The article presents a model for symbolic music generation that is controlled by emotions. This model combines diffusion models with Generative Adversarial Networks (GANs). While diffusion models have been successful…
Continue readingThe article discusses the use of Denoising Diffusion Probabilistic Models (DDPM) in the generation of symbolic music with desired composer styles. The authors propose the combination of a vector quantized…
Continue readingThe study presents a novel 1D deep learning tool, CaReNet-V1, for breast cancer subtype evaluation and biochemical content evaluation using micro-FTIR hyperspectral images. The tool effectively classified cancer and adjacent…
Continue readingThe study focuses on using deep learning to predict breast cancer subtypes and biomarker levels. Four biomarkers – Estrogen Receptor (ER), Progesterone Receptor (PR), HER2, and Ki67 – are used…
Continue readingThis academic paper highlights the use of Explainable AI (XAI) and Deep Learning in detecting faults in rolling element bearings. The authors propose a unique, domain-specific feature attribution framework that…
Continue readingThe paper focuses on the detection of heart disease through ECG images using advanced technologies, specifically vision transformer models. These models include Google-Vit, Microsoft-Beit, and Swin-Tiny. The study emphasizes the…
Continue readingThe paper presents a framework for evaluating image-based anomaly detectors using synthetically generated validation data. This method, which does not require additional training or fine-tuning, uses a small support set…
Continue readingThe paper discusses the use of Microscaling (MX) data formats in deep learning applications. MX data formats combine a per-block scaling factor with narrow floating-point and integer types for individual…
Continue readingThe paper discusses the potential of Deep Learning in extracting relevant information from raw EEG data. It highlights the challenge of processing EEG data from different experiments due to varying…
Continue readingThis paper proposes a novel deep learning-based framework for solving variational problems, with a focus on optimal transport. The dynamical formulation of optimal transport can be extended through different choices…
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