Pathologies of Neural Models Make Interpretations Difficult

One way to interpret neural model predictions is to highlight the most important input features---for example, a heatmap visualization over the words in an input sentence. In existing interpretation methods for NLP, a word's importance is determined by either input perturbation---measuring the decrease in model confidence when that word is removed---or by the gradient with respect to that word. To understand the limitations of these methods, we use input reduction, which iteratively removes the least important word from the input. This exposes pathological behaviors of neural models: the remaining words appear nonsensical to humans and are not the ones determined as important by interpretation methods. As we confirm with human experiments, the reduced examples lack information to support the prediction of any label, but models still make the same predictions with high confidence. To explain these counterintuitive results, we draw connections to adversarial examples and confidence calibration: pathological behaviors reveal difficulties in interpreting neural models trained with maximum likelihood. To mitigate their deficiencies, we fine-tune the models by encouraging high entropy outputs on reduced examples. Fine-tuned models become more interpretable under input reduction without accuracy loss on regular examples.Comment: EMNLP 2018 camera read

Accounting for decarbonisation and reducing capital at risk in the S&P500

This document is the Accepted Manuscript version of the following article: Colin Haslam, Nick Tsitsianis, Glen Lehman, Tord Andersson, and John Malamatenios, ‘Accounting for decarbonisation and reducing capital at risk in the S&P500’, Accounting Forum, Vol. 42 91): 119-129, March 2018. Under embargo until 7 August 2019. The final, definitive version is available online at doi: https://doi.org/10.1016/j.accfor.2018.01.004.This article accounts for carbon emissions in the S&P 500 and explores the extent to which capital is at risk from decarbonising value chains. At a global level it is proving difficult to decouple carbon emissions from GDP growth. Top-down legal and regulatory arrangements envisaged by the Kyoto Protocol are practically redundant given inconsistent political commitment to mitigating global climate change and promoting sustainability. The United Nations Environment Programme (UNEP) and European Commission (EC) are promoting the role of financial markets and financial institutions as drivers of behavioural change mobilising capital allocations to decarbonise corporate activity.Peer reviewe

Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

We present a simulation-based study using deep convolutional neural networks (DCNNs) to identify neutrino interaction vertices in the MINERvA passive targets region, and illustrate the application of domain adversarial neural networks (DANNs) in this context. DANNs are designed to be trained in one domain (simulated data) but tested in a second domain (physics data) and utilize unlabeled data from the second domain so that during training only features which are unable to discriminate between the domains are promoted. MINERvA is a neutrino-nucleus scattering experiment using the NuMI beamline at Fermilab. $A$-dependent cross sections are an important part of the physics program, and these measurements require vertex finding in complicated events. To illustrate the impact of the DANN we used a modified set of simulation in place of physics data during the training of the DANN and then used the label of the modified simulation during the evaluation of the DANN. We find that deep learning based methods offer significant advantages over our prior track-based reconstruction for the task of vertex finding, and that DANNs are able to improve the performance of deep networks by leveraging available unlabeled data and by mitigating network performance degradation rooted in biases in the physics models used for training.Comment: 41 page

Studies On The Potential Impacts Of The New Basel Capital Accord

In April 2003, the Basel Committee on Banking Supervision published the third consultative paper (CP3) of the new Basel Capital Accord relating to the prudential regulation of banks, which was followed in July 2003 by the EU Commission’s draft directive with the same contents, but slightly different detailed rules (Capital Adequacy Directive, CAD3). During the consultative process both organisations expect comments from the players affected by the new capital regulation, thus from the central banks of each country as well. The significance of the new capital regulation is underlined by the fact that the Basel recommendation will soon be followed by the European Union’s directive (presumably in 2004), the implementation of which will be one of the largest regulative challenges for Hungary. Accordingly, the Magyar Nemzeti Bank pays special attention to preparing the implementation of the Basel II/CAD3 capital accords, laying the groundwork for the adaptation and carrying out the necessary background analyses. Our main objective in the first phase of this rather complex and far-reaching project was – through participation in the legislative process – to analyse the issues important and relevant for the MNB, as well as to assess the potential consequences of implementation in Hungary. During such analyses we focused on the macro-prudential consequences. Accordingly, we carried out a detailed assessment of five topics.Basel Capital Accord, Pro-cyclicality, Credit risk, Market Risk, Regulation, Corporate governance.

Gateway Electromagnetic Environmental Effects (E3) Requirements

Electromagnetic Compatibility (EMC) is essential to the success of any vehicle design that incorporates a complex assortment of electronic, electrical, and electromechanical systems and sub-systems that is expected to meet operational and performance requirements while exposed to a changing set of electromagnetic environments composed of both man-made and naturally occurring threats. The combined aspects of these environments are known as Electromagnetic Environmental Effects (E3). The attainment of EMC is accomplished through the application of sound engineering principles and practices that enable a complex vehicle or vehicles to operate successfully when exposed to the effects of its expected and/or specified electromagnetic environments