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Found 7 papers, 2 papers with code
AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations
However, MTL research faces two challenges: 1) effectively modeling the relationships between tasks to enable knowledge sharing, and 2) jointly learning task-specific and shared knowledge.
MolMiner: You only look once for chemical structure recognition
Due to a backlog of decades and an increasing amount of these printed literature, there is a high demand for the translation of printed depictions into machine-readable formats, which is known as Optical Chemical Structure Recognition (OCSR).
DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction
To overcome the challenge brought by DHEN's deeper and multi-layer structure in training, we propose a novel co-designed training system that can further improve the training efficiency of DHEN.
Deep Learning Model of Dock by Dock Process Significantly Accelerate the Process of Docking-based Virtual Screening
Howerver, a majority of compouds with low docking scores could waste most of the computational resources.
Hallucination Improves Few-Shot Object Detection
One critical factor in improving few-shot detection is to address the lack of variation in training data.
Cooperating RPN's Improve Few-Shot Object Detection
Learning to detect an object in an image from very few training examples - few-shot object detection - is challenging, because the classifier that sees proposal boxes has very little training data.
The Effect of Explicit Structure Encoding of Deep Neural Networks for Symbolic Music Generation
With recent breakthroughs in artificial neural networks, deep generative models have become one of the leading techniques for computational creativity.
