467 research outputs found
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing
Semi-supervised learning is crucial for alleviating labelling burdens in
people-centric sensing. However, human-generated data inherently suffer from
distribution shift in semi-supervised learning due to the diverse biological
conditions and behavior patterns of humans. To address this problem, we propose
a generic distributionally robust model for semi-supervised learning on
distributionally shifted data. Considering both the discrepancy and the
consistency between the labeled data and the unlabeled data, we learn the
latent features that reduce person-specific discrepancy and preserve
task-specific consistency. We evaluate our model in a variety of people-centric
recognition tasks on real-world datasets, including intention recognition,
activity recognition, muscular movement recognition and gesture recognition.
The experiment results demonstrate that the proposed model outperforms the
state-of-the-art methods.Comment: 8 pages, accepted by AAAI201
The integrated system optimization based on the boiler combustion and denitration with denitration operating cost consideration
Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data
Diffusion models achieve state-of-the-art performance in various generation
tasks. However, their theoretical foundations fall far behind. This paper
studies score approximation, estimation, and distribution recovery of diffusion
models, when data are supported on an unknown low-dimensional linear subspace.
Our result provides sample complexity bounds for distribution estimation using
diffusion models. We show that with a properly chosen neural network
architecture, the score function can be both accurately approximated and
efficiently estimated. Furthermore, the generated distribution based on the
estimated score function captures the data geometric structures and converges
to a close vicinity of the data distribution. The convergence rate depends on
the subspace dimension, indicating that diffusion models can circumvent the
curse of data ambient dimensionality.Comment: 52 pages, 4 figure
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