3 research outputs found
Variation-based Cause Effect Identification
Mining genuine mechanisms underlying the complex data generation process in
real-world systems is a fundamental step in promoting interpretability of, and
thus trust in, data-driven models. Therefore, we propose a variation-based
cause effect identification (VCEI) framework for causal discovery in bivariate
systems from a single observational setting. Our framework relies on the
principle of independence of cause and mechanism (ICM) under the assumption of
an existing acyclic causal link, and offers a practical realization of this
principle. Principally, we artificially construct two settings in which the
marginal distributions of one covariate, claimed to be the cause, are
guaranteed to have non-negligible variations. This is achieved by re-weighting
samples of the marginal so that the resultant distribution is notably distinct
from this marginal according to some discrepancy measure. In the causal
direction, such variations are expected to have no impact on the effect
generation mechanism. Therefore, quantifying the impact of these variations on
the conditionals reveals the genuine causal direction. Moreover, we formulate
our approach in the kernel-based maximum mean discrepancy, lifting all
constraints on the data types of cause-and-effect covariates, and rendering
such artificial interventions a convex optimization problem. We provide a
series of experiments on real and synthetic data showing that VCEI is, in
principle, competitive to other cause effect identification frameworks
Multi-level Attention Model for Weakly Supervised Audio Classification
In this paper, we propose a multi-level attention model to solve the weakly
labelled audio classification problem. The objective of audio classification is
to predict the presence or absence of audio events in an audio clip. Recently,
Google published a large scale weakly labelled dataset called Audio Set, where
each audio clip contains only the presence or absence of the audio events,
without the onset and offset time of the audio events. Our multi-level
attention model is an extension to the previously proposed single-level
attention model. It consists of several attention modules applied on
intermediate neural network layers. The output of these attention modules are
concatenated to a vector followed by a multi-label classifier to make the final
prediction of each class. Experiments shown that our model achieves a mean
average precision (mAP) of 0.360, outperforms the state-of-the-art single-level
attention model of 0.327 and Google baseline of 0.314.Comment: 5 pages, 3 figures, Submitted to Eusipco 201