Papers by Mehrdad Alizadeh

2 papers
A Corpus for Visual Question Answering Annotated with Frame Semantic Information (2020.lrec-1)

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Challenge: Visual Question Answering (VQA) is a computer vision problem.
Approach: They propose to annotate a visual question answering dataset with verb semantics to help the model understand verbs.
Outcome: The proposed system is built on the imSitu dataset annotated with verb semantic information.
LLM in a flash: Efficient Large Language Model Inference with Limited Memory (2024.acl-long)

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Challenge: Large language models (LLMs) have high computational and memory requirements, especially for devices with limited memory.
Approach: They propose a method that stores model parameters in flash memory but brings them on demand to DRAM . authors propose two techniques to optimize for reading data in larger, more contiguous chunks .
Outcome: The proposed method reduces the volume of data transferred from flash and reads data in larger, more contiguous chunks.

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