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Found 15 papers, 4 papers with code
OCRT: Boosting Foundation Models in the Open World with Object-Concept-Relation Triad
Although foundation models (FMs) claim to be powerful, their generalization ability significantly decreases when faced with distribution shifts, weak supervision, or malicious attacks in the open world.
Bootstrap Segmentation Foundation Model under Distribution Shift via Object-Centric Learning
Foundation models have made incredible strides in achieving zero-shot or few-shot generalization, leveraging prompt engineering to mimic the problem-solving approach of human intelligence.
Mixstyle-Entropy: Domain Generalization with Causal Intervention and Perturbation
In this paper, we propose a novel and holistic framework based on causality, named InPer, designed to enhance model generalization by incorporating causal intervention during training and causal perturbation during testing.
SUBLLM: A Novel Efficient Architecture with Token Sequence Subsampling for LLM
In inference, it boosts speeds by up to 37% and reduces memory by 1GB per GPU.
Layer-wise Representation Fusion for Compositional Generalization
Inspired by this, we propose LRF, a novel \textbf{L}ayer-wise \textbf{R}epresentation \textbf{F}usion framework for CG, which learns to fuse previous layers' information back into the encoding and decoding process effectively through introducing a \emph{fuse-attention module} at each encoder and decoder layer.
Data-Driven Outage Restoration Time Prediction via Transfer Learning with Cluster Ensembles
This paper develops a data-driven approach to accurately predict the restoration time of outages under different scales and factors.
Synthetic Active Distribution System Generation via Unbalanced Graph Generative Adversarial Network
The basic idea is to learn the distribution of random walks both over a real-world system and across each phase of line segments, capturing the underlying local properties of an individual real-world distribution network and generating specific synthetic networks accordingly.
Mitigating Smart Meter Asynchrony Error Via Multi-Objective Low Rank Matrix Recovery
Smart meters (SMs) are being widely deployed by distribution utilities across the U. S.
Distribution Grid Modeling Using Smart Meter Data
The knowledge of distribution grid models, including topologies and line impedances, is essential to grid monitoring, control and protection.
Multi-Source Data Fusion Outage Location in Distribution Systems via Probabilistic Graph Models
A novel aspect of the proposed approach is that it takes multi-source evidence and the complex structure of distribution systems into account using a probabilistic graphical method.
A Hierarchical Deep Actor-Critic Learning Method for Joint Distribution System State Estimation
To maintain monitoring accuracy, the two levels exchange boundary information with each other at the secondary nodes, including transformer voltages (first layer to second layer) and active/reactive total power injection (second layer to first layer).
Computational Efficiency
Hierarchical Reinforcement Learning
Learning Latent Interactions for Event classification via Graph Neural Networks and PMU Data
One essential application is the use of PMU data for real-time event identification.
Disaggregating Customer-level Behind-the-Meter PV Generation Using Smart Meter Data and Solar Exemplars
In most cases, PVs are installed behind-the-meter (BTM), and only the net demand is recorded.
Learning-Based Real-Time Event Identification Using Rich Real PMU Data
A large-scale deployment of phasor measurement units (PMUs) that reveal the inherent physical laws of power systems from a data perspective enables an enhanced awareness of power system operation.
Singular Perturbation-based Large-Signal Order Reduction of Microgrids for Stability and Accuracy Synthesis with Control
Higher-level controller design and stability analysis of such high-order systems are usually intractable and computation-costly.
