Search Results for author:
Found 9 papers, 2 papers with code
Searching to Exploit Memorization Effect in Learning with Noisy Labels
Sample selection approaches are popular in robust learning from noisy labels.
Loss-aware Curriculum Learning for Heterogeneous Graph Neural Networks
Heterogeneous Graph Neural Networks (HGNNs) are a class of deep learning models designed specifically for heterogeneous graphs, which are graphs that contain different types of nodes and edges.
Ranked #1 on
Node Property Prediction
on ogbn-mag
Positive-Unlabeled Node Classification with Structure-aware Graph Learning
We also propose a regularizer to align the model with graph structure.
An Adaptive Policy to Employ Sharpness-Aware Minimization
Sharpness-aware minimization (SAM), which searches for flat minima by min-max optimization, has been shown to be useful in improving model generalization.
Leveraging per Image-Token Consistency for Vision-Language Pre-training
(2) Under-utilization of the unmasked tokens: CMLM primarily focuses on the masked token but it cannot simultaneously leverage other tokens to learn vision-language associations.
AutoWeird: Weird Translational Scoring Function Identified by Random Search
This scoring function, called AutoWeird, only uses tail entity and relation in a triplet to compute its plausibility score.
Ranked #2 on
Link Property Prediction
on ogbl-wikikg2
Topology-aware Tensor Decomposition for Meta-graph Learning
The proposed topology-aware tensor decomposition is easy to use and simple to implement, and it can be taken as a plug-in part to upgrade many existing works, including node classification and recommendation on heterogeneous graphs.
TRACE: Tensorizing and Generalizing Supernets from Neural Architecture Search
Recently, a special kind of graph, i. e., supernet, which allows two nodes connected by multi-choice edges, has exhibited its power in neural architecture search (NAS) by searching better architectures for computer vision (CV) and natural language processing (NLP) tasks.
Searching to Exploit Memorization Effect in Learning from Corrupted Labels
Sample selection approaches are popular in robust learning from noisy labels.
