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Found 4 papers, 3 papers with code
Interactive Molecular Discovery with Natural Language
Natural language is expected to be a key medium for various human-machine interactions in the era of large language models.
Towards AI-Empowered Crowdsourcing
Crowdsourcing, in which human intelligence and productivity is dynamically mobilized to tackle tasks too complex for automation alone to handle, has grown to be an important research topic and inspired new businesses (e. g., Uber, Airbnb).
Training Networks in Null Space of Feature Covariance for Continual Learning
To balance plasticity and stability of network in continual learning, in this paper, we propose a novel network training algorithm called Adam-NSCL, which sequentially optimizes network parameters in the null space of previous tasks.
HyperAdam: A Learnable Task-Adaptive Adam for Network Training
Deep neural networks are traditionally trained using human-designed stochastic optimization algorithms, such as SGD and Adam.
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