Search Results for author:
Found 8 papers, 5 papers with code
Single-Cell RNA-seq Synthesis with Latent Diffusion Model
The single-cell RNA sequencing (scRNA-seq) technology enables researchers to study complex biological systems and diseases with high resolution.
Learning from Emergence: A Study on Proactively Inhibiting the Monosemantic Neurons of Artificial Neural Networks
We validate our conjecture that monosemanticity brings about performance change at different model scales on a variety of neural networks and benchmark datasets in different areas, including language, image, and physics simulation tasks.
Search to Fine-tune Pre-trained Graph Neural Networks for Graph-level Tasks
Given a pre-trained GNN, we propose to search to fine-tune pre-trained graph neural networks for graph-level tasks (S2PGNN), which adaptively design a suitable fine-tuning framework for the given labeled data on the downstream task.
Noise2Info: Noisy Image to Information of Noise for Self-Supervised Image Denoising
However, such a method is highly sensitive to the standard deviation \sigma_n of noises injected to clean images, where \sigma_n is inaccessible without knowing clean images.
AutoGEL: An Automated Graph Neural Network with Explicit Link Information
However, existing AutoGNN works mainly adopt an implicit way to model and leverage the link information in the graphs, which is not well regularized to the link prediction task on graphs, and limits the performance of AutoGNN for other graph tasks.
Message Function Search for Hyper-relational Knowledge Graph
In this paper, we first unify a search space of message functions that enables both structures and operators to be searchable.
Efficient Relation-aware Scoring Function Search for Knowledge Graph Embedding
The scoring function, which measures the plausibility of triplets in knowledge graphs (KGs), is the key to ensure the excellent performance of KG embedding, and its design is also an important problem in the literature.
Searching to Sparsify Tensor Decomposition for N-ary Relational Data
Recently, tensor decomposition methods have been introduced into N-ary relational data and become state-of-the-art on embedding learning.
