pwc-archive/papers-with-abstracts huggingface dataset:
Backprop-Q: Generalized Backpropagation for Stochastic Computation Graphs
Authors: Xiaoran Xu, Songpeng Zu, Yuan Zhang, Hanning Zhou, Wei Feng
Date: 2018-07-25 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

In real-world scenarios, it is appealing to learn a model carrying out
stochastic operations internally, known as stochastic computation graphs
(SCGs), rather than learning a deterministic mapping. However, standard
backpropagation is not applicable to SCGs. We attempt to address this issue
from the angle of cost propagation, with local surrogate costs, called
Q-functions, constructed and learned for each stochastic node in an SCG. Then,
the SCG can be trained based on these surrogate costs using standard
backpropagation. We propose the entire framework as a solution to generalize
backpropagation for SCGs, which resembles an actor-critic architecture but
based on a graph. For broad applicability, we study a variety of SCG structures
from one cost to multiple costs. We utilize recent advances in reinforcement
learning (RL) and variational Bayes (VB), such as off-policy critic learning
and unbiased-and-low-variance gradient estimation, and review them in the
context of SCGs. The generalized backpropagation extends transported learning
signals beyond gradients between stochastic nodes while preserving the benefit
of backpropagating gradients through deterministic nodes. Experimental
suggestions and concerns are listed to help design and test any specific model
using this framework.
Pre-trainable Reservoir Computing with Recursive Neural Gas
Authors: Luca Carcano, Emanuele Plebani, Danilo Pietro Pau, Marco Piastra
Date: 2018-07-25 00:00:00

Echo State Networks (ESN) are a class of Recurrent Neural Networks (RNN) that
has gained substantial popularity due to their effectiveness, ease of use and
potential for compact hardware implementation. An ESN contains the three
network layers input, reservoir and readout where the reservoir is the truly
recurrent network. The input and reservoir layers of an ESN are initialized at
random and never trained afterwards and the training of the ESN is applied to
the readout layer only. The alternative of Recursive Neural Gas (RNG) is one of
the many proposals of fully-trainable reservoirs that can be found in the
literature. Although some improvements in performance have been reported with
RNG, to the best of authors' knowledge, no experimental comparative results are
known with benchmarks for which ESN is known to yield excellent results. This
work describes an accurate model of RNG together with some extensions to the
models presented in the literature and shows comparative results on three
well-known and accepted datasets. The experimental results obtained show that,
under specific circumstances, RNG-based reservoirs can achieve better
performance.
How good is my GAN?
Authors: Konstantin Shmelkov, Cordelia Schmid, Karteek Alahari
Date: 2018-07-25 00:00:00
Proceeding: ECCV 2018 9
Tasks: Diversity, General Classification, image-classification, Image Classification

Generative adversarial networks (GANs) are one of the most popular methods
for generating images today. While impressive results have been validated by
visual inspection, a number of quantitative criteria have emerged only
recently. We argue here that the existing ones are insufficient and need to be
in adequation with the task at hand. In this paper we introduce two measures
based on image classification---GAN-train and GAN-test, which approximate the
recall (diversity) and precision (quality of the image) of GANs respectively.
We evaluate a number of recent GAN approaches based on these two measures and
demonstrate a clear difference in performance. Furthermore, we observe that the
increasing difficulty of the dataset, from CIFAR10 over CIFAR100 to ImageNet,
shows an inverse correlation with the quality of the GANs, as clearly evident
from our measures.
Prototype Discovery using Quality-Diversity
Authors: Alexander Hagg, Alexander Asteroth, Thomas Bäck
Date: 2018-07-25 00:00:00
Tasks: Dimensionality Reduction, Diversity

An iterative computer-aided ideation procedure is introduced, building on
recent quality-diversity algorithms, which search for diverse as well as
high-performing solutions. Dimensionality reduction is used to define a
similarity space, in which solutions are clustered into classes. These classes
are represented by prototypes, which are presented to the user for selection.
In the next iteration, quality-diversity focuses on searching within the
selected class. A quantitative analysis is performed on a 2D airfoil, and a
more complex 3D side view mirror domain shows how computer-aided ideation can
help to enhance engineers' intuition while allowing their design decisions to
influence the design process.
Attention Mechanisms for Object Recognition with Event-Based Cameras
Authors: Marco Cannici, Marco Ciccone, Andrea Romanoni, Matteo Matteucci
Date: 2018-07-25 00:00:00
Tasks: Event-based vision, Object Recognition, Translation

Event-based cameras are neuromorphic sensors capable of efficiently encoding
visual information in the form of sparse sequences of events. Being
biologically inspired, they are commonly used to exploit some of the
computational and power consumption benefits of biological vision. In this
paper we focus on a specific feature of vision: visual attention. We propose
two attentive models for event based vision: an algorithm that tracks events
activity within the field of view to locate regions of interest and a
fully-differentiable attention procedure based on DRAW neural model. We
highlight the strengths and weaknesses of the proposed methods on four
datasets, the Shifted N-MNIST, Shifted MNIST-DVS, CIFAR10-DVS and N-Caltech101
collections, using the Phased LSTM recognition network as a baseline reference
model obtaining improvements in terms of both translation and scale invariance.
Fast Dynamic Convolutional Neural Networks for Visual Tracking
Authors: Zhiyan Cui, Na Lu
Date: 2018-06-29 00:00:00
Tasks: Visual Tracking

Most of the existing tracking methods based on CNN(convolutional neural
networks) are too slow for real-time application despite the excellent tracking
precision compared with the traditional ones. In this paper, a fast dynamic
visual tracking algorithm combining CNN based MDNet(Multi-Domain Network) and
RoIAlign was developed. The major problem of MDNet also lies in the time
efficiency. Considering the computational complexity of MDNet is mainly caused
by the large amount of convolution operations and fine-tuning of the network
during tracking, a RoIPool layer which could conduct the convolution over the
whole image instead of each RoI is added to accelerate the convolution and a
new strategy of fine-tuning the fully-connected layers is used to accelerate
the update. With RoIPool employed, the computation speed has been increased but
the tracking precision has dropped simultaneously. RoIPool could lose some
positioning precision because it can not handle locations represented by
floating numbers. So RoIAlign, instead of RoIPool, which can process floating
numbers of locations by bilinear interpolation has been added to the network.
The results show the target localization precision has been improved and it
hardly increases the computational cost. These strategies can accelerate the
processing and make it 7x faster than MDNet with very low impact on precision
and it can run at around 7 fps. The proposed algorithm has been evaluated on
two benchmarks: OTB100 and VOT2016, on which high precision and speed have been
obtained. The influence of the network structure and training data are also
discussed with experiments.
Supervised and Semi-Supervised Deep Neural Networks for CSI-Based Authentication
Authors: Qian Wang, Hang Li, Zhi Chen, Dou Zhao, Shuang Ye, Jiansheng Cai
Date: 2018-07-25 00:00:00

From the viewpoint of physical-layer authentication, spoofing attacks can be
foiled by checking channel state information (CSI). Existing CSI-based
authentication algorithms mostly require a deep knowledge of the channel to
deliver decent performance. In this paper, we investigate CSI-based
authenticators that can spare the effort to predetermine channel properties by
utilizing deep neural networks (DNNs). We first propose a convolutional neural
network (CNN)-enabled authenticator that is able to extract the local features
in CSI. Next, we employ the recurrent neural network (RNN) to capture the
dependencies between different frequencies in CSI. In addition, we propose to
use the convolutional recurrent neural network (CRNN)---a combination of the
CNN and the RNN---to learn local and contextual information in CSI for user
authentication. To effectively train these DNNs, one needs a large amount of
labeled channel records. However, it is often expensive to label large channel
observations in the presence of a spoofer. In view of this, we further study a
case in which only a small part of the the channel observations are labeled. To
handle it, we extend these DNNs-enabled approaches into semi-supervised ones.
This extension is based on a semi-supervised learning technique that employs
both the labeled and unlabeled data to train a DNN. To be specific, our
semi-supervised method begins by generating pseudo labels for the unlabeled
channel samples through implementing the K-means algorithm in a semi-supervised
manner. Subsequently, both the labeled and pseudo labeled data are exploited to
pre-train a DNN, which is then fine-tuned based on the labeled channel records.
Propensity score estimation using classification and regression trees in the presence of missing covariate data
Authors: Bas B. L. Penning de Vries, Maarten van Smeden, Rolf H. H. Groenwold
Date: 2018-07-25 00:00:00
Tasks: General Classification, Imputation, regression

Data mining and machine learning techniques such as classification and
regression trees (CART) represent a promising alternative to conventional
logistic regression for propensity score estimation. Whereas incomplete data
preclude the fitting of a logistic regression on all subjects, CART is
appealing in part because some implementations allow for incomplete records to
be incorporated in the tree fitting and provide propensity score estimates for
all subjects. Based on theoretical considerations, we argue that the automatic
handling of missing data by CART may however not be appropriate. Using a series
of simulation experiments, we examined the performance of different approaches
to handling missing covariate data; (i) applying the CART algorithm directly to
the (partially) incomplete data, (ii) complete case analysis, and (iii)
multiple imputation. Performance was assessed in terms of bias in estimating
exposure-outcome effects \add{among the exposed}, standard error, mean squared
error and coverage. Applying the CART algorithm directly to incomplete data
resulted in bias, even in scenarios where data were missing completely at
random. Overall, multiple imputation followed by CART resulted in the best
performance. Our study showed that automatic handling of missing data in CART
can cause serious bias and does not outperform multiple imputation as a means
to account for missing data.
Unbounded Output Networks for Classification
Authors: Stefan Elfwing, Eiji Uchibe, Kenji Doya
Date: 2018-07-25 00:00:00
Tasks: Classification, General Classification

We proposed the expected energy-based restricted Boltzmann machine (EE-RBM)
as a discriminative RBM method for classification. Two characteristics of the
EE-RBM are that the output is unbounded and that the target value of correct
classification is set to a value much greater than one. In this study, by
adopting features of the EE-RBM approach to feed-forward neural networks, we
propose the UnBounded output network (UBnet) which is characterized by three
features: (1) unbounded output units; (2) the target value of correct
classification is set to a value much greater than one; and (3) the models are
trained by a modified mean-squared error objective. We evaluate our approach
using the MNIST, CIFAR-10, and CIFAR-100 benchmark datasets. We first
demonstrate, for shallow UBnets on MNIST, that a setting of the target value
equal to the number of hidden units significantly outperforms a setting of the
target value equal to one, and it also outperforms standard neural networks by
about 25\%. We then validate our approach by achieving high-level
classification performance on the three datasets using unbounded output
residual networks. We finally use MNIST to analyze the learned features and
weights, and we demonstrate that UBnets are much more robust against
adversarial examples than the standard approach of using a softmax output layer
and training the networks by a cross-entropy objective.
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
Authors: Xingang Pan, Ping Luo, Jianping Shi, Xiaoou Tang
Date: 2018-07-25 00:00:00
Proceeding: ECCV 2018 9
Tasks: All-day Semantic Segmentation, Domain Adaptation, Domain Generalization, Robust Object Detection, Vocal Bursts Valence Prediction

Convolutional neural networks (CNNs) have achieved great successes in many computer vision problems. Unlike existing works that designed CNN architectures to improve performance on a single task of a single domain and not generalizable, we present IBN-Net, a novel convolutional architecture, which remarkably enhances a CNN's modeling ability on one domain (e.g. Cityscapes) as well as its generalization capacity on another domain (e.g. GTA5) without finetuning. IBN-Net carefully integrates Instance Normalization (IN) and Batch Normalization (BN) as building blocks, and can be wrapped into many advanced deep networks to improve their performances. This work has three key contributions. (1) By delving into IN and BN, we disclose that IN learns features that are invariant to appearance changes, such as colors, styles, and virtuality/reality, while BN is essential for preserving content related information. (2) IBN-Net can be applied to many advanced deep architectures, such as DenseNet, ResNet, ResNeXt, and SENet, and consistently improve their performance without increasing computational cost. (3) When applying the trained networks to new domains, e.g. from GTA5 to Cityscapes, IBN-Net achieves comparable improvements as domain adaptation methods, even without using data from the target domain. With IBN-Net, we won the 1st place on the WAD 2018 Challenge Drivable Area track, with an mIoU of 86.18%.
Deterministic consensus maximization with biconvex programming
Authors: Zhipeng Cai, Tat-Jun Chin, Huu Le, David Suter
Date: 2018-07-25 00:00:00
Proceeding: ECCV 2018 9

Consensus maximization is one of the most widely used robust fitting
paradigms in computer vision, and the development of algorithms for consensus
maximization is an active research topic. In this paper, we propose an
efficient deterministic optimization algorithm for consensus maximization.
Given an initial solution, our method conducts a deterministic search that
forcibly increases the consensus of the initial solution. We show how each
iteration of the update can be formulated as an instance of biconvex
programming, which we solve efficiently using a novel biconvex optimization
algorithm. In contrast to our algorithm, previous consensus improvement
techniques rely on random sampling or relaxations of the objective function,
which reduce their ability to significantly improve the initial consensus. In
fact, on challenging instances, the previous techniques may even return a worse
off solution. Comprehensive experiments show that our algorithm can
consistently and greatly improve the quality of the initial solution, without
substantial cost.
Distinctive-attribute Extraction for Image Captioning
Authors: Boeun Kim, Young Han Lee, Hyedong Jung, Choongsang Cho
Date: 2018-07-25 00:00:00
Tasks: Attribute, Attribute Extraction, Image Captioning

Image captioning, an open research issue, has been evolved with the progress
of deep neural networks. Convolutional neural networks (CNNs) and recurrent
neural networks (RNNs) are employed to compute image features and generate
natural language descriptions in the research. In previous works, a caption
involving semantic description can be generated by applying additional
information into the RNNs. In this approach, we propose a distinctive-attribute
extraction (DaE) which explicitly encourages significant meanings to generate
an accurate caption describing the overall meaning of the image with their
unique situation. Specifically, the captions of training images are analyzed by
term frequency-inverse document frequency (TF-IDF), and the analyzed semantic
information is trained to extract distinctive-attributes for inferring
captions. The proposed scheme is evaluated on a challenge data, and it improves
an objective performance while describing images in more detail.
"Bilingual Expert" Can Find Translation Errors
Authors: Kai Fan, Jiayi Wang, Bo Li, Fengming Zhou, Boxing Chen, Luo Si
Date: 2018-07-25 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

Recent advances in statistical machine translation via the adoption of neural
sequence-to-sequence models empower the end-to-end system to achieve
state-of-the-art in many WMT benchmarks. The performance of such machine
translation (MT) system is usually evaluated by automatic metric BLEU when the
golden references are provided for validation. However, for model inference or
production deployment, the golden references are prohibitively available or
require expensive human annotation with bilingual expertise. In order to
address the issue of quality evaluation (QE) without reference, we propose a
general framework for automatic evaluation of translation output for most WMT
quality evaluation tasks. We first build a conditional target language model
with a novel bidirectional transformer, named neural bilingual expert model,
which is pre-trained on large parallel corpora for feature extraction. For QE
inference, the bilingual expert model can simultaneously produce the joint
latent representation between the source and the translation, and real-valued
measurements of possible erroneous tokens based on the prior knowledge learned
from parallel data. Subsequently, the features will further be fed into a
simple Bi-LSTM predictive model for quality evaluation. The experimental
results show that our approach achieves the state-of-the-art performance in the
quality estimation track of WMT 2017/2018.
Semantics Meet Saliency: Exploring Domain Affinity and Models for Dual-Task Prediction
Authors: Md Amirul Islam, Mahmoud Kalash, Neil D. B. Bruce
Date: 2018-07-25 00:00:00

Much research has examined models for prediction of semantic labels or
instances including dense pixel-wise prediction. The problem of predicting
salient objects or regions of an image has also been examined in a similar
light. With that said, there is an apparent relationship between these two
problem domains in that the composition of a scene and associated semantic
categories is certain to play into what is deemed salient. In this paper, we
explore the relationship between these two problem domains. This is carried out
in constructing deep neural networks that perform both predictions together
albeit with different configurations for flow of conceptual information related
to each distinct problem. This is accompanied by a detailed analysis of object
co-occurrences that shed light on dataset bias and semantic precedence specific
to individual categories.
Multi-Agent Reinforcement Learning: A Report on Challenges and Approaches
Authors: Sanyam Kapoor
Date: 2018-07-25 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement Learning (RL) is a learning paradigm concerned with learning to
control a system so as to maximize an objective over the long term. This
approach to learning has received immense interest in recent times and success
manifests itself in the form of human-level performance on games like
\textit{Go}. While RL is emerging as a practical component in real-life
systems, most successes have been in Single Agent domains. This report will
instead specifically focus on challenges that are unique to Multi-Agent Systems
interacting in mixed cooperative and competitive environments. The report
concludes with advances in the paradigm of training Multi-Agent Systems called
\textit{Decentralized Actor, Centralized Critic}, based on an extension of MDPs
called \textit{Decentralized Partially Observable MDP}s, which has seen a
renewed interest lately.
Topics in Random Matrices and Statistical Machine Learning
Authors: Sushma Kumari
Date: 2018-07-25 00:00:00
Tasks: BIG-bench Machine Learning

This thesis consists of two independent parts: random matrices, which form
the first one-third of this thesis, and machine learning, which constitutes the
remaining part. The main results of this thesis are as follows: a necessary and
sufficient condition for the inverse moments of $(m,n,\beta)$-Laguerre matrices
and compound Wishart matrices to be finite; the universal weak consistency and
the strong consistency of the $k$-nearest neighbor rule in metrically
sigma-finite dimensional spaces and metrically finite dimensional spaces
respectively. In Part I, the Chapter 1 introduces the $(m,n,\beta)$-Laguerre
matrix, Wishart and compound Wishart matrix and their joint eigenvalue
distribution. While in Chapter 2, a necessary and sufficient condition to have
finite inverse moments has been derived. In Part II, the Chapter 1 introduces
the various notions of metric dimension and differentiation property followed
by our proof for the necessary part of Preiss' result. Further, Chapter 2 gives
an introduction to the mathematical concepts in statistical machine learning
and then the $k$-nearest neighbor rule is presented in Chapter 3 with a proof
of Stone's theorem. In chapters 4 and 5, we present our main results and some
possible future directions based on it.
Video Storytelling: Textual Summaries for Events
Authors: Junnan Li, Yongkang Wong, Qi Zhao, Mohan S. Kankanhalli
Date: 2018-07-25 00:00:00
Tasks: Diversity, Reinforcement Learning, Sentence

Bridging vision and natural language is a longstanding goal in computer vision and multimedia research. While earlier works focus on generating a single-sentence description for visual content, recent works have studied paragraph generation. In this work, we introduce the problem of video storytelling, which aims at generating coherent and succinct stories for long videos. Video storytelling introduces new challenges, mainly due to the diversity of the story and the length and complexity of the video. We propose novel methods to address the challenges. First, we propose a context-aware framework for multimodal embedding learning, where we design a Residual Bidirectional Recurrent Neural Network to leverage contextual information from past and future. Second, we propose a Narrator model to discover the underlying storyline. The Narrator is formulated as a reinforcement learning agent which is trained by directly optimizing the textual metric of the generated story. We evaluate our method on the Video Story dataset, a new dataset that we have collected to enable the study. We compare our method with multiple state-of-the-art baselines, and show that our method achieves better performance, in terms of quantitative measures and user study.
3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration
Authors: Zi Jian Yew, Gim Hee Lee
Date: 2018-07-25 00:00:00
Proceeding: ECCV 2018 9
Tasks: Point Cloud Registration

In this paper, we propose the 3DFeat-Net which learns both 3D feature
detector and descriptor for point cloud matching using weak supervision. Unlike
many existing works, we do not require manual annotation of matching point
clusters. Instead, we leverage on alignment and attention mechanisms to learn
feature correspondences from GPS/INS tagged 3D point clouds without explicitly
specifying them. We create training and benchmark outdoor Lidar datasets, and
experiments show that 3DFeat-Net obtains state-of-the-art performance on these
gravity-aligned datasets.