pwc-archive/papers-with-abstracts huggingface dataset:
Category-level 6D Object Pose Recovery in Depth Images
Authors: Caner Sahin, Tae-Kyun Kim
Date: 2018-08-01 00:00:00
Tasks: 6D Pose Estimation using RGB, Graph Matching, Object, Pose Estimation

Intra-class variations, distribution shifts among source and target domains
are the major challenges of category-level tasks. In this study, we address
category-level full 6D object pose estimation in the context of depth modality,
introducing a novel part-based architecture that can tackle the above-mentioned
challenges. Our architecture particularly adapts the distribution shifts
arising from shape discrepancies, and naturally removes the variations of
texture, illumination, pose, etc., so we call it as "Intrinsic Structure
Adaptor (ISA)". We engineer ISA based on the followings: i) "Semantically
Selected Centers (SSC)" are proposed in order to define the "6D pose" at the
level of categories. ii) 3D skeleton structures, which we derive as
shape-invariant features, are used to represent the parts extracted from the
instances of given categories, and privileged one-class learning is employed
based on these parts. iii) Graph matching is performed during training in such
a way that the adaptation/generalization capability of the proposed
architecture is improved across unseen instances. Experiments validate the
promising performance of the proposed architecture on both synthetic and real
datasets.
A Deep Neural Model Of Emotion Appraisal
Authors: Pablo Barros, Emilia Barakova, Stefan Wermter
Date: 2018-08-01 00:00:00
Tasks: Developmental Learning, model

Emotional concepts play a huge role in our daily life since they take part
into many cognitive processes: from the perception of the environment around us
to different learning processes and natural communication. Social robots need
to communicate with humans, which increased also the popularity of affective
embodied models that adopt different emotional concepts in many everyday tasks.
However, there is still a gap between the development of these solutions and
the integration and development of a complex emotion appraisal system, which is
much necessary for true social robots. In this paper, we propose a deep neural
model which is designed in the light of different aspects of developmental
learning of emotional concepts to provide an integrated solution for internal
and external emotion appraisal. We evaluate the performance of the proposed
model with different challenging corpora and compare it with state-of-the-art
models for external emotion appraisal. To extend the evaluation of the proposed
model, we designed and collected a novel dataset based on a Human-Robot
Interaction (HRI) scenario. We deployed the model in an iCub robot and
evaluated the capability of the robot to learn and describe the affective
behavior of different persons based on observation. The performed experiments
demonstrate that the proposed model is competitive with the state of the art in
describing emotion behavior in general. In addition, it is able to generate
internal emotional concepts that evolve through time: it continuously forms and
updates the formed emotional concepts, which is a step towards creating an
emotional appraisal model grounded in the robot experiences.
Robbins-Monro conditions for persistent exploration learning strategies
Authors: Dmitry B. Rokhlin
Date: 2018-08-01 00:00:00
Tasks: Q-Learning

We formulate simple assumptions, implying the Robbins-Monro conditions for
the $Q$-learning algorithm with the local learning rate, depending on the
number of visits of a particular state-action pair (local clock) and the number
of iteration (global clock). It is assumed that the Markov decision process is
communicating and the learning policy ensures the persistent exploration. The
restrictions are imposed on the functional dependence of the learning rate on
the local and global clocks. The result partially confirms the conjecture of
Bradkte (1994).
Tumor Delineation For Brain Radiosurgery by a ConvNet and Non-Uniform Patch Generation
Authors: Egor Krivov, Valery Kostjuchenko, Alexandra Dalechina, Boris Shirokikh, Gleb karchuk, Alexander Denisenko, Andrey Golanov, Mikhail Belyaev
Date: 2018-08-01 00:00:00
Tasks: Lesion Segmentation, Segmentation

Deep learning methods are actively used for brain lesion segmentation. One of
the most popular models is DeepMedic, which was developed for segmentation of
relatively large lesions like glioma and ischemic stroke. In our work, we
consider segmentation of brain tumors appropriate to stereotactic radiosurgery
which limits typical lesion sizes. These differences in target volumes lead to
a large number of false negatives (especially for small lesions) as well as to
an increased number of false positives for DeepMedic. We propose a new
patch-sampling procedure to increase network performance for small lesions. We
used a 6-year dataset from a stereotactic radiosurgery center. To evaluate our
approach, we conducted experiments with the three most frequent brain tumors:
metastasis, meningioma, schwannoma. In addition to cross-validation, we
estimated quality on a hold-out test set which was collected several years
later than the train one. The experimental results show solid improvements in
both cases.
Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy
Authors: Yuan Xie, Boyi Liu, Qiang Liu, Zhaoran Wang, Yuan Zhou, Jian Peng
Date: 2018-08-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Off-policy evaluation, Recommendation Systems

When learning from a batch of logged bandit feedback, the discrepancy between
the policy to be learned and the off-policy training data imposes statistical
and computational challenges. Unlike classical supervised learning and online
learning settings, in batch contextual bandit learning, one only has access to
a collection of logged feedback from the actions taken by a historical policy,
and expect to learn a policy that takes good actions in possibly unseen
contexts. Such a batch learning setting is ubiquitous in online and interactive
systems, such as ad platforms and recommendation systems. Existing approaches
based on inverse propensity weights, such as Inverse Propensity Scoring (IPS)
and Policy Optimizer for Exponential Models (POEM), enjoy unbiasedness but
often suffer from large mean squared error. In this work, we introduce a new
approach named Maximum Likelihood Inverse Propensity Scoring (MLIPS) for batch
learning from logged bandit feedback. Instead of using the given historical
policy as the proposal in inverse propensity weights, we estimate a maximum
likelihood surrogate policy based on the logged action-context pairs, and then
use this surrogate policy as the proposal. We prove that MLIPS is
asymptotically unbiased, and moreover, has a smaller nonasymptotic mean squared
error than IPS. Such an error reduction phenomenon is somewhat surprising as
the estimated surrogate policy is less accurate than the given historical
policy. Results on multi-label classification problems and a large- scale ad
placement dataset demonstrate the empirical effectiveness of MLIPS.
Furthermore, the proposed surrogate policy technique is complementary to
existing error reduction techniques, and when combined, is able to consistently
boost the performance of several widely used approaches.
Experience, Imitation and Reflection; Confucius' Conjecture and Machine Learning
Authors: Amir Ramezani Dooraki
Date: 2018-08-01 00:00:00
Tasks: BIG-bench Machine Learning

Artificial intelligence recently had a great advancements caused by the
emergence of new processing power and machine learning methods. Having said
that, the learning capability of artificial intelligence is still at its
infancy comparing to the learning capability of human and many animals. Many of
the current artificial intelligence applications can only operate in a very
orchestrated, specific environments with an extensive training set that exactly
describes the conditions that will occur during execution time. Having that in
mind, and considering the several existing machine learning methods this
question rises that 'What are some of the best ways for a machine to learn?'
Regarding the learning methods of human, Confucius' point of view is that they
are by experience, imitation and reflection. This paper tries to explore and
discuss regarding these three ways of learning and their implementations in
machines by having a look at how they happen in minds.
Model selection by minimum description length: Lower-bound sample sizes for the Fisher information approximation
Authors: Daniel W. Heck, Morten Moshagen, Edgar Erdfelder
Date: 2018-08-01 00:00:00
Tasks: Model Selection

The Fisher information approximation (FIA) is an implementation of the
minimum description length principle for model selection. Unlike information
criteria such as AIC or BIC, it has the advantage of taking the functional form
of a model into account. Unfortunately, FIA can be misleading in finite
samples, resulting in an inversion of the correct rank order of complexity
terms for competing models in the worst case. As a remedy, we propose a
lower-bound $N'$ for the sample size that suffices to preclude such errors. We
illustrate the approach using three examples from the family of multinomial
processing tree models.
Binarized Convolutional Neural Networks for Efficient Inference on GPUs
Authors: Mir Khan, Heikki Huttunen, Jani Boutellier
Date: 2018-08-01 00:00:00
Tasks: Binarization, GPU, image-classification, Image Classification

Convolutional neural networks have recently achieved significant
breakthroughs in various image classification tasks. However, they are
computationally expensive,which can make their feasible mplementation on
embedded and low-power devices difficult. In this paper convolutional neural
network binarization is implemented on GPU-based platforms for real-time
inference on resource constrained devices. In binarized networks, all weights
and intermediate computations between layers are quantized to +1 and -1,
allowing multiplications and additions to be replaced with bit-wise operations
between 32-bit words. This representation completely eliminates the need for
floating point multiplications and additions and decreases both the
computational load and the memory footprint compared to a full-precision
network implemented in floating point, making it well-suited for
resource-constrained environments. We compare the performance of our
implementation with an equivalent floating point implementation on one desktop
and two embedded GPU platforms. Our implementation achieves a maximum speed up
of 7. 4X with only 4.4% loss in accuracy compared to a reference
implementation.
Connecting Visual Experiences using Max-flow Network with Application to Visual Localization
Authors: A. H. Abdul Hafez, Nakul Agarwal, C. V. Jawahar
Date: 2018-08-01 00:00:00
Tasks: Autonomous Navigation, Visual Localization

We are motivated by the fact that multiple representations of the environment
are required to stand for the changes in appearance with time and for changes
that appear in a cyclic manner. These changes are, for example, from day to
night time, and from day to day across seasons. In such situations, the robot
visits the same routes multiple times and collects different appearances of it.
Multiple visual experiences usually find robotic vision applications like
visual localization, mapping, place recognition, and autonomous navigation. The
novelty in this paper is an algorithm that connects multiple visual experiences
via aligning multiple image sequences. This problem is solved by finding the
maximum flow in a directed graph flow-network, whose vertices represent the
matches between frames in the test and reference sequences. Edges of the graph
represent the cost of these matches. The problem of finding the best match is
reduced to finding the minimum-cut surface, which is solved as a maximum flow
network problem. Application to visual localization is considered in this paper
to show the effectiveness of the proposed multiple image sequence alignment
method, without loosing its generality. Experimental evaluations show that the
precision of sequence matching is improved by considering multiple visual
sequences for the same route, and that the method performs favorably against
state-of-the-art single representation methods like SeqSLAM and ABLE-M.
Beetle Swarm Optimization Algorithm:Theory and Application
Authors: Tiantian Wang, Long Yang
Date: 2018-08-01 00:00:00

In this paper, a new meta-heuristic algorithm, called beetle swarm optimization algorithm, is proposed by enhancing the performance of swarm optimization through beetle foraging principles. The performance of 23 benchmark functions is tested and compared with widely used algorithms, including particle swarm optimization algorithm, genetic algorithm (GA) and grasshopper optimization algorithm . Numerical experiments show that the beetle swarm optimization algorithm outperforms its counterparts. Besides, to demonstrate the practical impact of the proposed algorithm, two classic engineering design problems, namely, pressure vessel design problem and himmelblaus optimization problem, are also considered and the proposed beetle swarm optimization algorithm is shown to be competitive in those applications.
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks
Authors: Chu Wang, Yan-Ming Zhang, Cheng-Lin Liu
Date: 2018-08-01 00:00:00
Tasks: Anomaly Detection, Intrusion Detection, Network Intrusion Detection

Anomaly detection aims to detect abnormal events by a model of normality. It
plays an important role in many domains such as network intrusion detection,
criminal activity identity and so on. With the rapidly growing size of
accessible training data and high computation capacities, deep learning based
anomaly detection has become more and more popular. In this paper, a new
domain-based anomaly detection method based on generative adversarial networks
(GAN) is proposed. Minimum likelihood regularization is proposed to make the
generator produce more anomalies and prevent it from converging to normal data
distribution. Proper ensemble of anomaly scores is shown to improve the
stability of discriminator effectively. The proposed method has achieved
significant improvement than other anomaly detection methods on Cifar10 and UCI
datasets.
Towards Machine Learning on data from Professional Cyclists
Authors: Agrin Hilmkil, Oscar Ivarsson, Moa Johansson, Dan Kuylenstierna, Teun van Erp
Date: 2018-08-01 00:00:00
Tasks: BIG-bench Machine Learning

Professional sports are developing towards increasingly scientific training
methods with increasing amounts of data being collected from laboratory tests,
training sessions and competitions. In cycling, it is standard to equip
bicycles with small computers recording data from sensors such as power-meters,
in addition to heart-rate, speed, altitude etc. Recently, machine learning
techniques have provided huge success in a wide variety of areas where large
amounts of data (big data) is available. In this paper, we perform a pilot
experiment on machine learning to model physical response in elite cyclists. As
a first experiment, we show that it is possible to train a LSTM machine
learning algorithm to predict the heart-rate response of a cyclist during a
training session. This work is a promising first step towards developing more
elaborate models based on big data and machine learning to capture performance
aspects of athletes.
MaxMin Linear Initialization for Fuzzy C-Means
Authors: Aybükë Oztürk, Stéphane Lallich, Jérôme Darmont, Sylvie Yona Waksman
Date: 2018-08-01 00:00:00
Tasks: Clustering, Text Categorization

Clustering is an extensive research area in data science. The aim of
clustering is to discover groups and to identify interesting patterns in
datasets. Crisp (hard) clustering considers that each data point belongs to one
and only one cluster. However, it is inadequate as some data points may belong
to several clusters, as is the case in text categorization. Thus, we need more
flexible clustering. Fuzzy clustering methods, where each data point can belong
to several clusters, are an interesting alternative. Yet, seeding iterative
fuzzy algorithms to achieve high quality clustering is an issue. In this paper,
we propose a new linear and efficient initialization algorithm MaxMin Linear to
deal with this problem. Then, we validate our theoretical results through
extensive experiments on a variety of numerical real-world and artificial
datasets. We also test several validity indices, including a new validity index
that we propose, Transformed Standardized Fuzzy Difference (TSFD).
Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models
Authors: Jiawei Zhang, Yang Wang, Piero Molino, Lezhi Li, David S. Ebert
Date: 2018-08-01 00:00:00
Tasks: BIG-bench Machine Learning

Interpretation and diagnosis of machine learning models have gained renewed
interest in recent years with breakthroughs in new approaches. We present
Manifold, a framework that utilizes visual analysis techniques to support
interpretation, debugging, and comparison of machine learning models in a more
transparent and interactive manner. Conventional techniques usually focus on
visualizing the internal logic of a specific model type (i.e., deep neural
networks), lacking the ability to extend to a more complex scenario where
different model types are integrated. To this end, Manifold is designed as a
generic framework that does not rely on or access the internal logic of the
model and solely observes the input (i.e., instances or features) and the
output (i.e., the predicted result and probability distribution). We describe
the workflow of Manifold as an iterative process consisting of three major
phases that are commonly involved in the model development and diagnosis
process: inspection (hypothesis), explanation (reasoning), and refinement
(verification). The visual components supporting these tasks include a
scatterplot-based visual summary that overviews the models' outcome and a
customizable tabular view that reveals feature discrimination. We demonstrate
current applications of the framework on the classification and regression
tasks and discuss other potential machine learning use scenarios where Manifold
can be applied.
A Pseudo Multi-Exposure Fusion Method Using Single Image
Authors: Yuma Kinoshita, Sayaka Shiota, Hitoshi Kiya
Date: 2018-08-01 00:00:00
Tasks: Multi-Exposure Image Fusion

This paper proposes a novel pseudo multi-exposure image fusion method based
on a single image. Multi-exposure image fusion is used to produce images
without saturation regions, by using photos with different exposures. However,
it is difficult to take photos suited for the multi-exposure image fusion when
we take a photo of dynamic scenes or record a video. In addition, the
multi-exposure image fusion cannot be applied to existing images with a single
exposure or videos. The proposed method enables us to produce pseudo
multi-exposure images from a single image. To produce multi-exposure images,
the proposed method utilizes the relationship between the exposure values and
pixel values, which is obtained by assuming that a digital camera has a linear
response function. Moreover, it is shown that the use of a local contrast
enhancement method allows us to produce pseudo multi-exposure images with
higher quality. Most of conventional multi-exposure image fusion methods are
also applicable to the proposed multi-exposure images. Experimental results
show the effectiveness of the proposed method by comparing the proposed one
with conventional ones.
Reinforced Evolutionary Neural Architecture Search
Authors: Yukang Chen, Gaofeng Meng, Qian Zhang, Shiming Xiang, Chang Huang, Lisen Mu, Xinggang Wang
Date: 2018-08-01 00:00:00
Tasks: Neural Architecture Search, Semantic Segmentation

Neural Architecture Search (NAS) is an important yet challenging task in
network design due to its high computational consumption. To address this
issue, we propose the Reinforced Evolutionary Neural Architecture Search (RE-
NAS), which is an evolutionary method with the reinforced mutation for NAS. Our
method integrates reinforced mutation into an evolution algorithm for neural
architecture exploration, in which a mutation controller is introduced to learn
the effects of slight modifications and make mutation actions. The reinforced
mutation controller guides the model population to evolve efficiently.
Furthermore, as child models can inherit parameters from their parents during
evolution, our method requires very limited computational resources. In
experiments, we conduct the proposed search method on CIFAR-10 and obtain a
powerful network architecture, RENASNet. This architecture achieves a
competitive result on CIFAR-10. The explored network architecture is
transferable to ImageNet and achieves a new state-of-the-art accuracy, i.e.,
75.7% top-1 accuracy with 5.36M parameters on mobile ImageNet. We further test
its performance on semantic segmentation with DeepLabv3 on the PASCAL VOC.
RENASNet outperforms MobileNet-v1, MobileNet-v2 and NASNet. It achieves 75.83%
mIOU without being pre-trained on COCO.
Graph R-CNN for Scene Graph Generation
Authors: Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, Devi Parikh
Date: 2018-08-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Graph Generation, Scene Graph Generation

We propose a novel scene graph generation model called Graph R-CNN, that is
both effective and efficient at detecting objects and their relations in
images. Our model contains a Relation Proposal Network (RePN) that efficiently
deals with the quadratic number of potential relations between objects in an
image. We also propose an attentional Graph Convolutional Network (aGCN) that
effectively captures contextual information between objects and relations.
Finally, we introduce a new evaluation metric that is more holistic and
realistic than existing metrics. We report state-of-the-art performance on
scene graph generation as evaluated using both existing and our proposed
metrics.
Monolingual and Cross-lingual Zero-shot Style Transfer
Authors: Elizaveta Korotkova, Maksym Del, Mark Fishel
Date: 2018-08-01 00:00:00
Tasks: Machine Translation, Style Transfer, Translation

We introduce the task of zero-shot style transfer between different
languages. Our training data includes multilingual parallel corpora, but does
not contain any parallel sentences between styles, similarly to the recent
previous work. We propose a unified multilingual multi-style machine
translation system design, that allows to perform zero-shot style conversions
during inference; moreover, it does so both monolingually and cross-lingually.
Our model allows to increase the presence of dissimilar styles in corpus by up
to 3 times, easily learns to operate with various contractions, and provides
reasonable lexicon swaps as we see from manual evaluation.