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Found 12 papers, 9 papers with code
Self-Improved Learning for Scalable Neural Combinatorial Optimization
The end-to-end neural combinatorial optimization (NCO) method shows promising performance in solving complex combinatorial optimization problems without the need for expert design.
Smooth Tchebycheff Scalarization for Multi-Objective Optimization
Multi-objective optimization problems can be found in many real-world applications, where the objectives often conflict each other and cannot be optimized by a single solution.
Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalization
The results show that the unified model demonstrates superior performance in the eleven VRPs, reducing the average gap to around 5% from over 20% in the existing approach and achieving a significant performance boost on benchmark datasets as well as a real-world logistics application.
An Example of Evolutionary Computation + Large Language Model Beating Human: Design of Efficient Guided Local Search
Recently, we have proposed a novel Algorithm Evolution using Large Language Model (AEL) framework for automatic algorithm design.
Unlocking Deep Learning: A BP-Free Approach for Parallel Block-Wise Training of Neural Networks
This includes decisions on how to decouple network blocks and which auxiliary networks to use for each block.
DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic Models
Performing magnetic resonance imaging (MRI) reconstruction from under-sampled k-space data can accelerate the procedure to acquire MRI scans and reduce patients' discomfort.
Large Language Model for Multi-objective Evolutionary Optimization
It is also promising to see the operator only learned from a few instances can have robust generalization performance on unseen problems with quite different patterns and settings.
Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization
Neural combinatorial optimization (NCO) is a promising learning-based approach for solving challenging combinatorial optimization problems without specialized algorithm design by experts.
CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection
This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection.
Ranked #1 on
Action Triplet Detection
on CholecT50 (Challenge)
Cross-Domain Few-Shot Relation Extraction via Representation Learning and Domain Adaptation
Since the prototype is necessary for obtaining relationships between entities in the latent space, we suggest learning more interpretable and efficient prototypes from prior knowledge and the intrinsic semantics of relations to extract new relations in various domains more effectively.
A Generalized Scalarization Method for Evolutionary Multi-Objective Optimization
We analyze how GR can no longer avoid mismatches when $L_{\infty}$ is replaced by another $L_{p}$ with $p\in [1,\infty)$, and find that the $L_p$-based ($1\leq p<\infty$) subproblems having inconsistently large preference regions.
Dynamic Multi-objective Ensemble of Acquisition Functions in Batch Bayesian Optimization
To select batch candidate solutions, we rank these non-dominated solutions into several layers according to their relative performance on the three acquisition functions.
