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Found 13 papers, 0 papers with code
VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections
Therefore, mini-batch training for graph transformers is a promising direction, but limited samples in each mini-batch can not support effective dense attention to encode informative representations.
Quantifying Policy Administration Cost in an Active Learning Framework
To the best of our knowledge, this is the first work to employ an active learning framework to study the cost of policy deliberation and demonstrate the cost advantage of heuristic policy administration.
Empowering Working Memory for Large Language Model Agents
The limitations of traditional LLM memory designs are analyzed, including their isolation of distinct dialog episodes and lack of persistent memory links.
Hierarchical Multi-Marginal Optimal Transport for Network Alignment
To depict high-order relationships across multiple networks, the FGW distance is generalized to the multi-marginal setting, based on which networks can be aligned jointly.
LLM-Rec: Personalized Recommendation via Prompting Large Language Models
Notably, the success of LLM-Rec lies in its prompting strategies, which effectively tap into the language model's comprehension of both general and specific item characteristics.
GPatcher: A Simple and Adaptive MLP Model for Alleviating Graph Heterophily
While graph heterophily has been extensively studied in recent years, a fundamental research question largely remains nascent: How and to what extent will graph heterophily affect the prediction performance of graph neural networks (GNNs)?
Dynamic Transfer Learning across Graphs
Transferring knowledge across graphs plays a pivotal role in many high-stake domains, ranging from transportation networks to e-commerce networks, from neuroscience to finance.
Do We Really Need Complicated Model Architectures For Temporal Networks?
Recurrent neural network (RNN) and self-attention mechanism (SAM) are the de facto methods to extract spatial-temporal information for temporal graph learning.
A Novel Feeder-level Microgrid Unit Commitment Algorithm Considering Cold-load Pickup, Phase Balancing, and Reconfiguration
This paper presents a novel 2-stage microgrid unit commitment (Microgrid-UC) algorithm considering cold-load pickup (CLPU) effects, three-phase load balancing requirements, and feasible reconfiguration options.
Retrieval Based Time Series Forecasting
In this paper, we show both theoretically and empirically that the uncertainty could be effectively reduced by retrieving relevant time series as references.
Reinforcement Learning for Volt-Var Control: A Novel Two-stage Progressive Training Strategy
In Stage 1(individual training), while holding all the other agents inactive, we separately train each agent to obtain its own optimal VVC actions in the action space: {consume, generate, do-nothing}.
A TCN-based Hybrid Forecasting Framework for Hours-ahead Utility-scale PV Forecasting
The hybrid framework consists of two forecasting models: a physics-based trend forecasting (TF) model and a data-driven cloud-event forecasting (CF) model.
A Meta-learning based Distribution System Load Forecasting Model Selection Framework
This paper presents a meta-learning based, automatic distribution system load forecasting model selection framework.
