4 research outputs found
On the Rationality of Explanations in Classification Algorithms
This paper is a first step towards studying the rationality of explanations produced by up-to-date AI systems. Based on the thesis that designing rational explanations for accomplishing trustworthy AI is fundamental for ethics in AI, we study the rationality criteria that explanations in classification algorithms have to meet. In this way, we identify, define, and exemplify characteristic criteria of rational explanations in classification algorithms
Composition of Saliency Metrics for Channel Pruning with a Myopic Oracle
The computation and memory needed for Convolutional Neural Network (CNN)
inference can be reduced by pruning weights from the trained network. Pruning
is guided by a pruning saliency, which heuristically approximates the change in
the loss function associated with the removal of specific weights. Many pruning
signals have been proposed, but the performance of each heuristic depends on
the particular trained network. This leaves the data scientist with a difficult
choice. When using any one saliency metric for the entire pruning process, we
run the risk of the metric assumptions being invalidated, leading to poor
decisions being made by the metric. Ideally we could combine the best aspects
of different saliency metrics. However, despite an extensive literature review,
we are unable to find any prior work on composing different saliency metrics.
The chief difficulty lies in combining the numerical output of different
saliency metrics, which are not directly comparable.
We propose a method to compose several primitive pruning saliencies, to
exploit the cases where each saliency measure does well. Our experiments show
that the composition of saliencies avoids many poor pruning choices identified
by individual saliencies. In most cases our method finds better selections than
even the best individual pruning saliency
Learning to Reweight for Graph Neural Network
Graph Neural Networks (GNNs) show promising results for graph tasks. However,
existing GNNs' generalization ability will degrade when there exist
distribution shifts between testing and training graph data. The cardinal
impetus underlying the severe degeneration is that the GNNs are architected
predicated upon the I.I.D assumptions. In such a setting, GNNs are inclined to
leverage imperceptible statistical correlations subsisting in the training set
to predict, albeit it is a spurious correlation. In this paper, we study the
problem of the generalization ability of GNNs in Out-Of-Distribution (OOD)
settings. To solve this problem, we propose the Learning to Reweight for
Generalizable Graph Neural Network (L2R-GNN) to enhance the generalization
ability for achieving satisfactory performance on unseen testing graphs that
have different distributions with training graphs. We propose a novel nonlinear
graph decorrelation method, which can substantially improve the
out-of-distribution generalization ability and compares favorably to previous
methods in restraining the over-reduced sample size. The variables of the graph
representation are clustered based on the stability of the correlation, and the
graph decorrelation method learns weights to remove correlations between the
variables of different clusters rather than any two variables. Besides, we
interpose an efficacious stochastic algorithm upon bi-level optimization for
the L2R-GNN framework, which facilitates simultaneously learning the optimal
weights and GNN parameters, and avoids the overfitting problem. Experimental
results show that L2R-GNN greatly outperforms baselines on various graph
prediction benchmarks under distribution shifts