A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers.
[NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training
[ICLR 2020] Lite Transformer with Long-Short Range Attention
[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning; [NeurIPS 2022] MCUNetV3: On-Device Training Under 256KB Memory
[ACL'20] HAT: Hardware-Aware Transformers for Efficient Natural Language Processing