Search Results for author:
Found 11 papers, 8 papers with code
Subgraph-level Universal Prompt Tuning
In the evolving landscape of machine learning, the adaptation of pre-trained models through prompt tuning has become increasingly prominent.
MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker Joints
Molecular core structures and R-groups are essential concepts in drug development.
Co-attention Graph Pooling for Efficient Pairwise Graph Interaction Learning
Graph Neural Networks (GNNs) have proven to be effective in processing and learning from graph-structured data.
Towards Flexible Time-to-event Modeling: Optimizing Neural Networks via Rank Regression
The proposed method is a semiparametric approach to AFT modeling that does not impose any distributional assumptions on the survival time distribution.
Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction
Graph Neural Networks (GNNs) have been widely applied to various fields for learning over graph-structured data.
MSTR: Multi-Scale Transformer for End-to-End Human-Object Interaction Detection
Human-Object Interaction (HOI) detection is the task of identifying a set of <human, object, interaction> triplets from an image.
Towards simple time-to-event modeling: optimizing neural networks via rank regression
Time-to-event analysis, also known as survival analysis, aims to predict the first occurred event time, conditional on a set of features.
HOTR: End-to-End Human-Object Interaction Detection with Transformers
Human-Object Interaction (HOI) detection is a task of identifying "a set of interactions" in an image, which involves the i) localization of the subject (i. e., humans) and target (i. e., objects) of interaction, and ii) the classification of the interaction labels.
Ranked #16 on
Human-Object Interaction Detection
on V-COCO
Robust Long-Term Object Tracking via Improved Discriminative Model Prediction
We propose an improved discriminative model prediction method for robust long-term tracking based on a pre-trained short-term tracker.
Self-Attention Graph Pooling
In particular, studies have focused on generalizing convolutional neural networks to graph data, which includes redefining the convolution and the downsampling (pooling) operations for graphs.
Ranked #4 on
Graph Classification
on FRANKENSTEIN
Typeface Completion with Generative Adversarial Networks
However, in designing a typeface, it is difficult to keep the style of various characters consistent, especially for languages with lots of morphological variations such as Chinese.
