pwc-archive/papers-with-abstracts huggingface dataset:
Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors
Authors: Dmitry Baranchuk, Artem Babenko, Yury Malkov
Date: 2018-02-07 00:00:00
Proceeding: ECCV 2018 9
Tasks: Retrieval

This work addresses the problem of billion-scale nearest neighbor search. The
state-of-the-art retrieval systems for billion-scale databases are currently
based on the inverted multi-index, the recently proposed generalization of the
inverted index structure. The multi-index provides a very fine-grained
partition of the feature space that allows extracting concise and accurate
short-lists of candidates for the search queries. In this paper, we argue that
the potential of the simple inverted index was not fully exploited in previous
works and advocate its usage both for the highly-entangled deep descriptors and
relatively disentangled SIFT descriptors. We introduce a new retrieval system
that is based on the inverted index and outperforms the multi-index by a large
margin for the same memory consumption and construction complexity. For
example, our system achieves the state-of-the-art recall rates several times
faster on the dataset of one billion deep descriptors compared to the efficient
implementation of the inverted multi-index from the FAISS library.
Modelling Preference Data with the Wallenius Distribution
Authors: Clara Grazian, Fabrizio Leisen, Brunero Liseo
Date: 2017-01-27 00:00:00
Tasks: General Classification

The Wallenius distribution is a generalisation of the Hypergeometric
distribution where weights are assigned to balls of different colours. This
naturally defines a model for ranking categories which can be used for
classification purposes. Since, in general, the resulting likelihood is not
analytically available, we adopt an approximate Bayesian computational (ABC)
approach for estimating the importance of the categories. We illustrate the
performance of the estimation procedure on simulated datasets. Finally, we use
the new model for analysing two datasets about movies ratings and Italian
academic statisticians' journal preferences. The latter is a novel dataset
collected by the authors.
Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks
Authors: Yaqi Liu, Qingxiao Guan, Xianfeng Zhao, Yun Cao
Date: 2017-06-13 00:00:00
Tasks: Segmentation

In this paper, we propose to utilize Convolutional Neural Networks (CNNs) and
the segmentation-based multi-scale analysis to locate tampered areas in digital
images. First, to deal with color input sliding windows of different scales, a
unified CNN architecture is designed. Then, we elaborately design the training
procedures of CNNs on sampled training patches. With a set of robust
multi-scale tampering detectors based on CNNs, complementary tampering
possibility maps can be generated. Last but not least, a segmentation-based
method is proposed to fuse the maps and generate the final decision map. By
exploiting the benefits of both the small-scale and large-scale analyses, the
segmentation-based multi-scale analysis can lead to a performance leap in
forgery localization of CNNs. Numerous experiments are conducted to demonstrate
the effectiveness and efficiency of our method.
From Selective Deep Convolutional Features to Compact Binary Representations for Image Retrieval
Authors: Thanh-Toan Do, Tuan Hoang, Dang-Khoa Le Tan, Huu Le, Tam V. Nguyen, Ngai-Man Cheung
Date: 2018-02-07 00:00:00
Tasks: Image Retrieval, Retrieval

In the large-scale image retrieval task, the two most important requirements
are the discriminability of image representations and the efficiency in
computation and storage of representations. Regarding the former requirement,
Convolutional Neural Network (CNN) is proven to be a very powerful tool to
extract highly discriminative local descriptors for effective image search.
Additionally, in order to further improve the discriminative power of the
descriptors, recent works adopt fine-tuned strategies. In this paper, taking a
different approach, we propose a novel, computationally efficient, and
competitive framework. Specifically, we firstly propose various strategies to
compute masks, namely SIFT-mask, SUM-mask, and MAX-mask, to select a
representative subset of local convolutional features and eliminate redundant
features. Our in-depth analyses demonstrate that proposed masking schemes are
effective to address the burstiness drawback and improve retrieval accuracy.
Secondly, we propose to employ recent embedding and aggregating methods which
can significantly boost the feature discriminability. Regarding the computation
and storage efficiency, we include a hashing module to produce very compact
binary image representations. Extensive experiments on six image retrieval
benchmarks demonstrate that our proposed framework achieves the
state-of-the-art retrieval performances.
Combining Learned and Analytical Models for Predicting Action Effects from Sensory Data
Authors: Alina Kloss, Stefan Schaal, Jeannette Bohg
Date: 2017-10-11 00:00:00
Tasks: Open-Ended Question Answering

One of the most basic skills a robot should possess is predicting the effect of physical interactions with objects in the environment. This enables optimal action selection to reach a certain goal state. Traditionally, dynamics are approximated by physics-based analytical models. These models rely on specific state representations that may be hard to obtain from raw sensory data, especially if no knowledge of the object shape is assumed. More recently, we have seen learning approaches that can predict the effect of complex physical interactions directly from sensory input. It is however an open question how far these models generalize beyond their training data. In this work, we investigate the advantages and limitations of neural network based learning approaches for predicting the effects of actions based on sensory input and show how analytical and learned models can be combined to leverage the best of both worlds. As physical interaction task, we use planar pushing, for which there exists a well-known analytical model and a large real-world dataset. We propose to use a convolutional neural network to convert raw depth images or organized point clouds into a suitable representation for the analytical model and compare this approach to using neural networks for both, perception and prediction. A systematic evaluation of the proposed approach on a very large real-world dataset shows two main advantages of the hybrid architecture. Compared to a pure neural network, it significantly (i) reduces required training data and (ii) improves generalization to novel physical interaction.
ShakeDrop Regularization for Deep Residual Learning
Authors: Yoshihiro Yamada, Masakazu Iwamura, Takuya Akiba, Koichi Kise
Date: 2018-02-07 00:00:00

Overfitting is a crucial problem in deep neural networks, even in the latest network architectures. In this paper, to relieve the overfitting effect of ResNet and its improvements (i.e., Wide ResNet, PyramidNet, and ResNeXt), we propose a new regularization method called ShakeDrop regularization. ShakeDrop is inspired by Shake-Shake, which is an effective regularization method, but can be applied to ResNeXt only. ShakeDrop is more effective than Shake-Shake and can be applied not only to ResNeXt but also ResNet, Wide ResNet, and PyramidNet. An important key is to achieve stability of training. Because effective regularization often causes unstable training, we introduce a training stabilizer, which is an unusual use of an existing regularizer. Through experiments under various conditions, we demonstrate the conditions under which ShakeDrop works well.
Efficient and Effective Quantum Compiling for Entanglement-based Machine Learning on IBM Q Devices
Authors: Davide Ferrari, Michele Amoretti
Date: 2018-01-08 00:00:00
Tasks: BIG-bench Machine Learning, Quantum Machine Learning

Quantum compiling means fast, device-aware implementation of quantum algorithms (i.e., quantum circuits, in the quantum circuit model of computation). In this paper, we present a strategy for compiling IBM Q -aware, low-depth quantum circuits that generate Greenberger-Horne-Zeilinger (GHZ) entangled states. The resulting compiler can replace the QISKit compiler for the specific purpose of obtaining improved GHZ circuits. It is well known that GHZ states have several practical applications, including quantum machine learning. We illustrate our experience in implementing and querying a uniform quantum example oracle based on the GHZ circuit, for solving the classically hard problem of learning parity with noise.
Recent Advances in Neural Program Synthesis
Authors: Neel Kant
Date: 2018-02-07 00:00:00
Tasks: Object Recognition, Program induction, Program Synthesis, Translation

In recent years, deep learning has made tremendous progress in a number of
fields that were previously out of reach for artificial intelligence. The
successes in these problems has led researchers to consider the possibilities
for intelligent systems to tackle a problem that humans have only recently
themselves considered: program synthesis. This challenge is unlike others such
as object recognition and speech translation, since its abstract nature and
demand for rigor make it difficult even for human minds to attempt. While it is
still far from being solved or even competitive with most existing methods,
neural program synthesis is a rapidly growing discipline which holds great
promise if completely realized. In this paper, we start with exploring the
problem statement and challenges of program synthesis. Then, we examine the
fascinating evolution of program induction models, along with how they have
succeeded, failed and been reimagined since. Finally, we conclude with a
contrastive look at program synthesis and future research recommendations for
the field.
SlideRunner - A Tool for Massive Cell Annotations in Whole Slide Images
Authors: Marc Aubreville, Christof Bertram, Robert Klopfleisch, Andreas Maier
Date: 2018-02-07 00:00:00
Tasks: Mitosis Detection, whole slide images

Large-scale image data such as digital whole-slide histology images pose a
challenging task at annotation software solutions. Today, a number of good
solutions with varying scopes exist. For cell annotation, however, we find that
many do not match the prerequisites for fast annotations. Especially in the
field of mitosis detection, it is assumed that detection accuracy could
significantly benefit from larger annotation databases that are currently
however very troublesome to produce. Further, multiple independent (blind)
expert labels are a big asset for such databases, yet there is currently no
tool for this kind of annotation available.
  To ease this tedious process of expert annotation and grading, we introduce
SlideRunner, an open source annotation and visualization tool for digital
histopathology, developed in close cooperation with two pathologists.
SlideRunner is capable of setting annotations like object centers (for e.g.
cells) as well as object boundaries (e.g. for tumor outlines). It provides
single-click annotations as well as a blind mode for multi-annotations, where
the expert is directly shown the microscopy image containing the cells that he
has not yet rated.
Multi-View Bayesian Correlated Component Analysis
Authors: Simon Kamronn, Andreas Trier Poulsen, Lars Kai Hansen
Date: 2018-02-07 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Correlated component analysis as proposed by Dmochowski et al. (2012) is a
tool for investigating brain process similarity in the responses to multiple
views of a given stimulus. Correlated components are identified under the
assumption that the involved spatial networks are identical. Here we propose a
hierarchical probabilistic model that can infer the level of universality in
such multi-view data, from completely unrelated representations, corresponding
to canonical correlation analysis, to identical representations as in
correlated component analysis. This new model, which we denote Bayesian
correlated component analysis, evaluates favourably against three relevant
algorithms in simulated data. A well-established benchmark EEG dataset is used
to further validate the new model and infer the variability of spatial
representations across multiple subjects.
Outlier Detection for Robust Multi-dimensional Scaling
Authors: Leonid Blouvshtein, Daniel Cohen-Or
Date: 2018-02-07 00:00:00
Tasks: Dimensionality Reduction, Outlier Detection

Multi-dimensional scaling (MDS) plays a central role in data-exploration,
dimensionality reduction and visualization. State-of-the-art MDS algorithms are
not robust to outliers, yielding significant errors in the embedding even when
only a handful of outliers are present. In this paper, we introduce a technique
to detect and filter outliers based on geometric reasoning. We test the
validity of triangles formed by three points, and mark a triangle as broken if
its triangle inequality does not hold. The premise of our work is that unlike
inliers, outlier distances tend to break many triangles. Our method is tested
and its performance is evaluated on various datasets and distributions of
outliers. We demonstrate that for a reasonable amount of outliers, e.g., under
$20\%$, our method is effective, and leads to a high embedding quality.
Visual Interpretability for Deep Learning: a Survey
Authors: Quanshi Zhang, Song-Chun Zhu
Date: 2018-02-02 00:00:00
Tasks: Deep Learning, Explainable artificial intelligence, Survey

This paper reviews recent studies in understanding neural-network
representations and learning neural networks with interpretable/disentangled
middle-layer representations. Although deep neural networks have exhibited
superior performance in various tasks, the interpretability is always the
Achilles' heel of deep neural networks. At present, deep neural networks obtain
high discrimination power at the cost of low interpretability of their
black-box representations. We believe that high model interpretability may help
people to break several bottlenecks of deep learning, e.g., learning from very
few annotations, learning via human-computer communications at the semantic
level, and semantically debugging network representations. We focus on
convolutional neural networks (CNNs), and we revisit the visualization of CNN
representations, methods of diagnosing representations of pre-trained CNNs,
approaches for disentangling pre-trained CNN representations, learning of CNNs
with disentangled representations, and middle-to-end learning based on model
interpretability. Finally, we discuss prospective trends in explainable
artificial intelligence.
Annotation-Free and One-Shot Learning for Instance Segmentation of Homogeneous Object Clusters
Authors: Zheng Wu, Ruiheng Chang, Jiaxu Ma, Cewu Lu, Chi-Keung Tang
Date: 2018-02-01 00:00:00
Tasks: Instance Segmentation, One-Shot Learning, Semantic Segmentation

We propose a novel approach for instance segmen- tation given an image of
homogeneous object clus- ter (HOC). Our learning approach is one-shot be- cause
a single video of an object instance is cap- tured and it requires no human
annotation. Our in- tuition is that images of homogeneous objects can be
effectively synthesized based on structure and illumination priors derived from
real images. A novel solver is proposed that iteratively maximizes our
structured likelihood to generate realistic im- ages of HOC. Illumination
transformation scheme is applied to make the real and synthetic images share
the same illumination condition. Extensive experiments and comparisons are
performed to ver- ify our method. We build a dataset consisting of pixel-level
annotated images of HOC. The dataset and code will be published with the paper.
Bitewing Radiography Semantic Segmentation Base on Conditional Generative Adversarial Nets
Authors: Jiang Yun, Tan Ning, Zhang Hai, Peng Tingting
Date: 2018-02-07 00:00:00
Tasks: Generative Adversarial Network, Segmentation, Semantic Segmentation

Currently, Segmentation of bitewing radiograpy images is a very challenging
task. The focus of the study is to segment it into caries, enamel, dentin,
pulp, crowns, restoration and root canal treatments. The main method of
semantic segmentation of bitewing radiograpy images at this stage is the
U-shaped deep convolution neural network, but its accuracy is low. in order to
improve the accuracy of semantic segmentation of bitewing radiograpy images,
this paper proposes the use of Conditional Generative Adversarial network
(cGAN) combined with U-shaped network structure (U-Net) approach to semantic
segmentation of bitewing radiograpy images. The experimental results show that
the accuracy of cGAN combined with U-Net is 69.7%, which is 13.3% higher than
the accuracy of u-shaped deep convolution neural network of 56.4%.
Learning Representations from Road Network for End-to-End Urban Growth Simulation
Authors: Saptarshi Pal, Soumya K. Ghosh
Date: 2017-12-19 00:00:00
Tasks: Feature Engineering

From our experiences in the past, we have seen that the growth of cities is
very much dependent on the transportation networks. In mega cities,
transportation networks determine to a significant extent as to where the
people will move and houses will be built. Hence, transportation network data
is crucial to an urban growth prediction system. Existing works have used
manually derived distance based features based on the road networks to build
models on urban growth. But due to the non-generic and laborious nature of the
manual feature engineering process, we can shift to End-to-End systems which do
not rely on manual feature engineering. In this paper, we propose a method to
integrate road network data to an existing Rule based End-to-End framework
without manual feature engineering. Our method employs recurrent neural
networks to represent road networks in a structured way such that it can be
plugged into the previously proposed End-to-End framework. The proposed
approach enhances the performance in terms of Figure of Merit, Producer's
accuracy, User's accuracy and Overall accuracy of the existing Rule based
End-to-End framework.
An Empirical Evaluation of Deep Learning for ICD-9 Code Assignment using MIMIC-III Clinical Notes
Authors: Jinmiao Huang, Cesar Osorio, Luke Wicent Sy
Date: 2018-02-07 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Multi-Label Classification, Multi-Label Classification Of Biomedical Texts, Multi-Label Text Classification

Background and Objective: Code assignment is of paramount importance in many levels in modern hospitals, from ensuring accurate billing process to creating a valid record of patient care history. However, the coding process is tedious and subjective, and it requires medical coders with extensive training. This study aims to evaluate the performance of deep-learning-based systems to automatically map clinical notes to ICD-9 medical codes. Methods: The evaluations of this research are focused on end-to-end learning methods without manually defined rules. Traditional machine learning algorithms, as well as state-of-the-art deep learning methods such as Recurrent Neural Networks and Convolution Neural Networks, were applied to the Medical Information Mart for Intensive Care (MIMIC-III) dataset. An extensive number of experiments was applied to different settings of the tested algorithm. Results: Findings showed that the deep learning-based methods outperformed other conventional machine learning methods. From our assessment, the best models could predict the top 10 ICD-9 codes with 0.6957 F1 and 0.8967 accuracy and could estimate the top 10 ICD-9 categories with 0.7233 F1 and 0.8588 accuracy. Our implementation also outperformed existing work under certain evaluation metrics. Conclusion: A set of standard metrics was utilized in assessing the performance of ICD-9 code assignment on MIMIC-III dataset. All the developed evaluation tools and resources are available online, which can be used as a baseline for further research.
Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
Authors: Gabriel Lima Guimaraes, Benjamin Sanchez-Lengeling, Carlos Outeiral, Pedro Luis Cunha Farias, Alán Aspuru-Guzik
Date: 2017-05-30 00:00:00
Tasks: Molecular Graph Generation, Music Generation, Reinforcement Learning, Reinforcement Learning (RL)

In unsupervised data generation tasks, besides the generation of a sample
based on previous observations, one would often like to give hints to the model
in order to bias the generation towards desirable metrics. We propose a method
that combines Generative Adversarial Networks (GANs) and reinforcement learning
(RL) in order to accomplish exactly that. While RL biases the data generation
process towards arbitrary metrics, the GAN component of the reward function
ensures that the model still remembers information learned from data. We build
upon previous results that incorporated GANs and RL in order to generate
sequence data and test this model in several settings for the generation of
molecules encoded as text sequences (SMILES) and in the context of music
generation, showing for each case that we can effectively bias the generation
process towards desired metrics.
Self-Supervised Video Hashing with Hierarchical Binary Auto-encoder
Authors: Jingkuan Song, Hanwang Zhang, Xiangpeng Li, Lianli Gao, Meng Wang, Richang Hong
Date: 2018-02-07 00:00:00
Tasks: Binarization, Decoder, Retrieval, Video Retrieval

Existing video hash functions are built on three isolated stages: frame
pooling, relaxed learning, and binarization, which have not adequately explored
the temporal order of video frames in a joint binary optimization model,
resulting in severe information loss. In this paper, we propose a novel
unsupervised video hashing framework dubbed Self-Supervised Video Hashing
(SSVH), that is able to capture the temporal nature of videos in an end-to-end
learning-to-hash fashion. We specifically address two central problems: 1) how
to design an encoder-decoder architecture to generate binary codes for videos;
and 2) how to equip the binary codes with the ability of accurate video
retrieval. We design a hierarchical binary autoencoder to model the temporal
dependencies in videos with multiple granularities, and embed the videos into
binary codes with less computations than the stacked architecture. Then, we
encourage the binary codes to simultaneously reconstruct the visual content and
neighborhood structure of the videos. Experiments on two real-world datasets
(FCVID and YFCC) show that our SSVH method can significantly outperform the
state-of-the-art methods and achieve the currently best performance on the task
of unsupervised video retrieval.
Bayesian Optimization with Gradients
Authors: Jian Wu, Matthias Poloczek, Andrew Gordon Wilson, Peter I. Frazier
Date: 2017-03-13 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Bayesian Optimization, global-optimization

Bayesian optimization has been successful at global optimization of
expensive-to-evaluate multimodal objective functions. However, unlike most
optimization methods, Bayesian optimization typically does not use derivative
information. In this paper we show how Bayesian optimization can exploit
derivative information to decrease the number of objective function evaluations
required for good performance. In particular, we develop a novel Bayesian
optimization algorithm, the derivative-enabled knowledge-gradient (dKG), for
which we show one-step Bayes-optimality, asymptotic consistency, and greater
one-step value of information than is possible in the derivative-free setting.
Our procedure accommodates noisy and incomplete derivative information, comes
in both sequential and batch forms, and can optionally reduce the computational
cost of inference through automatically selected retention of a single
directional derivative. We also compute the d-KG acquisition function and its
gradient using a novel fast discretization-free technique. We show d-KG
provides state-of-the-art performance compared to a wide range of optimization
procedures with and without gradients, on benchmarks including logistic
regression, deep learning, kernel learning, and k-nearest neighbors.