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Found 5 papers, 1 papers with code
When Medical Imaging Met Self-Attention: A Love Story That Didn't Quite Work Out
To evaluate this trend for medical imaging, we extend two widely adopted convolutional architectures with different self-attention variants on two different medical datasets.
Reducing Bias in Pre-trained Models by Tuning while Penalizing Change
If later such a bias is discovered during inference or deployment, it is often necessary to acquire new data and retrain the model.
Embracing the black box: Heading towards foundation models for causal discovery from time series data
Our empirical findings suggest that causal discovery in a supervised manner is possible, assuming that the training and test time series samples share most of their dynamics.
Stabilizing Transformer-Based Action Sequence Generation For Q-Learning
To support the findings of this work, this paper seeks to provide an additional example of a Transformer-based RL method.
Conditioning of Reinforcement Learning Agents and its Policy Regularization Application
In this paper, we try to answer the following question: Can information about policy conditioning help to shape a more stable and general policy of reinforcement learning agents?
