Customer-obsessed science


Research areas
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June 25, 2025With large datasets, directly generating data ID codes from query embeddings is much more efficient than performing pairwise comparisons between queries and candidate responses.
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Large Language Models (LLMs) have brought with them an unprecedented interest in AI in society. This has enabled their use in several day to day applications such as virtual assistants or smart home agents. This integration with external tools also brings several risk areas where malicious actors may try to inject harmful instruc-tions in the user query (direct prompt injection) or in the retrieved information
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As computing environments become increasingly complex and distributed, the volume and complexity of security data generated across various systems have grown exponentially. Extracting useful insights from this security data is crucial for effective security analytics, anomaly detection, and threat identification. However, there is a lack of comprehensive evaluation benchmarks for assessing the performance
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CIKM 2024 Workshop on Graph Techniques for Adversarial Activity Analytics2024Graph outlier detection identifies substructures in graphs that significantly deviate from normal patterns. Traditional graph outlier detection methods are mostly limited to static graphs, which overlook the dynamic nature of real-world graphs and ignore temporal signals providing critical information for detecting outliers. Recently, Transformers revolutionized machine learning on time-series data. However
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ACM SoCC 20242024Large language models (LLMs) are ubiquitously powerful but prohibitively expensive to train, often requiring thousands of compute devices, typically GPUs. To reduce the cost of training LLMs for customers, Amazon Web Services (AWS) launched the Amazon EC2 trn1 instances, powered by AWS Trainium, Amazon’s homegrown deep-learning accelerator, as an alternative to distributed LLM training. The trn1 instances
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2024We propose a novel framework for pretraining and fine-tuning language models with the goal of determining whether two addresses represent the same physical building. Address matching and building authoritative address catalogues are important to many applications and businesses, such as delivery services, online retail, emergency services, logistics, etc. We propose to view a collection of addresses as
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