pwc-archive/papers-with-abstracts huggingface dataset:
Structural Deep Network Embedding
Authors: Daixin Wang1, Peng Cui1, Wenwu Zhu1
Date: 2016-06-01 00:00:00
Proceeding: KDD 2016 6
Tasks: Graph Classification, Link Prediction, Network Embedding

Networkembeddingisanimportantmethodtolearnlow-dimensional representations of vertexes in networks, aiming to capture and preserve the network structure. Almost all the existing network embeddingmethodsadoptshallowmodels. However,sincetheunderlyingnetworkstructureiscomplex, shallowmodelscannotcapture the highly non-linear network structure, resulting in sub-optimal network representations. Therefore, how to ﬁnd a method that is able to effectively capture the highly non-linear network structure and preserve the global and local structure is an open yet important problem. To solve this problem, in this paper we propose a StructuralDeepNetworkEmbeddingmethod,namelySDNE.More speciﬁcally, we ﬁrst propose a semi-supervised deep model, which has multiple layers of non-linear functions, thereby being able to capture the highly non-linear network structure. Then we propose to exploit the ﬁrst-order and second-order proximity jointly to preserve the network structure. The second-order proximity is used bytheunsupervisedcomponenttocapturetheglobalnetworkstructure. Whiletheﬁrst-orderproximityisusedasthesupervisedinformation in the supervised component to preserve the local network structure. By jointly optimizing them in the semi-supervised deep model, our method can preserve both the local and global network structureandisrobusttosparsenetworks. Empirically,weconduct the experiments on ﬁve real-world networks, including a language network, a citation network and three social networks. The results show that compared to the baselines, our method can reconstruct the original network signiﬁcantly better and achieves substantial gains in three applications, i.e. multi-label classiﬁcation, link prediction and visualization.
How do infinite width bounded norm networks look in function space?
Authors: Pedro Savarese, Itay Evron, Daniel Soudry, Nathan Srebro
Date: 2019-02-13 00:00:00

We consider the question of what functions can be captured by ReLU networks
with an unbounded number of units (infinite width), but where the overall
network Euclidean norm (sum of squares of all weights in the system, except for
an unregularized bias term for each unit) is bounded; or equivalently what is
the minimal norm required to approximate a given function. For functions $f :
\mathbb R \rightarrow \mathbb R$ and a single hidden layer, we show that the
minimal network norm for representing $f$ is $\max(\int |f''(x)| dx,
|f'(-\infty) + f'(+\infty)|)$, and hence the minimal norm fit for a sample is
given by a linear spline interpolation.
An Optimized Recurrent Unit for Ultra-Low-Power Keyword Spotting
Authors: Justice Amoh, Kofi Odame
Date: 2019-02-13 00:00:00
Tasks: Event Detection, Keyword Spotting

There is growing interest in being able to run neural networks on sensors,
wearables and internet-of-things (IoT) devices. However, the computational
demands of neural networks make them difficult to deploy on
resource-constrained edge devices.
  To meet this need, our work introduces a new recurrent unit architecture that
is specifically adapted for on-device low power acoustic event detection (AED).
The proposed architecture is based on the gated recurrent unit (`GRU') but
features optimizations that make it implementable on ultra-low power
micro-controllers such as the Arm Cortex M0+.
  Our new architecture, the Embedded Gated Recurrent Unit (eGRU) is
demonstrated to be highly efficient and suitable for short-duration AED and
keyword spotting tasks. A single eGRU cell is 60x faster and 10x smaller than a
GRU cell. Despite its optimizations, eGRU compares well with GRU across tasks
of varying complexities.
  The practicality of eGRU is investigated in a wearable acoustic event
detection application. An eGRU model is implemented and tested on the Arm
Cortex M0-based Atmel ATSAMD21E18 processor. The Arm M0+ implementation of the
eGRU model compares favorably with a full precision GRU that is running on a
workstation. The embedded eGRU model achieves a classification accuracy 95.3%,
which is only 2% less than the full precision GRU.
Improved Definition of NonStandard Neutrosophic Logic and Introduction to Neutrosophic Hyperreals
Authors: Florentin Smarandache
Date: 2018-11-24 00:00:00

O the third version of this response-paper to Imamura criticism, we recall that NonStandard Neutrosophic Logic was never used by neutrosophic community in no application, that the quarter of century old neutrosophic operators (1995) criticized by Imamura were never utilized since they were improved shortly after but he omits to tell their development, and that in real world applications we need to convert/approximate the NonStandard Analysis hyperreals, monads and binads to tiny intervals with the desired accuracy, otherwise they would be inapplicable. We point out several errors and false statements by Imamura with respect to the inf/sup of nonstandard subsets, also Imamura 'rigorous definition of neutrosophic logic' is wrong and the same for his definition of nonstandard unit interval, and we prove that there is not a total order on the set of hyperreals (because of the newly introduced Neutrosophic Hyperreals that are indeterminate), whence the transfer principle is questionable.
Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Network
Authors: Shengyu Zhao, Tingfung Lau, Ji Luo, Eric I-Chao Chang, Yan Xu
Date: 2019-02-13 00:00:00
Tasks: GPU, Image Registration, Medical Image Registration

3D medical image registration is of great clinical importance. However, supervised learning methods require a large amount of accurately annotated corresponding control points (or morphing), which are very difficult to obtain. Unsupervised learning methods ease the burden of manual annotation by exploiting unlabeled data without supervision. In this paper, we propose a new unsupervised learning method using convolutional neural networks under an end-to-end framework, Volume Tweening Network (VTN), for 3D medical image registration. We propose three innovative technical components: (1) An end-to-end cascading scheme that resolves large displacement; (2) An efficient integration of affine registration network; and (3) An additional invertibility loss that encourages backward consistency. Experiments demonstrate that our algorithm is 880x faster (or 3.3x faster without GPU acceleration) than traditional optimization-based methods and achieves state-of-theart performance in medical image registration.
Differentially Private Learning of Geometric Concepts
Authors: Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer
Date: 2019-02-13 00:00:00
Tasks: PAC learning

We present differentially private efficient algorithms for learning union of
polygons in the plane (which are not necessarily convex). Our algorithms
achieve $(\alpha,\beta)$-PAC learning and $(\epsilon,\delta)$-differential
privacy using a sample of size $\tilde{O}\left(\frac{1}{\alpha\epsilon}k\log
d\right)$, where the domain is $[d]\times[d]$ and $k$ is the number of edges in
the union of polygons.
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning
Authors: Qianwen Wang, Yao Ming, Zhihua Jin, Qiaomu Shen, Dongyu Liu, Micah J. Smith, Kalyan Veeramachaneni, Huamin Qu
Date: 2019-02-13 00:00:00
Tasks: AutoML, BIG-bench Machine Learning

To relieve the pain of manually selecting machine learning algorithms and
tuning hyperparameters, automated machine learning (AutoML) methods have been
developed to automatically search for good models. Due to the huge model search
space, it is impossible to try all models. Users tend to distrust automatic
results and increase the search budget as much as they can, thereby undermining
the efficiency of AutoML. To address these issues, we design and implement
ATMSeer, an interactive visualization tool that supports users in refining the
search space of AutoML and analyzing the results. To guide the design of
ATMSeer, we derive a workflow of using AutoML based on interviews with machine
learning experts. A multi-granularity visualization is proposed to enable users
to monitor the AutoML process, analyze the searched models, and refine the
search space in real time. We demonstrate the utility and usability of ATMSeer
through two case studies, expert interviews, and a user study with 13 end
users.
Wasserstein Barycenter Model Ensembling
Authors: Pierre Dognin, Igor Melnyk, Youssef Mroueh, Jerret Ross, Cicero dos Santos, Tom Sercu
Date: 2019-02-13 00:00:00
Tasks: Attribute, General Classification, Image Captioning, model, Word Embeddings

In this paper we propose to perform model ensembling in a multiclass or a
multilabel learning setting using Wasserstein (W.) barycenters. Optimal
transport metrics, such as the Wasserstein distance, allow incorporating
semantic side information such as word embeddings. Using W. barycenters to find
the consensus between models allows us to balance confidence and semantics in
finding the agreement between the models. We show applications of Wasserstein
ensembling in attribute-based classification, multilabel learning and image
captioning generation. These results show that the W. ensembling is a viable
alternative to the basic geometric or arithmetic mean ensembling.
Gated2Depth: Real-time Dense Lidar from Gated Images
Authors: Tobias Gruber, Frank Julca-Aguilar, Mario Bijelic, Werner Ritter, Klaus Dietmayer, Felix Heide
Date: 2019-02-13 00:00:00
Proceeding: ICCV 2019 10
Tasks: Scene Understanding

We present an imaging framework which converts three images from a gated camera into high-resolution depth maps with depth accuracy comparable to pulsed lidar measurements. Existing scanning lidar systems achieve low spatial resolution at large ranges due to mechanically-limited angular sampling rates, restricting scene understanding tasks to close-range clusters with dense sampling. Moreover, today's pulsed lidar scanners suffer from high cost, power consumption, large form-factors, and they fail in the presence of strong backscatter. We depart from point scanning and demonstrate that it is possible to turn a low-cost CMOS gated imager into a dense depth camera with at least 80m range - by learning depth from three gated images. The proposed architecture exploits semantic context across gated slices, and is trained on a synthetic discriminator loss without the need of dense depth labels. The proposed replacement for scanning lidar systems is real-time, handles back-scatter and provides dense depth at long ranges. We validate our approach in simulation and on real-world data acquired over 4,000km driving in northern Europe. Data and code are available at https://github.com/gruberto/Gated2Depth.
Explainable Text-Driven Neural Network for Stock Prediction
Authors: Linyi Yang, Zheng Zhang, Su Xiong, Lirui Wei, James Ng, Lina Xu, Ruihai Dong
Date: 2019-02-13 00:00:00
Tasks: Prediction, Stock Prediction, Stock Price Prediction

It has been shown that financial news leads to the fluctuation of stock
prices. However, previous work on news-driven financial market prediction
focused only on predicting stock price movement without providing an
explanation. In this paper, we propose a dual-layer attention-based neural
network to address this issue. In the initial stage, we introduce a
knowledge-based method to adaptively extract relevant financial news. Then, we
use input attention to pay more attention to the more influential news and
concatenate the day embeddings with the output of the news representation.
Finally, we use an output attention mechanism to allocate different weights to
different days in terms of their contribution to stock price movement. Thorough
empirical studies based upon historical prices of several individual stocks
demonstrate the superiority of our proposed method in stock price prediction
compared to state-of-the-art methods.
Learning to see across Domains and Modalities
Authors: Fabio Maria Carlucci
Date: 2019-02-13 00:00:00
Tasks: Domain Adaptation, image-classification, Image Classification, Object Recognition, Transfer Learning, Unsupervised Domain Adaptation

Deep learning has raised hopes and expectations as a general solution for
many applications; indeed it has proven effective, but it also showed a strong
dependence on large quantities of data. Luckily, it has been shown that, even
when data is scarce, a successful model can be trained by reusing prior
knowledge. Thus, developing techniques for transfer learning, in its broadest
definition, is a crucial element towards the deployment of effective and
accurate intelligent systems. This thesis will focus on a family of transfer
learning methods applied to the task of visual object recognition, specifically
image classification. Transfer learning is a general term, and specific
settings have been given specific names: when the learner has only access to
unlabeled data from the a target domain and labeled data from a different
domain (the source), the problem is known as that of "unsupervised domain
adaptation" (DA). The first part of this work will focus on three methods for
this setting: one of these methods deals with features, one with images while
the third one uses both. The second part will focus on the real life issues of
robotic perception, specifically RGB-D recognition. Robotic platforms are
usually not limited to color perception; very often they also carry a Depth
camera. Unfortunately, the depth modality is rarely used for visual recognition
due to the lack of pretrained models from which to transfer and little data to
train one on from scratch. Two methods for dealing with this scenario will be
presented: one using synthetic data and the other exploiting cross-modality
transfer learning.
3D Robot Pose Estimation from 2D Images
Authors: Christoph Heindl, Sebastian Zambal, Thomas Ponitz, Andreas Pichler, Josef Scharinger
Date: 2019-02-13 00:00:00
Tasks: Pose Estimation, Robot Pose Estimation

This paper considers the task of locating articulated poses of multiple
robots in images. Our approach simultaneously infers the number of robots in a
scene, identifies joint locations and estimates sparse depth maps around joint
locations. The proposed method applies staged convolutional feature detectors
to 2D image inputs and computes robot instance masks using a recurrent network
architecture. In addition, regression maps of most likely joint locations in
pixel coordinates together with depth information are computed. Compositing 3D
robot joint kinematics is accomplished by applying masks to joint readout maps.
Our end-to-end formulation is in contrast to previous work in which the
composition of robot joints into kinematics is performed in a separate
post-processing step. Despite the fact that our models are trained on
artificial data, we demonstrate generalizability to real world images.
Stable-Predictive Optimistic Counterfactual Regret Minimization
Authors: Gabriele Farina, Christian Kroer, Noam Brown, Tuomas Sandholm
Date: 2019-02-13 00:00:00
Tasks: counterfactual

The CFR framework has been a powerful tool for solving large-scale
extensive-form games in practice. However, the theoretical rate at which past
CFR-based algorithms converge to the Nash equilibrium is on the order of
$O(T^{-1/2})$, where $T$ is the number of iterations. In contrast, first-order
methods can be used to achieve a $O(T^{-1})$ dependence on iterations, yet
these methods have been less successful in practice. In this work we present
the first CFR variant that breaks the square-root dependence on iterations. By
combining and extending recent advances on predictive and stable regret
minimizers for the matrix-game setting we show that it is possible to leverage
"optimistic" regret minimizers to achieve a $O(T^{-3/4})$ convergence rate
within CFR. This is achieved by introducing a new notion of
stable-predictivity, and by setting the stability of each counterfactual regret
minimizer relative to its location in the decision tree. Experiments show that
this method is faster than the original CFR algorithm, although not as fast as
newer variants, in spite of their worst-case $O(T^{-1/2})$ dependence on
iterations.
Deep Divergence-Based Approach to Clustering
Authors: Michael Kampffmeyer, Sigurd Løkse, Filippo M. Bianchi, Lorenzo Livi, Arnt-Børre Salberg, Robert Jenssen
Date: 2019-02-13 00:00:00
Tasks: Clustering, Deep Clustering, Open-Ended Question Answering

A promising direction in deep learning research consists in learning
representations and simultaneously discovering cluster structure in unlabeled
data by optimizing a discriminative loss function. As opposed to supervised
deep learning, this line of research is in its infancy, and how to design and
optimize suitable loss functions to train deep neural networks for clustering
is still an open question. Our contribution to this emerging field is a new
deep clustering network that leverages the discriminative power of
information-theoretic divergence measures, which have been shown to be
effective in traditional clustering. We propose a novel loss function that
incorporates geometric regularization constraints, thus avoiding degenerate
structures of the resulting clustering partition. Experiments on synthetic
benchmarks and real datasets show that the proposed network achieves
competitive performance with respect to other state-of-the-art methods, scales
well to large datasets, and does not require pre-training steps.
Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection
Authors: Duong Nguyen, Oliver S. Kirsebom, Fábio Frazão, Ronan Fablet, Stan Matwin
Date: 2019-02-13 00:00:00
Tasks: Acoustic Novelty Detection, Feature Engineering, Novelty Detection

In this paper, we adapt Recurrent Neural Networks with Stochastic Layers,
which are the state-of-the-art for generating text, music and speech, to the
problem of acoustic novelty detection. By integrating uncertainty into the
hidden states, this type of network is able to learn the distribution of
complex sequences. Because the learned distribution can be calculated
explicitly in terms of probability, we can evaluate how likely an observation
is then detect low-probability events as novel. The model is robust, highly
unsupervised, end-to-end and requires minimum preprocessing, feature
engineering or hyperparameter tuning. An experiment on a benchmark dataset
shows that our model outperforms the state-of-the-art acoustic novelty
detectors.
Provable Low Rank Phase Retrieval
Authors: Seyedehsara Nayer, Praneeth Narayanamurthy, Namrata Vaswani
Date: 2019-02-13 00:00:00
Tasks: Matrix Completion, Retrieval

We study the Low Rank Phase Retrieval (LRPR) problem defined as follows: recover an $n \times q$ matrix $X^*$ of rank $r$ from a different and independent set of $m$ phaseless (magnitude-only) linear projections of each of its columns. To be precise, we need to recover $X^*$ from $y_k := |A_k{}' x^*_k|, k=1,2,\dots, q$ when the measurement matrices $A_k$ are mutually independent. Here $y_k$ is an $m$ length vector, $A_k$ is an $n \times m$ matrix, and $'$ denotes matrix transpose. The question is when can we solve LRPR with $m \ll n$? A reliable solution can enable fast and low-cost phaseless dynamic imaging, e.g., Fourier ptychographic imaging of live biological specimens. In this work, we develop the first provably correct approach for solving this LRPR problem. Our proposed algorithm, Alternating Minimization for Low-Rank Phase Retrieval (AltMinLowRaP), is an AltMin based solution and hence is also provably fast (converges geometrically). Our guarantee shows that AltMinLowRaP solves LRPR to $\epsilon$ accuracy, with high probability, as long as $m q \ge C n r^4 \log(1/\epsilon)$, the matrices $A_k$ contain i.i.d. standard Gaussian entries, and the right singular vectors of $X^*$ satisfy the incoherence assumption from matrix completion literature. Here $C$ is a numerical constant that only depends on the condition number of $X^*$ and on its incoherence parameter. Its time complexity is only $ C mq nr \log^2(1/\epsilon)$. Since even the linear (with phase) version of the above problem is not fully solved, the above result is also the first complete solution and guarantee for the linear case. Finally, we also develop a simple extension of our results for the dynamic LRPR setting.
Can We Automate Diagrammatic Reasoning?
Authors: Sk. Arif Ahmed, Debi Prosad Dogra, Samarjit Kar, Partha Pratim Roy, Dilip K. Prasad
Date: 2019-02-13 00:00:00
Tasks: Visual Reasoning

Learning to solve diagrammatic reasoning (DR) can be a challenging but
interesting problem to the computer vision research community. It is believed
that next generation pattern recognition applications should be able to
simulate human brain to understand and analyze reasoning of images. However,
due to the lack of benchmarks of diagrammatic reasoning, the present research
primarily focuses on visual reasoning that can be applied to real-world
objects. In this paper, we present a diagrammatic reasoning dataset that
provides a large variety of DR problems. In addition, we also propose a
Knowledge-based Long Short Term Memory (KLSTM) to solve diagrammatic reasoning
problems. Our proposed analysis is arguably the first work in this research
area. Several state-of-the-art learning frameworks have been used to compare
with the proposed KLSTM framework in the present context. Preliminary results
indicate that the domain is highly related to computer vision and pattern
recognition research with several challenging avenues.
Risk Prediction of Peer-to-Peer Lending Market by a LSTM Model with Macroeconomic Factor
Authors: Yan Wang, Xuelei Sherry Ni
Date: 2019-02-13 00:00:00
Tasks: Time Series Analysis

In the peer to peer (P2P) lending platform, investors hope to maximize their return while minimizing the risk through a comprehensive understanding of the P2P market. A low and stable average default rate across all the borrowers denotes a healthy P2P market and provides investors more confidence in a promising investment. Therefore, having a powerful model to describe the trend of the default rate in the P2P market is crucial. Different from previous studies that focus on modeling the default rate at the individual level, in this paper, we are the first to comprehensively explore the monthly trend of the default rate at the aggregative level for the P2P data from October 2007 to January 2016 in the US. We use the long short term memory (LSTM) approach to sequentially predict the default risk of the borrowers in Lending Club, which is the largest P2P lending platform in the US. Although being first applied in modeling the P2P sequential data, the LSTM approach shows its great potential by outperforming traditionally utilized time series models in our experiments. Furthermore, incorporating the macroeconomic feature \textit{unemp\_rate} (i.e., unemployment rate) can improve the LSTM performance by decreasing RMSE on both the training and the testing datasets. Our study can broaden the applications of the LSTM algorithm by using it on the sequential P2P data and guide the investors in making investment strategies.