Channel (or 3D filter) pruning serves as an effective way to accelerate the inference of
neural networks. There has been a flurry of algorithms that try to solve this practical problem …

Hyperparameter optimization constitutes a large part of typical modern machine learning
workflows. This arises from the fact that machine learning methods and corresponding …

Hyperparameter optimization (HPO) is a necessary step to ensure the best possible
performance of Machine Learning (ML) algorithms. Several methods have been developed …

Neural architecture search (NAS) has shown great promise in designing state-of-the-art
(SOTA) models that are both accurate and efficient. Recently, two-stage NAS, eg BigNAS …

Hyperparameter optimization constitutes a large part of typical modern machine learning
(ML) workflows. This arises from the fact that ML methods and corresponding preprocessing …

We aim to train a multi-task model such that users can adjust the desired compute budget
and relative importance of task performances after deployment, without retraining. This …

This work is an introduction and a survey for the Special Issue on Machine Intelligence at the
Edge. The authors argue that workloads that were formerly performed in the cloud are …

Embodied vision-based real-world systems, such as mobile robots, require a careful
balance between energy consumption, compute latency, and safety constraints to optimize …

To provide a flexible tradeoff between inference accuracy and resource requirement at
runtime, the slimmable neural network (SNN), a single network executable at different widths …

Scale variance among different sizes of body parts and objects is a challenging problem for
visual recognition tasks. Existing works usually design dedicated backbone or apply Neural …