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Found 8 papers, 4 papers with code
Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow
For tasks like code synthesis from natural language, code retrieval, and code summarization, data-driven models have shown great promise.
Addressing Multiple Salient Object Detection via Dual-Space Long-Range Dependencies
In this paper, we directly address the problem of detecting multiple salient objects across complex scenes.
ULSA: Unified Language of Synthesis Actions for Representation of Synthesis Protocols
This work is an important step towards creating a synthesis ontology and a solid foundation for autonomous robotic synthesis.
TadML: A fast temporal action detection with Mechanics-MLP
Temporal Action Detection(TAD) is a crucial but challenging task in video understanding. It is aimed at detecting both the type and start-end frame for each action instance in a long, untrimmed video. Most current models adopt both RGB and Optical-Flow streams for the TAD task.
Ranked #2 on
Action Detection
on THUMOS' 14
CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling
The simulation of large-scale systems with complex electron interactions remains one of the greatest challenges for the atomistic modeling of materials.
Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph
DSLAD is a self-supervised method with anomaly discrimination and representation learning decoupled for anomaly detection.
Catalysis distillation neural network for the few shot open catalyst challenge
To address the challenge of limited data in catalysis, we propose a machine learning approach based on MLP-Like and a framework called Catalysis Distillation Graph Neural Network (CDGNN).
Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions
The top 3 models are UIPs, the winning methodology for ML-guided materials discovery, achieving F1 scores of ~0. 6 for crystal stability classification and discovery acceleration factors (DAF) of up to 5x on the first 10k most stable predictions compared to dummy selection from our test set.
