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Found 8 papers, 2 papers with code
Learning to Denoise Unreliable Interactions for Link Prediction on Biomedical Knowledge Graph
By maximizing the mutual information between the reliable structure and smoothed semantic relations, DenoisedLP emphasizes the informative interactions for predicting relation-specific links.
Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction
Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs).
ViT-TTS: Visual Text-to-Speech with Scalable Diffusion Transformer
To mitigate the data scarcity in learning visual acoustic information, we 1) introduce a self-supervised learning framework to enhance both the visual-text encoder and denoiser decoder; 2) leverage the diffusion transformer scalable in terms of parameters and capacity to learn visual scene information.
AntCritic: Argument Mining for Free-Form and Visually-Rich Financial Comments
The task of argument mining aims to detect all possible argumentative components and identify their relationships automatically.
Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots
While motion planning of locomotion for legged robots has shown great success, motion planning for legged robots with dexterous multi-finger grasping is not mature yet.
AntPivot: Livestream Highlight Detection via Hierarchical Attention Mechanism
In recent days, streaming technology has greatly promoted the development in the field of livestream.
Deep learning for drug repurposing: methods, databases, and applications
Drug development is time-consuming and expensive.
DeepGS: Deep Representation Learning of Graphs and Sequences for Drug-Target Binding Affinity Prediction
Recently, with the increasing amount of affinity data available and the success of deep representation learning models on various domains, deep learning techniques have been applied to DTA prediction.
