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Found 5 papers, 2 papers with code
CES-KD: Curriculum-based Expert Selection for Guided Knowledge Distillation
Knowledge distillation (KD) is an effective tool for compressing deep classification models for edge devices.
Efficient Fine-Tuning of Compressed Language Models with Learners
We introduce Learner modules and priming, novel methods for fine-tuning that exploit the overparameterization of pre-trained language models to gain benefits in convergence speed and resource utilization.
Efficient Fine-Tuning of BERT Models on the Edge
FAR reduces fine-tuning time on the DistilBERT model and CoLA dataset by 30%, and time spent on memory operations by 47%.
Towards Knowledge-Augmented Visual Question Answering
We propose a model that captures the interactions between objects in a visual scene and entities in an external knowledge source.
ConceptBert: Concept-Aware Representation for Visual Question Answering
Given an image and a question in natural language, ConceptBert requires visual elements of the image and a Knowledge Graph (KG) to infer the correct answer.
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