Sentence examples for augmented likelihood from inspiring English sources

Starting from a Prior network trained on ChEMBL, the Agent is trained using the augmented likelihood of the SMILES generated.

We therefore denote an augmented likelihood (log P(A)_{mathbb {U}}) as a prior likelihood modulated by the desirability of a sequence: log P(A)_{mathbb {U}} = log P(A)_{Prior} + sigma S A where (sigma) is a scalar coefficient.

For the Agent based on the reduced Prior where (k=0.7), the lower prior likelihood of compounds similar to Celecoxib translates to a lower augmented likelihood, which lowers the average similarity of the structures generated by the Agent.

After 1000 steps, Celecoxib was the most commonly generated structure (about a third of the generated structures), followed by demethylated Celecoxib (also a third) whose SMILES is more likely according to the Prior with (log _e P = -15.2) but has a lower similarity ((J = 0.87)), resulting in an augmented likelihood equal to that of Celecoxib.

The return G(A) of a sequence A can in this case be seen as the agreement between the Agent likelihood (log P(A)_{mathbb {A}}) and the augmented likelihood: G(A) = -[log P(A)_{mathbb {U}} - log P(A)_{mathbb {A}}]^2The goal of the Agent is to learn a policy which maximizes the expected return, achieved by minimizing the cost function (J(Theta ) = -G).

We wish to sample from the joint posterior f θ, Y*∣ Y) of the parameters θ and the latent variables Y* given the data Y, using the fact that, by Bayes' theorem, (7) where L Y*, Y∣θ) is the approximated augmented likelihood.

For the Agent based on the reduced Prior where (k=1), the fact that Celecoxib and demethylated Celecoxib are given similar augmented likelihoods means that the average similarity converges to around 0.9 rather than 1.0.

Through learning an augmented episodic likelihood which is a composite of prior likelihood [17] and a user defined scoring function, the method aims to fine-tune an RNN pre-trained on the ChEMBL database [20] towards generating desirable compounds.

The model was tested on the task of generating sulphur free molecules as a proof of principle, and the method using augmented episodic likelihood was compared with traditional policy gradient methods.

Similarly, quality-augmented product of likelihood ratio fusion scheme has shown to improve the performance[22].

In Algorithm 2, randEgde returns a random edge from the augmented Set and edgeScore returns the likelihood of the augmented edge.