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arXiv 2023年

作者： Hennigen, Lucas Torroba Kim, Yoon Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory United States

Masked language models (MLM) do not explicitly define a distribution over language, i.e., they are not language models per se. However, recent work has implicitly treated them as such for the purposes of generation and scoring. This paper studies methods for deriving explicit joint distributions from MLMs, focusing on distributions over two tokens, which makes it possible to calculate exact distributional properties. We find that an approach based on identifying joints whose conditionals are closest to those of the MLM works well and outperforms existing Markov random field-based approaches. We further find that this derived model's conditionals can even occasionally outperform the original MLM's conditionals. © 2023, CC BY-SA.

关键词： Markov processes

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JTMA: Joint Multimodal Feature Fusion and Temporal Multi-head Attention for Humor Detection 4

JTMA: Joint Multimodal Feature Fusion and Temporal Multi-hea...

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4th Multimodal Sentiment Analysis Challenge and Workshop, MuSe 2023, In conjunction with ACM Multimedia 2023

作者： Li, Qi Xu, Yangyang Zhao, Zhuoer Tang, Shulei Zhang, Feixiang Wang, Ruotong Sun, Xiao Wang, Meng AHU-IAI AI Joint Laboratory Anhui University HeFei China Institute of Advanced Technology University of Science and Technology of China HeFei China School of Computer Science and Information Engineering Hefei University of Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Center ZhongJuYuan Intelligent Technology Co Ltd HeFei China School of Computer Science and Information Engineering Hefei University of Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Center HeFei China

ISBN: (纸本)9798400702709

In this paper, we propose a model named Joint multimodal feature fusion and Temporal Multi-head Attention (JTMA) to solve the MuSe-Humor sub-challenge in Multimodal Sentiment Analysis Challenge 2023. The goal of MuSe-Humor sub-challenge is to predict whether humor occurs in the given dataset that includes data from multiple modalities (e.g., text, audio and video). The cross-cultural testing presents a new challenge that makes it different from the previous years. To solve the above problems, the proposed model JTMA firstly uses a 1-D CNN to aggregate temporal information within the unimodal feature. Then the interactions of inter-modality and intra-modality are performed by the multimodal feature encoder module. Finally, we integrate the high-level representations learned from multiple modalities to accurately predict humor. The effectiveness of our proposed model is demonstrated through experimental results obtained on the official test set. Our model achieves an impressive AUC score of 0.8889, surpassing the performance of all other participants in the competition, and securing the Top 1 ranking. © 2023 ACM.

关键词： Sentiment analysis

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Tiling with Three Polygons is Undecidable

arXiv

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arXiv 2024年

作者： Demaine, Erik D. Langerman, Stefan Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Computer Science Department Université libre de Bruxelles Belgium

We prove that the following problem is co-RE-complete and thus undecidable: given three simple polygons, is there a tiling of the plane where every tile is an isometry of one of the three polygons (either allowing or forbidding reflections)? This result improves on the best previous construction which requires five polygons. Copyright © 2024, The Authors. All rights reserved.

关键词： Geometry

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Temporal-Aware Multimodal Feature Fusion for Sentiment Analysis 4

Temporal-Aware Multimodal Feature Fusion for Sentiment Analy...

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4th Multimodal Sentiment Analysis Challenge and Workshop, MuSe 2023, In conjunction with ACM Multimedia 2023

作者： Li, Qi Tang, Shulei Zhang, Feixiang Wang, Ruotong Xu, Yangyang Zhao, Zhuoer Sun, Xiao Wang, Meng AHU-IAI AI Joint Laboratory Anhui University HeFei China Institute of Advanced Technology University of Science and Technology of China HeFei China School of Computer Science and Information Engineering Hefei University of Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Center ZhongJuYuan Intelligent Technology Co Ltd HeFei China School of Computer Science and Information Engineering Hefei University of Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Center HeFei China

ISBN: (纸本)9798400702709

In this paper, we present a solution to the MuSe-Personalisation sub-challenge in the Multimodal Sentiment Analysis Challenge 2023. The task of MuSe-Personalisation aims to predict a time-continuous emotional value (i.e., arousal and valence) by using multimodal data. The MuSe-Personalisation sub-challenge faces the individual variations problem, resulting in poor generalization on unknown test sets. To solve the above problem, we first extract several informative visual features, and then propose a framework containing feature selection, feature learning and fusion strategy to discover the best combination of features for sentiment analysis. Finally, our method achieved the Top 1 performance in the MuSe-Personalisation sub-challenge, and the result in the combined CCC of physiological arousal and valence was 0.8681, outperforming the baseline system by a large margin (i.e., 10.42%) on the test set. © 2023 ACM.

关键词： Sentiment analysis

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CONTINUAL IMPROVEMENT OF THRESHOLD-BASED NOVELTY DETECTION

arXiv

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arXiv 2023年

作者： Ejilemele, Abe Mendez-Mendez, Jorge Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States

When evaluated in dynamic, open-world situations, neural networks struggle to detect unseen classes. This issue complicates the deployment of continual learners in realistic environments where agents are not explicitly informed when novel categories are encountered. A common family of techniques for detecting novelty relies on thresholds of similarity between observed data points and the data used for training. However, these methods often require manually specifying (ahead of time) the value of these thresholds, and are therefore incapable of adapting to the nature of the data. We propose a new method for automatically selecting these thresholds utilizing a linear search and leave-one-out cross-validation on the ID classes. We demonstrate that this novel method for selecting thresholds results in improved total accuracy on MNIST, Fashion MNIST, and CIFAR-10. © 2023, CC BY.

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VRLVMix: Combating Noisy Labels with Sample Selection based on Loss Variation 4

VRLVMix: Combating Noisy Labels with Sample Selection based ...

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4th International Conference on Big Data, artificial intelligence and Internet of Things Engineering, ICBAIE 2023

作者： Wang, Hailun Tu, Zhengzheng Jiang, Bo Ding, Yuhe Anhui University Institute of Artificial Intelligence Hefei Comprehensive National Science Center AHU-IAI Ai Joint Laboratory Hefei China Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350343618

Since deep neural networks can fully fit all data, including noisy labels, that is, mislabeled data, this will be detrimental to the robustness and generalization ability of the network. To address this problem, existing methods usually use small loss tricks to select clean samples for training. However, sample selection using only small loss techniques cannot distinguish between large loss 'hard' samples and noise samples. In this work, we analyze the reasons why clean samples are misidentified as noise samples and propose a balanced selection mechanism based on loss variation. This method sorts the amount of loss change, uses variance to count the rankings of multiple epochs to describe the stability of loss variation, and selects samples with stable loss as clean samples to reduce the misclassification of 'hard' samples. At the same time, semantic clustering is used to assist resampling and reweighting, thereby alleviating the negative impact 1 of class imbalance on sample selection. We conduct comparative experiments and ablation experiments on synthetic noise datasets and real-world datasets such as CIFAR-10/100 and Clothing1M. Our VRLVMix has been shown to outperform numerous state-of-the-art methods, as evidenced by the experimental results. Moreover, it demonstrates the ability to extract clean samples even from large loss sample. © 2023 IEEE.

关键词： Semantics

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Predicting Monthly Wheat Crop Coefficients in Morocco Using ANN Models with Limited Meteorological Data: A Study in the Draa-Tafilalet Region 9th

Predicting Monthly Wheat Crop Coefficients in Morocco Using ...

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9th International Conference on Smart City Applications, SCA 2024

作者： Ed-Daoudi, Rachid Ettaki, Badia Zerouaoui, Jamal LyRICA: Research Laboratory in Computer Science Data Sciences and Artificial Intelligence School of Information Sciences B.P: 6204 Rabat-Instituts 10100 Rabat Morocco Laboratory of Engineering Sciences and Modeling Faculty of Sciences Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco

ISBN: (纸本)9783031886522

The increasing population and water demand in North Africa, particularly in semi-arid and arid regions like Morocco, necessitate efficient water resource management. Morocco experiences significant water scarcity due to persistent droughts and rising agricultural water demand. Accurate estimation of crop water requirements, particularly crop evapotranspiration, influenced by climatic and crop-specific conditions, is vital for improving water use efficiency. This study focuses on wheat, a staple crop in Morocco's Draa-tafilalet region, encompassing Ouarzazate, Zagora, and Tinghir. Traditional methods for estimating the crop coefficient require comprehensive climatic datasets, often unavailable. Therefore, this study aims to develop and validate artificial neural network models to predict monthly Kc values for wheat using limited climatic data. The results showed that the optimal model for predicting crop coefficients used a minimum and maximum temperatures, along with solar radiation, applying different configurations of hidden neuron layers for each region: (7, 5) for Ouarzazate, (8, 6) for Zagora, and (9, 6) for Tinghir. The best model's accuracy was close to 1, showing a statistically significant accordance between observed and predicted values. This study highlights the model's high accuracy and recommends its use for predicting crop coefficients values with limited climatic factors. It also supports water users in developing and updating Kc databases for each region based on climatic conditions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Resource allocation

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Non-Gaussian Uncertainty Minimization Based Control of Stochastic Nonlinear Robotic Systems

arXiv

引用

arXiv 2023年

作者： Han, Weiqiao Jasour, Ashkan Williams, Brian Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States

In this paper, we consider the closed-loop control problem of nonlinear robotic systems in the presence of probabilistic uncertainties and disturbances. More precisely, we design a state feedback controller that minimizes deviations of the states of the system from the nominal state trajectories due to uncertainties and disturbances. Existing approaches to address the control problem of probabilistic systems are limited to particular classes of uncertainties and systems such as Gaussian uncertainties and processes and linearized systems. We present an approach that deals with nonlinear dynamics models and arbitrary known probabilistic uncertainties. We formulate the controller design problem as an optimization problem in terms of statistics of the probability distributions including moments and characteristic functions. In particular, in the provided optimization problem, we use moments and characteristic functions to propagate uncertainties throughout the nonlinear motion model of robotic systems. In order to reduce the tracking deviations, we minimize the uncertainty of the probabilistic states around the nominal trajectory by minimizing the trace and the determinant of the covariance matrix of the probabilistic states. To obtain the state feedback gains, we solve deterministic optimization problems in terms of moments, characteristic functions, and state feedback gains using off-the-shelf interior-point optimization solvers. To illustrate the performance of the proposed method, we compare our method with existing probabilistic control methods. © 2023, CC BY.

关键词： Optimization

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Cooperative Multi-source Data Trading

Cooperative Multi-source Data Trading

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Cheng, Jin Ding, Ningning Lui, John C. S. Huang, Jianwei The Chinese University of Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Hong Kong University of Science and Technology Data Science and Analytics Thrust Information Hub Guangzhou China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong The Chinese University of Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network Csijri Joint Research Centre on Smart Energy Storage Shenzhen China

ISBN: (纸本)9798350351255

In the era of big data, data trading significantly enhances data-driven technologies by facilitating data sharing. Despite the clear advantages often experienced by data users when incorporating multiple sources, the topic of multi-source data trading remains largely unexplored. This paper designs a novel data trading framework, which enables multi-source data trading through multi-source cooperation. The proposed framework aims to improve data usage efficiency and increase seller revenue. In particular, we model data sellers' cooperative decisions through the Nash bargaining framework and systematically outline the interactions between sellers and buyers as a two-stage Stackelberg game. A key contribution of this work is the consideration of coupling among diverse data products, which is essential but often overlooked in prior studies. We properly classify data's utility into endogenous and relational categories to disentangle the coupling. Despite the inherent non-convex nature of the optimization problem, we methodically derive the closed-form optimal solutions by decomposing the problem into several subproblems. Interestingly, we reveal that, under our proposed framework, sellers' revenue initially remains steady with the increase of product coupling level, but begins to rise once the level exceeds a certain threshold due to the substitute effect. Finally, experimental results show that our proposed framework can improve the seller's profit by up to 46.32% compared to traditional data trading methods in the current data market. © 2024 IEEE.

关键词： Marketplaces

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Time-aware Medication Recommendation via Intervention of Dynamic Treatment Regimes 25

Time-aware Medication Recommendation via Intervention of Dyn...

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34th ACM Web Conference, WWW 2025

作者： Li, Yishuo Zhang, Qi Lu, Wenpeng Peng, Xueping Zhang, Weiyu Si, Jiasheng Gong, Yongshun Hu, Liang Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Department of Computer Science and Technology Tongji University Shanghai China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia School of Software Shandong University Jinan China

ISBN: (纸本)9798400712746

Medication recommendation aims to suggest personalized drug combinations to patients based on their longitudinal medical histories stored in electronic health record (EHR) datasets. Patients’ Dynamic Treatment Regimes (DTRs) determine how patients’ drug combinations change along with the evolution of disease treatment. DTRs are effective for comprehending disease-treatment dynamics and for recommending a timely and personalized combination of medications for patients. However, existing medication recommender systems (MRSs) overlook the multiple treatment pathways generated by the intervention of DTRs and can only recommend a single treatment paradigm, ignoring the fact that patients may be at different treatment stages and thus require different treatment regime. Such disregard leads to a significant limitation in recommending personalized medication combinations tailored to different treatment stages, yielding greatly compromised accuracy and applicability of MRSs. Moreover, existing methods often overlook the time interval information over patients’ successive visits, which is critical to indicate patients’ treatment evolution. To address these significant gaps, we propose a Time-aware Medication Recommendation Framework via Intervention of Dynamic Treatment Regimes, called MR-DTR. To explicitly illustrate the intervention processes of DTRs on similar patients, we employ a co-guided graph to connect various patient sequences. In addition, to fully utilize the time interval information, we design a time-aware guidance mechanism dedicated to the co-guided graph to efficiently learn medication representation using the patient’s guidance information. We also introduce relative time intervals in the encoder to act as positional information. Extensive experiments on two real-world datasets demonstrate that MR-DTR surpasses state-of-the-art models in terms of recommendation performance. © 2025 Copyright held by the owner/author(s).

关键词： Electronic health record

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