AutoBNN: Probabilistic time series forecasting with compositional bayesian neural networks
… A BNN is a neural network with a probability distribution over weights rather than a fixed set of weights. …
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Learning the importance of training data under concept drift
… The objective of the weights is to improve the performance of M on future data. …
Mixed-input matrix multiplication performance optimizations
… The bulk of an LLM’s memory and compute are consumed by weights in matrix multiplication operations. …
SCIN: A new resource for representative dermatology images
…The majority of images in the SCIN dataset show early-stage concerns — more than half arose less than a week before the photo, and 30% arose less than a day before the…
MobileDiffusion: Rapid text-to-image generation on-device
… By using pre-trained weights to initialize the generator and the discriminator, the training becomes a fine-tuning process, which converges in less than 10K iterations. …
Graph neural networks in TensorFlow
… The training setup is completed by placing an output layer on top of the GNN’s hidden state for the labeled nodes, computing the loss to measure the prediction error , and updating model weights by backpropagation, as usual in any neural network training. …