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Found 10 papers, 4 papers with code
Parameter-efficient Prompt Learning for 3D Point Cloud Understanding
Finally, a lightweight PointAdapter module is arranged near target tasks to enhance prompt tuning for 3D point cloud understanding.
Graph-Skeleton: ~1% Nodes are Sufficient to Represent Billion-Scale Graph
In this paper, we argue that properly fetching and condensing the background nodes from massive web graph data might be a more economical shortcut to tackle the obstacles fundamentally.
Joint Trading and Scheduling among Coupled Carbon-Electricity-Heat-Gas Industrial Clusters
This paper presents a carbon-energy coupling management framework for an industrial park, where the carbon flow model accompanying multi-energy flows is adopted to track and suppress carbon emissions on the user side.
Hydrogen Supply Infrastructure Network Planning Approach towards Chicken-egg Conundrum
However, there is a strong causality between HFCVs and hydrogen refueling stations (HRSs): the planning decisions of HRSs could affect the hydrogen refueling demand of HFCVs, and the growth of demand would in turn stimulate the further investment in HRSs, which is also known as the ``chicken and egg'' conundrum.
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to Milliseconds
Network embedding, a graph representation learning method illustrating network topology by mapping nodes into lower-dimension vectors, is challenging to accommodate the ever-changing dynamic graphs in practice.
Distributionally Robust Day-ahead Scheduling for Power-traffic Network under a Potential Game Framework
In order to ensure the individual optimality of the two networks in a unified framework in day-ahead power scheduling, a two-stage distributionally robust centralized optimization model is established to carry out the equilibrium of power-transportation coupled network.
Neural Augmented Kalman Filtering with Bollinger Bands for Pairs Trading
In this work, we propose KalmenNet-aided Bollinger bands Pairs Trading (KBPT), a deep learning aided policy that augments the operation of KF-aided BB trading.
Relational Learning between Multiple Pulmonary Nodules via Deep Set Attention Transformers
In this study, we propose a multiple instance learning (MIL) approach and empirically prove the benefit to learn the relations between multiple nodules.
Evaluating and Boosting Uncertainty Quantification in Classification
Emergence of artificial intelligence techniques in biomedical applications urges the researchers to pay more attention on the uncertainty quantification (UQ) in machine-assisted medical decision making.
Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks
To address the limitations of existing magnitude-based pruning algorithms in cases where model weights or activations are of large and similar magnitude, we propose a novel perspective to discover parameter redundancy among channels and accelerate deep CNNs via channel pruning.
