Search Results for author:
Found 9 papers, 6 papers with code
Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies
To deal with such situations, we propose a novel time-series trend-following forecaster that can quickly adapt to new market conditions, referred to as regimes.
On Sequential Bayesian Inference for Continual Learning
Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks.
The Effectiveness of World Models for Continual Reinforcement Learning
World models power some of the most efficient reinforcement learning algorithms.
Efficient Adapter Transfer of Self-Supervised Speech Models for Automatic Speech Recognition
In this paper we propose applying adapters to wav2vec 2. 0 to reduce the number of parameters required for downstream ASR tasks, and increase scalability of the model to multiple tasks or languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Can Sequential Bayesian Inference Solve Continual Learning?
Previous work in Continual Learning (CL) has used sequential Bayesian inference to prevent forgetting and accumulate knowledge from previous tasks.
An Adapter Based Pre-Training for Efficient and Scalable Self-Supervised Speech Representation Learning
We evaluate by applying these language representations to automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Same State, Different Task: Continual Reinforcement Learning without Interference
In this paper we formalize this "interference" as distinct from the problem of forgetting.
Hierarchical Indian Buffet Neural Networks for Bayesian Continual Learning
We place an Indian Buffet process (IBP) prior over the structure of a Bayesian Neural Network (BNN), thus allowing the complexity of the BNN to increase and decrease automatically.
Practical Bayesian Learning of Neural Networks via Adaptive Optimisation Methods
We introduce a novel framework for the estimation of the posterior distribution over the weights of a neural network, based on a new probabilistic interpretation of adaptive optimisation algorithms such as AdaGrad and Adam.
