Search Results for author:
Found 8 papers, 3 papers with code
A Clustering Method with Graph Maximum Decoding Information
Despite its efficacy, the current clustering method utilizing the graph-based model overlooks the uncertainty associated with random walk access between nodes and the embedded structural information in the data.
A Multi-constraint and Multi-objective Allocation Model for Emergency Rescue in IoT Environment
Emergency relief operations are essential in disaster aftermaths, necessitating effective resource allocation to minimize negative impacts and maximize benefits.
A Survey on Game Playing Agents and Large Models: Methods, Applications, and Challenges
The swift evolution of Large-scale Models (LMs), either language-focused or multi-modal, has garnered extensive attention in both academy and industry.
Robustifying DARTS by Eliminating Information Bypass Leakage via Explicit Sparse Regularization
This naturally highlights the vital role of the sparsity of architecture parameters in the training phase which has not been well developed in the past.
Small Temperature is All You Need for Differentiable Architecture Search
DARTS then remaps the relaxed supernet back to the discrete space by one-off post-search pruning to obtain the final architecture (finalnet).
Rethink DARTS Search Space and Renovate a New Benchmark
DARTS search space (DSS) has become a canonical benchmark for NAS whereas some emerging works pointed out the issue of narrow accuracy range and claimed it would hurt the method ranking.
Enabling and Analyzing How to Efficiently Extract Information from Hybrid Long Documents with LLMs
We propose an Automated Financial Information Extraction (AFIE) framework that enhances LLMs' ability to comprehend and extract information from financial reports.
Delve into the Performance Degradation of Differentiable Architecture Search
Differentiable architecture search (DARTS) is widely considered to be easy to overfit the validation set which leads to performance degradation.
