@inproceedings {43347,
	title = {Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models},
	journal = { ACM International Conference on Information and Knowledge Management},
	year = {2023},
	abstract = {Causal Neural Network models have shown high levels of robustness to adversarial attacks as well as an increased capacity for generalisation tasks such as few-shot learning and rare-context classification compared to traditional Neural Networks. This robustness is argued to stem from the disentanglement of causal and confounder input signals. However, no quantitative study has yet measured the level of disentanglement achieved by these types of causal models or assessed how this relates to their adversarial robustness.\&nbsp;\&nbsp; Existing causal disentanglement metrics are not applicable to deterministic models trained on real-world datasets. We, therefore, utilise metrics of content/style disentanglement from the field of Computer Vision to measure different aspects of the causal disentanglement for four state-of-the-art causal Neural Network models. By re-implementing these models with a common ResNet18 architecture we are able to fairly measure their adversarial robustness on three standard image classification benchmarking datasets under seven common white-box attacks. We find a strong association (r=0.820, p=0.001) between the degree to which models decorrelate causal and confounder signals and their adversarial robustness. Additionally, we find a moderate negative association between the pixel-level information content of the confounder signal and adversarial robustness (r=-0.597, p=0.040).},
	author = {Preben Monteiro {Ness} and Marijan, Dusica and Bose, Sunanda}
}
@article {ragni2022increasing,
	title = {Increasing Context for Estimating Confidence Scores in Automatic Speech Recognition},
	journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},
	volume = {30},
	year = {2022},
	pages = {1319{\textendash}1329},
	publisher = { IEEE},
	author = {Ragni, Anton and Mark JF {Gales} and Rose, Oliver and Katherine M {Knill} and Kastanos, Alexandros and Li, Qiujia and Preben Monteiro {Ness}}
}
@inproceedings {li2019bi,
	title = {Bi-directional lattice recurrent neural networks for confidence estimation},
	journal = {ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
	year = {2019},
	pages = {6755{\textendash}6759},
	publisher = { IEEE},
	abstract = {The standard approach to mitigating errors made by an automatic
speech recognition system is to use confidence scores associated
with each predicted word. In the simplest case, these scores are
word posterior probabilities whilst more complex schemes utilise
bi-directional recurrent neural network (BiRNN) models. A number
of upstream and downstream applications, however, rely on confidence
scores assigned not only to 1-best hypotheses but to all words
found in confusion networks or lattices. These include but are not
limited to speaker adaptation, semi-supervised training and information 
retrieval. Although word posteriors could be used in those applications 
as confidence scores, they are known to have reliability issues. To make 
improved confidence scores more generally available, this paper shows how 
BiRNNs can be extended from 1-best sequences to confusion network and 
lattice structures. Experiments are conducted using one of the Cambridge
University submissions to the IARPA OpenKWS 2016 competition. The 
results show that confusion network and lattice-based BiRNNs can provide a 
significant improvement in confidence estimation.},
	keywords = {bi-directional recurrent neural network, confidence estimation, confusion network, lattice},
	author = {Li, Qiujia and Preben Monteiro {Ness} and Ragni, Anton and Mark JF {Gales}}
}
