209,060 research outputs found
Merging Decision Transformers: Weight Averaging for Forming Multi-Task Policies
Recent work has shown the promise of creating generalist, transformer-based,
models for language, vision, and sequential decision-making problems. To create
such models, we generally require centralized training objectives, data, and
compute. It is of interest if we can more flexibly create generalist policies
by merging together multiple, task-specific, individually trained policies. In
this work, we take a preliminary step in this direction through merging, or
averaging, subsets of Decision Transformers in parameter space trained on
different MuJoCo locomotion problems, forming multi-task models without
centralized training. We also demonstrate the importance of various
methodological choices when merging policies, such as utilizing common
pre-trained initializations, increasing model capacity, and utilizing Fisher
information for weighting parameter importance. In general, we believe research
in this direction could help democratize and distribute the process that forms
multi-task robotics policies. Our implementation is available at
https://github.com/daniellawson9999/merging-decision-transformers
Fast algorithm for calculating two-photon absorption spectra
We report a numerical calculation of the two-photon absorption coefficient of
electrons in a binding potential using the real-time real-space higher-order
difference method. By introducing random vector averaging for the intermediate
state, the task of evaluating the two-dimensional time integral is reduced to
calculating two one-dimensional integrals. This allows the reduction of the
computation load down to the same order as that for the linear response
function. The relative advantage of the method compared to the straightforward
multi-dimensional time integration is greater for the calculation of non-linear
response functions of higher order at higher energy resolution.Comment: 4 pages, 2 figures. It will be published in Phys. Rev. E on 1, March,
199
Learned Multi-Patch Similarity
Estimating a depth map from multiple views of a scene is a fundamental task
in computer vision. As soon as more than two viewpoints are available, one
faces the very basic question how to measure similarity across >2 image
patches. Surprisingly, no direct solution exists, instead it is common to fall
back to more or less robust averaging of two-view similarities. Encouraged by
the success of machine learning, and in particular convolutional neural
networks, we propose to learn a matching function which directly maps multiple
image patches to a scalar similarity score. Experiments on several multi-view
datasets demonstrate that this approach has advantages over methods based on
pairwise patch similarity.Comment: 10 pages, 7 figures, Accepted at ICCV 201
Sliced Multi-Marginal Optimal Transport
Multi-marginal optimal transport enables one to compare multiple probability
measures, which increasingly finds application in multi-task learning problems.
One practical limitation of multi-marginal transport is computational
scalability in the number of measures, samples and dimensionality. In this
work, we propose a multi-marginal optimal transport paradigm based on random
one-dimensional projections, whose (generalized) distance we term the sliced
multi-marginal Wasserstein distance. To construct this distance, we introduce a
characterization of the one-dimensional multi-marginal Kantorovich problem and
use it to highlight a number of properties of the sliced multi-marginal
Wasserstein distance. In particular, we show that (i) the sliced multi-marginal
Wasserstein distance is a (generalized) metric that induces the same topology
as the standard Wasserstein distance, (ii) it admits a dimension-free sample
complexity, (iii) it is tightly connected with the problem of barycentric
averaging under the sliced-Wasserstein metric. We conclude by illustrating the
sliced multi-marginal Wasserstein on multi-task density estimation and
multi-dynamics reinforcement learning problems
- …