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Found 6 papers, 2 papers with code
LATTE: Low-Precision Approximate Attention with Head-wise Trainable Threshold for Efficient Transformer
With the rise of Transformer models in NLP and CV domain, Multi-Head Attention has been proven to be a game-changer.
MPTQ-ViT: Mixed-Precision Post-Training Quantization for Vision Transformer
However, without considering the asymmetry in activations and relying on hand-crafted settings, these methods often struggle to maintain performance under low-bit quantization.
TSPTQ-ViT: Two-scaled post-training quantization for vision transformer
Due to the non-normally distributed values after Softmax and GeLU, post-training quantization on ViTs results in severe accuracy degradation.
C3-SL: Circular Convolution-Based Batch-Wise Compression for Communication-Efficient Split Learning
Based on the simulation results on CIFAR-10 and CIFAR-100, our method achieves a 16x compression ratio with negligible accuracy drops compared with the vanilla SL.
MAUS: A Dataset for Mental Workload Assessmenton N-back Task Using Wearable Sensor
Besides, we also presents a reproducible baseline system as a preliminary benchmark (The code of the baseline system on MAUS dataset is available on Github: https://github. com/rickwu11/MAUS\_dataset\_baseline\_system), which testing accuracy are 71. 6 %, 66. 7 %, and 59. 9 % in ECG, fingertip PPG, wristband PPG, respectively.
AdaBoost-assisted Extreme Learning Machine for Efficient Online Sequential Classification
In this paper, we propose an AdaBoost-assisted extreme learning machine for efficient online sequential classification (AOS-ELM).
