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Found 4 papers, 1 papers with code
Assessing the Portability of Parameter Matrices Trained by Parameter-Efficient Finetuning Methods
We compare the performance of ported modules with that of equivalent modules trained (i) from scratch, and (ii) from parameters sampled from the same distribution as the ported module.
PEFT-Ref: A Modular Reference Architecture and Typology for Parameter-Efficient Finetuning Techniques
Recent parameter-efficient finetuning (PEFT) techniques aim to improve over the considerable cost of fully finetuning large pretrained language models (PLM).
AfriVEC: Word Embedding Models for African Languages. Case Study of Fon and Nobiin
From Word2Vec to GloVe, word embedding models have played key roles in the current state-of-the-art results achieved in Natural Language Processing.
On the Reduction of Variance and Overestimation of Deep Q-Learning
The breakthrough of deep Q-Learning on different types of environments revolutionized the algorithmic design of Reinforcement Learning to introduce more stable and robust algorithms, to that end many extensions to deep Q-Learning algorithm have been proposed to reduce the variance of the target values and the overestimation phenomena.
