pwc-archive/papers-with-abstracts huggingface dataset:
Click-Through Rate Prediction Using Graph Neural Networks and Online Learning
Authors: Farzaneh Rajabi, Jack Siyuan He
Date: 2021-05-09 00:00:00
Tasks: Binary Classification, Click-Through Rate Prediction, Collaborative Filtering, Recommendation Systems

Recommendation systems have been extensively studied by many literature in the past and are ubiquitous in online advertisement, shopping industry/e-commerce, query suggestions in search engines, and friend recommendation in social networks. Moreover, restaurant/music/product/movie/news/app recommendations are only a few of the applications of a recommender system. A small percent improvement on the CTR prediction accuracy has been mentioned to add millions of dollars of revenue to the advertisement industry. Click-Through-Rate (CTR) prediction is a special version of recommender system in which the goal is predicting whether or not a user is going to click on a recommended item. A content-based recommendation approach takes into account the past history of the user's behavior, i.e. the recommended products and the users reaction to them. So, a personalized model that recommends the right item to the right user at the right time is the key to building such a model. On the other hand, the so-called collaborative filtering approach incorporates the click history of the users who are very similar to a particular user, thereby helping the recommender to come up with a more confident prediction for that particular user by leveraging the wider knowledge of users who share their taste in a connected network of users. In this project, we are interested in building a CTR predictor using Graph Neural Networks complemented by an online learning algorithm that models such dynamic interactions. By framing the problem as a binary classification task, we have evaluated this system both on the offline models (GNN, Deep Factorization Machines) with test-AUC of 0.7417 and on the online learning model with test-AUC of 0.7585 using a sub-sampled version of Criteo public dataset consisting of 10,000 data points.
Slash or burn: Power line and vegetation classification for wildfire prevention
Authors: Austin Park, Farzaneh Rajabi, Ross Weber
Date: 2021-05-09 00:00:00
Tasks: Feature Engineering, Management, Transfer Learning

Electric utilities are struggling to manage increasing wildfire risk in a hotter and drier climate. Utility transmission and distribution lines regularly ignite destructive fires when they make contact with surrounding vegetation. Trimming vegetation to maintain the separation from utility assets is as critical to safety as it is difficult. Each utility has tens of thousands of linear miles to manage, poor knowledge of where those assets are located, and no way to prioritize trimming. Feature-enhanced convolutional neural networks (CNNs) have proven effective in this problem space. Histograms of oriented gradients (HOG) and Hough transforms are used to increase the salience of the linear structures like power lines and poles. Data is frequently taken from drone or satellite footage, but Google Street View offers an even more scalable and lower cost solution. This paper uses $1,320$ images scraped from Street View, transfer learning on popular CNNs, and feature engineering to place images in one of three classes: (1) no utility systems, (2) utility systems with no overgrown vegetation, or (3) utility systems with overgrown vegetation. The CNN output thus yields a prioritized vegetation management system and creates a geotagged map of utility assets as a byproduct. Test set accuracy with reached $80.15\%$ using VGG11 with a trained first layer and classifier, and a model ensemble correctly classified $88.88\%$ of images with risky vegetation overgrowth.
Human Gait State Prediction Using Cellular Automata and Classification Using ELM
Authors: Vijay Bhaskar Semwal, Neha Gaud, G. C. Nandi
Date: 2021-05-08 00:00:00

In this research article, we have reported periodic cellular automata rules for different gait state prediction and classification of the gait data using extreme machine Leaning (ELM). This research is the first attempt to use cellular automaton to understand the complexity of bipedal walk. Due to nonlinearity, varying configurations throughout the gait cycle and the passive joint located at the unilateral foot-ground contact in bipedal walk resulting variation of dynamic descriptions and control laws from phase to phase for human gait is making difficult to predict the bipedal walk states. We have designed the cellular automata rules which will predict the next gait state of bipedal steps based on the previous two neighbour states. We have designed cellular automata rules for normal walk. The state prediction will help to correctly design the bipedal walk. The normal walk depends on next two states and has total 8 states. We have considered the current and previous states to predict next state. So we have formulated 16 rules using cellular automata, 8 rules for each leg. The priority order maintained using the fact that if right leg in swing phase then left leg will be in stance phase. To validate the model we have classified the gait Data using ELM [1] and achieved accuracy 60%. We have explored the trajectories and compares with another gait trajectories. Finally we have presented the error analysis for different joints.
AnomalyHop: An SSL-based Image Anomaly Localization Method
Authors: Kaitai Zhang, Bin Wang, Wei Wang, Fahad Sohrab, Moncef Gabbouj, C. -C. Jay Kuo
Date: 2021-05-08 00:00:00
Tasks: Anomaly Localization, Benchmarking

An image anomaly localization method based on the successive subspace learning (SSL) framework, called AnomalyHop, is proposed in this work. AnomalyHop consists of three modules: 1) feature extraction via successive subspace learning (SSL), 2) normality feature distributions modeling via Gaussian models, and 3) anomaly map generation and fusion. Comparing with state-of-the-art image anomaly localization methods based on deep neural networks (DNNs), AnomalyHop is mathematically transparent, easy to train, and fast in its inference speed. Besides, its area under the ROC curve (ROC-AUC) performance on the MVTec AD dataset is 95.9%, which is among the best of several benchmarking methods. Our codes are publicly available at Github.
Stability and Generalization of Stochastic Gradient Methods for Minimax Problems
Authors: Yunwen Lei, Zhenhuan Yang, Tianbao Yang, Yiming Ying
Date: 2021-05-08 00:00:00
Tasks: Generalization Bounds

Many machine learning problems can be formulated as minimax problems such as Generative Adversarial Networks (GANs), AUC maximization and robust estimation, to mention but a few. A substantial amount of studies are devoted to studying the convergence behavior of their stochastic gradient-type algorithms. In contrast, there is relatively little work on their generalization, i.e., how the learning models built from training examples would behave on test examples. In this paper, we provide a comprehensive generalization analysis of stochastic gradient methods for minimax problems under both convex-concave and nonconvex-nonconcave cases through the lens of algorithmic stability. We establish a quantitative connection between stability and several generalization measures both in expectation and with high probability. For the convex-concave setting, our stability analysis shows that stochastic gradient descent ascent attains optimal generalization bounds for both smooth and nonsmooth minimax problems. We also establish generalization bounds for both weakly-convex-weakly-concave and gradient-dominated problems.
Improving Deep Learning Performance for Predicting Large-Scale Porous-Media Flow through Feature Coarsening
Authors: BiCheng Yan, Dylan Robert Harp, Bailian Chen, Rajesh J. Pawar
Date: 2021-05-08 00:00:00

Physics-based simulation for fluid flow in porous media is a computational technology to predict the temporal-spatial evolution of state variables (e.g. pressure) in porous media, and usually requires high computational expense due to its nonlinearity and the scale of the study domain. This letter describes a deep learning (DL) workflow to predict the pressure evolution as fluid flows in large-scale 3D heterogeneous porous media. In particular, we apply feature coarsening technique to extract the most representative information and perform the training and prediction of DL at the coarse scale, and further recover the resolution at the fine scale by 2D piecewise cubic interpolation. We validate the DL approach that is trained from physics-based simulation data to predict pressure field in a field-scale 3D geologic CO_2 storage reservoir. We evaluate the impact of feature coarsening on DL performance, and observe that the feature coarsening can not only decrease training time by >74% and reduce memory consumption by >75%, but also maintains temporal error <1.5%. Besides, the DL workflow provides predictive efficiency with ~1400 times speedup compared to physics-based simulation.
PIM-DRAM: Accelerating Machine Learning Workloads using Processing in Commodity DRAM
Authors: Sourjya Roy, Mustafa Ali, Anand Raghunathan
Date: 2021-05-08 00:00:00
Tasks: BIG-bench Machine Learning, GPU, Medical Diagnosis

Deep Neural Networks (DNNs) have transformed the field of machine learning and are widely deployed in many applications involving image, video, speech and natural language processing. The increasing compute demands of DNNs have been widely addressed through Graphics Processing Units (GPUs) and specialized accelerators. However, as model sizes grow, these von Neumann architectures require very high memory bandwidth to keep the processing elements utilized as a majority of the data resides in the main memory. Processing in memory has been proposed as a promising solution for the memory wall bottleneck for ML workloads. In this work, we propose a new DRAM-based processing-in-memory (PIM) multiplication primitive coupled with intra-bank accumulation to accelerate matrix vector operations in ML workloads. The proposed multiplication primitive adds < 1% area overhead and does not require any change in the DRAM peripherals. Therefore, the proposed multiplication can be easily adopted in commodity DRAM chips. Subsequently, we design a DRAM-based PIM architecture, data mapping scheme and dataflow for executing DNNs within DRAM. System evaluations performed on networks like AlexNet, VGG16 and ResNet18 show that the proposed architecture, mapping, and data flow can provide up to 19.5x speedup over an NVIDIA Titan Xp GPU highlighting the need to overcome the memory bottleneck in future generations of DNN hardware.
Generative Actor-Critic: An Off-policy Algorithm Using the Push-forward Model
Authors: Lingwei Peng, Hui Qian, Zhebang Shen, Chao Zhang, Fei Li
Date: 2021-05-08 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, Recommendation Systems

Model-free deep reinforcement learning has achieved great success in many domains, such as video games, recommendation systems and robotic control tasks. In continuous control tasks, widely used policies with Gaussian distributions results in ineffective exploration of environments and limited performance of algorithms in many cases. In this paper, we propose a density-free off-policy algorithm, Generative Actor-Critic(GAC), using the push-forward model to increase the expressiveness of policies, which also includes an entropy-like technique, MMD-entropy regularizer, to balance the exploration and exploitation. Additionnally, we devise an adaptive mechanism to automatically scale this regularizer, which further improves the stability and robustness of GAC. The experiment results show that push-forward policies possess desirable features, such as multi-modality, which can improve the efficiency of exploration and asymptotic performance of algorithms obviously.
Mental Models of Adversarial Machine Learning
Authors: Lukas Bieringer, Kathrin Grosse, Michael Backes, Battista Biggio, Katharina Krombholz
Date: 2021-05-08 00:00:00
Tasks: BIG-bench Machine Learning

Although machine learning is widely used in practice, little is known about practitioners' understanding of potential security challenges. In this work, we close this substantial gap and contribute a qualitative study focusing on developers' mental models of the machine learning pipeline and potentially vulnerable components. Similar studies have helped in other security fields to discover root causes or improve risk communication. Our study reveals two \facets of practitioners' mental models of machine learning security. Firstly, practitioners often confuse machine learning security with threats and defences that are not directly related to machine learning. Secondly, in contrast to most academic research, our participants perceive security of machine learning as not solely related to individual models, but rather in the context of entire workflows that consist of multiple components. Jointly with our additional findings, these two facets provide a foundation to substantiate mental models for machine learning security and have implications for the integration of adversarial machine learning into corporate workflows, \new{decreasing practitioners' reported uncertainty}, and appropriate regulatory frameworks for machine learning security.
Business Entity Matching with Siamese Graph Convolutional Networks
Authors: Evgeny Krivosheev, Mattia Atzeni, Katsiaryna Mirylenka, Paolo Scotton, Christoph Miksovic, Anton Zorin
Date: 2021-05-08 00:00:00
Tasks: Data Integration, Deep Learning

Data integration has been studied extensively for decades and approached from different angles. However, this domain still remains largely rule-driven and lacks universal automation. Recent developments in machine learning and in particular deep learning have opened the way to more general and efficient solutions to data-integration tasks. In this paper, we demonstrate an approach that allows modeling and integrating entities by leveraging their relations and contextual information. This is achieved by combining siamese and graph neural networks to effectively propagate information between connected entities and support high scalability. We evaluated our approach on the task of integrating data about business entities, demonstrating that it outperforms both traditional rule-based systems and other deep learning approaches.
A Crossover That Matches Diverse Parents Together in Evolutionary Algorithms
Authors: Maciej Świechowski
Date: 2021-05-08 00:00:00
Tasks: Evolutionary Algorithms

Crossover and mutation are the two main operators that lead to new solutions in evolutionary approaches. In this article, a new method of performing the crossover phase is presented. The problem of choice is evolutionary decision tree construction. The method aims at finding such individuals that together complement each other. Hence we say that they are diversely specialized. We propose the way of calculating the so-called complementary fitness. In several empirical experiments, we evaluate the efficacy of the method proposed in four variants and compare it to a fitness-rank-based approach. One variant emerges clearly as the best approach, whereas the remaining ones are below the baseline.
Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
Authors: Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu
Date: 2021-05-08 00:00:00
Proceeding: ACL 2021 5
Tasks: Chinese Named Entity Recognition, Chunking, Named Entity Recognition, Named Entity Recognition (NER), Retrieval, Sentence

Recent advances in Named Entity Recognition (NER) show that document-level contexts can significantly improve model performance. In many application scenarios, however, such contexts are not available. In this paper, we propose to find external contexts of a sentence by retrieving and selecting a set of semantically relevant texts through a search engine, with the original sentence as the query. We find empirically that the contextual representations computed on the retrieval-based input view, constructed through the concatenation of a sentence and its external contexts, can achieve significantly improved performance compared to the original input view based only on the sentence. Furthermore, we can improve the model performance of both input views by Cooperative Learning, a training method that encourages the two input views to produce similar contextual representations or output label distributions. Experiments show that our approach can achieve new state-of-the-art performance on 8 NER data sets across 5 domains.
Diversifying Neural Text Generation with Part-of-Speech Guided Softmax and Sampling
Authors: Zhixian Yang, Pengxuan Xu, Xiaojun Wan
Date: 2021-05-08 00:00:00
Proceeding: COLING 2022 10
Tasks: Diversity, POS, Text Generation

Neural text generation models are likely to suffer from the low-diversity problem. Various decoding strategies and training-based methods have been proposed to promote diversity only by exploiting contextual features, but rarely do they consider incorporating syntactic structure clues. In this work, we propose using linguistic annotation, i.e., part-of-speech (POS), to guide the text generation. In detail, we introduce POS Guided Softmax to explicitly model two posterior probabilities: (i) next-POS, and (ii) next-token from the vocabulary of the target POS. A POS Guided Sampling strategy is further proposed to address the low-diversity problem by enriching the diversity of POS. Extensive experiments and human evaluations show that, compared with existing state-of-the-art methods, our POS Guided Softmax and Sampling (POSG) can generate more diverse text while maintaining comparable quality.
On Guaranteed Optimal Robust Explanations for NLP Models
Authors: Emanuele La Malfa, Agnieszka Zbrzezny, Rhiannon Michelmore, Nicola Paoletti, Marta Kwiatkowska
Date: 2021-05-08 00:00:00
Tasks: Sentiment Analysis

We build on abduction-based explanations for ma-chine learning and develop a method for computing local explanations for neural network models in natural language processing (NLP). Our explanations comprise a subset of the words of the in-put text that satisfies two key features: optimality w.r.t. a user-defined cost function, such as the length of explanation, and robustness, in that they ensure prediction invariance for any bounded perturbation in the embedding space of the left out words. We present two solution algorithms, respectively based on implicit hitting sets and maximum universal subsets, introducing a number of algorithmic improvements to speed up convergence of hard instances. We show how our method can be con-figured with different perturbation sets in the em-bedded space and used to detect bias in predictions by enforcing include/exclude constraints on biased terms, as well as to enhance existing heuristic-based NLP explanation frameworks such as Anchors. We evaluate our framework on three widely used sentiment analysis tasks and texts of up to100words from SST, Twitter and IMDB datasets,demonstrating the effectiveness of the derived explanations.
Loss Tolerant Federated Learning
Authors: Pengyuan Zhou, Pei Fang, Pan Hui
Date: 2021-05-08 00:00:00
Tasks: Fairness, Federated Learning

Federated learning has attracted attention in recent years for collaboratively training data on distributed devices with privacy-preservation. The limited network capacity of mobile and IoT devices has been seen as one of the major challenges for cross-device federated learning. Recent solutions have been focusing on threshold-based client selection schemes to guarantee the communication efficiency. However, we find this approach can cause biased client selection and results in deteriorated performance. Moreover, we find that the challenge of network limit may be overstated in some cases and the packet loss is not always harmful. In this paper, we explore the loss tolerant federated learning (LT-FL) in terms of aggregation, fairness, and personalization. We use ThrowRightAway (TRA) to accelerate the data uploading for low-bandwidth-devices by intentionally ignoring some packet losses. The results suggest that, with proper integration, TRA and other algorithms can together guarantee the personalization and fairness performance in the face of packet loss below a certain fraction (10%-30%).
Facial Emotion Recognition: State of the Art Performance on FER2013
Authors: Yousif Khaireddin, Zhuofa Chen
Date: 2021-05-08 00:00:00
Tasks: Computational Efficiency, Emotion Recognition, Facial Emotion Recognition, Facial Expression Recognition (FER)

Facial emotion recognition (FER) is significant for human-computer interaction such as clinical practice and behavioral description. Accurate and robust FER by computer models remains challenging due to the heterogeneity of human faces and variations in images such as different facial pose and lighting. Among all techniques for FER, deep learning models, especially Convolutional Neural Networks (CNNs) have shown great potential due to their powerful automatic feature extraction and computational efficiency. In this work, we achieve the highest single-network classification accuracy on the FER2013 dataset. We adopt the VGGNet architecture, rigorously fine-tune its hyperparameters, and experiment with various optimization methods. To our best knowledge, our model achieves state-of-the-art single-network accuracy of 73.28 % on FER2013 without using extra training data.
Multimodal and Contrastive Learning for Click Fraud Detection
Authors: Weibin Li, Qiwei Zhong, Qingyang Zhao, Hongchun Zhang, Xiaonan Meng
Date: 2021-05-08 00:00:00
Tasks: Contrastive Learning, Fraud Detection

Advertising click fraud detection plays one of the vital roles in current E-commerce websites as advertising is an essential component of its business model. It aims at, given a set of corresponding features, e.g., demographic information of users and statistical features of clicks, predicting whether a click is fraudulent or not in the community. Recent efforts attempted to incorporate attributed behavior sequence and heterogeneous network for extracting complex features of users and achieved significant effects on click fraud detection. In this paper, we propose a Multimodal and Contrastive learning network for Click Fraud detection (MCCF). Specifically, motivated by the observations on differences of demographic information, behavior sequences and media relationship between fraudsters and genuine users on E-commerce platform, MCCF jointly utilizes wide and deep features, behavior sequence and heterogeneous network to distill click representations. Moreover, these three modules are integrated by contrastive learning and collaboratively contribute to the final predictions. With the real-world datasets containing 2.54 million clicks on Alibaba platform, we investigate the effectiveness of MCCF. The experimental results show that the proposed approach is able to improve AUC by 7.2% and F1-score by 15.6%, compared with the state-of-the-art methods.