476 citations
- Karlsruhe Institute of TechnologyDE16 papers
- University of MünsterDE16 papers
- Lawrence Berkeley National LaboratoryUS15 papers
- Czech Academy of Sciences, Nuclear Physics InstituteCZ14 papers
- Institute for Nuclear ResearchRU14 papers
- Massachusetts Institute of TechnologyUS14 papers
- Max Planck Institute for PhysicsDE14 papers
- Technical University of MunichDE14 papers
- University of North Carolina at Chapel HillUS14 papers
- University of WashingtonUS14 papers
- University of WuppertalDE14 papers
- Carnegie Mellon UniversityUS13 papers
9 papers · 1 filter
A Practical Guide to Streaming Continual Learning
Andrea Cossu, Federico Giannini, Giacomo Ziffer +5
Continual Learning (CL) and Streaming Machine Learning (SML) study the ability of agents to learn from a stream of non-stationary data. Despite sharing some similarities, they addr…
Continuous-Flow Data-Rate-Aware CNN Inference on FPGA
Tobias Habermann, Michael Mecik, Zhenyu Wang +3
Among hardware accelerators for deep-learning inference, data flow implementations offer low latency and high throughput capabilities. In these architectures, each neuron is mapped…
A Study of Continual Learning Methods for Q-Learning
Benedikt Bagus, Alexander Gepperth
We present an empirical study on the use of continual learning (CL) methods in a reinforcement learning (RL) scenario, which, to the best of our knowledge, has not been described b…
An Investigation of Replay-based Approaches for Continual Learning
Benedikt Bagus, Alexander Gepperth
Continual learning (CL) is a major challenge of machine learning (ML) and describes the ability to learn several tasks sequentially without catastrophic forgetting (CF). Recent wor…
Continual Learning with Fully Probabilistic Models
Benedikt Pfülb, Alexander Gepperth, Benedikt Bagus
We present an approach for continual learning (CL) that is based on fully probabilistic (or generative) models of machine learning. In contrast to, e.g., GANs that are "generative"…
A Study of Deep Learning for Network Traffic Data Forecasting
Benedikt Pfülb, Christoph Hardegen, Alexander Gepperth +1
We present a study of deep learning applied to the domain of network traffic data forecasting. This is a very important ingredient for network traffic engineering, e.g., intelligen…