A review of deep learning techniques for speech processing
The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …
learning. The use of multiple processing layers has enabled the creation of models capable …
A survey on lora of large language models
Abstract Low-Rank Adaptation (LoRA), which updates the dense neural network layers with
pluggable low-rank matrices, is one of the best performed parameter efficient fine-tuning …
pluggable low-rank matrices, is one of the best performed parameter efficient fine-tuning …
Fine-tuning language models with just forward passes
Fine-tuning language models (LMs) has yielded success on diverse downstream tasks, but
as LMs grow in size, backpropagation requires a prohibitively large amount of memory …
as LMs grow in size, backpropagation requires a prohibitively large amount of memory …
Task arithmetic in the tangent space: Improved editing of pre-trained models
Task arithmetic has recently emerged as a cost-effective and scalable approach to edit pre-
trained models directly in weight space: By adding the fine-tuned weights of different tasks …
trained models directly in weight space: By adding the fine-tuned weights of different tasks …
Trak: Attributing model behavior at scale
SM Park, K Georgiev, A Ilyas, G Leclerc… - ar** both
task-specific and general-purpose machine learning systems, including develo** models …
task-specific and general-purpose machine learning systems, including develo** models …
Sample based explanations via generalized representers
We propose a general class of sample based explanations of machine learning models,
which we term generalized representers. To measure the effect of a training sample on a …
which we term generalized representers. To measure the effect of a training sample on a …