In this paper, we propose a novel layer-adaptive weight-pruning approach for Deep Neural
Networks (DNNs) that addresses the challenge of optimizing the output distortion …

We study the neural network (NN) compression problem, viewing the tension between the
compression ratio and NN performance through the lens of rate-distortion theory. We choose …

Implicit neural representations (INRs) for various data types have gained popularity in the
field of deep learning owing to their effectiveness. However, previous studies on INRs have …

Allocating different bit widths to different channels and quantizing them independently bring
higher quantization precision and accuracy. Most of prior works use equal bit width to …

Deep neural networks (DNNs) have been widely used in many artificial intelligence (AI)
tasks. However, deploying them brings significant challenges due to the huge cost of …

With neural networks growing deeper and feature maps growing larger, limited
communication bandwidth with external memory (or DRAM) and power constraints become …

This work focuses on the efficient quantization of convolutional neural networks (CNNs).
Specifically, we introduce a method called non-uniform uniform quantization (NUUQ), a …

Intermediate deep visual feature compression and transmission is an emerging research
topic, which enables a good balance among computing load, bandwidth usage and …

State space models (SSMs) have emerged as a powerful framework for modelling long-
range dependencies in sequence data. Unlike traditional recurrent neural networks (RNNs) …

STATISTICAL METHODS FOR EFFICIENT AND TRUSTWORTHY MACHINE LEARNING A
DISSERTATION SUBMITTED TO THE DEPARTMENT OF ELECTRICAL ENGI Page 1 …