning in latent variable models as they allow us to
leverage the rich representational power of neural networks to obtain flexible
approximations of the posterior of latent variables as well as tight evidence
lower bounds (ELBOs). Combined with stochastic variational inference, this
provides a methodology scaling to large datasets. However, for this methodology
to be practically efficient, it is necessary to obtain low-variance unbiased
estimators of the ELBO and its gradients with respect to the parameters of
interest. While the use of Markov chain Monte Carlo (MCMC) techniques such as
Hamiltonian Monte Carlo (HMC) has been previously suggested to achieve this
[23, 26], the proposed methods require specifying reverse kernels which have a
large impact on performance. Additionally, the resulting unbiased estimator of
the ELBO for most MCMC kernels is typically not amenable to the
reparameterization trick. We show here how to optimally select reverse kernels
in this setting and, by building upon Hamiltonian Importance Sampling (HIS)
[17], we obtain a scheme that provides low-variance unbiased estimators of the
ELBO and its gradients using the reparameterization trick. This allows us to
develop a Hamiltonian Variational Auto-Encoder (HVAE). This method can be
reinterpreted as a target-informed normalizing flow [20] which, within our
context, only requires a few evaluations of the gradient of the sampled
likelihood and trivial Jacobian calculations at each iteration.
Lightweight Probabilistic Deep Networks
Authors: Jochen Gast, Stefan Roth
Date: 2018-05-29 00:00:00
Proceeding: CVPR 2018 6

Even though probabilistic treatments of neural networks have a long history,
they have not found widespread use in practice. Sampling approaches are often
too slow already for simple networks. The size of the inputs and the depth of
typical CNN architectures in computer vision only compound this problem.
Uncertainty in neural networks has thus been largely ignored in practice,
despite the fact that it may provide important information about the
reliability of predictions and the inner workings of the network. In this
paper, we introduce two lightweight approaches to making supervised learning
with probabilistic deep networks practical: First, we suggest probabilistic
output layers for classification and regression that require only minimal
changes to existing networks. Second, we employ assumed density filtering and
show that activation uncertainties can be propagated in a practical fashion
through the entire network, again with minor changes. Both probabilistic
networks retain the predictive power of the deterministic counterpart, but
yield uncertainties that correlate well with the empirical error induced by
their predictions. Moreover, the robustness to adversarial examples is
significantly increased.
Long Short-Term Memory Networks for CSI300 Volatility Prediction with Baidu Search Volume
Authors: Yu-Long Zhou, Ren-Jie Han, Qian Xu, Wei-Ke Zhang
Date: 2018-05-29 00:00:00

Intense volatility in financial markets affect humans worldwide. Therefore,
relatively accurate prediction of volatility is critical. We suggest that
massive data sources resulting from human interaction with the Internet may
offer a new perspective on the behavior of market participants in periods of
large market movements. First we select 28 key words, which are related to
finance as indicators of the public mood and macroeconomic factors. Then those
28 words of the daily search volume based on Baidu index are collected
manually, from June 1, 2006 to October 29, 2017. We apply a Long Short-Term
Memory neural network to forecast CSI300 volatility using those search volume
data. Compared to the benchmark GARCH model, our forecast is more accurate,
which demonstrates the effectiveness of the LSTM neural network in volatility
forecasting.