pwc-archive/papers-with-abstracts huggingface dataset:
Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in Badlands
Authors: Rohitash Chandra, Danial Azam, R. Dietmar Müller, Tristan Salles, Sally Cripps
Date: 2018-05-02 00:00:00
Tasks: Bayesian Inference, Uncertainty Quantification

Bayesian inference provides a rigorous methodology for estimation and uncertainty quantification of parameters in geophysical forward models. Badlands (basin and landscape dynamics model) is a landscape evolution model that simulates topography development at various space and time scales. Badlands consists of a number of geophysical parameters that needs estimation with appropriate uncertainty quantification; given the observed present-day ground truth such as surface topography and the stratigraphy of sediment deposition through time. The inference of unknown parameters is challenging due to the scarcity of data, sensitivity of the parameter setting and complexity of the Badlands model. In this paper, we take a Bayesian approach to provide inference using Markov chain Monte Carlo sampling (MCMC). We present \textit{Bayeslands}; a Bayesian framework for Badlands that fuses information obtained from complex forward models with observational data and prior knowledge. As a proof-of-concept, we consider a synthetic and real-world topography with two parameters for Bayeslands inference, namely precipitation and erodibility. The results of the experiments show that Bayeslands yields a promising distribution of the parameters. Moreover, we demonstrate the challenge in sampling irregular and multi-modal posterior distributions using a likelihood surface that has a range of sub-optimal modes.
Functional ASP with Intensional Sets: Application to Gelfond-Zhang Aggregates
Authors: Pedro Cabalar, Jorge Fandinno, Luis Fariñas del Cerro, David Pearce
Date: 2018-05-02 00:00:00

In this paper, we propose a variant of Answer Set Programming (ASP) with
evaluable functions that extends their application to sets of objects,
something that allows a fully logical treatment of aggregates. Formally, we
start from the syntax of First Order Logic with equality and the semantics of
Quantified Equilibrium Logic with evaluable functions (QELF). Then, we proceed
to incorporate a new kind of logical term, intensional set (a construct
commonly used to denote the set of objects characterised by a given formula),
and to extend QELF semantics for this new type of expression. In our extended
approach, intensional sets can be arbitrarily used as predicate or function
arguments or even nested inside other intensional sets, just as regular
first-order logical terms. As a result, aggregates can be naturally formed by
the application of some evaluable function (count, sum, maximum, etc) to a set
of objects expressed as an intensional set. This approach has several
advantages. First, while other semantics for aggregates depend on some
syntactic transformation (either via a reduct or a formula translation), the
QELF interpretation treats them as regular evaluable functions, providing a
compositional semantics and avoiding any kind of syntactic restriction. Second,
aggregates can be explicitly defined now within the logical language by the
simple addition of formulas that fix their meaning in terms of multiple
applications of some (commutative and associative) binary operation. For
instance, we can use recursive rules to define sum in terms of integer
addition. Last, but not least, we prove that the semantics we obtain for
aggregates coincides with the one defined by Gelfond and Zhang for the Alog
language, when we restrict to that syntactic fragment. (Under consideration for
acceptance in TPLP)
Convolutional Sequence to Sequence Model for Human Dynamics
Authors: Chen Li, Zhen Zhang, Wee Sun Lee, Gim Hee Lee
Date: 2018-05-02 00:00:00
Proceeding: CVPR 2018 6
Tasks: Decoder, Human Dynamics, Human Pose Forecasting, model

Human motion modeling is a classic problem in computer vision and graphics.
Challenges in modeling human motion include high dimensional prediction as well
as extremely complicated dynamics.We present a novel approach to human motion
modeling based on convolutional neural networks (CNN). The hierarchical
structure of CNN makes it capable of capturing both spatial and temporal
correlations effectively. In our proposed approach,a convolutional long-term
encoder is used to encode the whole given motion sequence into a long-term
hidden variable, which is used with a decoder to predict the remainder of the
sequence. The decoder itself also has an encoder-decoder structure, in which
the short-term encoder encodes a shorter sequence to a short-term hidden
variable, and the spatial decoder maps the long and short-term hidden variable
to motion predictions. By using such a model, we are able to capture both
invariant and dynamic information of human motion, which results in more
accurate predictions. Experiments show that our algorithm outperforms the
state-of-the-art methods on the Human3.6M and CMU Motion Capture datasets. Our
code is available at the project website.
Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks
Authors: Sascha Wirges, Tom Fischer, Jesus Balado Frias, Christoph Stiller
Date: 2018-05-02 00:00:00
Tasks: General Classification, object-detection, Object Detection, Sensor Fusion

A detailed environment perception is a crucial component of automated
vehicles. However, to deal with the amount of perceived information, we also
require segmentation strategies. Based on a grid map environment
representation, well-suited for sensor fusion, free-space estimation and
machine learning, we detect and classify objects using deep convolutional
neural networks. As input for our networks we use a multi-layer grid map
efficiently encoding 3D range sensor information. The inference output consists
of a list of rotated bounding boxes with associated semantic classes. We
conduct extensive ablation studies, highlight important design considerations
when using grid maps and evaluate our models on the KITTI Bird's Eye View
benchmark. Qualitative and quantitative benchmark results show that we achieve
robust detection and state of the art accuracy solely using top-view grid maps
from range sensor data.
MX-LSTM: mixing tracklets and vislets to jointly forecast trajectories and head poses
Authors: Irtiza Hasan, Francesco Setti, Theodore Tsesmelis, Alessio Del Bue, Fabio Galasso, Marco Cristani
Date: 2018-05-02 00:00:00
Proceeding: CVPR 2018 6
Tasks: Trajectory Forecasting

Recent approaches on trajectory forecasting use tracklets to predict the
future positions of pedestrians exploiting Long Short Term Memory (LSTM)
architectures. This paper shows that adding vislets, that is, short sequences
of head pose estimations, allows to increase significantly the trajectory
forecasting performance. We then propose to use vislets in a novel framework
called MX-LSTM, capturing the interplay between tracklets and vislets thanks to
a joint unconstrained optimization of full covariance matrices during the LSTM
backpropagation. At the same time, MX-LSTM predicts the future head poses,
increasing the standard capabilities of the long-term trajectory forecasting
approaches. With standard head pose estimators and an attentional-based social
pooling, MX-LSTM scores the new trajectory forecasting state-of-the-art in all
the considered datasets (Zara01, Zara02, UCY, and TownCentre) with a dramatic
margin when the pedestrians slow down, a case where most of the forecasting
approaches struggle to provide an accurate solution.
Learning with Opponent-Learning Awareness
Authors: Jakob N. Foerster, Richard Y. Chen, Maruan Al-Shedivat, Shimon Whiteson, Pieter Abbeel, Igor Mordatch
Date: 2017-09-13 00:00:00
Tasks: Multi-agent Reinforcement Learning, Reinforcement Learning

Multi-agent settings are quickly gathering importance in machine learning.
This includes a plethora of recent work on deep multi-agent reinforcement
learning, but also can be extended to hierarchical RL, generative adversarial
networks and decentralised optimisation. In all these settings the presence of
multiple learning agents renders the training problem non-stationary and often
leads to unstable training or undesired final results. We present Learning with
Opponent-Learning Awareness (LOLA), a method in which each agent shapes the
anticipated learning of the other agents in the environment. The LOLA learning
rule includes a term that accounts for the impact of one agent's policy on the
anticipated parameter update of the other agents. Results show that the
encounter of two LOLA agents leads to the emergence of tit-for-tat and
therefore cooperation in the iterated prisoners' dilemma, while independent
learning does not. In this domain, LOLA also receives higher payouts compared
to a naive learner, and is robust against exploitation by higher order
gradient-based methods. Applied to repeated matching pennies, LOLA agents
converge to the Nash equilibrium. In a round robin tournament we show that LOLA
agents successfully shape the learning of a range of multi-agent learning
algorithms from literature, resulting in the highest average returns on the
IPD. We also show that the LOLA update rule can be efficiently calculated using
an extension of the policy gradient estimator, making the method suitable for
model-free RL. The method thus scales to large parameter and input spaces and
nonlinear function approximators. We apply LOLA to a grid world task with an
embedded social dilemma using recurrent policies and opponent modelling. By
explicitly considering the learning of the other agent, LOLA agents learn to
cooperate out of self-interest. The code is at github.com/alshedivat/lola.
The Power of Linear Recurrent Neural Networks
Authors: Frieder Stolzenburg, Sandra Litz, Olivia Michael, Oliver Obst
Date: 2018-02-09 00:00:00
Tasks: Stock Price Prediction, Time Series, Time Series Analysis

Recurrent neural networks are a powerful means to cope with time series. We show how autoregressive linear, i.e., linearly activated recurrent neural networks (LRNNs) can approximate any time-dependent function f(t). The approximation can effectively be learned by simply solving a linear equation system; no backpropagation or similar methods are needed. Furthermore, and this is the main contribution of this article, the size of an LRNN can be reduced significantly in one step after inspecting the spectrum of the network transition matrix, i.e., its eigenvalues, by taking only the most relevant components. Therefore, in contrast to other approaches, we do not only learn network weights but also the network architecture. LRNNs have interesting properties: They end up in ellipse trajectories in the long run and allow the prediction of further values and compact representations of functions. We demonstrate this by several experiments, among them multiple superimposed oscillators (MSO), robotic soccer (RoboCup), and stock price prediction. LRNNs outperform the previous state-of-the-art for the MSO task with a minimal number of units.
Translating LPOD and CR-Prolog2 into Standard Answer Set Programs
Authors: Joohyung Lee, Zhun Yang
Date: 2018-05-02 00:00:00

Logic Programs with Ordered Disjunction (LPOD) is an extension of standard
answer set programs to handle preference using the construct of ordered
disjunction, and CR-Prolog2 is an extension of standard answer set programs
with consistency restoring rules and LPOD-like ordered disjunction. We present
reductions of each of these languages into the standard ASP language, which
gives us an alternative way to understand the extensions in terms of the
standard ASP language.
Boosting Noise Robustness of Acoustic Model via Deep Adversarial Training
Authors: Bin Liu, Shuai Nie, Yaping Zhang, Dengfeng Ke, Shan Liang, Wenju Liu1
Date: 2018-05-02 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition

In realistic environments, speech is usually interfered by various noise and
reverberation, which dramatically degrades the performance of automatic speech
recognition (ASR) systems. To alleviate this issue, the commonest way is to use
a well-designed speech enhancement approach as the front-end of ASR. However,
more complex pipelines, more computations and even higher hardware costs
(microphone array) are additionally consumed for this kind of methods. In
addition, speech enhancement would result in speech distortions and mismatches
to training. In this paper, we propose an adversarial training method to
directly boost noise robustness of acoustic model. Specifically, a jointly
compositional scheme of generative adversarial net (GAN) and neural
network-based acoustic model (AM) is used in the training phase. GAN is used to
generate clean feature representations from noisy features by the guidance of a
discriminator that tries to distinguish between the true clean signals and
generated signals. The joint optimization of generator, discriminator and AM
concentrates the strengths of both GAN and AM for speech recognition.
Systematic experiments on CHiME-4 show that the proposed method significantly
improves the noise robustness of AM and achieves the average relative error
rate reduction of 23.38% and 11.54% on the development and test set,
respectively.
A Deep Network for Arousal-Valence Emotion Prediction with Acoustic-Visual Cues
Authors: Songyou Peng, Le Zhang, Yutong Ban, Meng Fang, Stefan Winkler
Date: 2018-05-02 00:00:00

In this paper, we comprehensively describe the methodology of our submissions to the One-Minute Gradual-Emotion Behavior Challenge 2018.
A Probabilistic Extension of Action Language BC+
Authors: Joohyung Lee, Yi Wang
Date: 2018-05-02 00:00:00

We present a probabilistic extension of action language BC+. Just like BC+ is
defined as a high-level notation of answer set programs for describing
transition systems, the proposed language, which we call pBC+, is defined as a
high-level notation of LPMLN programs---a probabilistic extension of answer set
programs. We show how probabilistic reasoning about transition systems, such as
prediction, postdiction, and planning problems, as well as probabilistic
diagnosis for dynamic domains, can be modeled in pBC+ and computed using an
implementation of LPMLN.
Interpretable Fully Convolutional Classification of Intrapapillary Capillary Loops for Real-Time Detection of Early Squamous Neoplasia
Authors: Luis C. Garcia-Peraza-Herrera, Martin Everson, Wenqi Li, Inmanol Luengo, Lorenz Berger, Omer Ahmad, Laurence Lovat, Hsiu-Po Wang, Wen-Lun Wang, Rehan Haidry, Danail Stoyanov, Tom Vercauteren, Sebastien Ourselin
Date: 2018-05-02 00:00:00
Tasks: General Classification

In this work, we have concentrated our efforts on the interpretability of
classification results coming from a fully convolutional neural network.
Motivated by the classification of oesophageal tissue for real-time detection
of early squamous neoplasia, the most frequent kind of oesophageal cancer in
Asia, we present a new dataset and a novel deep learning method that by means
of deep supervision and a newly introduced concept, the embedded Class
Activation Map (eCAM), focuses on the interpretability of results as a design
constraint of a convolutional network. We present a new approach to visualise
attention that aims to give some insights on those areas of the oesophageal
tissue that lead a network to conclude that the images belong to a particular
class and compare them with those visual features employed by clinicians to
produce a clinical diagnosis. In comparison to a baseline method which does not
feature deep supervision but provides attention by grafting Class Activation
Maps, we improve the F1-score from 87.3% to 92.7% and provide more detailed
attention maps.
Adversarially Robust Generalization Requires More Data
Authors: Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Mądry
Date: 2018-04-30 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: General Classification, image-classification, Image Classification

Machine learning models are often susceptible to adversarial perturbations of
their inputs. Even small perturbations can cause state-of-the-art classifiers
with high "standard" accuracy to produce an incorrect prediction with high
confidence. To better understand this phenomenon, we study adversarially robust
learning from the viewpoint of generalization. We show that already in a simple
natural data model, the sample complexity of robust learning can be
significantly larger than that of "standard" learning. This gap is information
theoretic and holds irrespective of the training algorithm or the model family.
We complement our theoretical results with experiments on popular image
classification datasets and show that a similar gap exists here as well. We
postulate that the difficulty of training robust classifiers stems, at least
partially, from this inherently larger sample complexity.
A Data-Driven Residential Transformer Overloading Risk Assessment Method
Authors: Ming Dong, Benzhe Li, Alex Nassif
Date: 2018-05-02 00:00:00
Tasks: Clustering, Management

Residential transformer population is a critical type of asset that many electric utility companies have been attempting to manage proactively and effectively to reduce unexpected failures and life losses that are often caused by transformer overloading. Within the typical power asset portfolio, the residential transformer asset is often large in population, having lowest reliability design, lacking transformer loading data and susceptible to customer loading behaviors such as adoption of distributed energy resources and electric vehicles. On the bright side, the availability of more residential operation data along with the advancement of data analytics techniques have provided a new path to further our understanding of local residential transformer overloading risks statistically. This research developed a new data-driven method to combine clustering analysis and the simulation of transformer temperature rise and insulation life loss to quantitatively and statistically assess the overloading risk of residential transformer population in one area and suggest proper risk management measures according to the assessment results. Case studies from an actual Canadian utility company have been presented and discussed in detail to demonstrate the applicability and usefulness of the proposed method.
An Interactive Greedy Approach to Group Sparsity in High Dimensions
Authors: Wei Qian, Wending Li, Yasuhiro Sogawa, Ryohei Fujimaki, Xitong Yang, Ji Liu
Date: 2017-07-10 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Vocal Bursts Intensity Prediction

Sparsity learning with known grouping structure has received considerable
attention due to wide modern applications in high-dimensional data analysis.
Although advantages of using group information have been well-studied by
shrinkage-based approaches, benefits of group sparsity have not been
well-documented for greedy-type methods, which much limits our understanding
and use of this important class of methods. In this paper, generalizing from a
popular forward-backward greedy approach, we propose a new interactive greedy
algorithm for group sparsity learning and prove that the proposed greedy-type
algorithm attains the desired benefits of group sparsity under high dimensional
settings. An estimation error bound refining other existing methods and a
guarantee for group support recovery are also established simultaneously. In
addition, we incorporate a general M-estimation framework and introduce an
interactive feature to allow extra algorithm flexibility without compromise in
theoretical properties. The promising use of our proposal is demonstrated
through numerical evaluations including a real industrial application in human
activity recognition at home. Supplementary materials for this article are
available online.
Semantic Channel and Shannon's Channel Mutually Match for Multi-Label Classification
Authors: Chenguang Lu
Date: 2018-05-02 00:00:00
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

A group of transition probability functions form a Shannon's channel whereas
a group of truth functions form a semantic channel. Label learning is to let
semantic channels match Shannon's channels and label selection is to let
Shannon's channels match semantic channels. The Channel Matching (CM) algorithm
is provided for multi-label classification. This algorithm adheres to maximum
semantic information criterion which is compatible with maximum likelihood
criterion and regularized least squares criterion. If samples are very large,
we can directly convert Shannon's channels into semantic channels by the third
kind of Bayes' theorem; otherwise, we can train truth functions with parameters
by sampling distributions. A label may be a Boolean function of some atomic
labels. For simplifying learning, we may only obtain the truth functions of
some atomic label. For a given label, instances are divided into three kinds
(positive, negative, and unclear) instead of two kinds as in popular studies so
that the problem with binary relevance is avoided. For each instance, the
classifier selects a compound label with most semantic information or richest
connotation. As a predictive model, the semantic channel does not change with
the prior probability distribution (source) of instances. It still works when
the source is changed. The classifier changes with the source, and hence can
overcome class-imbalance problem. It is shown that the old population's
increasing will change the classifier for label "Old" and has been impelling
the semantic evolution of "Old". The CM iteration algorithm for unseen instance
classification is introduced.
Towards Interpretable Face Recognition
Authors: Bangjie Yin, Luan Tran, Haoxiang Li, Xiaohui Shen, Xiaoming Liu
Date: 2018-05-02 00:00:00
Proceeding: ICCV 2019 10
Tasks: Diversity, Face Recognition

Deep CNNs have been pushing the frontier of visual recognition over past years. Besides recognition accuracy, strong demands in understanding deep CNNs in the research community motivate developments of tools to dissect pre-trained models to visualize how they make predictions. Recent works further push the interpretability in the network learning stage to learn more meaningful representations. In this work, focusing on a specific area of visual recognition, we report our efforts towards interpretable face recognition. We propose a spatial activation diversity loss to learn more structured face representations. By leveraging the structure, we further design a feature activation diversity loss to push the interpretable representations to be discriminative and robust to occlusions. We demonstrate on three face recognition benchmarks that our proposed method is able to improve face recognition accuracy with easily interpretable face representations.
Deep Depth Completion of a Single RGB-D Image
Authors: Yinda Zhang, Thomas Funkhouser
Date: 2018-03-25 00:00:00
Proceeding: CVPR 2018 6
Tasks: Depth Completion, Depth Estimation

The goal of our work is to complete the depth channel of an RGB-D image.
Commodity-grade depth cameras often fail to sense depth for shiny, bright,
transparent, and distant surfaces. To address this problem, we train a deep
network that takes an RGB image as input and predicts dense surface normals and
occlusion boundaries. Those predictions are then combined with raw depth
observations provided by the RGB-D camera to solve for depths for all pixels,
including those missing in the original observation. This method was chosen
over others (e.g., inpainting depths directly) as the result of extensive
experiments with a new depth completion benchmark dataset, where holes are
filled in training data through the rendering of surface reconstructions
created from multiview RGB-D scans. Experiments with different network inputs,
depth representations, loss functions, optimization methods, inpainting
methods, and deep depth estimation networks show that our proposed approach
provides better depth completions than these alternatives.