Search Results for author:
Found 7 papers, 0 papers with code
Predicting Ground Reaction Force from Inertial Sensors
Our aim in this paper is to determine if data collected with inertial measurement units (IMUs), that can be worn by athletes during outdoor runs, can be used to predict GRF with sufficient accuracy to allow the analysis of its derived biomechanical variables (e. g., contact time and loading rate).
Queue Scheduling with Adversarial Bandit Learning
In this paper, we study scheduling of a queueing system with zero knowledge of instantaneous network conditions.
Inference Latency Prediction at the Edge
With the growing workload of inference tasks on mobile devices, state-of-the-art neural architectures (NAs) are typically designed through Neural Architecture Search (NAS) to identify NAs with good tradeoffs between accuracy and efficiency (e. g., latency).
Defending against Poisoning Backdoor Attacks on Federated Meta-learning
Federated learning allows multiple users to collaboratively train a shared classification model while preserving data privacy.
Achieving Transparency Report Privacy in Linear Time
However, a provably formal study of the impact to data subjects' privacy caused by the utility of releasing an ATR (that investigates transparency and fairness), is yet to be addressed in the literature.
Backdoor Attacks on Federated Meta-Learning
Federated learning allows multiple users to collaboratively train a shared classification model while preserving data privacy.
Throughput Prediction of Asynchronous SGD in TensorFlow
Our approach is able to model the interaction of multiple nodes and the scheduling of concurrent transmissions between the parameter server and each node.
