3 citations · 3 across the 6 of their papers we have counts for
7 papers
Breaking Chains with Trees: Model-Parallel Deep Learning with Time Complexity
Neeraj Mohan Sushma, Aditya Nagarsekar, Cabrel Teguemne Fokam +4
Modern deep neural networks are trained using error backpropagation, which requires sequential forward and backward computations across network layers. As these networks become dee…
Dynamic sparsity in tree-structured feed-forward layers at scale
Reza Sedghi, Robin Schiewer, Anand Subramoney +1
At typical context lengths, the feed-forward MLP block accounts for a large share of a transformer's compute budget, motivating sparse alternatives to dense MLP blocks. We study sp…
Probing Length Generalization in Mamba via Image Reconstruction
Jan Rathjens, Robin Schiewer, Laurenz Wiskott +1
Mamba has attracted widespread interest as a general-purpose sequence model due to its low computational complexity and competitive performance relative to transformers. However, i…
Exploring the limits of Hierarchical World Models in Reinforcement Learning
Robin Schiewer, Anand Subramoney, Laurenz Wiskott
Hierarchical model-based reinforcement learning (HMBRL) aims to combine the benefits of better sample efficiency of model based reinforcement learning (MBRL) with the abstraction c…
Benchmarks for Physical Reasoning AI
Andrew Melnik, Robin Schiewer, Moritz Lange +4
Physical reasoning is a crucial aspect in the development of general AI systems, given that human learning starts with interacting with the physical world before progressing to mor…
Modular Networks Prevent Catastrophic Interference in Model-Based Multi-Task Reinforcement Learning
Robin Schiewer, Laurenz Wiskott
In a multi-task reinforcement learning setting, the learner commonly benefits from training on multiple related tasks by exploiting similarities among them. At the same time, the t…
Latent Representation Prediction Networks
Hlynur Davíð Hlynsson, Merlin Schüler, Robin Schiewer +2
Deeply-learned planning methods are often based on learning representations that are optimized for unrelated tasks. For example, they might be trained on reconstructing the environ…