283 research outputs found
Knowledge-based Transfer Learning Explanation
Machine learning explanation can significantly boost machine learning's
application in decision making, but the usability of current methods is limited
in human-centric explanation, especially for transfer learning, an important
machine learning branch that aims at utilizing knowledge from one learning
domain (i.e., a pair of dataset and prediction task) to enhance prediction
model training in another learning domain. In this paper, we propose an
ontology-based approach for human-centric explanation of transfer learning.
Three kinds of knowledge-based explanatory evidence, with different
granularities, including general factors, particular narrators and core
contexts are first proposed and then inferred with both local ontologies and
external knowledge bases. The evaluation with US flight data and DBpedia has
presented their confidence and availability in explaining the transferability
of feature representation in flight departure delay forecasting.Comment: Accepted by International Conference on Principles of Knowledge
Representation and Reasoning, 201
Validating Sample Average Approximation Solutions with Negatively Dependent Batches
Sample-average approximations (SAA) are a practical means of finding
approximate solutions of stochastic programming problems involving an extremely
large (or infinite) number of scenarios. SAA can also be used to find estimates
of a lower bound on the optimal objective value of the true problem which, when
coupled with an upper bound, provides confidence intervals for the true optimal
objective value and valuable information about the quality of the approximate
solutions. Specifically, the lower bound can be estimated by solving multiple
SAA problems (each obtained using a particular sampling method) and averaging
the obtained objective values. State-of-the-art methods for lower-bound
estimation generate batches of scenarios for the SAA problems independently. In
this paper, we describe sampling methods that produce negatively dependent
batches, thus reducing the variance of the sample-averaged lower bound
estimator and increasing its usefulness in defining a confidence interval for
the optimal objective value. We provide conditions under which the new sampling
methods can reduce the variance of the lower bound estimator, and present
computational results to verify that our scheme can reduce the variance
significantly, by comparison with the traditional Latin hypercube approach
Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph
Multi-modal aspect-based sentiment classification (MABSC) is task of
classifying the sentiment of a target entity mentioned in a sentence and an
image. However, previous methods failed to account for the fine-grained
semantic association between the image and the text, which resulted in limited
identification of fine-grained image aspects and opinions. To address these
limitations, in this paper we propose a new approach called SeqCSG, which
enhances the encoder-decoder sentiment classification framework using
sequential cross-modal semantic graphs. SeqCSG utilizes image captions and
scene graphs to extract both global and local fine-grained image information
and considers them as elements of the cross-modal semantic graph along with
tokens from tweets. The sequential cross-modal semantic graph is represented as
a sequence with a multi-modal adjacency matrix indicating relationships between
elements. Experimental results show that the approach outperforms existing
methods and achieves state-of-the-art performance on two standard datasets.
Further analysis has demonstrated that the model can implicitly learn the
correlation between fine-grained information of the image and the text with the
given target. Our code is available at https://github.com/zjukg/SeqCSG.Comment: ICANN 2023, https://github.com/zjukg/SeqCS
- …