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Found 8 papers, 3 papers with code
Unsupervised Domain Adaptation within Deep Foundation Latent Spaces
Through quantitative analysis, as well as qualitative interpretations of decision making, we demonstrate that the suggested method can improve upon existing baselines, as well as showcase the limitations of such approach yet to be solved.
Towards interpretable-by-design deep learning algorithms
The proposed framework named IDEAL (Interpretable-by-design DEep learning ALgorithms) recasts the standard supervised classification problem into a function of similarity to a set of prototypes derived from the training data, while taking advantage of existing latent spaces of large neural networks forming so-called Foundation Models (FM).
Imbedding Deep Neural Networks
Continuous-depth neural networks, such as Neural ODEs, have refashioned the understanding of residual neural networks in terms of non-linear vector-valued optimal control problems.
Skillful Precipitation Nowcasting using Deep Generative Models of Radar
To address these challenges, we present a Deep Generative Model for the probabilistic nowcasting of precipitation from radar.
A review of radar-based nowcasting of precipitation and applicable machine learning techniques
A 'nowcast' is a type of weather forecast which makes predictions in the very short term, typically less than two hours - a period in which traditional numerical weather prediction can be limited.
On-Policy Trust Region Policy Optimisation with Replay Buffers
Building upon the recent success of deep reinforcement learning methods, we investigate the possibility of on-policy reinforcement learning improvement by reusing the data from several consecutive policies.
Aggregated Sparse Attention for Steering Angle Prediction
In this paper, we apply the attention mechanism to autonomous driving for steering angle prediction.
Combination of Supervised and Reinforcement Learning For Vision-Based Autonomous Control
In this article we propose a model-free control method, which uses a combination of reinforcement and supervised learning for autonomous control and paves the way towards policy based control in real world environments.
