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Found 9 papers, 5 papers with code
Rethinking Performance Measures of RNA Secondary Structure Problems
Accurate RNA secondary structure prediction is vital for understanding cellular regulation and disease mechanisms.
Constrained Parameter Regularization
Instead of applying a single constant penalty to all parameters, we enforce an upper bound on a statistical measure (e. g., the L$_2$-norm) of parameter groups.
RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement
Using medical image segmentation as the evaluation environment, we show that latent feature recycling enables the network to iteratively refine initial predictions even beyond the iterations seen during training, converging towards an improved decision.
Towards Automated Design of Riboswitches
Experimental screening and selection pipelines for the discovery of novel riboswitches are expensive, time-consuming, and inefficient.
Scalable Deep Learning for RNA Secondary Structure Prediction
The field of RNA secondary structure prediction has made significant progress with the adoption of deep learning techniques.
Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design
Our world is ambiguous and this is reflected in the data we use to train our algorithms.
Hyperparameter Transfer Across Developer Adjustments
After developer adjustments to a machine learning (ML) algorithm, how can the results of an old hyperparameter optimization (HPO) automatically be used to speedup a new HPO?
Sample-Efficient Automated Deep Reinforcement Learning
Despite significant progress in challenging problems across various domains, applying state-of-the-art deep reinforcement learning (RL) algorithms remains challenging due to their sensitivity to the choice of hyperparameters.
Neural Architecture Evolution in Deep Reinforcement Learning for Continuous Control
Current Deep Reinforcement Learning algorithms still heavily rely on handcrafted neural network architectures.
