pwc-archive/papers-with-abstracts huggingface dataset:
Analyzing Human-Human Interactions: A Survey
Authors: Alexandros Stergiou, Ronald Poppe
Date: 2018-07-31 00:00:00
Tasks: Action Recognition, Survey, Temporal Action Localization

Many videos depict people, and it is their interactions that inform us of their activities, relation to one another and the cultural and social setting. With advances in human action recognition, researchers have begun to address the automated recognition of these human-human interactions from video. The main challenges stem from dealing with the considerable variation in recording setting, the appearance of the people depicted and the coordinated performance of their interaction. This survey provides a summary of these challenges and datasets to address these, followed by an in-depth discussion of relevant vision-based recognition and detection methods. We focus on recent, promising work based on deep learning and convolutional neural networks (CNNs). Finally, we outline directions to overcome the limitations of the current state-of-the-art to analyze and, eventually, understand social human actions.
On-line Adaptative Curriculum Learning for GANs
Authors: Thang Doan, Joao Monteiro, Isabela Albuquerque, Bogdan Mazoure, Audrey Durand, Joelle Pineau, R. Devon Hjelm
Date: 2018-07-31 00:00:00
Tasks: Multi-Armed Bandits, Stochastic Optimization

Generative Adversarial Networks (GANs) can successfully approximate a
probability distribution and produce realistic samples. However, open questions
such as sufficient convergence conditions and mode collapse still persist. In
this paper, we build on existing work in the area by proposing a novel
framework for training the generator against an ensemble of discriminator
networks, which can be seen as a one-student/multiple-teachers setting. We
formalize this problem within the full-information adversarial bandit
framework, where we evaluate the capability of an algorithm to select mixtures
of discriminators for providing the generator with feedback during learning. To
this end, we propose a reward function which reflects the progress made by the
generator and dynamically update the mixture weights allocated to each
discriminator. We also draw connections between our algorithm and stochastic
optimization methods and then show that existing approaches using multiple
discriminators in literature can be recovered from our framework. We argue that
less expressive discriminators are smoother and have a general coarse grained
view of the modes map, which enforces the generator to cover a wide portion of
the data distribution support. On the other hand, highly expressive
discriminators ensure samples quality. Finally, experimental results show that
our approach improves samples quality and diversity over existing baselines by
effectively learning a curriculum. These results also support the claim that
weaker discriminators have higher entropy improving modes coverage. Keywords:
multiple discriminators, curriculum learning, multiple resolutions
discriminators, multi-armed bandits, generative adversarial networks, smooth
discriminators, multi-discriminator gan training, multiple experts.
Graph-Based Recommendation System
Authors: Kaige Yang, Laura Toni
Date: 2018-07-31 00:00:00
Tasks: Recommendation Systems

In this work, we study recommendation systems modelled as contextual
multi-armed bandit (MAB) problems. We propose a graph-based recommendation
system that learns and exploits the geometry of the user space to create
meaningful clusters in the user domain. This reduces the dimensionality of the
recommendation problem while preserving the accuracy of MAB. We then study the
effect of graph sparsity and clusters size on the MAB performance and provide
exhaustive simulation results both in synthetic and in real-case datasets.
Simulation results show improvements with respect to state-of-the-art MAB
algorithms.
Practical Constrained Optimization of Auction Mechanisms in E-Commerce Sponsored Search Advertising
Authors: Gang Bai, Zhihui Xie, Liang Wang
Date: 2018-07-31 00:00:00

Sponsored search in E-commerce platforms such as Amazon, Taobao and Tmall
provides sellers an effective way to reach potential buyers with most relevant
purpose. In this paper, we study the auction mechanism optimization problem in
sponsored search on Alibaba's mobile E-commerce platform. Besides generating
revenue, we are supposed to maintain an efficient marketplace with plenty of
quality users, guarantee a reasonable return on investment (ROI) for
advertisers, and meanwhile, facilitate a pleasant shopping experience for the
users. These requirements essentially pose a constrained optimization problem.
Directly optimizing over auction parameters yields a discontinuous, non-convex
problem that denies effective solutions. One of our major contribution is a
practical convex optimization formulation of the original problem. We devise a
novel re-parametrization of auction mechanism with discrete sets of
representative instances. To construct the optimization problem, we build an
auction simulation system which estimates the resulted business indicators of
the selected parameters by replaying the auctions recorded from real online
requests. We summarized the experiments on real search traffics to analyze the
effects of fidelity of auction simulation, the efficacy under various
constraint targets and the influence of regularization. The experiment results
show that with proper entropy regularization, we are able to maximize revenue
while constraining other business indicators within given ranges.
Leveraging Knowledge Graph Embedding Techniques for Industry 4.0 Use Cases
Authors: Martina Garofalo, Maria Angela Pellegrino, Abdulrahman Altabba, Michael Cochez
Date: 2018-07-31 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering, Graph Embedding, Knowledge Graph Embedding, Management

Industry is evolving towards Industry 4.0, which holds the promise of
increased flexibility in manufacturing, better quality and improved
productivity. A core actor of this growth is using sensors, which must capture
data that can used in unforeseen ways to achieve a performance not achievable
without them. However, the complexity of this improved setting is much greater
than what is currently used in practice. Hence, it is imperative that the
management cannot only be performed by human labor force, but part of that will
be done by automated algorithms instead. A natural way to represent the data
generated by this large amount of sensors, which are not acting measuring
independent variables, and the interaction of the different devices is by using
a graph data model. Then, machine learning could be used to aid the Industry
4.0 system to, for example, perform predictive maintenance. However, machine
learning directly on graphs, needs feature engineering and has scalability
issues. In this paper we discuss methods to convert (embed) the graph in a
vector space, such that it becomes feasible to use traditional machine learning
methods for Industry 4.0 settings.
The Importance of Context When Recommending TV Content: Dataset and Algorithms
Authors: Miklas S. Kristoffersen, Sven E. Shepstone, Zheng-Hua Tan
Date: 2018-07-30 00:00:00
Tasks: Recommendation Systems

Home entertainment systems feature in a variety of usage scenarios with one or more simultaneous users, for whom the complexity of choosing media to consume has increased rapidly over the last decade. Users' decision processes are complex and highly influenced by contextual settings, but data supporting the development and evaluation of context-aware recommender systems are scarce. In this paper we present a dataset of self-reported TV consumption enriched with contextual information of viewing situations. We show how choice of genre associates with, among others, the number of present users and users' attention levels. Furthermore, we evaluate the performance of predicting chosen genres given different configurations of contextual information, and compare the results to contextless predictions. The results suggest that including contextual features in the prediction cause notable improvements, and both temporal and social context show significant contributions.
Speaker Recognition from Raw Waveform with SincNet
Authors: Mirco Ravanelli, Yoshua Bengio
Date: 2018-07-29 00:00:00
Tasks: Speaker Identification, Speaker Recognition, Speaker Verification

Deep learning is progressively gaining popularity as a viable alternative to i-vectors for speaker recognition. Promising results have been recently obtained with Convolutional Neural Networks (CNNs) when fed by raw speech samples directly. Rather than employing standard hand-crafted features, the latter CNNs learn low-level speech representations from waveforms, potentially allowing the network to better capture important narrow-band speaker characteristics such as pitch and formants. Proper design of the neural network is crucial to achieve this goal. This paper proposes a novel CNN architecture, called SincNet, that encourages the first convolutional layer to discover more meaningful filters. SincNet is based on parametrized sinc functions, which implement band-pass filters. In contrast to standard CNNs, that learn all elements of each filter, only low and high cutoff frequencies are directly learned from data with the proposed method. This offers a very compact and efficient way to derive a customized filter bank specifically tuned for the desired application. Our experiments, conducted on both speaker identification and speaker verification tasks, show that the proposed architecture converges faster and performs better than a standard CNN on raw waveforms.
SEA: A Combined Model for Heat Demand Prediction
Authors: Jiyang Xie, Jiaxin Guo, Zhanyu Ma, Jing-Hao Xue, Qie Sun, Hailong Li, Jun Guo
Date: 2018-07-28 00:00:00
Tasks: model, Prediction

Heat demand prediction is a prominent research topic in the area of
intelligent energy networks. It has been well recognized that periodicity is
one of the important characteristics of heat demand. Seasonal-trend
decomposition based on LOESS (STL) algorithm can analyze the periodicity of a
heat demand series, and decompose the series into seasonal and trend
components. Then, predicting the seasonal and trend components respectively,
and combining their predictions together as the heat demand prediction is a
possible way to predict heat demand. In this paper, STL-ENN-ARIMA (SEA), a
combined model, was proposed based on the combination of the Elman neural
network (ENN) and the autoregressive integrated moving average (ARIMA) model,
which are commonly applied to heat demand prediction. ENN and ARIMA are used to
predict seasonal and trend components, respectively. Experimental results
demonstrate that the proposed SEA model has a promising performance.
Web-STAR: A Visual Web-Based IDE for a Story Comprehension System
Authors: Christos Rodosthenous, Loizos Michael
Date: 2018-07-28 00:00:00

We present Web-STAR, an online platform for story understanding built on top
of the STAR reasoning engine for STory comprehension through ARgumentation. The
platform includes a web-based IDE, integration with the STAR system, and a web
service infrastructure to support integration with other systems that rely on
story understanding functionality to complete their tasks. The platform also
delivers a number of "social" features, including a community repository for
public story sharing with a built-in commenting system, and tools for
collaborative story editing that can be used for team development projects and
for educational purposes.
Diverse Image-to-Image Translation via Disentangled Representations
Authors: Hsin-Ying Lee, Hung-Yu Tseng, Jia-Bin Huang, Maneesh Kumar Singh, Ming-Hsuan Yang
Date: 2018-08-02 00:00:00
Proceeding: ECCV 2018 9
Tasks: Attribute, Diversity, Domain Adaptation, Image-to-Image Translation, Multimodal Unsupervised Image-To-Image Translation, Perceptual Distance, Synthetic-to-Real Translation, Translation

Image-to-image translation aims to learn the mapping between two visual
domains. There are two main challenges for many applications: 1) the lack of
aligned training pairs and 2) multiple possible outputs from a single input
image. In this work, we present an approach based on disentangled
representation for producing diverse outputs without paired training images. To
achieve diversity, we propose to embed images onto two spaces: a
domain-invariant content space capturing shared information across domains and
a domain-specific attribute space. Our model takes the encoded content features
extracted from a given input and the attribute vectors sampled from the
attribute space to produce diverse outputs at test time. To handle unpaired
training data, we introduce a novel cross-cycle consistency loss based on
disentangled representations. Qualitative results show that our model can
generate diverse and realistic images on a wide range of tasks without paired
training data. For quantitative comparisons, we measure realism with user study
and diversity with a perceptual distance metric. We apply the proposed model to
domain adaptation and show competitive performance when compared to the
state-of-the-art on the MNIST-M and the LineMod datasets.
Inferring Parameters Through Inverse Multiobjective Optimization
Authors: Chaosheng Dong, Bo Zeng
Date: 2018-08-02 00:00:00
Tasks: Decision Making, Multiobjective Optimization

Given a set of human's decisions that are observed, inverse optimization has
been developed and utilized to infer the underlying decision making problem.
The majority of existing studies assumes that the decision making problem is
with a single objective function, and attributes data divergence to noises,
errors or bounded rationality, which, however, could lead to a corrupted
inference when decisions are tradeoffs among multiple criteria. In this paper,
we take a data-driven approach and design a more sophisticated inverse
optimization formulation to explicitly infer parameters of a multiobjective
decision making problem from noisy observations. This framework, together with
our mathematical analyses and advanced algorithm developments, demonstrates a
strong capacity in estimating critical parameters, decoupling "interpretable"
components from noises or errors, deriving the denoised \emph{optimal}
decisions, and ensuring statistical significance. In particular, for the whole
decision maker population, if suitable conditions hold, we will be able to
understand the overall diversity and the distribution of their preferences over
multiple criteria, which is important when a precise inference on every single
decision maker is practically unnecessary or infeasible. Numerical results on a
large number of experiments are reported to confirm the effectiveness of our
unique inverse optimization model and the computational efficacy of the
developed algorithms.
Streaming Kernel PCA with $\tilde{O}(\sqrt{n})$ Random Features
Authors: Enayat Ullah, Poorya Mianjy, Teodor V. Marinov, Raman Arora
Date: 2018-08-02 00:00:00

We study the statistical and computational aspects of kernel principal
component analysis using random Fourier features and show that under mild
assumptions, $O(\sqrt{n} \log n)$ features suffices to achieve
$O(1/\epsilon^2)$ sample complexity. Furthermore, we give a memory efficient
streaming algorithm based on classical Oja's algorithm that achieves this rate.
Machine Learning of Space-Fractional Differential Equations
Authors: Mamikon Gulian, Maziar Raissi, Paris Perdikaris, George Karniadakis
Date: 2018-08-02 00:00:00
Tasks: BIG-bench Machine Learning, regression

Data-driven discovery of "hidden physics" -- i.e., machine learning of differential equation models underlying observed data -- has recently been approached by embedding the discovery problem into a Gaussian Process regression of spatial data, treating and discovering unknown equation parameters as hyperparameters of a modified "physics informed" Gaussian Process kernel. This kernel includes the parametrized differential operators applied to a prior covariance kernel. We extend this framework to linear space-fractional differential equations. The methodology is compatible with a wide variety of fractional operators in $\mathbb{R}^d$ and stationary covariance kernels, including the Matern class, and can optimize the Matern parameter during training. We provide a user-friendly and feasible way to perform fractional derivatives of kernels, via a unified set of d-dimensional Fourier integral formulas amenable to generalized Gauss-Laguerre quadrature. The implementation of fractional derivatives has several benefits. First, it allows for discovering fractional-order PDEs for systems characterized by heavy tails or anomalous diffusion, bypassing the analytical difficulty of fractional calculus. Data sets exhibiting such features are of increasing prevalence in physical and financial domains. Second, a single fractional-order archetype allows for a derivative of arbitrary order to be learned, with the order itself being a parameter in the regression. This is advantageous even when used for discovering integer-order equations; the user is not required to assume a "dictionary" of derivatives of various orders, and directly controls the parsimony of the models being discovered. We illustrate on several examples, including fractional-order interpolation of advection-diffusion and modeling relative stock performance in the S&P 500 with alpha-stable motion via a fractional diffusion equation.
Learning Actionable Representations from Visual Observations
Authors: Debidatta Dwibedi, Jonathan Tompson, Corey Lynch, Pierre Sermanet
Date: 2018-08-02 00:00:00
Tasks: continuous-control, Continuous Control, Reinforcement Learning

In this work we explore a new approach for robots to teach themselves about
the world simply by observing it. In particular we investigate the
effectiveness of learning task-agnostic representations for continuous control
tasks. We extend Time-Contrastive Networks (TCN) that learn from visual
observations by embedding multiple frames jointly in the embedding space as
opposed to a single frame. We show that by doing so, we are now able to encode
both position and velocity attributes significantly more accurately. We test
the usefulness of this self-supervised approach in a reinforcement learning
setting. We show that the representations learned by agents observing
themselves take random actions, or other agents perform tasks successfully, can
enable the learning of continuous control policies using algorithms like
Proximal Policy Optimization (PPO) using only the learned embeddings as input.
We also demonstrate significant improvements on the real-world Pouring dataset
with a relative error reduction of 39.4% for motion attributes and 11.1% for
static attributes compared to the single-frame baseline. Video results are
available at https://sites.google.com/view/actionablerepresentations .
Cyberbullying Detection -- Technical Report 2/2018, Department of Computer Science AGH, University of Science and Technology
Authors: Michał Ptaszyński, Gniewosz Leliwa, Mateusz Piech, Aleksander Smywiński-Pohl
Date: 2018-08-02 00:00:00

The research described in this paper concerns automatic cyberbullying
detection in social media. There are two goals to achieve: building a gold
standard cyberbullying detection dataset and measuring the performance of the
Samurai cyberbullying detection system. The Formspring dataset provided in a
Kaggle competition was re-annotated as a part of the research. The annotation
procedure is described in detail and, unlike many other recent data annotation
initiatives, does not use Mechanical Turk for finding people willing to perform
the annotation. The new annotation compared to the old one seems to be more
coherent since all tested cyberbullying detection system performed better on
the former. The performance of the Samurai system is compared with 5 commercial
systems and one well-known machine learning algorithm, used for classifying
textual content, namely Fasttext. It turns out that Samurai scores the best in
all measures (accuracy, precision and recall), while Fasttext is the
second-best performing algorithm.
The Lyapunov Neural Network: Adaptive Stability Certification for Safe Learning of Dynamical Systems
Authors: Spencer M. Richards, Felix Berkenkamp, Andreas Krause
Date: 2018-08-02 00:00:00

Learning algorithms have shown considerable prowess in simulation by allowing
robots to adapt to uncertain environments and improve their performance.
However, such algorithms are rarely used in practice on safety-critical
systems, since the learned policy typically does not yield any safety
guarantees. That is, the required exploration may cause physical harm to the
robot or its environment. In this paper, we present a method to learn accurate
safety certificates for nonlinear, closed-loop dynamical systems. Specifically,
we construct a neural network Lyapunov function and a training algorithm that
adapts it to the shape of the largest safe region in the state space. The
algorithm relies only on knowledge of inputs and outputs of the dynamics,
rather than on any specific model structure. We demonstrate our method by
learning the safe region of attraction for a simulated inverted pendulum.
Furthermore, we discuss how our method can be used in safe learning algorithms
together with statistical models of dynamical systems.
BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation
Authors: Changqian Yu, Jingbo Wang, Chao Peng, Changxin Gao, Gang Yu, Nong Sang
Date: 2018-08-02 00:00:00
Proceeding: ECCV 2018 9
Tasks: Dichotomous Image Segmentation, Real-Time Semantic Segmentation, Segmentation, Semantic Segmentation, Thermal Image Segmentation

Semantic segmentation requires both rich spatial information and sizeable
receptive field. However, modern approaches usually compromise spatial
resolution to achieve real-time inference speed, which leads to poor
performance. In this paper, we address this dilemma with a novel Bilateral
Segmentation Network (BiSeNet). We first design a Spatial Path with a small
stride to preserve the spatial information and generate high-resolution
features. Meanwhile, a Context Path with a fast downsampling strategy is
employed to obtain sufficient receptive field. On top of the two paths, we
introduce a new Feature Fusion Module to combine features efficiently. The
proposed architecture makes a right balance between the speed and segmentation
performance on Cityscapes, CamVid, and COCO-Stuff datasets. Specifically, for a
2048x1024 input, we achieve 68.4% Mean IOU on the Cityscapes test dataset with
speed of 105 FPS on one NVIDIA Titan XP card, which is significantly faster
than the existing methods with comparable performance.
Semi-blind source separation with multichannel variational autoencoder
Authors: Hirokazu Kameoka, Li Li, Shota Inoue, Shoji Makino
Date: 2018-08-02 00:00:00
Tasks: blind source separation, Decoder

This paper proposes a multichannel source separation technique called the
multichannel variational autoencoder (MVAE) method, which uses a conditional
VAE (CVAE) to model and estimate the power spectrograms of the sources in a
mixture. By training the CVAE using the spectrograms of training examples with
source-class labels, we can use the trained decoder distribution as a universal
generative model capable of generating spectrograms conditioned on a specified
class label. By treating the latent space variables and the class label as the
unknown parameters of this generative model, we can develop a
convergence-guaranteed semi-blind source separation algorithm that consists of
iteratively estimating the power spectrograms of the underlying sources as well
as the separation matrices. In experimental evaluations, our MVAE produced
better separation performance than a baseline method.