pwc-archive/papers-with-abstracts huggingface dataset:
Feature-based factorized Bilinear Similarity Model for Cold-Start Top-n Item Recommendation
Authors: Mohit Sharma, Jiayu Zhou, Junling Hu, George Karypis
Date: 2019-04-22 00:00:00

Recommending new items to existing users has remained a challenging problem
due to absence of user's past preferences for these items. The user
personalized non-collaborative methods based on item features can be used to
address this item cold-start problem. These methods rely on similarities
between the target item and user's previous preferred items. While computing
similarities based on item features, these methods overlook the interactions
among the features of the items and consider them independently. Modeling
interactions among features can be helpful as some features, when considered
together, provide a stronger signal on the relevance of an item when compared
to case where features are considered independently. To address this important
issue, in this work we introduce the Feature-based factorized Bilinear
Similarity Model (FBSM), which learns factorized bilinear similarity model for
TOP-n recommendation of new items, given the information about items preferred
by users in past as well as the features of these items. We carry out extensive
empirical evaluations on benchmark datasets, and we find that the proposed FBSM
approach improves upon traditional non-collaborative methods in terms of
recommendation performance. Moreover, the proposed approach also learns
insightful interactions among item features from data, which lead to deep
understanding on how these interactions contribute to personalized
recommendation.
Adaptive Matrix Completion for the Users and the Items in Tail
Authors: Mohit Sharma, George Karypis
Date: 2019-04-22 00:00:00
Tasks: Collaborative Filtering, Low-Rank Matrix Completion, Matrix Completion, Recommendation Systems

Recommender systems are widely used to recommend the most appealing items to users. These recommendations can be generated by applying collaborative filtering methods. The low-rank matrix completion method is the state-of-the-art collaborative filtering method. In this work, we show that the skewed distribution of ratings in the user-item rating matrix of real-world datasets affects the accuracy of matrix-completion-based approaches. Also, we show that the number of ratings that an item or a user has positively correlates with the ability of low-rank matrix-completion-based approaches to predict the ratings for the item or the user accurately. Furthermore, we use these insights to develop four matrix completion-based approaches, i.e., Frequency Adaptive Rating Prediction (FARP), Truncated Matrix Factorization (TMF), Truncated Matrix Factorization with Dropout (TMF + Dropout) and Inverse Frequency Weighted Matrix Factorization (IFWMF), that outperforms traditional matrix-completion-based approaches for the users and the items with few ratings in the user-item rating matrix.
AutoEncoders for Training Compact Deep Learning RF Classifiers for Wireless Protocols
Authors: Silvija Kokalj-Filipovic, Rob Miller, Joshua Morman
Date: 2019-04-13 00:00:00
Tasks: Denoising

We show that compact fully connected (FC) deep learning networks trained to
classify wireless protocols using a hierarchy of multiple denoising
autoencoders (AEs) outperform reference FC networks trained in a typical way,
i.e., with a stochastic gradient based optimization of a given FC architecture.
Not only is the complexity of such FC network, measured in number of trainable
parameters and scalar multiplications, much lower than the reference FC and
residual models, its accuracy also outperforms both models for nearly all
tested SNR values (0 dB to 50dB). Such AE-trained networks are suited for
in-situ protocol inference performed by simple mobile devices based on noisy
signal measurements. Training is based on the data transmitted by real devices,
and collected in a controlled environment, and systematically augmented by a
policy-based data synthesis process by adding to the signal any subset of
impairments commonly seen in a wireless receiver.
Smart Laptop Bag with Machine Learning for Activity Recognition
Authors: Dwij Sukeshkumar Sheth, Shantanu Singh, Prakhar S Mathur, Vydeki D
Date: 2019-04-14 00:00:00
Tasks: Activity Recognition, BIG-bench Machine Learning, Cloud Computing

In todays world of smart living, the smart laptop bag, presented in this paper, provides a better solution to keep track of our precious possessions and monitoring them in real time. As the world moves towards a much tech-savvy direction, the novel laptop bag discussed here facilitates the user to perform location tracking, ambiance monitoring, user-state monitoring etc. in one device. The innovative design uses cloud computing and machine learning algorithms to monitor the health of the user and many parameters of the bag. The emergency alert system in this bag could be trained to send appropriate notifications to emergency contacts of the user, in case of abnormal health conditions or theft of the bag. The experimental smart laptop bag uses deep neural network, which was trained and tested over the various parameters from the bag and produces above 95% accurate results.
Temporal Unet: Sample Level Human Action Recognition using WiFi
Authors: Fei Wang, Yunpeng Song, Jimuyang Zhang, Jinsong Han, Dong Huang
Date: 2019-04-19 00:00:00
Tasks: Action Localization, Action Recognition, Action Segmentation, Semantic Segmentation, Sign Language Recognition, Temporal Action Localization

Human doing actions will result in WiFi distortion, which is widely explored
for action recognition, such as the elderly fallen detection, hand sign
language recognition, and keystroke estimation. As our best survey, past work
recognizes human action by categorizing one complete distortion series into one
action, which we term as series-level action recognition. In this paper, we
introduce a much more fine-grained and challenging action recognition task into
WiFi sensing domain, i.e., sample-level action recognition. In this task, every
WiFi distortion sample in the whole series should be categorized into one
action, which is a critical technique in precise action localization,
continuous action segmentation, and real-time action recognition. To achieve
WiFi-based sample-level action recognition, we fully analyze approaches in
image-based semantic segmentation as well as in video-based frame-level action
recognition, then propose a simple yet efficient deep convolutional neural
network, i.e., Temporal Unet. Experimental results show that Temporal Unet
achieves this novel task well. Codes have been made publicly available at
https://github.com/geekfeiw/WiSLAR.
XTreePath: A generalization of XPath to handle real world structural variation
Authors: Joseph Paul Cohen, Wei Ding, Abraham Bagherjeiran
Date: 2015-05-06 00:00:00

We discuss a key problem in information extraction which deals with wrapper
failures due to changing content templates. A good proportion of wrapper
failures are due to HTML templates changing to cause wrappers to become
incompatible after element inclusion or removal in a DOM (Tree representation
of HTML). We perform a large-scale empirical analyses of the causes of shift
and mathematically quantify the levels of domain difficulty based on entropy.
We propose the XTreePath annotation method to captures contextual node
information from the training DOM. We then utilize this annotation in a
supervised manner at test time with our proposed Recursive Tree Matching method
which locates nodes most similar in context recursively using the tree edit
distance. The search is based on a heuristic function that takes into account
the similarity of a tree compared to the structure that was present in the
training data. We evaluate XTreePath using 117,422 pages from 75 diverse
websites in 8 vertical markets. Our XTreePath method consistently outperforms
XPath and a current commercial system in terms of successful extractions in a
blackbox test. We make our code and datasets publicly available online.
Style Transfer by Relaxed Optimal Transport and Self-Similarity
Authors: Nicholas Kolkin, Jason Salavon, Greg Shakhnarovich
Date: 2019-04-29 00:00:00
Proceeding: CVPR 2019 6
Tasks: Style Transfer

Style transfer algorithms strive to render the content of one image using the style of another. We propose Style Transfer by Relaxed Optimal Transport and Self-Similarity (STROTSS), a new optimization-based style transfer algorithm. We extend our method to allow user-specified point-to-point or region-to-region control over visual similarity between the style image and the output. Such guidance can be used to either achieve a particular visual effect or correct errors made by unconstrained style transfer. In order to quantitatively compare our method to prior work, we conduct a large-scale user study designed to assess the style-content tradeoff across settings in style transfer algorithms. Our results indicate that for any desired level of content preservation, our method provides higher quality stylization than prior work. Code is available at https://github.com/nkolkin13/STROTSS
Unsupervised Feature Learning for Point Cloud by Contrasting and Clustering With Graph Convolutional Neural Network
Authors: Ling Zhang, Zhigang Zhu
Date: 2019-04-28 00:00:00
Tasks: Clustering, Object, Point Cloud Classification

To alleviate the cost of collecting and annotating large-scale point cloud datasets, we propose an unsupervised learning approach to learn features from unlabeled point cloud "3D object" dataset by using part contrasting and object clustering with deep graph neural networks (GNNs). In the contrast learning step, all the samples in the 3D object dataset are cut into two parts and put into a "part" dataset. Then a contrast learning GNN (ContrastNet) is trained to verify whether two randomly sampled parts from the part dataset belong to the same object. In the cluster learning step, the trained ContrastNet is applied to all the samples in the original 3D object dataset to extract features, which are used to group the samples into clusters. Then another GNN for clustering learning (ClusterNet) is trained to predict the cluster ID of all the training samples. The contrasting learning forces the ContrastNet to learn high-level semantic features of objects but probably ignores low-level features, while the ClusterNet improves the quality of learned features by being trained to discover objects that probably belong to the same semantic categories by the use of cluster IDs. We have conducted extensive experiments to evaluate the proposed framework on point cloud classification tasks. The proposed unsupervised learning approach obtained comparable performance to the state-of-the-art unsupervised learning methods that used much more complicated network structures. The code of this work is publicly available via: https://github.com/lingzhang1/ContrastNet.
Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks
Authors: Xiang He, Sibei Yang, Guanbin Li?, Haofeng Li, Huiyou Chang, Yizhou Yu
Date: 2019-04-27 00:00:00
Tasks: Image Segmentation, Lesion Segmentation, Segmentation, Semantic Segmentation, Skin Lesion Segmentation

Recent progress in biomedical image segmentation based on deep convolutional
neural networks (CNNs) has drawn much attention. However, its vulnerability
towards adversarial samples cannot be overlooked. This paper is the first one
that discovers that all the CNN-based state-of-the-art biomedical image
segmentation models are sensitive to adversarial perturbations. This limits the
deployment of these methods in safety-critical biomedical fields. In this
paper, we discover that global spatial dependencies and global contextual
information in a biomedical image can be exploited to defend against
adversarial attacks. To this end, non-local context encoder (NLCE) is proposed
to model short- and long range spatial dependencies and encode global contexts
for strengthening feature activations by channel-wise attention. The NLCE
modules enhance the robustness and accuracy of the non-local context encoding
network (NLCEN), which learns robust enhanced pyramid feature representations
with NLCE modules, and then integrates the information across different levels.
Experiments on both lung and skin lesion segmentation datasets have
demonstrated that NLCEN outperforms any other state-of-the-art biomedical image
segmentation methods against adversarial attacks. In addition, NLCE modules can
be applied to improve the robustness of other CNN-based biomedical image
segmentation methods.
Classification and Detection in Mammograms with Weak Supervision via Dual Branch Deep Neural Net
Authors: Ran Bakalo, Rami Ben-Ari, Jacob Goldberger
Date: 2019-04-28 00:00:00
Tasks: Classification, General Classification, Multi-class Classification, TAG

The high cost of generating expert annotations, poses a strong limitation for
supervised machine learning methods in medical imaging. Weakly supervised
methods may provide a solution to this tangle. In this study, we propose a
novel deep learning architecture for multi-class classification of mammograms
according to the severity of their containing anomalies, having only a global
tag over the image. The suggested scheme further allows localization of the
different types of findings in full resolution. The new scheme contains a dual
branch network that combines region-level classification with region ranking.
We evaluate our method on a large multi-center mammography dataset including
$\sim$3,000 mammograms with various anomalies and demonstrate the advantages of
the proposed method over a previous weakly-supervised strategy.
The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning
Authors: Steffen Wolf, Alberto Bailoni, Constantin Pape, Nasim Rahaman, Anna Kreshuk, Ullrich Köthe, Fred A. Hamprecht
Date: 2019-04-25 00:00:00
Tasks: Clustering, graph partitioning, Segmentation

Image partitioning, or segmentation without semantics, is the task of decomposing an image into distinct segments, or equivalently to detect closed contours. Most prior work either requires seeds, one per segment; or a threshold; or formulates the task as multicut / correlation clustering, an NP-hard problem. Here, we propose an efficient algorithm for graph partitioning, the "Mutex Watershed''. Unlike seeded watershed, the algorithm can accommodate not only attractive but also repulsive cues, allowing it to find a previously unspecified number of segments without the need for explicit seeds or a tunable threshold. We also prove that this simple algorithm solves to global optimality an objective function that is intimately related to the multicut / correlation clustering integer linear programming formulation. The algorithm is deterministic, very simple to implement, and has empirically linearithmic complexity. When presented with short-range attractive and long-range repulsive cues from a deep neural network, the Mutex Watershed gives the best results currently known for the competitive ISBI 2012 EM segmentation benchmark.
Self-Attention Capsule Networks for Object Classification
Authors: Assaf Hoogi, Brian Wilcox, Yachee Gupta, Daniel L. Rubin
Date: 2019-04-29 00:00:00
Tasks: Classification, General Classification, Image Classification, Object

We propose a novel architecture for object classification, called Self-Attention Capsule Networks (SACN). SACN is the first model that incorporates the Self-Attention mechanism as an integral layer within the Capsule Network (CapsNet). While the Self-Attention mechanism supplies a long-range dependencies, results in selecting the more dominant image regions to focus on, the CapsNet analyzes the relevant features and their spatial correlations inside these regions only. The features are extracted in the convolutional layer. Then, the Self-Attention layer learns to suppress irrelevant regions based on features analysis and highlights salient features useful for a specific task. The attention map is then fed into the CapsNet primary layer that is followed by a classification layer. The proposed SACN model was designed to solve two main limitations of the baseline CapsNet - analysis of complex data and significant computational load. In this work, we use a shallow CapsNet architecture and compensates for the absence of a deeper network by using the Self-Attention module to significantly improve the results. The proposed Self-Attention CapsNet architecture was extensively evaluated on six different datasets, mainly on three different medical sets, in addition to the natural MNIST, SVHN and CIFAR10. The model was able to classify images and their patches with diverse and complex backgrounds better than the baseline CapsNet. As a result, the proposed Self-Attention CapsNet significantly improved classification performance within and across different datasets and outperformed the baseline CapsNet, ResNet-18 and DenseNet-40 not only in classification accuracy but also in robustness.
Unsupervised Data Augmentation for Consistency Training
Authors: Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, Quoc V. Le
Date: 2019-04-29 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Data Augmentation, Image Augmentation, Image Classification, Semi-Supervised Image Classification, Sentiment Analysis, Text Classification, Transfer Learning

Semi-supervised learning lately has shown much promise in improving deep learning models when labeled data is scarce. Common among recent approaches is the use of consistency training on a large amount of unlabeled data to constrain model predictions to be invariant to input noise. In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning. By substituting simple noising operations with advanced data augmentation methods such as RandAugment and back-translation, our method brings substantial improvements across six language and three vision tasks under the same consistency training framework. On the IMDb text classification dataset, with only 20 labeled examples, our method achieves an error rate of 4.20, outperforming the state-of-the-art model trained on 25,000 labeled examples. On a standard semi-supervised learning benchmark, CIFAR-10, our method outperforms all previous approaches and achieves an error rate of 5.43 with only 250 examples. Our method also combines well with transfer learning, e.g., when finetuning from BERT, and yields improvements in high-data regime, such as ImageNet, whether when there is only 10% labeled data or when a full labeled set with 1.3M extra unlabeled examples is used. Code is available at https://github.com/google-research/uda.
Optimal Sparse Decision Trees
Authors: Xiyang Hu, Cynthia Rudin, Margo Seltzer
Date: 2019-04-29 00:00:00
Proceeding: NeurIPS 2019 12

Decision tree algorithms have been among the most popular algorithms for interpretable (transparent) machine learning since the early 1980's. The problem that has plagued decision tree algorithms since their inception is their lack of optimality, or lack of guarantees of closeness to optimality: decision tree algorithms are often greedy or myopic, and sometimes produce unquestionably suboptimal models. Hardness of decision tree optimization is both a theoretical and practical obstacle, and even careful mathematical programming approaches have not been able to solve these problems efficiently. This work introduces the first practical algorithm for optimal decision trees for binary variables. The algorithm is a co-design of analytical bounds that reduce the search space and modern systems techniques, including data structures and a custom bit-vector library. Our experiments highlight advantages in scalability, speed, and proof of optimality. The code is available at https://github.com/xiyanghu/OSDT.
HIGAN: Cosmic Neutral Hydrogen with Generative Adversarial Networks
Authors: Juan Zamudio-Fernandez, Atakan Okan, Francisco Villaescusa-Navarro, Seda Bilaloglu, Asena Derin Cengiz, Siyu He, Laurence Perreault Levasseur, Shirley Ho
Date: 2019-04-29 00:00:00
Tasks: Clustering, CPU

One of the most promising ways to observe the Universe is by detecting the
21cm emission from cosmic neutral hydrogen (HI) through radio-telescopes. Those
observations can shed light on fundamental astrophysical questions only if
accurate theoretical predictions are available. In order to maximize the
scientific return of these surveys, those predictions need to include different
observables and be precise on non-linear scales. Currently, one of the best
ways to achieve this is via cosmological hydrodynamic simulations; however, the
computational cost of these simulations is high -- tens of millions of CPU
hours. In this work, we use Wasserstein Generative Adversarial Networks (WGANs)
to generate new high-resolution ($35~h^{-1}{\rm kpc}$) 3D realizations of
cosmic HI at $z=5$. We do so by sampling from a 100-dimension manifold, learned
by the generator, that characterizes the fully non-linear abundance and
clustering of cosmic HI from the state-of-the-art simulation IllustrisTNG. We
show that different statistical properties of the produced samples -- 1D PDF,
power spectrum, bispectrum, and void size function -- match very well those of
IllustrisTNG, and outperform state-of-the-art models such as Halo Occupation
Distributions (HODs). Our WGAN samples reproduce the abundance of HI across 9
orders of magnitude, from the Ly$\alpha$ forest to Damped Lyman Absorbers. WGAN
can produce new samples orders of magnitude faster than hydrodynamic
simulations.
Adversarial Training for Free!
Authors: Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein
Date: 2019-04-29 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Adversarial Attack, Adversarial Defense, Domain Generalization, General Classification

Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks. Unfortunately, the high cost of generating strong adversarial examples makes standard adversarial training impractical on large-scale problems like ImageNet. We present an algorithm that eliminates the overhead cost of generating adversarial examples by recycling the gradient information computed when updating model parameters. Our "free" adversarial training algorithm achieves comparable robustness to PGD adversarial training on the CIFAR-10 and CIFAR-100 datasets at negligible additional cost compared to natural training, and can be 7 to 30 times faster than other strong adversarial training methods. Using a single workstation with 4 P100 GPUs and 2 days of runtime, we can train a robust model for the large-scale ImageNet classification task that maintains 40% accuracy against PGD attacks. The code is available at https://github.com/ashafahi/free_adv_train.
SEALion: a Framework for Neural Network Inference on Encrypted Data
Authors: Tim van Elsloo, Giorgio Patrini, Hamish Ivey-Law
Date: 2019-04-29 00:00:00
Tasks: Privacy Preserving, Transfer Learning

We present SEALion: an extensible framework for privacy-preserving machine
learning with homomorphic encryption. It allows one to learn deep neural
networks that can be seamlessly utilized for prediction on encrypted data. The
framework consists of two layers: the first is built upon TensorFlow and SEAL
and exposes standard algebra and deep learning primitives; the second
implements a Keras-like syntax for training and inference with neural networks.
Given a required level of security, a user is abstracted from the details of
the encoding and the encryption scheme, allowing quick prototyping. We present
two applications that exemplifying the extensibility of our proposal, which are
also of independent interest: i) improving efficiency of neural network
inference by an activity sparsifier and ii) transfer learning by querying a
server-side Variational AutoEncoder that can handle encrypted data.