223 citations · 389 across the 45 of their papers we have counts for
5 papers · 2 filters
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition
Lucas Liebenwein, Alaa Maalouf, Oren Gal +2
We present a novel global compression framework for deep neural networks that automatically analyzes each layer to identify the optimal per-layer compression ratio, while simultane…
Closed-form Continuous-time Neural Models
Ramin Hasani, Mathias Lechner, Alexander Amini +5
Continuous-time neural processes are performant sequential decision-makers that are built by differential equations (DE). However, their expressive power when they are deployed on…
Sparse Flows: Pruning Continuous-depth Models
Lucas Liebenwein, Ramin Hasani, Alexander Amini +1
Continuous deep learning architectures enable learning of flexible probabilistic models for predictive modeling as neural ordinary differential equations (ODEs), and for generative…
Causal Navigation by Continuous-time Neural Networks
Charles Vorbach, Ramin Hasani, Alexander Amini +2
Imitation learning enables high-fidelity, vision-based learning of policies within rich, photorealistic environments. However, such techniques often rely on traditional discrete-ti…
Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing
Axel Brunnbauer, Luigi Berducci, Andreas Brandstätter +4
World models learn behaviors in a latent imagination space to enhance the sample-efficiency of deep reinforcement learning (RL) algorithms. While learning world models for high-dim…