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Working with time series is difficult due to the high dimensionality of the data, erroneous or extraneous data, and large datasets. At the core of time series data analytics there are (a) a time ...
Artificial intelligence is forcing engineering teams to rethink data infrastructure. With embeddings powering search, recommendation engines, fraud detection, and retrieval-augmented generation (RAG), ...
After developing a local AI Agent I faced the issue of extreme hallucinations. In order to solve it, one of the best solutions I found was to use Vector Databases coupled with your AI Agent. This ...
Quantum computation of the energy of molecules and materials is one of the most promising applications of fault-tolerant quantum computers. Practical applications require development of quantum ...
Abstract: This work studies black-box adversarial attacks against deep neural networks, where the attacker only has access to the query feedback from the target model. The current state-of-the-art ...
A Deep-Reinforcement Learning Approach for activity optimization in mobile edge computing (MEC) network. Mobile edge computing (MEC) networks bring computing and storage capabilities closer to edge ...
Laryngopharyngeal reflux (LPR) is a prevalent disease affecting a high proportion of patients seeking laryngology consultation. Diagnosis is made subjectively based on history, symptoms, and ...
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