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Found 5 papers, 2 papers with code
Towards Robust and Efficient Continual Language Learning
As the application space of language models continues to evolve, a natural question to ask is how we can quickly adapt models to new tasks.
When Does Re-initialization Work?
However, when deployed alongside other carefully tuned regularization techniques, re-initialization methods offer little to no added benefit for generalization, although optimal generalization performance becomes less sensitive to the choice of learning rate and weight decay hyperparameters.
ProSper -- A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions
The library widens the scope of dictionary learning approaches beyond implementations of standard approaches such as ICA, NMF or standard L1 sparse coding.
Variational Memory Addressing in Generative Models
Aiming to augment generative models with external memory, we interpret the output of a memory module with stochastic addressing as a conditional mixture distribution, where a read operation corresponds to sampling a discrete memory address and retrieving the corresponding content from memory.
Reweighted Wake-Sleep
The wake-sleep algorithm relies on training not just the directed generative model but also a conditional generative model (the inference network) that runs backward from visible to latent, estimating the posterior distribution of latent given visible.
