In this paper, we tackle the high computational overhead of transformers for lightweight image super-resolution. (SR). Motivated by the observations of self-attention's inter-layer repetition, we ...
Abstract: Graph Convolution Networks (GCNs) have achieved remarkable success in representation of structured graph data. As we know that traditional GCNs are generally defined on the fixed first-order ...
Abstract: The morphological undecimated wavelet (MUW) is an efficient feature extraction algorithm for bearing fault diagnosis. Currently, the researched MUW is mainly focused on background noise ...
KernelOptimizer is an open-source tool that automates CUDA kernel optimization for PyTorch workloads using large language models (LLMs). Inspired by Stanford CRFM’s fast kernel research, it leverages ...
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