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Found 25 papers, 7 papers with code
基于层次注意力机制和门机制的属性级别情感分析(Aspect-level Sentiment Analysis Based on Hierarchical Attention and Gate Networks)
近年来, 作为细粒度的属性级别情感分析在商业界和学术界受到越来越多的关注, 其目的在于识别一个句子中多个属性词所对应的情感极性。目前, 在解决属性级别情感分析问题的绝大多数工作都集中在注意力机制的设计上, 以此突出上下文和属性词中不同词对于属性级别情感分析的贡献, 同时使上下文和属性词之间相互关联。本文提出使用层次注意力机制和门机制处理属性级别情感分析任务, 在得到属性词的隐藏状态之后, 通过注意力机制得到属性词新的表示, 然后利用属性词新的表示和注意力机制进一步得到上下文新的表示, 层次注意力机制的设计使得上下文和属性词的表达更加准确;同时通过门机制选择对属性词而言上下文中有用的信息, 以此丰富上下文的表达, 在SemEval 2014 Task4和Twitter数据集上的实验结果表明本文提出模型的有效性。
FedEP: Tailoring Attention to Heterogeneous Data Distribution with Entropy Pooling for Decentralized Federated Learning
Federated Learning (FL) performance is highly influenced by data distribution across clients, and non-Independent and Identically Distributed (non-IID) leads to a slower convergence of the global model and a decrease in model effectiveness.
De-VertiFL: A Solution for Decentralized Vertical Federated Learning
Among the FL paradigm, horizontal FL, where clients share the same set of features but different data samples, has been extensively studied in both centralized and decentralized settings.
Convolutional Beamspace Beamforming for Low-Complexity Far-Field and Near-Field MU-MIMO Communications
The commonly used linear processing schemes include the maximum-ratio combining (MRC), zero-forcing (ZF) and minimum mean squared error (MMSE) beamforming, which may result in the unfavorable performance or complexity as the antenna number grows.
Learning Deep Tree-based Retriever for Efficient Recommendation: Theory and Method
Although advancements in deep learning have significantly enhanced the recommendation accuracy of deep recommendation models, these methods still suffer from low recommendation efficiency.
Enhancing Spatial Multiplexing and Interference Suppression for Near- and Far-Field Communications with Sparse MIMO
Given the same number of array elements, the performance of sparse MIMO is compared with conventional MIMO.
NeuroBind: Towards Unified Multimodal Representations for Neural Signals
Understanding neural activity and information representation is crucial for advancing knowledge of brain function and cognition.
This&That: Language-Gesture Controlled Video Generation for Robot Planning
We propose a robot learning method for communicating, planning, and executing a wide range of tasks, dubbed This&That.
IFA: Interaction Fidelity Attention for Entire Lifelong Behaviour Sequence Modeling
The lifelong user behavior sequence provides abundant information of user preference and gains impressive improvement in the recommendation task, however increases computational consumption significantly.
Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning
Despite vision-language models' (VLMs) remarkable capabilities as versatile visual assistants, two substantial challenges persist within the existing VLM frameworks: (1) lacking task diversity in pretraining and visual instruction tuning, and (2) annotation error and bias in GPT-4 synthesized instruction tuning data.
Binding Touch to Everything: Learning Unified Multimodal Tactile Representations
We introduce UniTouch, a unified tactile model for vision-based touch sensors connected to multiple modalities, including vision, language, and sound.
Sentinel: An Aggregation Function to Secure Decentralized Federated Learning
Decentralized Federated Learning (DFL) emerges as an innovative paradigm to train collaborative models, addressing the single point of failure limitation.
Knowledge Solver: Teaching LLMs to Search for Domain Knowledge from Knowledge Graphs
In some previous works, additional modules like graph neural networks (GNNs) are trained on retrieved knowledge from external knowledge bases, aiming to mitigate the problem of lacking domain-specific knowledge.
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation
Recent research has shown that the integration of Reinforcement Learning (RL) with Moving Target Defense (MTD) can enhance cybersecurity in Internet-of-Things (IoT) devices.
RCVaR: an Economic Approach to Estimate Cyberattacks Costs using Data from Industry Reports
Therefore, managing risk exposure and cybersecurity strategies is essential for digitized companies that want to survive in competitive markets.
Fedstellar: A Platform for Decentralized Federated Learning
To overcome these challenges, this paper presents Fedstellar, a platform extended from p2pfl library and designed to train FL models in a decentralized, semi-decentralized, and centralized fashion across diverse federations of physical or virtualized devices.
Self-Supervised Video Forensics by Audio-Visual Anomaly Detection
Manipulated videos often contain subtle inconsistencies between their visual and audio signals.
Ranked #3 on
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on FakeAVCeleb
MTCSNN: Multi-task Clinical Siamese Neural Network for Diabetic Retinopathy Severity Prediction
The novelty of this project is to utilize the ordinal information among labels and add a new regression task, which can help the model learn more discriminative feature embedding for fine-grained classification tasks.
Reinforcement Routing on Proximity Graph for Efficient Recommendation
Instead of searching the nearest neighbor for the query, we search the item with maximum inner product with query on the proximity graph.
AVA-AVD: Audio-Visual Speaker Diarization in the Wild
Audio-visual speaker diarization aims at detecting "who spoke when" using both auditory and visual signals.
Scalable Data Annotation Pipeline for High-Quality Large Speech Datasets Development
Our A/B testing and pilot results demonstrated the HITL pipeline can improve annotation speed and capacity by at least 80% and quality is comparable to or higher than manual double pass annotation.
Exploiting Network Structures to Improve Semantic Representation for the Financial Domain
This paper presents the participation of the MiniTrue team in the FinSim-3 shared task on learning semantic similarities for the financial domain in English language.
A Simple Voting Mechanism for Online Sexist Content Identification
This paper presents the participation of the MiniTrue team in the EXIST 2021 Challenge on the sexism detection in social media task for English and Spanish.
Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences
Evolutionary algorithms (EAs) are general-purpose optimization algorithms, inspired by natural evolution.
Reinforced Product Metadata Selection for Helpfulness Assessment of Customer Reviews
Given a product, a selector (agent) learns from both the keys in the product metadata and one of its reviews to take an action that selects the correct value, and a successive predictor (network) makes the free-text review attend to this value to obtain better neural representations for helpfulness assessment.
