5 citations · 9 across the 11 of their papers we have counts for
5 papers · 1 filter
Boosting Entropy with Bell Box Quantization
Ningfeng Yang, Tor M. Aamodt
Quantization-Aware Pre-Training (QAPT) is an effective technique to reduce the compute and memory overhead of Deep Neural Networks while improving their energy efficiency on edge d…
Improving the Straight-Through Estimator with Zeroth-Order Information
Ningfeng Yang, Tor M. Aamodt
We study the problem of training neural networks with quantized parameters. Learning low-precision quantized parameters by enabling computation of gradients via the Straight-Throug…
Learning Label Encodings for Deep Regression
Deval Shah, Tor M. Aamodt
Deep regression networks are widely used to tackle the problem of predicting a continuous value for a given input. Task-specialized approaches for training regression networks have…
Label Encoding for Regression Networks
Deval Shah, Zi Yu Xue, Tor M. Aamodt
Deep neural networks are used for a wide range of regression problems. However, there exists a significant gap in accuracy between specialized approaches and generic direct regress…
Sparse Weight Activation Training
Md Aamir Raihan, Tor M. Aamodt
Neural network training is computationally and memory intensive. Sparse training can reduce the burden on emerging hardware platforms designed to accelerate sparse computations, bu…