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Found 6 papers, 1 papers with code
Continual Learning Under Language Shift
The recent increase in data and model scale for language model pre-training has led to huge training costs.
FMM-Head: Enhancing Autoencoder-based ECG anomaly detection with prior knowledge
Detecting anomalies in electrocardiogram data is crucial to identifying deviations from normal heartbeat patterns and providing timely intervention to at-risk patients.
DeepGANTT: A Scalable Deep Learning Scheduler for Backscatter Networks
Without the need to retrain, DeepGANTT generalizes to networks 6x larger in the number of nodes and 10x larger in the number of tags than those used for training, breaking the scalability limitations of the optimal scheduler and reducing carrier utilization by up to 50% compared to the state-of-the-art heuristic.
Predicting Treatment Outcome from Patient Texts:The Case of Internet-Based Cognitive Behavioural Therapy
We investigate the feasibility of applying standard text categorisation methods to patient text in order to predict treatment outcome in Internet-based cognitive behavioural therapy.
Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking
Existing approaches to solving combinatorial optimization problems on graphs suffer from the need to engineer each problem algorithmically, with practical problems recurring in many instances.
Modelling Reservoir Computing with the Discrete Nonlinear Schrödinger Equation
We formulate, using the discrete nonlinear Schroedinger equation (DNLS), a general approach to encode and process information based on reservoir computing.
Data Analysis, Statistics and Probability
