3 papers
cs.LG2026
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…
cs.CL2026
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…
cs.LG2026
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…