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Found 4 papers, 0 papers with code
Orthogonalising gradients to speedup neural network optimisation
The optimisation of neural networks can be sped up by orthogonalising the gradients before the optimisation step, ensuring the diversification of the learned representations.
Generalisation and the Geometry of Class Separability
Recent results in deep learning show that considering only the capacity of machines does not adequately explain the generalisation performance we can observe.
Linear Disentangled Representations and Unsupervised Action Estimation
In this work we empirically show that linear disentangled representations are not generally present in standard VAE models and that they instead require altering the loss landscape to induce them.
Extended Formulations for Online Linear Bandit Optimization
On-line linear optimization on combinatorial action sets (d-dimensional actions) with bandit feedback, is known to have complexity in the order of the dimension of the problem.
