pwc-archive/papers-with-abstracts huggingface dataset:
VirtuosoNet: A Hierarchical RNN-based System for Modeling Expressive Piano Performance
Authors: Dasaem Jeong, Taegyun Kwon, Yoojin Kim, Kyogu Lee, Juhan Nam
Date: 2019-11-04 00:00:00
Proceeding: ISMIR 2019 11
Tasks: Music Performance Rendering

In this paper, we present our application of deep neural network to modeling piano performance, which imitates the expressive control of tempo, dynamics, articulations and pedaling from pianists. Our model consists of recurrent neural networks with hierarchical attention and conditional variational autoencoder. The model takes a sequence of note-level score features extracted from MusicXML as input and predicts piano performance features of the corresponding notes. To render musical expressions consistently over long-term sections, we first predict tempo and dynamics in measure-level and, based on the result, refine them in note-level. The evaluation through listening test shows that our model achieves a more human-like expressiveness compared to previous models. We also share the dataset we used for the experiment.
An adaptive denoising recommendation algorithm for causal separation bias
Authors: Zhangqiuling, Xuhuayang, Wangjianfang
Date: 2023-11-15 00:00:00
Proceeding: Conference 2023 11
Tasks: Causal Inference, Denoising, Multi-Task Learning, Recommendation Systems, Selection bias

In recommender systems, user selection bias often influences user-item interactions, e.g., users are more likely to rate their previously preferred or popular items. Existing methods can leverage the impact of selection bias in user ratings on the evaluation and optimization of recommendation system. However, these methods either inevitably contain a large amount of noise in the sampling process or suffer from the confound between users’ conformity and interests. Inspired by the recent success of causal inference, in this work we propose a novel method to separate popularity biases for recommendation, named adaptive denoising and causal inference algorithm (ADA). We first compute the average rating of all feedback items of each user as the basis in converting explicit feedback to implicit feedback, and then obtain the true positive implicit data through adaptive denoising method. In addition, we separate the confounding of users’ conformity and interest in the selection bias by causal inference. Specifically, we construct a multi-task learning model with regularization loss functions. Experimental results on the two datasets demonstrate the superiority of our ADA model over state-of-the-art methods in recommendation accuracy.
Second-order Confidence Network for Early Classification of Time Series
Authors: Junwei Lv, Yuqi Chu, Jun Hu, Peipei Li, Xuegang Hu
Date: 2023-12-19 00:00:00
Proceeding: ACM Transactions on Intelligent Systems and Technology 2023 12
Tasks: Early  Classification, Time Series

Time series data are ubiquitous in a variety of disciplines. Early classification of time series, which aims to predict the class label of a time series as early and accurately as possible, is a significant but challenging task in many time-sensitive applications. Existing approaches mainly utilize heuristic stopping rules to capture stopping signals from the prediction results of time series classifiers. However, heuristic stopping rules can only capture obvious stopping signals, which makes these approaches give either correct but late predictions or early but incorrect predictions. To tackle the problem, we propose a novel second-order confidence network for early classification of time series, which can automatically learn to capture implicit stopping signals in early time series in a unified framework. The proposed model leverages deep neural models to capture temporal patterns and outputs second-order confidence to reflect the implicit stopping signals. Specifically, our model exploits the data not only from a time step but also from the probability sequence to capture stopping signals. By combining stopping signals from the classifier output and the second-order confidence, we design a more robust trigger to decide whether or not to request more observations from future time steps. Experimental results show that our approach can achieve superior results in early classification compared to state-of-the-art approaches.
De novo Drug Design using Reinforcement Learning with Multiple GPT Agents
Authors: Xiuyuan Hu, Guoqing Liu, Yang Zhao, Hao Zhang
Date: 2023-12-21 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Diversity, Drug Design, reinforcement-learning, Reinforcement Learning

De novo drug design is a pivotal issue in pharmacology and a new area of focus in AI for science research. A central challenge in this field is to generate molecules with specific properties while also producing a wide range of diverse candidates. Although advanced technologies such as transformer models and reinforcement learning have been applied in drug design, their potential has not been fully realized. Therefore, we propose MolRL-MGPT, a reinforcement learning algorithm with multiple GPT agents for drug molecular generation. To promote molecular diversity, we encourage the agents to collaborate in searching for desirable molecules in diverse directions. Our algorithm has shown promising results on the GuacaMol benchmark and exhibits efficacy in designing inhibitors against SARS-CoV-2 protein targets. The codes are available at: https://github.com/HXYfighter/MolRL-MGPT.
Image Classifier Based Generative Method for Planar Antenna Design
Authors: Yang Zhong, Weiping Dou, Andrew Cohen, Dia'a Bisharat, Yuandong Tian, Jiang Zhu, Qing Huo Liu
Date: 2023-12-16 00:00:00

To extend the antenna design on printed circuit boards (PCBs) for more engineers of interest, we propose a simple method that models PCB antennas with a few basic components. By taking two separate steps to decide their geometric dimensions and positions, antenna prototypes can be facilitated with no experience required. Random sampling statistics relate to the quality of dimensions are used in selecting among dimension candidates. A novel image-based classifier using a convolutional neural network (CNN) is introduced to further determine the positions of these fixed-dimension components. Two examples from wearable products have been chosen to examine the entire workflow. Their final designs are realistic and their performance metrics are not inferior to the ones designed by experienced engineers.
Aspect Oriented Suggestion Extraction from Online Reviews
Authors: Tharushi Jayasekara, Ruvan Weerasinghe, Viraj Welgama
Date: 2021-12-15 00:00:00
Proceeding: 20th IEEE International Conference on Machine Learning and Applications (ICMLA) 2021 12
Tasks: Aspect Extraction, Language Modelling, Suggestion mining

In the world of business, products need to evolve adjusting to the needs of the customer to ensure customer satisfaction. The abundance of opinionated text on the internet contains suggestions made by users that can be used to improve products. These suggestions are important to multiple stakeholders; as product improvements for businesses or as tips and advice to consumers. Until now, the extraction of suggestions has usually been defined as a problem of classifying sentences into suggestion and non-suggestion classes. No work has attempted to differentiate suggestions on the basis of intended receiver. Extracting these suggestions with respect to the aspects that reviewers are not satisfied with, could be used as a potential solution to improve products. To address these shortcomings, this study proposes a novel task decomposition called Aspect Oriented Suggestion Extraction to identify product improvements. It contains three main subtasks: suggestion classification, beneficiary classification and aspect extraction. The proposed approach proved to be very effective in determining suggestions from non-suggestions, achieving an F-score of 91% with BERT pretrained language model, outperforming state-of-the-art methods on suggestion mining. Experimental results on hotel reviews show the effectiveness of the techniques. The proposed framework, being the first of its nature, yields promising results.
Selectivity Estimation for Range Predicates using Lightweight Models
Authors: Anshuman Dutt, Chi Wang, Azade Nazi, Srikanth Kandula, Vivek Narasayya, Surajit Chaudhuri
Date: 2019-05-01 00:00:00
Proceeding: Proceedings of the VLDB Endowment 2019 5
Tasks: Feature Engineering, regression

Query optimizers depend on selectivity estimates of query predicates to produce a good execution plan. When a query contains multiple predicates, today’s optimizers use a variety of assumptions, such as independence between predicates, to estimate selectivity. While such techniques have the benefit of fast estimation and small memory footprint, they often incur large selectivity estimation errors. In this work, we reconsider selectivity estimation as a regression problem. We explore application of neural networks and tree-based ensembles to the important problem of selectivity estimation of multi-dimensional range predicates. While their straightforward application does not outperform even simple baselines, we propose two simple yet effective design choices, i.e., regression label transformation and feature engineering, motivated by the selectivity estimation context. Through extensive empirical evaluation across a variety of datasets, we show that the proposed models deliver both highly accurate estimates as well as fast estimation.
MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video
Authors: Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Richang Hong, Tat-Seng Chua
Date: 2019-10-19 00:00:00
Proceeding: ACM International Conference on Multimedia 2019 10
Tasks: Microvideo Recommendation, Micro-video recommendations, Multimedia recommendation, Multi-Media Recommendation, Multi-modal Recommendation, Multimodal Recommendation, Representation Learning

Personalized recommendation plays a central role in many online content sharing platforms. To provide quality micro-video recommendation service, it is of crucial importance to consider the interactions between users and items (i.e. micro-videos) as well as the item contents from various modalities (e.g. visual, acoustic, and textual). Existing works on multimedia recommendation largely exploit multi-modal contents to enrich item representations, while less effort is made to leverage information interchange between users and items to enhance user representations and further capture user's fine-grained preferences on different modalities. In this paper, we propose to exploit user-item interactions to guide the representation learning in each modality, and further personalized micro-video recommendation. We design a Multi-modal Graph Convolution Network (MMGCN) framework built upon the message-passing idea of graph neural networks, which can yield modal-specific representations of users and micro-videos to better capture user preferences. Specifically, we construct a user-item bipartite graph in each modality, and enrich the representation of each node with the topological structure and features of its neighbors. Through extensive experiments on three publicly available datasets, Tiktok, Kwai, and MovieLens, we demonstrate that our proposed model is able to significantly outperform state-of-the-art multi-modal recommendation methods.
LightGT: A Light Graph Transformer for Multimedia Recommendation
Authors: Yinwei Wei, Wenqi Liu, Fan Liu, Xiang Wang, Liqiang Nie, Tat-Seng Chua
Date: 2023-07-18 00:00:00
Proceeding: SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval 2023 7
Tasks: Collaborative Filtering, Microvideo Recommendation, Micro-video recommendations, Multimedia recommendation, Multi-Media Recommendation, Multimodal Recommendation, Recommendation Systems

Multimedia recommendation methods aim to discover the user preference on the multi-modal information to enhance the collaborative filtering (CF) based recommender system. Nevertheless, they seldom consider the impact of feature extraction on the user preference modeling and prediction of the user-item interaction, as the extracted features contain excessive information irrelevant to the recommendation.
To capture the informative features from the extracted ones, we resort to Transformer model to establish the correlation between the items historically interacted by the same user. Considering its challenges in effectiveness and efficiency, we propose a novel Transformer-based recommendation model, termed as Light Graph Transformer model (LightGT). Therein, we develop a modal-specific embedding and a layer-wise position encoder for the effective similarity measurement, and present a light self-attention block to improve the efficiency of self-attention scoring. Based on these designs, we can effectively and efficiently learn the user preference from the off-the-shelf items' features to predict the user-item interactions. Conducting extensive experiments on Movielens, Tiktok and Kwai datasets, we demonstrate that LigthGT significantly outperforms the state-of-the-art baselines with less time. Our code is publicly available at: https://github.com/Liuwq-bit/LightGT.
MaxwellNet: Physics-driven deep neural network training based on Maxwell’s equations
Authors: Joowon Lim;Demetri Psaltis
Date: 2022-01-01 00:00:00
Proceeding: journal 2022 1

Maxwell’s equations govern light propagation and its interaction with matter. Therefore, the solution of Maxwell’s equations using computational electromagnetic simulations plays a critical role in understanding light–matter interaction and designing optical elements. Such simulations are often time-consuming, and recent activities have been described to replace or supplement them with trained deep neural networks (DNNs). Such DNNs typically require extensive, computationally demanding simulations using conventional electromagnetic solvers to compose the training dataset. In this paper, we present a novel scheme to train a DNN that solves Maxwell’s equations speedily and accurately without relying on other computational electromagnetic solvers. Our approach is to train a DNN using the residual of Maxwell’s equations as the physics-driven loss function for a network that finds the electric field given the spatial distribution of the material property. We demonstrate it by training a single network that simultaneously finds multiple solutions of various aspheric micro-lenses. Furthermore, we exploit the speed of this network in a novel inverse design scheme to design a micro-lens that maximizes a desired merit function. We believe that our approach opens up a novel way for light simulation and optical design of photonic devices.
Preserving Privacy Through Dememorization: An Unlearning Technique For Mitigating Memorization Risks In Language Models
Authors: Aly Kassem, Omar Mahmoud, Sherif Saad
Date: 2023-12-09 00:00:00
Proceeding: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing 2023 12
Tasks: Language Modeling, Language Modelling, Memorization

Large Language models (LLMs) are trained on vast amounts of data, including sensitive information that poses a risk to personal privacy if exposed. LLMs have shown the ability to memorize and reproduce portions of their training data when prompted by adversaries. Prior research has focused on addressing this memorization issue and preventing verbatim replication through techniques like knowledge unlearning and data pre-processing. However, these methods have limitations regarding the number of protected samples, limited privacy types, and potentially lower-quality generative models. To tackle this challenge more effectively, we propose “DeMem,” a novel unlearning approach that utilizes an efficient reinforcement learning feedback loop via proximal policy optimization. By fine-tuning the language model with a negative similarity score as a reward signal, we incentivize the LLMs to learn a paraphrasing policy to unlearn the pre-training data. Our experiments demonstrate that DeMem surpasses strong baselines and state-of-the-art methods in terms of its ability to generalize and strike a balance between maintaining privacy and LLM performance.
D-STGCNT: A Dense Spatio-Temporal Graph Conv-GRU Network based on transformer for assessment of patient physical rehabilitation
Authors: Youssef Mourchid, Rim Slama
Date: 2023-12-21 00:00:00

This paper tackles the challenge of automatically assessing physical rehabilitation exercises for patients who perform the exercises without clinician supervision. The objective is to provide a quality score to ensure correct performance and achieve desired results. To achieve this goal, a new graph-based model, the Dense Spatio-Temporal Graph Conv-GRU Network with Transformer, is introduced. This model combines a modified version of STGCN and transformer architectures for efficient handling of spatio-temporal data. The key idea is to consider skeleton data respecting its non-linear structure as a graph and detecting joints playing the main role in each rehabilitation exercise. Dense connections and GRU mechanisms are used to rapidly process large 3D skeleton inputs and effectively model temporal dynamics. The transformer encoder's attention mechanism focuses on relevant parts of the input sequence, making it useful for evaluating rehabilitation exercises. The evaluation of our proposed approach on the KIMORE and UI-PRMD datasets highlighted its potential, surpassing state-of-the-art methods in terms of accuracy and computational time. This resulted in faster and more accurate learning and assessment of rehabilitation exercises. Additionally, our model provides valuable feedback through qualitative illustrations, effectively highlighting the significance of joints in specific exercises.
AQuA: A Benchmarking Tool for Label Quality Assessment
Authors: Mononito Goswami, Vedant Sanil, Arjun Choudhry, Arvind Srinivasan, Chalisa Udompanyawit, Artur Dubrawski
Date: 2023-06-15 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Benchmarking, Label Error Detection, Model Selection

Machine learning (ML) models are only as good as the data they are trained on. But recent studies have found datasets widely used to train and evaluate ML models, e.g. ImageNet, to have pervasive labeling errors. Erroneous labels on the train set hurt ML models' ability to generalize, and they impact evaluation and model selection using the test set. Consequently, learning in the presence of labeling errors is an active area of research, yet this field lacks a comprehensive benchmark to evaluate these methods. Most of these methods are evaluated on a few computer vision datasets with significant variance in the experimental protocols. With such a large pool of methods and inconsistent evaluation, it is also unclear how ML practitioners can choose the right models to assess label quality in their data. To this end, we propose a benchmarking environment AQuA to rigorously evaluate methods that enable machine learning in the presence of label noise. We also introduce a design space to delineate concrete design choices of label error detection models. We hope that our proposed design space and benchmark enable practitioners to choose the right tools to improve their label quality and that our benchmark enables objective and rigorous evaluation of machine learning tools facing mislabeled data.
Single-Image Specular Highlight Removal via Real-World Dataset Construction
Authors: Zhongqi Wu, Chuanqing Zhuang, Jian Shi, Jianwei Guo, Jun Xiao, Xiaopeng Zhang, Dong-Ming Yan
Date: 2021-08-27 00:00:00
Proceeding: TMM 2021 8
Tasks: Generative Adversarial Network, Highlight Detection, highlight removal, Image Segmentation, Semantic Segmentation

Specular reflections pose great challenges on various multimedia and computer vision tasks, e.g. , image segmentation, detection and matching. In this paper, we build a large-scale Paired Specular-Diffuse (PSD) image dataset, where the images are carefully captured by using real-world objects and the ground-truth specular-free diffuse images are provided. To the best of our knowledge, this is the first real-world benchmark dataset for specular highlight removal task, which is useful for evaluating and encouraging new deep learning-based approaches. Given this dataset, we present a novel Generative Adversarial Network (GAN) for specular highlight removal from a single image by introducing the detection of specular reflection information as a guidance. Our network also makes full use of the attention mechanism and is able to directly model the mapping relation between the diffuse area and the specular highlight area without any explicit estimation of the illumination. Experimental results demonstrate that the proposed network is more effective to remove specular reflection components with the guidance of specular highlight detection than recent state-of-the-art methods.
Causal Effect Estimation on Hierarchical Spatial Graph Data
Authors: Koh Takeuchi, Ryo Nishida, Hisashi Kashima, Masaki Onishi
Date: 2023-08-06 00:00:00
Proceeding: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2023 8
Tasks: Causal Inference, Time Series

Estimating individual treatment effects from observational data is a fundamental problem in causal inference. To accurately estimate treatment effects in the spatial domain, we need to address certain aspects such as how to use the spatial coordinates of covariates and treatments and how the covariates and the treatments interact spatially. We introduce a new problem of predicting treatment effects on time series outcomes from spatial graph data with a hierarchical structure. To address this problem, we propose a spatial intervention neural network (SINet) that leverages the hierarchical structure of spatial graphs to learn a rich representation of the covariates and the treatments and exploits this representation to predict a time series of treatment outcome. Using a multi-agent simulator, we synthesized a crowd movement guidance dataset and conduct experiments to estimate the conditional average treatment effect, where we considered the initial locations of the crowds as covariates, route guidance as a treatment, and number of agents reaching a goal at each time stamp as the outcome. We employed state-of-the-art spatio-temporal graph neural networks and neural network-based causal inference methods as baselines, and show that our proposed method outperformed baselines both quantitatively and qualitatively.
Analyses and Concerns in Precision Medicine: A Statistical Perspective
Authors: Xiaofei Chen
Date: 2024-01-12 00:00:00
Tasks: Data Integration, Data Visualization

This article explores the critical role of statistical analysis in precision medicine. It discusses how personalized healthcare is enhanced by statistical methods that interpret complex, multidimensional datasets, focusing on predictive modeling, machine learning algorithms, and data visualization techniques. The paper addresses challenges in data integration and interpretation, particularly with diverse data sources like electronic health records (EHRs) and genomic data. It also delves into ethical considerations such as patient privacy and data security. In addition, the paper highlights the evolution of statistical analysis in medicine, core statistical methodologies in precision medicine, and future directions in the field, emphasizing the integration of artificial intelligence (AI) and machine learning (ML).