890 research outputs found
Fuzzy logic based intention recognition in STS processes
This paper represents a fuzzy logic based classifier that is able to recognise human users' intention of standing up from their behaviours in terms of the force they apply to the ground. The research reported focused on the selection of meaningful input data to the classifier and on the determination of fuzzy sets that best represent the intention information hidden in the force data. The classifier is a component of a robot chair which provides the users with assistance to stand up based on the recognised intention by the classifier
A Viscosity Solution Theory of Stochastic Hamilton-Jacobi-Bellman equations in the Wasserstein Space
This paper is devoted to a viscosity solution theory of the stochastic
Hamilton-Jacobi-Bellman equation in the Wasserstein spaces for the mean-field
type control problem which allows for random coefficients and may thus be
non-Markovian. The value function of the control problem is proven to be the
unique viscosity solution. The major challenge lies in the mixture of the lack
of local compactness of the Wasserstein spaces and the non-Markovian setting
with random coefficients and various techniques are used, including Ito
processes parameterized by random measures, the conditional law invariance of
the value function, a novel tailor-made compact subset of measure-valued
processes, finite dimensional approximations via stochastic n-player
differential games with common noises, and so on.Comment: 41 page
Minimum Cost Active Labeling
Labeling a data set completely is important for groundtruth generation. In
this paper, we consider the problem of minimum-cost labeling: classifying all
images in a large data set with a target accuracy bound at minimum dollar cost.
Human labeling can be prohibitive, so we train a classifier to accurately label
part of the data set. However, training the classifier can be expensive too,
particularly with active learning. Our min-cost labeling uses a variant of
active learning to learn a model to predict the optimal training set size for
the classifier that minimizes overall cost, then uses active learning to train
the classifier to maximize the number of samples the classifier can correctly
label. We validate our approach on well-known public data sets such as Fashion,
CIFAR-10, and CIFAR-100. In some cases, our approach has 6X lower overall cost
relative to human labeling, and is always cheaper than the cheapest active
learning strategy
SDCL: Self-Distillation Contrastive Learning for Chinese Spell Checking
Due to the ambiguity of homophones, Chinese Spell Checking (CSC) has
widespread applications. Existing systems typically utilize BERT for text
encoding. However, CSC requires the model to account for both phonetic and
graphemic information. To adapt BERT to the CSC task, we propose a token-level
self-distillation contrastive learning method. We employ BERT to encode both
the corrupted and corresponding correct sentence. Then, we use contrastive
learning loss to regularize corrupted tokens' hidden states to be closer to
counterparts in the correct sentence. On three CSC datasets, we confirmed our
method provides a significant improvement above baselines
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