116 research outputs found
Topological restrictions on Anosov representations
We characterize groups admitting Anosov representations into
, projective Anosov representations into
, and Borel Anosov representations into
. More generally, we obtain bounds on the
cohomological dimension of groups admitting -Anosov representations into
and offer several characterizations of Benoist
representations
Anosov representations, strongly convex cocompact groups and weak eigenvalue gaps
We provide characterizations of Anosov representations of word hyperbolic
groups into real semisimple Lie groups in terms of equivariant limit maps, the
Cartan property and the uniform gap summation property of \cite{GGKW}. As an
application we obtain a characterization of strongly convex cocompact subgroups
of the projective linear group . We also compute
the H\"older exponent of the Anosov limit maps of an Anosov representation in
terms of the Cartan and Lyapunov projection of the image.Comment: 45 pages, Comments welcom
The H\"older exponent of Anosov limit maps
Let be a non-elementary word hyperbolic group and a
visual metric on its Gromov boundary . For an
-Anosov representation , where or , we
calculate the H\"older exponent of the Anosov limit map
of in terms of the moduli of
eigenvalues of elements in and the stable translation length on
. If is either irreducible or
spans and is
-Anosov, then attains its H\"older exponent. We also
provide an analogous calculation for the exponent of the inverse limit map of
-hyperconvex representations. Finally, we exhibit examples of non
semisimple -Anosov representations of surface groups in
whose Anosov limit map in
does not attain its H\"older exponent.Comment: 24 page
Federated Fine-Tuning of Foundation Models via Probabilistic Masking
Foundation Models (FMs) have revolutionized machine learning with their
adaptability and high performance across tasks; yet, their integration into
Federated Learning (FL) is challenging due to substantial communication
overhead from their extensive parameterization. Current communication-efficient
FL strategies, such as gradient compression, reduce bitrates to around
bit-per-parameter (bpp). However, these approaches fail to harness the
characteristics of FMs, with their large number of parameters still posing a
challenge to communication efficiency, even at these bitrate regimes. In this
work, we present DeltaMask, a novel method that efficiently fine-tunes FMs in
FL at an ultra-low bitrate, well below 1 bpp. DeltaMask employs stochastic
masking to detect highly effective subnetworks within FMs and leverage
stochasticity and sparsity in client masks to compress updates into a compact
grayscale image using probabilistic filters, deviating from traditional weight
training approaches. Our comprehensive evaluations across various datasets and
architectures demonstrate DeltaMask efficiently achieves bitrates as low as
0.09 bpp, enhancing communication efficiency while maintaining FMs performance,
as measured on 8 datasets and 5 pre-trained models of various network
architectures.Comment: 19 pages, 9 figure
New nonlinear hyperbolic groups
We construct nonlinear hyperbolic groups which are large, torsion‐free, one‐ended, and admit a finite K(π,1). Our examples are built from superrigid cocompact rank one lattices via amalgamated free products and HNN extensions.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149490/1/blms12248.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149490/2/blms12248_am.pd
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