Zobrazeno 1 - 10
of 476
pro vyhledávání: '"Lin, Alex P."'
Deriving meaningful representations from complex, high-dimensional data in unsupervised settings is crucial across diverse machine learning applications. This paper introduces a framework for multi-scale graph network embedding based on spectral grap
Externí odkaz:
http://arxiv.org/abs/2406.02778
Autor:
Lin, Alex Tong, Osher, Stanley J.
We introduce a method called MASCOT (Multi-Agent Shape Control with Optimal Transport) to compute optimal control solutions of agents with shape/formation/density constraints. For example, we might want to apply shape constraints on the agents -- per
Externí odkaz:
http://arxiv.org/abs/2207.00129
We provide a method to identify system parameters of dynamical systems, called ID-ODE -- Inference by Differentiation and Observing Delay Embeddings. In this setting, we are given a dataset of trajectories from a dynamical system with system paramete
Externí odkaz:
http://arxiv.org/abs/2203.06269
Living organisms are primarily made of cells. Identifying them and characterizing their geometry and spatial distribution is a first step towards building multi-scale models of these biomaterials. We propose a method to count cells using nuclei in an
Externí odkaz:
http://arxiv.org/abs/2112.10670
Existing goal-oriented dialogue datasets focus mainly on identifying slots and values. However, customer support interactions in reality often involve agents following multi-step procedures derived from explicitly-defined company policies as well. To
Externí odkaz:
http://arxiv.org/abs/2104.00783
We introduce a new method for training generative adversarial networks by applying the Wasserstein-2 metric proximal on the generators. The approach is based on Wasserstein information geometry. It defines a parametrization invariant natural gradient
Externí odkaz:
http://arxiv.org/abs/2102.06862
Inverse problems consist of recovering a signal from a collection of noisy measurements. These are typically cast as optimization problems, with classic approaches using a data fidelity term and an analytic regularizer that stabilizes recovery. Recen
Externí odkaz:
http://arxiv.org/abs/2008.02200
We present APAC-Net, an alternating population and agent control neural network for solving stochastic mean field games (MFGs). Our algorithm is geared toward high-dimensional instances of MFGs that are beyond reach with existing solution methods. We
Externí odkaz:
http://arxiv.org/abs/2002.10113
We propose regularization strategies for learning discriminative models that are robust to in-class variations of the input data. We use the Wasserstein-2 geometry to capture semantically meaningful neighborhoods in the space of images, and define a
Externí odkaz:
http://arxiv.org/abs/1909.06860
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