3 citations · 13 across the 10 of their papers we have counts for
6 papers · 1 filter
Investigating the Benefits of Projection Head for Representation Learning
Yihao Xue, Eric Gan, Jiayi Ni +2
An effective technique for obtaining high-quality representations is adding a projection head on top of the encoder during training, then discarding it and using the pre-projection…
Hadamard Domain Training with Integers for Class Incremental Quantized Learning
Martin Schiemer, Clemens JS Schaefer, Jayden Parker Vap +4
Continual learning is a desirable feature in many modern machine learning applications, which allows in-field adaptation and updating, ranging from accommodating distribution shift…
Augmenting Hessians with Inter-Layer Dependencies for Mixed-Precision Post-Training Quantization
Clemens JS Schaefer, Navid Lambert-Shirzad, Xiaofan Zhang +7
Efficiently serving neural network models with low latency is becoming more challenging due to increasing model complexity and parameter count. Model quantization offers a solution…
Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression
Yihao Xue, Siddharth Joshi, Eric Gan +2
Contrastive learning (CL) has emerged as a powerful technique for representation learning, with or without label supervision. However, supervised CL is prone to collapsing represen…
The Hardware Impact of Quantization and Pruning for Weights in Spiking Neural Networks
Clemens JS Schaefer, Pooria Taheri, Mark Horeni +1
Energy efficient implementations and deployments of Spiking neural networks (SNNs) have been of great interest due to the possibility of developing artificial systems that can achi…
Mixed Precision Post Training Quantization of Neural Networks with Sensitivity Guided Search
Clemens JS Schaefer, Elfie Guo, Caitlin Stanton +7
Serving large-scale machine learning (ML) models efficiently and with low latency has become challenging owing to increasing model size and complexity. Quantizing models can simult…