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Found 5 papers, 5 papers with code
Detecting Music Performance Errors with Transformers
Beginner musicians often struggle to identify specific errors in their performances, such as playing incorrect notes or rhythms.
Token Turing Machines are Efficient Vision Models
By ensuring that there are fewer process tokens than memory tokens, we are able to reduce the inference time of the network while maintaining its accuracy.
Pruning One More Token is Enough: Leveraging Latency-Workload Non-Linearities for Vision Transformers on the Edge
For similar latency (within 5. 2% or 7ms) across devices we achieve 78. 6%-84. 5% ImageNet1K accuracy, while the state-of-the-art, Token Merging, achieves 45. 8%-85. 4%.
An automated approach for improving the inference latency and energy efficiency of pretrained CNNs by removing irrelevant pixels with focused convolutions
Computer vision often uses highly accurate Convolutional Neural Networks (CNNs), but these deep learning models are associated with ever-increasing energy and computation requirements.
Analysis of Failures and Risks in Deep Learning Model Converters: A Case Study in the ONNX Ecosystem
We find that the node conversion stage of a model converter accounts for ~75% of the defects and 33% of reported failure are related to semantically incorrect models.
