pwc-archive/papers-with-abstracts huggingface dataset:
Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions
Authors: Feng Yu, Zhaocheng Liu, Qiang Liu, Haoli Zhang, Shu Wu, Liang Wang
Date: 2020-01-01 00:00:00
Proceeding: CIKM 2020 1
Tasks: Click-Through Rate Prediction, Feature Engineering

Click-Through Rate (CTR) prediction is a crucial task for various online applications, such as recommendation and online advertising. The task of CTR prediction is to predict the probability of users' clicking behaviors, with high-dimensional input features. To avoid heavy handcrafted feature engineering, the core topic of CTR prediction is the automatic interactions of the input features. Factorization Machine (FM) is an effective approach for modeling second-order feature interactions. Recently, FM has been extended for modeling higher-order feature interactions, such as xDeepFM and Higher-Order Factorization Machine (HOFM). However, these approaches are with either high complexity or iterative computation consuming much time and space. To overcome above problems, we express arbitrary-order FM in the form of power sums according to Newton's identities. Accordingly, we propose a novel Interaction Machine (IM) model. IM is an efficient and exact implementation of high-order FM, whose time complexity linearly grows with the order of interactions and the number of feature fields. Via IM, we can conduct arbitrary-order feature interactions in a very simple way. Moreover, we perform IM together with deep neural networks, and the resulted DeepIM model is more efficient than xDeepFM with comparable or even better performance. We conduct experiments on two real-world datasets, in which effectiveness and efficiency of both IM and DeepIM are strongly verified.
Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions
Authors: Feng Yu, Zhaocheng Liu, Qiang Liu, Haoli Zhang, Shu Wu, Liang Wang
Date: 2020-01-01 00:00:00
Tasks: Click-Through Rate Prediction, Feature Engineering

Click-Through Rate (CTR) prediction is a crucial task for various online applications, such as recommendation and online advertising. The task of CTR prediction is to predict the probability of users' clicking behaviors, with high-dimensional input features. To avoid heavy handcrafted feature engineering, the core topic of CTR prediction is the automatic interactions of the input features. Factorization Machine (FM) is an effective approach for modeling second-order feature interactions. Recently, FM has been extended for modeling higher-order feature interactions, such as xDeepFM and Higher-Order Factorization Machine (HOFM). However, these approaches are with either high complexity or iterative computation consuming much time and space. To overcome above problems, we express arbitrary-order FM in the form of power sums according to Newton's identities. Accordingly, we propose a novel Interaction Machine (IM) model. IM is an efficient and exact implementation of high-order FM, whose time complexity linearly grows with the order of interactions and the number of feature fields. Via IM, we can conduct arbitrary-order feature interactions in a very simple way. Moreover, we perform IM together with deep neural networks, and the resulted DeepIM model is more efficient than xDeepFM with comparable or even better performance. We conduct experiments on two real-world datasets, in which effectiveness and efficiency of both IM and DeepIM are strongly verified.
Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features
Authors: Ying Shan, T. Ryan Hoens, Jian Jiao, Haijing Wang, Dong Yu, JC Mao
Date: 2016-01-01 00:00:00
Proceeding: KDD 2016 1
Tasks: Feature Engineering, GPU

Manually crafted combinatorial features have been the “secret sauce” behind many successful models. For web-scale applications, however, the variety and volume of features make these manually crafted features expensive to create, maintain, and deploy. This paper proposes the Deep Crossing model which is a deep neural network that automatically combines features to produce superior models. The input of Deep Crossing is a set of individual features that can be either dense or sparse. The important crossing features are discovered implicitly by the networks, which are comprised of an embedding and stacking layer, as well as a cascade of Residual Units.

Deep Crossing is implemented with a modeling tool called the Computational Network Tool Kit (CNTK), powered by a multi-GPU platform. It was able to build, from scratch, two web-scale models for a major paid search engine, and achieve superior results with only a sub-set of the features used in the production models. This demonstrates the potential of using Deep Crossing as a general modeling paradigm to improve existing products, as well as to speed up the development of new models with a fraction of the investment in feature engineering and acquisition of deep domain knowledge.
A Numerical Evaluation of the Accuracy of Influence Maximization Algorithms
Authors: Hautahi Kingi, Li-An Daniel Wang, Tom Shafer, Minh Huynh, MikeTrinh, Aaron Heuser
Date: 2020-08-24 00:00:00
Proceeding: Springer 2020 8
Tasks: GPU

We develop an algorithm to compute exact solutions to the influence maximization problem using concepts from reverse influence sampling (RIS). We implement the algorithm using GPU resources to evaluate the empirical accuracy of theoretically-guaranteed greedy and RIS approximate solutions. We find that the approximations yield solutions that are remarkably close to optimal — usually achieving greater than 99% of the optimal influence spread. These results are consistent across a wide range of network structures.
Cost-aware Targeted Viral Marketing in billion-scale networks
Authors: Hung T. Nguyen, Thang N. Dinh, My T. Thai
Date: 2016-07-28 00:00:00
Proceeding: IEEE 2016 7
Tasks: Marketing

Online social networks have been one of the most effective platforms for marketing and advertising. Through the “world-of-mouth” exchanges, so-called viral marketing, the influence and product adoption can spread from few key influencers to billions of users in the network. To identify those key influencers, a great amount of work has been devoted for the Influence Maximization (IM) problem that seeks a set of k seed users that maximize the expected influence. Unfortunately, IM encloses two impractical assumptions: 1) any seed user can be acquired with the same cost and 2) all users are equally interested in the advertisement. In this paper, we propose a new problem, called Cost-aware Targeted Viral Marketing (CTVM), to find the most cost-effective seed users who can influence the most relevant users to the advertisement. Since CTVM is NP-hard, we design an efficient (1 - 1/√e-ϵ - e)-approximation algorithm, named BCT, to solve the problem in billion-scale networks. Comparing with IM algorithms, we show that BCT is both theoretically and experimentally faster than the state-of-the-arts while providing better solution quality. Moreover, we prove that under the Linear Threshold model, BCT is the first sub-linear time algorithm for CTVM (and IM) in dense networks. In our experiments with a Twitter dataset, containing 1.46 billions of social relations and 106 millions tweets, BCT can identify key influencers in each trending topic in only few minutes.
Biomedical Named Entity Recognition using Conditional Random Fields and Rich Feature Sets
Authors: Burr Settles
Date: 2004-08-28 00:00:00
Proceeding: Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (NLPBA/BioNLP) 2004 8
Tasks: Management, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

As the wealth of biomedical knowledge in the form of literature increases, there is a rising need for effective natural language processing tools to assist in organizing, curating, and retrieving this information. To that end, named entity recognition (the task of identifying words and phrases in free text that belong to certain classes of interest) is an important first step for many of these larger information management goals.

In recent years, much attention has been focused on the problem of recognizing gene and protein mentions in biomedical abstracts. This paper presents a framework for simultaneously recognizing occurrences of PROTEIN, DNA, RNA, CELL-LINE, and CELL-TYPE entity classes using Conditional Random Fields with a variety of traditional and novel features. I show that this approach can achieve an overall F1 measure around 70, which seems to be the current state of the art.

The system described here was developed as part of the BioNLP/NLPBA 2004 shared task. Experiments were conducted on a training and evaluation set provided by the task organizers.
Closing the Loop: Fast, Interactive Semi-Supervised Annotation With Queries on Features and Instances
Authors: Burr Settles
Date: 2011-07-01 00:00:00
Proceeding: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing 2011 7
Tasks: Active Learning, Information Extraction, Sentiment Analysis, Text Classification

This paper describes DUALIST, an active learning annotation paradigm which solicits and learns from labels on both features (e.g., words) and instances (e.g., documents). We present a novel semi-supervised training algorithm developed for this setting, which is (1) fast enough to support real-time interactive speeds, and (2) at least as accurate as preexisting methods for learning with mixed feature and instance labels. Human annotators in user studies were able to produce near-state-of-the-art classifiers—on several corpora in a variety of application domains—with only a few minutes of effort.
A Sleeping, Recovering Bandit Algorithm for Optimizing Recurring Notifications
Authors: Kevin P. Yancey, Burr Settles
Date: 2020-08-23 00:00:00
Proceeding: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2020 8
Tasks: Multi-Armed Bandits

Many online and mobile applications rely on daily emails and push notifications to increase and maintain user engagement. The multi-armed bandit approach provides a useful framework for optimizing the content of these notifications, but a number of complications (such as novelty effects and conditional eligibility) make conventional bandit algorithms unsuitable in practice. In this paper, we introduce the Recovering Difference Softmax Algorithm to address the particular challenges of this problem domain, and use it to successfully optimize millions of daily reminders for the online language-learning app Duolingo. This lead to a 0.5%. increase in total daily active users (DAUs) and a 2%, increase in new user retention over a strong baseline. We provide technical details of its design and deployment, and demonstrate its efficacy through both offline and online evaluation experiments.
SCAN: Sequence-character Aware Network for Text Recognition
Authors: Heba Hassan, Marwan Torki, Mohamed E. Hussein
Date: 2021-01-08 00:00:00
Proceeding: International Conference of Computer Vision Theory and Application 2021 1
Tasks: Decoder, Semantic Segmentation

Text recognition continues to be a challenging problem in the context of text reading in natural scenes. Bearing
in mind the sequential nature of text, the problem is usually posed as a sequence prediction problem from a
whole-word image. Alternatively, it can also be posed as a character prediction problem. The latter approach
is typically more robust to challenging word shapes. Attempting to find the sweet spot that attains the best
of the two approaches, we propose Sequence-Character Aware Network (SCAN). SCAN starts by locating
and recognizing the characters and then generates the word using a sequence-based approach. It comprises
two modules: a semantic-segmentation-based character prediction, and an encoder-decoder network for word
generation. The training is done over two stages. In the first stage, we adopt a multi-task training technique
with both character-level and word-level losses and trainable loss weighting. In the second stage, the character level
loss is removed, enabling the use of data with only word-level annotations. Experiments are conducted
on several datasets for both regular and irregular text, showing state of the art performance of the proposed
approach. It also shows that the proposed approach is robust against noisy word detection.
One model Packs Thousands of Items with Recurrent Conditional Query Learning
Authors: Dongda Li, Zhaoquan Gu, Yuexuan Wang, Changwei Ren, Francis C. M. Lau
Date: 2021-11-12 00:00:00
Tasks: Combinatorial Optimization

Recent studies have revealed that neural combinatorial optimization (NCO) has advantages over conventional algorithms in many combinatorial optimization problems such as routing, but it is less efficient for more complicated optimization tasks such as packing which involves mutually conditioned action spaces. In this paper, we propose a Recurrent Conditional Query Learning (RCQL) method to solve both 2D and 3D packing problems. We first embed states by a recurrent encoder, and then adopt attention with conditional queries from previous actions. The conditional query mechanism fills the information gap between learning steps, which shapes the problem as a Markov decision process. Benefiting from the recurrence, a single RCQL model is capable of handling different sizes of packing problems. Experiment results show that RCQL can effectively learn strong heuristics for offline and online strip packing problems (SPPs), outperforming a wide range of baselines in space utilization ratio. RCQL reduces the average bin gap ratio by 1.83% in offline 2D 40-box cases and 7.84% in 3D cases compared with state-of-the-art methods. Meanwhile, our method also achieves 5.64% higher space utilization ratio for SPPs with 1000 items than the state of the art.
Time in a Box: Advancing Knowledge Graph Completion with Temporal Scopes
Authors: Ling Cai, Krzysztof Janowic, Bo Yan, Rui Zhu, Gengchen Mai
Date: 2021-11-12 00:00:00
Tasks: Knowledge Base Completion, Knowledge Graph Completion, Link Prediction, Time-interval Prediction, valid

Almost all statements in knowledge bases have a temporal scope during which they are valid. Hence, knowledge base completion (KBC) on temporal knowledge bases (TKB), where each statement \textit{may} be associated with a temporal scope, has attracted growing attention. Prior works assume that each statement in a TKB \textit{must} be associated with a temporal scope. This ignores the fact that the scoping information is commonly missing in a KB. Thus prior work is typically incapable of handling generic use cases where a TKB is composed of temporal statements with/without a known temporal scope. In order to address this issue, we establish a new knowledge base embedding framework, called TIME2BOX, that can deal with atemporal and temporal statements of different types simultaneously. Our main insight is that answers to a temporal query always belong to a subset of answers to a time-agnostic counterpart. Put differently, time is a filter that helps pick out answers to be correct during certain periods. We introduce boxes to represent a set of answer entities to a time-agnostic query. The filtering functionality of time is modeled by intersections over these boxes. In addition, we generalize current evaluation protocols on time interval prediction. We describe experiments on two datasets and show that the proposed method outperforms state-of-the-art (SOTA) methods on both link prediction and time prediction.
NRC-GAMMA: Introducing a Novel Large Gas Meter Image Dataset
Authors: Ashkan Ebadi, Patrick Paul, Sofia Auer, Stéphane Tremblay
Date: 2021-11-12 00:00:00
Tasks: Meter Reading

Automatic meter reading technology is not yet widespread. Gas, electricity, or water accumulation meters reading is mostly done manually on-site either by an operator or by the homeowner. In some countries, the operator takes a picture as reading proof to confirm the reading by checking offline with another operator and/or using it as evidence in case of conflicts or complaints. The whole process is time-consuming, expensive, and prone to errors. Automation can optimize and facilitate such labor-intensive and human error-prone processes. With the recent advances in the fields of artificial intelligence and computer vision, automatic meter reading systems are becoming more viable than ever. Motivated by the recent advances in the field of artificial intelligence and inspired by open-source open-access initiatives in the research community, we introduce a novel large benchmark dataset of real-life gas meter images, named the NRC-GAMMA dataset. The data were collected from an Itron 400A diaphragm gas meter on January 20, 2020, between 00:05 am and 11:59 pm. We employed a systematic approach to label the images, validate the labellings, and assure the quality of the annotations. The dataset contains 28,883 images of the entire gas meter along with 57,766 cropped images of the left and the right dial displays. We hope the NRC-GAMMA dataset helps the research community to design and implement accurate, innovative, intelligent, and reproducible automatic gas meter reading solutions.
Catastrophe, Compounding & Consistency in Choice
Authors: Chris Gagne, Peter Dayan
Date: 2021-11-12 00:00:00
Tasks: Decision Making

Conditional value-at-risk (CVaR) precisely characterizes the influence that rare, catastrophic events can exert over decisions. Such characterizations are important for both normal decision-making and for psychiatric conditions such as anxiety disorders -- especially for sequences of decisions that might ultimately lead to disaster. CVaR, like other well-founded risk measures, compounds in complex ways over such sequences -- and we recently formalized three structurally different forms in which risk either averages out or multiplies. Unfortunately, existing cognitive tasks fail to discriminate these approaches well; here, we provide examples that highlight their unique characteristics, and make formal links to temporal discounting for the two of the approaches that are time consistent. These examples can ground future experiments with the broader aim of characterizing risk attitudes, especially for longer horizon problems and in psychopathological populations.
Two steps to risk sensitivity
Authors: Chris Gagne, Peter Dayan
Date: 2021-11-12 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Decision Making, Distributional Reinforcement Learning, Reinforcement Learning (RL), Sensitivity, Vocal Bursts Valence Prediction

Distributional reinforcement learning (RL) -- in which agents learn about all the possible long-term consequences of their actions, and not just the expected value -- is of great recent interest. One of the most important affordances of a distributional view is facilitating a modern, measured, approach to risk when outcomes are not completely certain. By contrast, psychological and neuroscientific investigations into decision making under risk have utilized a variety of more venerable theoretical models such as prospect theory that lack axiomatically desirable properties such as coherence. Here, we consider a particularly relevant risk measure for modeling human and animal planning, called conditional value-at-risk (CVaR), which quantifies worst-case outcomes (e.g., vehicle accidents or predation). We first adopt a conventional distributional approach to CVaR in a sequential setting and reanalyze the choices of human decision-makers in the well-known two-step task, revealing substantial risk aversion that had been lurking under stickiness and perseveration. We then consider a further critical property of risk sensitivity, namely time consistency, showing alternatives to this form of CVaR that enjoy this desirable characteristic. We use simulations to examine settings in which the various forms differ in ways that have implications for human and animal planning and behavior.
AWD3: Dynamic Reduction of the Estimation Bias
Authors: Dogan C. Cicek, Enes Duran, Baturay Saglam, Kagan Kaya, Furkan B. Mutlu, Suleyman S. Kozat
Date: 2021-11-12 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, OpenAI Gym, Reinforcement Learning (RL)

Value-based deep Reinforcement Learning (RL) algorithms suffer from the estimation bias primarily caused by function approximation and temporal difference (TD) learning. This problem induces faulty state-action value estimates and therefore harms the performance and robustness of the learning algorithms. Although several techniques were proposed to tackle, learning algorithms still suffer from this bias. Here, we introduce a technique that eliminates the estimation bias in off-policy continuous control algorithms using the experience replay mechanism. We adaptively learn the weighting hyper-parameter beta in the Weighted Twin Delayed Deep Deterministic Policy Gradient algorithm. Our method is named Adaptive-WD3 (AWD3). We show through continuous control environments of OpenAI gym that our algorithm matches or outperforms the state-of-the-art off-policy policy gradient learning algorithms.
Explainability and the Fourth AI Revolution
Authors: Loizos Michael
Date: 2021-11-12 00:00:00

This chapter discusses AI from the prism of an automated process for the organization of data, and exemplifies the role that explainability has to play in moving from the current generation of AI systems to the next one, where the role of humans is lifted from that of data annotators working for the AI systems to that of collaborators working with the AI systems.
Multiway Storage Modification Machines
Authors: J. -M. Chauvet
Date: 2021-11-12 00:00:00

We present a parallel version of Sch\"onhage's Storage Modification Machine, the Multiway Storage Modification Machine (MWSMM). Like the alternative Association Storage Modification Machine of Tromp and van Emde Boas, MWSMMs recognize in polynomial time what Turing Machines recognize in polynomial space. Falling thus into the Second Machine Class, the MWSMM is a parallel machine model conforming to the Parallel Computation Thesis. We illustrate MWSMMs by a simple implementation of Wolfram's String Substitution System.
STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks
Authors: Guannan Lou, Yuze Liu, Tiehua Zhang, Xi Zheng
Date: 2021-11-12 00:00:00
Tasks: Federated Learning

We present a spatial-temporal federated learning framework for graph neural networks, namely STFL. The framework explores the underlying correlation of the input spatial-temporal data and transform it to both node features and adjacency matrix. The federated learning setting in the framework ensures data privacy while achieving a good model generalization. Experiments results on the sleep stage dataset, ISRUC_S3, illustrate the effectiveness of STFL on graph prediction tasks.
Causal Multi-Agent Reinforcement Learning: Review and Open Problems
Authors: St John Grimbly, Jonathan Shock, Arnu Pretorius
Date: 2021-11-12 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper serves to introduce the reader to the field of multi-agent reinforcement learning (MARL) and its intersection with methods from the study of causality. We highlight key challenges in MARL and discuss these in the context of how causal methods may assist in tackling them. We promote moving toward a 'causality first' perspective on MARL. Specifically, we argue that causality can offer improved safety, interpretability, and robustness, while also providing strong theoretical guarantees for emergent behaviour. We discuss potential solutions for common challenges, and use this context to motivate future research directions.