Summary: Capability of generalization in learning of neural networks from examples can be modelled using regularization, which has been developed as a tool... Krková Věra - 《Logic Journal of Igpl》 被引量: 22发表: 2004年 Learning from Data as an Optimization and Inverse Problem V. Kurkova...
If extra information is not desired to be introduced, then calculating the phase directly from a propagated intensity measurement is an ill-posed problem. We can overcome such difficulty through incorporating prior knowledge. This is also known as regularization. In the Gerchberg-Saxton (GS) algorith...
The error scales quadratically in the episode length, as opposed to the linear scaling in standard RL. If a mistake is made that puts the agent in a part of the state space that the expert did not visit, the agent has no data to learn a policy from. In supervised learning,(x,y)\s...
For this reason, we focus on weak forms of BPTT with relatively small temporal horizons, in which we model only K time steps of feedback into the past from an error signal E (truncated BPTT as defined above). In our experiments the size of K — which we report as a percentage of ...
In all these examples, we could directly obtain the structure of the NN from the forward model and known parameters. However, in these approaches, there are some difficulties that consist of the determination of the structure of the NN. For example, in the first example, obtaining the structur...
Apart from battery technology, there has been a significant development taking place in the field of super capacitors as they provide a fast-charging alternative to batteries [203]. In [204], the authors developed an approach to enhance the performance of Micro Air Vehicles (MAVs) through innova...
Like ProteinMPNN, this is an inverse folding method that attempts to go from a structure (protein backbone atom coordinates) to a sequence. While ProteinMPNN was trained on experimentally determined strutures, this method, called ESM-IF, uses 12 million structures predicted by AlphaFold as its tr...
The variational homoencoder: learning to learn high capacity generative models from few examples. Hewitt, Nye, Gane, Jaakkola, Tenebaum https://arxiv.org/abs/1807.08919 Wasserstein variational inference. Ambrogioni, Guclu, Gucluturk, Hinne, Maris, van Gerven https://arxiv.org/abs/1805.11284 The...
This follows from the fact that ambiguity can be resolved, unambiguously, by epistemic actions. To illustrate the distinction between belief-based and belief-free policies, consider the following examples: a predator (e.g., an owl) has to locate a prey (e.g., a field mouse). In this ...
(x). Let us consider supervised labels y. The task is to obtain the optimal parameters from examples such that the student network approximates the behavior of the labels. Let us denote by l(x, w) a loss when input signal x is processed by a network having parameter w. A typical ...