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Found 65 papers, 25 papers with code
Coordinated Power Smoothing Control for Wind Storage Integrated System with Physics-informed Deep Reinforcement Learning
The Wind Storage Integrated System with Power Smoothing Control (PSC) has emerged as a promising solution to ensure both efficient and reliable wind energy generation.
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
Hard-Synth: Synthesizing Diverse Hard Samples for ASR using Zero-Shot TTS and LLM
Existing research primarily utilizes additional text data and predefined speech styles supported by TTS models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Legal Evalutions and Challenges of Large Language Models
In this paper, we review legal testing methods based on Large Language Models (LLMs), using the OPENAI o1 model as a case study to evaluate the performance of large models in applying legal provisions.
Evaluation of OpenAI o1: Opportunities and Challenges of AGI
-Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis.
Large Language Model Should Understand Pinyin for Chinese ASR Error Correction
Large language models can enhance automatic speech recognition systems through generative error correction.
GAIA -- A Large Language Model for Advanced Power Dispatch
This paper introduces GAIA, the pioneering Large Language Model (LLM) tailored for power dispatch tasks.
ElecBench: a Power Dispatch Evaluation Benchmark for Large Language Models
In response to the urgent demand for grid stability and the complex challenges posed by renewable energy integration and electricity market dynamics, the power sector increasingly seeks innovative technological solutions.
Self-supervised Graph Neural Network for Mechanical CAD Retrieval
In this work, we propose GC-CAD, a self-supervised contrastive graph neural network-based method for mechanical CAD retrieval that directly models parameterized CAD raw files.
WenetSpeech4TTS: A 12,800-hour Mandarin TTS Corpus for Large Speech Generation Model Benchmark
Furthermore, we have created subsets of varying sizes, categorized by segment quality scores to allow for TTS model training and fine-tuning.
ESIHGNN: Event-State Interactions Infused Heterogeneous Graph Neural Network for Conversational Emotion Recognition
Toward this end, we propose a novel graph-based approach, namely Event-State Interactions infused Heterogeneous Graph Neural Network (ESIHGNN), which incorporates the speaker's emotional state and constructs a heterogeneous event-state interaction graph to model the conversation.
Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods
For each role, we summarize the methodologies, analyze the specific RL challenges that are mitigated, and provide insights into future directions.
EventRL: Enhancing Event Extraction with Outcome Supervision for Large Language Models
In this study, we present EventRL, a reinforcement learning approach developed to enhance event extraction for large language models (LLMs).
Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models
This paper proposes a novel conceptual prototype for designing versatile graph learning methods with LLMs, with a particular focus on the "where" and "how" perspectives.
Towards Fair, Robust and Efficient Client Contribution Evaluation in Federated Learning
In this paper, we introduce a novel method called Fair, Robust, and Efficient Client Assessment (FRECA) for quantifying client contributions in FL.
Power System Fault Diagnosis with Quantum Computing and Efficient Gate Decomposition
Power system fault diagnosis is crucial for identifying the location and causes of faults and providing decision-making support for power dispatchers.
Free Lunch for Federated Remote Sensing Target Fine-Grained Classification: A Parameter-Efficient Framework
Remote Sensing Target Fine-grained Classification (TFGC) is of great significance in both military and civilian fields.
A Novel Dual-robot Accurate Calibration Method Using Convex Optimization and Lie Derivative
Traditional linear methods are inadequate to decouple and simultaneously solve the unknown matrices due to their intercoupling.
Ophtha-LLaMA2: A Large Language Model for Ophthalmology
In recent years, pre-trained large language models (LLMs) have achieved tremendous success in the field of Natural Language Processing (NLP).
Joint Training or Not: An Exploration of Pre-trained Speech Models in Audio-Visual Speaker Diarization
The scarcity of labeled audio-visual datasets is a constraint for training superior audio-visual speaker diarization systems.
Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service
Then, we formulate the initial client pool selection problem into an optimization problem that aims to maximize the overall scores of selected clients within a given budget and propose a greedy algorithm to solve it.
Applying Large Language Models to Power Systems: Potential Security Threats
Applying large language models (LLMs) to modern power systems presents a promising avenue for enhancing decision-making and operational efficiency.
Holistic Evaluation of GPT-4V for Biomedical Imaging
GPT-4V represents a breakthrough in artificial general intelligence (AGI) for computer vision, with applications in the biomedical domain.
Evaluating multiple large language models in pediatric ophthalmology
IMPORTANCE The response effectiveness of different large language models (LLMs) and various individuals, including medical students, graduate students, and practicing physicians, in pediatric ophthalmology consultations, has not been clearly established yet.
Evaluating Large Language Models in Ophthalmology
Methods: A 100-item ophthalmology single-choice test was administered to three different LLMs (GPT-3. 5, GPT-4, and PaLM2) and three different professional levels (medical undergraduates, medical masters, and attending physicians), respectively.
Customising General Large Language Models for Specialised Emotion Recognition Tasks
This indicates the strong transferability and feasibility of LLMs in the field of emotion recognition.
Benchmarking Large Language Models with Augmented Instructions for Fine-grained Information Extraction
Information Extraction (IE) is an essential task in Natural Language Processing.
ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data
Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels.
A Versatile Graph Learning Approach through LLM-based Agent
In this paper, we propose to explore versatile graph learning approaches with LLM-based agents, and the key insight is customizing the graph learning procedures for diverse graphs and tasks.
Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models
After the inception of emotion recognition or affective computing, it has increasingly become an active research topic due to its broad applications.
Automated 3D Pre-Training for Molecular Property Prediction
Next, to automatically fuse these three generative tasks, we design a surrogate metric using the \textit{total energy} to search for weight distribution of the three pretext task since total energy corresponding to the quality of 3D conformer. Extensive experiments on 2D molecular graphs are conducted to demonstrate the accuracy, efficiency and generalization ability of the proposed 3D PGT compared to various pre-training baselines.
MASK-CNN-Transformer For Real-Time Multi-Label Weather Recognition
The proposed model called MASK-Convolutional Neural Network-Transformer (MASK-CT) is based on the Transformer, the convolutional process, and the MASK mechanism.
TempEE: Temporal-Spatial Parallel Transformer for Radar Echo Extrapolation Beyond Auto-Regression
TempEE avoids using auto-regression and instead employs a one-step forward strategy to prevent cumulative error spreading during the extrapolation process.
Exploring the Feasibility of ChatGPT for Event Extraction
While ChatGPT has demonstrated impressive results in tasks like machine translation, text summarization, and question answering, it presents challenges when used for complex tasks like event extraction.
Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification
In recent years, Graph Neural Networks (GNNs) have been popular in the graph classification task.
Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction
We present Mask-then-Fill, a flexible and effective data augmentation framework for event extraction.
Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach
Despite the success, we observe two aspects that can be further improved: (a) enhancing the ego feature information extraction from node itself which is more reliable in extracting the intra-class information; (b) designing node-wise GNNs can better adapt to the nodes with different homophily ratios.
Ranked #4 on
Node Classification
on Actor
TSUP Speaker Diarization System for Conversational Short-phrase Speaker Diarization Challenge
In this challenge, we explore three kinds of typical speaker diarization systems, which are spectral clustering(SC) based diarization, target-speaker voice activity detection(TS-VAD) and end-to-end neural diarization(EEND) respectively.
Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Search
Aiming at two molecular graph datasets and one protein association subgraph dataset in OGB graph classification task, we design a graph neural network framework for graph classification task by introducing PAS(Pooling Architecture Search).
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020
Researchers naturally adopt Automated Machine Learning on Graph Learning, aiming to reduce the human effort and achieve generally top-performing GNNs, but their methods focus more on the architecture search.
Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering
Representations of events described in text are important for various tasks.
Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network
Heterogeneous Graph Neural Network (HGNN) has been successfully employed in various tasks, but we cannot accurately know the importance of different design dimensions of HGNNs due to diverse architectures and applied scenarios.
Graph Neural Networks for Double-Strand DNA Breaks Prediction
Double-strand DNA breaks (DSBs) are a form of DNA damage that can cause abnormal chromosomal rearrangements.
Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective
To enjoy the benefits while alleviating the corresponding deficiencies of these two manners, we learn to design the topology of GNNs in a novel feature fusion perspective which is dubbed F$^2$GNN.
Learn Layer-wise Connections in Graph Neural Networks
In recent years, Graph Neural Networks (GNNs) have shown superior performance on diverse applications on real-world datasets.
Nondestructive Testing of Composite Fibre Materials with Hyperspectral Imaging : Evaluative Studies in the EU H2020 FibreEUse Project
Through capturing spectral data from a wide frequency range along with the spatial information, hyperspectral imaging (HSI) can detect minor differences in terms of temperature, moisture and chemical composition.
Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platform
A public instance of Codabench (https://www. codabench. org/) is open to everyone, free of charge, and allows benchmark organizers to compare fairly submissions, under the same setting (software, hardware, data, algorithms), with custom protocols and data formats.
Multi-Relational Graph based Heterogeneous Multi-Task Learning in Community Question Answering
To tackle this challenge, we develop a multi-relational graph based MTL model called Heterogeneous Multi-Task Graph Isomorphism Network (HMTGIN) which efficiently solves heterogeneous CQA tasks.
Pooling Architecture Search for Graph Classification
To address this problem, we propose to use neural architecture search (NAS) to search for adaptive pooling architectures for graph classification.
TabGNN: Multiplex Graph Neural Network for Tabular Data Prediction
Tabular data prediction (TDP) is one of the most popular industrial applications, and various methods have been designed to improve the prediction performance.
Search to aggregate neighborhood for graph neural network
In this work, to obtain the data-specific GNN architectures and address the computational challenges facing by NAS approaches, we propose a framework, which tries to Search to Aggregate NEighborhood (SANE), to automatically design data-specific GNN architectures.
Efficient Graph Neural Architecture Search
To obtain state-of-the-art (SOAT) data-specific GNN architectures, researchers turn to the neural architecture search (NAS) methods.
DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks
Specifically, we search for a meta graph, which can capture more complex semantic relations than a meta path, to determine how graph neural networks (GNNs) propagate messages along different types of edges.
Machine-learning-enhanced time-of-flight mass spectrometry analysis
Mass spectrometry is a widespread approach to work out what are the constituents of a material.
Simplifying Architecture Search for Graph Neural Network
Recent years have witnessed the popularity of Graph Neural Networks (GNN) in various scenarios.
Phase-coherent asynchronous optical sampling system
Mutual phase coherence is the most crucial factor in asynchronous optical sampling system, and its enhancement is commonly achieved by phase-locking both femtosecond lasers to a shared narrow-linewidth cavity-stabilized laser.
Optics Instrumentation and Detectors
Vertex-reinforced Random Walk for Network Embedding
In this paper, we study the fundamental problem of random walk for network embedding.
Motif Enhanced Recommendation over Heterogeneous Information Network
In this paper, we propose to use motifs to capture higher-order relations among nodes of same type in a HIN and develop the motif-enhanced meta-path (MEMP) to combine motif-based higher-order relations with edge-based first-order relations.
Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction
An effective content recommendation in modern social media platforms should benefit both creators to bring genuine benefits to them and consumers to help them get really interesting content.
Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
Deep learning based methods have been widely used in industrial recommendation systems (RSs).
Ranked #10 on
Recommendation Systems
on MovieLens 1M
POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion
In particular, there exist two requirements for fashion outfit recommendation: the Compatibility of the generated fashion outfits, and the Personalization in the recommendation process.
Multi-Interest Network with Dynamic Routing for Recommendation at Tmall
Industrial recommender systems usually consist of the matching stage and the ranking stage, in order to handle the billion-scale of users and items.
Ranked #1 on
Information Retrieval
on Amazon
A General End-to-end Diagnosis Framework for Manufacturing Systems
A central challenge in manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications.
Automated Machine Learning: From Principles to Practices
Machine learning (ML) methods have been developing rapidly, but configuring and selecting proper methods to achieve a desired performance is increasingly difficult and tedious.
Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba
Using online A/B test, we show that the online Click-Through-Rate (CTRs) are improved comparing to the previous recommendation methods widely used in Taobao, further demonstrating the effectiveness and feasibility of our proposed methods in Taobao's live production environment.
Side Information Fusion for Recommender Systems over Heterogeneous Information Network
Collaborative filtering (CF) has been one of the most important and popular recommendation methods, which aims at predicting users' preferences (ratings) based on their past behaviors.
