Category: Deep Learning Architectures

Abstract The exponential growth of big data in RNA biology (RB) has led to the development of deep learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL studies in other fields, the successful implementation of DL in RB depends heavily on the effective utilization of large-scale datasets from public databases. In achieving this goal, data encoding methods, learning algorithms, and techniques that align well with biological domain knowledge have played pivotal roles. In this review, we provide guiding principles for applying these DL concepts to various…

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It’s time to celebrate the incredible women leading the way in AI! Nominate your inspiring leaders for VentureBeat’s Women in AI Awards today before June 18. Learn More Matrix multiplications (MatMul) are the most computationally expensive operations in large language models (LLM) using the Transformer architecture. As LLMs scale to larger sizes, the cost of MatMul grows significantly, increasing memory usage and latency during training and inference.  In their paper, the researchers introduce MatMul-free language models that achieve performance on par with state-of-the-art Transformers while requiring far less memory during…

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Abstract Deep learning-based methods have demonstrated high classification performance in the detection of cardiovascular diseases from electrocardiograms (ECGs). However, their blackbox character and the associated lack of interpretability limit their clinical applicability. To overcome existing limitations, we present a novel deep learning architecture for interpretable ECG analysis (xECGArch). For the first time, short- and long-term features are analyzed by two independent convolutional neural networks (CNNs) and combined into an ensemble, which is extended by methods of explainable artificial intelligence (xAI) to whiten the blackbox. To demonstrate the trustworthiness of xECGArch,…

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A data-driven approach empowers businesses to make informed decisions based on accurate predictions and forecasts, leading to improved operational efficiency and resource optimization. Machine learning (ML) systems have the remarkable ability to continuously learn and adapt, improving their performance over time as they are exposed to more data. This self-learning capability ensures that organizations can stay ahead of the curve, responding dynamically to changing market conditions and customer preferences, ultimately driving innovation and enhancing competitiveness. By leveraging the power of machine learning on AWS, businesses can unlock benefits that enhance…

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In general, discriminative and/or decoding techniques identify objects and infer what is “true” and what is “fake”. As a principle, generative AI systems create objects such as pictures, audio, writing samples, and outline anything that computer-controlled systems like 3D printers can build [62]. Most often, generative and discriminative or decoding systems operate paired in generative adversarial network (to be introduced soon) models setting the business-as-usual rather than state-of-the-art of today´s AI industry. Typically, a system labeled as generative AI is self-learning, it uses unsupervised learning (but can use other types of machine…

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