Exploration of Novel Machine Learning Techniques for Optimizing Embeddings and Improving Retrieval Performance in Large Scale Systems
Keywords:
Machine Learning, Embeddings, Retrieval Systems, Optimization, Large-Scale Systems, Neural NetworksAbstract
In recent years, the field of machine learning has advanced significantly, with novel techniques emerging to optimize embeddings for improved retrieval performance in large-scale systems. This paper explores state-of-the-art methods that leverage advanced neural architectures, optimization algorithms, and hybrid techniques to enhance embeddings and retrieval tasks. A focus is placed on embedding generation, fine-tuning, and deployment in real-world systems such as search engines, recommender systems, and natural language processing applications. The findings suggest that optimizing embeddings not only improves retrieval accuracy but also significantly reduces computational overhead in large-scale scenarios.
References
Mikolov, T., et al. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv preprint arXiv:1301.3781.
Pennington, J., Socher, R., & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. EMNLP.
Devlin, J., et al. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.
Chen, T., et al. (2020). A Simple Framework for Contrastive Learning of Visual Representations. arXiv preprint arXiv:2002.05709.
He, K., et al. (2020). Momentum Contrast for Unsupervised Visual Representation Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence.
Vaswani, Ashish, et al. "Attention Is All You Need." Advances in Neural Information Processing Systems, 2017, pp. 5998–6008.
Karpukhin, Vladimir, et al. "Dense Passage Retrieval for Open-Domain Question Answering." Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020.
Guu, Kelvin, et al. "Realm: Retrieval-Augmented Language Model Pre-Training." arXiv preprint, 2020. arXiv:2002.08909.
Johnson, Jeff, Matthijs Douze, and Hervé Jégou. "Billion-Scale Similarity Search with GPUs." IEEE Transactions on Big Data, 2019.
Reimers, Nils, and Iryna Gurevych. "Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks." Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, 2019.
Radford, Alec, et al. "Learning Transferable Visual Models from Natural Language Supervision." Proceedings of the International Conference on Machine Learning, 2021.
Yamada, Ikuya, et al. "Efficient Passage Retrieval with Dense Representations and Query-Driven Indexing." arXiv preprint, 2020. arXiv:2004.14245.
Ni, Jianmo, et al. "Large Dual Encoders Are Generalizable Retrievers." arXiv preprint, 2021. arXiv:2112.07899.
Gao, Luyu, et al. "Condenser: A Pre-Training Architecture for Dense Retrieval." arXiv preprint, 2021. arXiv:2104.08253.
Xiong, Lee, et al. "Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval." arXiv preprint, 2021. arXiv:2007.00808.
