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Found 6 papers, 2 papers with code
AiSAQ: All-in-Storage ANNS with Product Quantization for DRAM-free Information Retrieval
Despite it claims to save memory usage by loading compressed vectors by product quantization (PQ), its memory usage increases in proportion to the scale of datasets.
RaLLe: A Framework for Developing and Evaluating Retrieval-Augmented Large Language Models
Retrieval-augmented large language models (R-LLMs) combine pre-trained large language models (LLMs) with information retrieval systems to improve the accuracy of factual question-answering.
SimplyRetrieve: A Private and Lightweight Retrieval-Centric Generative AI Tool
Large Language Model (LLM) based Generative AI systems have seen significant progress in recent years.
Can a Frozen Pretrained Language Model be used for Zero-shot Neural Retrieval on Entity-centric Questions?
However, it has been shown that the existing dense retrievers do not generalize well not only out of domain but even in domain such as Wikipedia, especially when a named entity in a question is a dominant clue for retrieval.
Ranked #4 on
Passage Retrieval
on EntityQuestions
Revisiting a kNN-based Image Classification System with High-capacity Storage
Moreover, users cannot verify the validity of inference results or evaluate the contribution of knowledge to the results.
Ranked #1 on
Incremental Learning
on ImageNet - 10 steps
(using extra training data)
Prune or quantize? Strategy for Pareto-optimally low-cost and accurate CNN
These findings are available not only to improve the Pareto frontier for accuracy vs. computational cost, but also give us some new insights on deep neural network.
