The study proposes Kernel-Eigen Pair Sparse Variational Gaussian Processes (KEP-SVGP) for building uncertainty-aware self-attention in transformers. The asymmetry of attention kernels is addressed using Kernel SVD (KSVD), yielding reduced complexity….
Continue readingThis article focuses on the capabilities of transformer networks like ChatGPT and other Large Language Models (LLMs). These networks use an encoding vector to transform a complete input sequence, such…
Continue readingThe article presents a novel method for efficient Conformer-based end-to-end automatic speech recognition. The Conformer block uses a self-attention mechanism to capture global information and a convolutional neural network to…
Continue readingThe article ‘How Much Context Does My Attention-Based ASR System Need?’ by Robert Flynn and Anton Ragni of the University of Sheffield, UK, investigates the effect of using longer acoustic…
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