pwc-archive/papers-with-abstracts huggingface dataset:
Personalized Dialogue Generation with Diversified Traits
Authors: Yinhe Zheng, Guanyi Chen, Minlie Huang, Song Liu, Xuan Zhu
Date: 2019-01-28 00:00:00
Tasks: Dialogue Generation

Endowing a dialogue system with particular personality traits is essential to deliver more human-like conversations. However, due to the challenge of embodying personality via language expression and the lack of large-scale persona-labeled dialogue data, this research problem is still far from well-studied. In this paper, we investigate the problem of incorporating explicit personality traits in dialogue generation to deliver personalized dialogues. To this end, firstly, we construct PersonalDialog, a large-scale multi-turn dialogue dataset containing various traits from a large number of speakers. The dataset consists of 20.83M sessions and 56.25M utterances from 8.47M speakers. Each utterance is associated with a speaker who is marked with traits like Age, Gender, Location, Interest Tags, etc. Several anonymization schemes are designed to protect the privacy of each speaker. This large-scale dataset will facilitate not only the study of personalized dialogue generation, but also other researches on sociolinguistics or social science. Secondly, to study how personality traits can be captured and addressed in dialogue generation, we propose persona-aware dialogue generation models within the sequence to sequence learning framework. Explicit personality traits (structured by key-value pairs) are embedded using a trait fusion module. During the decoding process, two techniques, namely persona-aware attention and persona-aware bias, are devised to capture and address trait-related information. Experiments demonstrate that our model is able to address proper traits in different contexts. Case studies also show interesting results for this challenging research problem.
ErasureHead: Distributed Gradient Descent without Delays Using Approximate Gradient Coding
Authors: Hongyi Wang, Zachary Charles, Dimitris Papailiopoulos
Date: 2019-01-28 00:00:00

We present ErasureHead, a new approach for distributed gradient descent (GD)
that mitigates system delays by employing approximate gradient coding. Gradient
coded distributed GD uses redundancy to exactly recover the gradient at each
iteration from a subset of compute nodes. ErasureHead instead uses approximate
gradient codes to recover an inexact gradient at each iteration, but with
higher delay tolerance. Unlike prior work on gradient coding, we provide a
performance analysis that combines both delay and convergence guarantees. We
establish that down to a small noise floor, ErasureHead converges as quickly as
distributed GD and has faster overall runtime under a probabilistic delay
model. We conduct extensive experiments on real world datasets and distributed
clusters and demonstrate that our method can lead to significant speedups over
both standard and gradient coded GD.
Support Feature Machines
Authors: Tomasz Maszczyk, Włodzisław Duch
Date: 2019-01-28 00:00:00
Tasks: BIG-bench Machine Learning

Support Vector Machines (SVMs) with various kernels have played dominant role
in machine learning for many years, finding numerous applications. Although
they have many attractive features interpretation of their solutions is quite
difficult, the use of a single kernel type may not be appropriate in all areas
of the input space, convergence problems for some kernels are not uncommon, the
standard quadratic programming solution has $O(m^3)$ time and $O(m^2)$ space
complexity for $m$ training patterns. Kernel methods work because they
implicitly provide new, useful features. Such features, derived from various
kernels and other vector transformations, may be used directly in any machine
learning algorithm, facilitating multiresolution, heterogeneous models of data.
Therefore Support Feature Machines (SFM) based on linear models in the extended
feature spaces, enabling control over selection of support features, give at
least as good results as any kernel-based SVMs, removing all problems related
to interpretation, scaling and convergence. This is demonstrated for a number
of benchmark datasets analyzed with linear discrimination, SVM, decision trees
and nearest neighbor methods.
Neural eliminators and classifiers
Authors: Włodzisław Duch, Rafał Adamczak, Yoichi Hayashi
Date: 2019-01-28 00:00:00
Tasks: Classification, General Classification

Classification may not be reliable for several reasons: noise in the data,
insufficient input information, overlapping distributions and sharp definition
of classes. Faced with several possibilities neural network may in such cases
still be useful if instead of a classification elimination of improbable
classes is done. Eliminators may be constructed using classifiers assigning new
cases to a pool of several classes instead of just one winning class.
Elimination may be done with the help of several classifiers using modified
error functions. A real life medical application of neural network is presented
illustrating the usefulness of elimination.
Convolutional Neural Networks with Layer Reuse
Authors: Okan Köpüklü, Maryam Babaee, Stefan Hörmann, Gerhard Rigoll
Date: 2019-01-28 00:00:00
Tasks: image-classification, Image Classification

A convolutional layer in a Convolutional Neural Network (CNN) consists of
many filters which apply convolution operation to the input, capture some
special patterns and pass the result to the next layer. If the same patterns
also occur at the deeper layers of the network, why wouldn't the same
convolutional filters be used also in those layers? In this paper, we propose a
CNN architecture, Layer Reuse Network (LruNet), where the convolutional layers
are used repeatedly without the need of introducing new layers to get a better
performance. This approach introduces several advantages: (i) Considerable
amount of parameters are saved since we are reusing the layers instead of
introducing new layers, (ii) the Memory Access Cost (MAC) can be reduced since
reused layer parameters can be fetched only once, (iii) the number of
nonlinearities increases with layer reuse, and (iv) reused layers get gradient
updates from multiple parts of the network. The proposed approach is evaluated
on CIFAR-10, CIFAR-100 and Fashion-MNIST datasets for image classification
task, and layer reuse improves the performance by 5.14%, 5.85% and 2.29%,
respectively. The source code and pretrained models are publicly available.
A Simple Method to Reduce Off-chip Memory Accesses on Convolutional Neural Networks
Authors: Doyun Kim, Kyoung-Young Kim, Sangsoo Ko, Sanghyuck Ha
Date: 2019-01-28 00:00:00

For convolutional neural networks, a simple algorithm to reduce off-chip
memory accesses is proposed by maximally utilizing on-chip memory in a neural
process unit. Especially, the algorithm provides an effective way to process a
module which consists of multiple branches and a merge layer. For Inception-V3
on Samsung's NPU in Exynos, our evaluation shows that the proposed algorithm
makes off-chip memory accesses reduced by 1/50, and accordingly achieves 97.59
% reduction in the amount of feature-map data to be transferred from/to
off-chip memory.
Hybrid Machine Learning Approach to Popularity Prediction of Newly Released Contents for Online Video Streaming Service
Authors: Hongjun Jeon, Wonchul Seo, Eunjeong Lucy Park, Sungchul Choi
Date: 2019-01-28 00:00:00
Tasks: BIG-bench Machine Learning, Hybrid Machine Learning, Marketing

In the industry of video content providers such as VOD and IPTV, predicting
the popularity of video contents in advance is critical not only from a
marketing perspective but also from a network optimization perspective. By
predicting whether the content will be successful or not in advance, the
content file, which is large, is efficiently deployed in the proper service
providing server, leading to network cost optimization. Many previous studies
have done view count prediction research to do this. However, the studies have
been making predictions based on historical view count data from users. In this
case, the contents had been published to the users and already deployed on a
service server. These approaches make possible to efficiently deploy a content
already published but are impossible to use for a content that is not be
published. To address the problems, this research proposes a hybrid machine
learning approach to the classification model for the popularity prediction of
newly video contents which is not published. In this paper, we create a new
variable based on the related content of the specific content and divide entire
dataset by the characteristics of the contents. Next, the prediction is
performed using XGBoosting and deep neural net based model according to the
data characteristics of the cluster. Our model uses metadata for contents for
prediction, so we use categorical embedding techniques to solve the sparsity of
categorical variables and make them learn efficiently for the deep neural net
model. As well, we use the FTRL-proximal algorithm to solve the problem of the
view-count volatility of video content. We achieve overall better performance
than the previous standalone method with a dataset from one of the top
streaming service company.
Fast Hierarchical Depth Map Computation from Stereo
Authors: Vinay Kaushik, Brejesh lall
Date: 2019-01-28 00:00:00

Disparity by Block Matching stereo is usually used in applications with
limited computational power in order to get depth estimates. However, the
research on simple stereo methods has been lesser than the energy based
counterparts which promise a better quality depth map with more potential for
future improvements. Semi-global-matching (SGM) methods offer good performance
and easy implementation but suffer from the problem of very high memory
footprint because it's working on the full disparity space image. On the other
hand, Block matching stereo needs much less memory. In this paper, we introduce
a novel multi-scale-hierarchical block-matching approach using a pyramidal
variant of depth and cost functions which drastically improves the results of
standard block matching stereo techniques while preserving the low memory
footprint and further reducing the complexity of standard block matching. We
tested our new multi block matching scheme on the Middlebury stereo benchmark.
For the Middlebury benchmark we get results that are only slightly worse than
state of the art SGM implementations.
TuckER: Tensor Factorization for Knowledge Graph Completion
Authors: Ivana Balažević, Carl Allen, Timothy M. Hospedales
Date: 2019-01-28 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Knowledge Graph Completion, Knowledge Graphs, Link Prediction, Prediction

Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferring missing facts based on existing ones. We propose TuckER, a relatively straightforward but powerful linear model based on Tucker decomposition of the binary tensor representation of knowledge graph triples. TuckER outperforms previous state-of-the-art models across standard link prediction datasets, acting as a strong baseline for more elaborate models. We show that TuckER is a fully expressive model, derive sufficient bounds on its embedding dimensionalities and demonstrate that several previously introduced linear models can be viewed as special cases of TuckER.
ML for Flood Forecasting at Scale
Authors: Sella Nevo, Vova Anisimov, Gal Elidan, Ran El-Yaniv, Pete Giencke, Yotam Gigi, Avinatan Hassidim, Zach Moshe, Mor Schlesinger, Guy Shalev, Ajai Tirumali, Ami Wiesel, Oleg Zlydenko, Yossi Matias
Date: 2019-01-28 00:00:00

Effective riverine flood forecasting at scale is hindered by a multitude of
factors, most notably the need to rely on human calibration in current
methodology, the limited amount of data for a specific location, and the
computational difficulty of building continent/global level models that are
sufficiently accurate. Machine learning (ML) is primed to be useful in this
scenario: learned models often surpass human experts in complex
high-dimensional scenarios, and the framework of transfer or multitask learning
is an appealing solution for leveraging local signals to achieve improved
global performance. We propose to build on these strengths and develop ML
systems for timely and accurate riverine flood prediction.
Enhancing Quality for VVC Compressed Videos by Jointly Exploiting Spatial Details and Temporal Structure
Authors: Xiandong Meng, Xuan Deng, Shuyuan Zhu, Bing Zeng
Date: 2019-01-28 00:00:00
Tasks: Video Compression

In this paper, we propose a quality enhancement network of versatile video coding (VVC) compressed videos by jointly exploiting spatial details and temporal structure (SDTS). The proposed network consists of a temporal structure fusion subnet and a spatial detail enhancement subnet. The former subnet is used to estimate and compensate the temporal motion across frames, and the latter subnet is used to reduce the compression artifacts and enhance the reconstruction quality of compressed video. Experimental results demonstrate the effectiveness of our SDTS-based method.
From-Below Boolean Matrix Factorization Algorithm Based on MDL
Authors: Tatiana Makhalova, Martin Trnecka
Date: 2019-01-28 00:00:00

During the past few years Boolean matrix factorization (BMF) has become an
important direction in data analysis. The minimum description length principle
(MDL) was successfully adapted in BMF for the model order selection.
Nevertheless, a BMF algorithm performing good results from the standpoint of
standard measures in BMF is missing. In this paper, we propose a novel
from-below Boolean matrix factorization algorithm based on formal concept
analysis. The algorithm utilizes the MDL principle as a criterion for the
factor selection. On various experiments we show that the proposed algorithm
outperforms---from different standpoints---existing state-of-the-art BMF
algorithms.
Physical-Layer Supervised Learning Assisted by an Entangled Sensor Network
Authors: Quntao Zhuang, Zheshen Zhang
Date: 2019-01-28 00:00:00
Tasks: Data Compression

Many existing quantum supervised learning (SL) schemes consider data given a priori in a classical description. With only noisy intermediate-scale quantum (NISQ) devices available in the near future, their quantum speedup awaits the development of quantum random access memories (qRAMs) and fault-tolerant quantum computing. There, however, also exist a multitude of SL tasks whose data are acquired by sensors, e.g., pattern classification based on data produced by imaging sensors. Solving such SL tasks naturally requires an integrated approach harnessing tools from both quantum sensing and quantum computing. We introduce supervised learning assisted by an entangled sensor network (SLAEN) as a means to carry out SL tasks at the physical layer. The entanglement shared by the sensors in SLAEN boosts the performance of extracting global features of the object under investigation. We leverage SLAEN to construct an entanglement-assisted support-vector machine for data classification and entanglement-assisted principal component analyzer for data compression. In both schemes, variational circuits are employed to seek the optimum entangled probe states and measurement settings to maximize the entanglement-enabled {enhancement}. We observe that SLAEN enjoys an appreciable entanglement-enabled performance gain, even in the presence of loss, over conventional strategies in which classical data are acquired by separable sensors and subsequently processed by classical SL algorithms. SLAEN is realizable with available technology, opening a viable route toward building NISQ devices that offer unmatched performance beyond what the optimum classical device is able to afford.
Fairness in representation: quantifying stereotyping as a representational harm
Authors: Mohsen Abbasi, Sorelle A. Friedler, Carlos Scheidegger, Suresh Venkatasubramanian
Date: 2019-01-28 00:00:00
Tasks: BIG-bench Machine Learning, Fairness

While harms of allocation have been increasingly studied as part of the
subfield of algorithmic fairness, harms of representation have received
considerably less attention. In this paper, we formalize two notions of
stereotyping and show how they manifest in later allocative harms within the
machine learning pipeline. We also propose mitigation strategies and
demonstrate their effectiveness on synthetic datasets.
Out-of-Sample Testing for GANs
Authors: Pablo Sánchez-Martín, Pablo M. Olmos, Fernando Pérez-Cruz
Date: 2019-01-28 00:00:00

We propose a new method to evaluate GANs, namely EvalGAN. EvalGAN relies on a
test set to directly measure the reconstruction quality in the original sample
space (no auxiliary networks are necessary), and it also computes the
(log)likelihood for the reconstructed samples in the test set. Further, EvalGAN
is agnostic to the GAN algorithm and the dataset. We decided to test it on
three state-of-the-art GANs over the well-known CIFAR-10 and CelebA datasets.
Improving Myocardium Segmentation in Cardiac CT Angiography using Spectral Information
Authors: Steffen Bruns, Jelmer M. Wolterink, Robbert W. van Hamersvelt, Majd Zreik, Tim Leiner, Ivana Išgum
Date: 2018-09-27 00:00:00
Tasks: Data Augmentation, Myocardium Segmentation, Segmentation

Accurate segmentation of the left ventricle myocardium in cardiac CT
angiography (CCTA) is essential for e.g. the assessment of myocardial
perfusion. Automatic deep learning methods for segmentation in CCTA might
suffer from differences in contrast-agent attenuation between training and test
data due to non-standardized contrast administration protocols and varying
cardiac output. We propose augmentation of the training data with virtual
mono-energetic reconstructions from a spectral CT scanner which show different
attenuation levels of the contrast agent. We compare this to an augmentation by
linear scaling of all intensity values, and combine both types of augmentation.
We train a 3D fully convolutional network (FCN) with 10 conventional CCTA
images and corresponding virtual mono-energetic reconstructions acquired on a
spectral CT scanner, and evaluate on 40 CCTA scans acquired on a conventional
CT scanner. We show that training with data augmentation using virtual
mono-energetic images improves upon training with only conventional images
(Dice similarity coefficient (DSC) 0.895 $\pm$ 0.039 vs. 0.846 $\pm$ 0.125). In
comparison, training with data augmentation using linear scaling improves the
DSC to 0.890 $\pm$ 0.039. Moreover, combining the results of both augmentation
methods leads to a DSC of 0.901 $\pm$ 0.036, showing that both augmentations
lead to different local improvements of the segmentations. Our results indicate
that virtual mono-energetic images improve the generalization of an FCN used
for myocardium segmentation in CCTA images.
CURE: Curvature Regularization For Missing Data Recovery
Authors: Bin Dong, Haocheng Ju, Yiping Lu, Zuoqiang Shi
Date: 2019-01-28 00:00:00
Tasks: Image Inpainting

Missing data recovery is an important and yet challenging problem in imaging and data science. Successful models often adopt certain carefully chosen regularization. Recently, the low dimension manifold model (LDMM) was introduced by S.Osher et al. and shown effective in image inpainting. They observed that enforcing low dimensionality on image patch manifold serves as a good image regularizer. In this paper, we observe that having only the low dimension manifold regularization is not enough sometimes, and we need smoothness as well. For that, we introduce a new regularization by combining the low dimension manifold regularization with a higher order Curvature Regularization, and we call this new regularization CURE for short. The key step of solving CURE is to solve a biharmonic equation on a manifold. We further introduce a weighted version of CURE, called WeCURE, in a similar manner as the weighted nonlocal Laplacian (WNLL) method. Numerical experiments for image inpainting and semi-supervised learning show that the proposed CURE and WeCURE significantly outperform LDMM and WNLL respectively.
Interpretable Complex-Valued Neural Networks for Privacy Protection
Authors: Liyao Xiang, Haotian Ma, Hao Zhang, Yifan Zhang, Jie Ren, Quanshi Zhang
Date: 2019-01-28 00:00:00
Proceeding: ICLR 2020 1

Previous studies have found that an adversary attacker can often infer unintended input information from intermediate-layer features. We study the possibility of preventing such adversarial inference, yet without too much accuracy degradation. We propose a generic method to revise the neural network to boost the challenge of inferring input attributes from features, while maintaining highly accurate outputs. In particular, the method transforms real-valued features into complex-valued ones, in which the input is hidden in a randomized phase of the transformed features. The knowledge of the phase acts like a key, with which any party can easily recover the output from the processing result, but without which the party can neither recover the output nor distinguish the original input. Preliminary experiments on various datasets and network structures have shown that our method significantly diminishes the adversary's ability in inferring about the input while largely preserves the resulting accuracy.
On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis
Authors: Kohei Hayashi, Masaaki Imaizumi, Yuichi Yoshida
Date: 2019-01-28 00:00:00
Tasks: regression

In this paper, we study random subsampling of Gaussian process regression,
one of the simplest approximation baselines, from a theoretical perspective.
Although subsampling discards a large part of training data, we show provable
guarantees on the accuracy of the predictive mean/variance and its
generalization ability. For analysis, we consider embedding kernel matrices
into graphons, which encapsulate the difference of the sample size and enables
us to evaluate the approximation and generalization errors in a unified manner.
The experimental results show that the subsampling approximation achieves a
better trade-off regarding accuracy and runtime than the Nystr\"{o}m and random
Fourier expansion methods.
Target Tracking for Contextual Bandits: Application to Demand Side Management
Authors: Margaux Brégère, Pierre Gaillard, Yannig Goude, Gilles Stoltz
Date: 2019-01-28 00:00:00
Tasks: Management, Multi-Armed Bandits

We propose a contextual-bandit approach for demand side management by offering price incentives. More precisely, a target mean consumption is set at each round and the mean consumption is modeled as a complex function of the distribution of prices sent and of some contextual variables such as the temperature, weather, and so on. The performance of our strategies is measured in quadratic losses through a regret criterion. We offer $T^{2/3}$ upper bounds on this regret (up to poly-logarithmic terms)---and even faster rates under stronger assumptions---for strategies inspired by standard strategies for contextual bandits (like LinUCB, see Li et al., 2010). Simulations on a real data set gathered by UK Power Networks, in which price incentives were offered, show that our strategies are effective and may indeed manage demand response by suitably picking the price levels.
Secure multi-party linear regression at plaintext speed
Authors: Jonathan M. Bloom
Date: 2019-01-28 00:00:00
Tasks: feature selection, regression

We detail distributed algorithms for scalable, secure multiparty linear regression and feature selection at essentially the same speed as plaintext regression. While the core geometric ideas are simple, the recognition of their broad utility when combined is novel. Our scheme opens the door to efficient and secure genome-wide association studies across multiple biobanks.