Deep neural networks have gained tremendous success in a broad range of machine learning tasks due to its remarkable capability to learn semantic-rich features from high-dimensional data. However, they often require large-scale labelled data to successfully learn such features, which significantly hinders their adaption into unsupervised learning tasks, such as anomaly detection and clustering, and limits their applications into critical domains where obtaining massive labelled data is prohibitively expensive. To enable downstream unsupervised learning on those domains, in this work we propose to learn features without using any labelled data by training neural networks to predict data distances in a randomly projected space. Random mapping is a theoretical proven approach to obtain approximately preserved distances. To well predict these random distances, the representation learner is optimised to learn genuine class structures that are implicitly embedded in the randomly projected space. Experimental results on 19 real-world datasets show our learned representations substantially outperform state-of-the-art competing methods in both anomaly detection and clustering tasks. Unsupervised representation learning aims at automatically extracting expressive feature representations from data without any manually labelled data. Due to the remarkable capability to learn semantic-rich features, deep neural networks have been becoming one widely-used technique to empower a broad range of machine learning tasks. One main issue with these deep learning techniques is that a massive amount of labelled data is typically required to successfully learn these expressive features. As a result, their transformation power is largely reduced for tasks that are unsupervised in nature, such as anomaly detection and clustering. This is also true to critical domains, such as healthcare and fintech, where collecting massive labelled data is prohibitively expensive and/or is impossible to scale. To bridge this gap, in this work we explore fully unsupervised representation learning techniques to enable downstream unsupervised learning methods on those critical domains. In recent years, many unsupervised representation learning methods (Mikolov et al., 2013a; Le & Mikolov, 2014; Misra et al., 2016; Lee et al., 2017; Gidaris et al., 2018) have been introduced, of which most are self-supervised approaches that formulate the problem as an annotation free pretext task.

Comparing business process variants using event logs is a common use case in process mining. Existing techniques for process variant analysis detect statistically-significant differences between variants at the level of individual entities (such as process activities) and their relationships (e.g. directly-follows relations between activities). This may lead to a proliferation of differences due to the low level of granularity in which such differences are captured. This paper presents a novel approach to detect statistically-significant differences between variants at the level of entire process traces (i.e. sequences of directly-follows relations). The cornerstone of this approach is a technique to learn a directly follows graph called mutual fingerprint from the event logs of the two variants. A mutual fingerprint is a lossless encoding of a set of traces and their duration using discrete wavelet transformation. This structure facilitates the understanding of statistical differences along the control-flow and performance dimensions. The approach has been evaluated using real-life event logs against two baselines. The results show that at a trace level, the baselines cannot always reveal the differences discovered by our approach, or can detect spurious differences.

Predictive process analytics has recently gained significant attention, and yet its successful adoption in organisations relies on how well users can trust the predictions of the underlying machine learning algorithms that are often applied and recognised as a `black-box'. Without understanding the rationale of the black-box machinery, there will be a lack of trust in the predictions, a reluctance to use the predictions, and in the worse case, consequences of an incorrect decision based on the prediction. In this paper, we emphasise the importance of interpreting the predictive models in addition to the evaluation using conventional metrics, such as accuracy, in the context of predictive process monitoring. We review existing studies on business process monitoring benchmarks for predicting process outcomes and remaining time. We derive explanations that present the behaviour of the entire predictive model as well as explanations describing a particular prediction. These explanations are used to reveal data leakages, assess the interpretability of features used by the model, and the degree of the use of process knowledge in the existing benchmark models. Findings from this exploratory study motivate the need to incorporate interpretability in predictive process analytics.

Australia Abstract Stability of recurrent models is closely linked with trainability, generalizability and in some applications, safety. Methods that train stable recurrent neural networks, however, do so at a significant cost to expressibility. We propose an implicit model structure that allows for a convex parametriza-tion of stable models using contraction analysis of nonlinear systems. Using these stability conditions we propose a new approach to model initialization and then provide a number of empirical results comparing the performance of our proposed model set to previous stable RNNs and vanilla RNNs. By carefully controlling stability in the model, we observe a significant increase in the speed of training and model performance. Keywords: System Identification, Contraction, Stability, Recurrent Neural Network, V anishing Gradient, Exploding Gradient, Nonlinear Systems, Echo State Network Notation Most of our notation is standard.

A growing body of work shows that many problems in fairness, accountability, transparency, and ethics in machine learning systems are rooted in decisions surrounding the data collection and annotation process. In spite of its fundamental nature however, data collection remains an overlooked part of the machine learning (ML) pipeline. In this paper, we argue that a new specialization should be formed within ML that is focused on methodologies for data collection and annotation: efforts that require institutional frameworks and procedures. Specifically for sociocultural data, parallels can be drawn from archives and libraries. Archives are the longest standing communal effort to gather human information and archive scholars have already developed the language and procedures to address and discuss many challenges pertaining to data collection such as consent, power, inclusivity, transparency, and ethics & privacy. We discuss these five key approaches in document collection practices in archives that can inform data collection in sociocultural ML. By showing data collection practices from another field, we encourage ML research to be more cognizant and systematic in data collection and draw from interdisciplinary expertise.

Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities. However, controlling attributes of the generated language (e.g. switching topic or sentiment) is difficult without modifying the model architecture or fine-tuning on attribute-specific data and entailing the significant cost of retraining. We propose a simple alternative: the Plug and Play Language Model (PPLM) for controllable language generation, which combines a pretrained LM with one or more simple attribute classifiers that guide text generation without any further training of the LM. In the canonical scenario we present, the attribute models are simple classifiers consisting of a user-specified bag of words or a single learned layer with 100,000 times fewer parameters than the LM. Sampling entails a forward and backward pass in which gradients from the attribute model push the LM's hidden activations and thus guide the generation. Model samples demonstrate control over a range of topics and sentiment styles, and extensive automated and human annotated evaluations show attribute alignment and fluency. PPLMs are flexible in that any combination of differentiable attribute models may be used to steer text generation, which will allow for diverse and creative applications beyond the examples given in this paper.

In Australia, five major companies are involved in a trial run of eight principles developed as part of the government AI Ethics Framework. The idea behind the principles is to ensure that AI systems benefit individuals, society and the environment; respect human rights; don't discriminate; and uphold privacy rights and data protection.

Authorship identification is a process in which the author of a text is identified. Most known literary texts can easily be attributed to a certain author because they are, for example, signed. Yet sometimes we find unfinished pieces of work or a whole bunch of manuscripts with a wide variety of possible authors. In order to assess the importance of such a manuscript, it is vital to know who wrote it. In this work, we aim to develop a machine learning framework to effectively determine authorship. We formulate the task as a single-label multi-class text categorization problem and propose a supervised machine learning framework incorporating stylometric features. This task is highly interdisciplinary in that it takes advantage of machine learning, information retrieval, and natural language processing. We present an approach and a model which learns the differences in writing style between $50$ different authors and is able to predict the author of a new text with high accuracy. The accuracy is seen to increase significantly after introducing certain linguistic stylometric features along with text features.

Audio Player failed to load. Try to Download directly (2.19 MB) Space to play or pause, M to mute, left and right arrows to seek, up and down arrows for volume. Australia's Human Rights Commission is calling for a moratorium on the introduction of some new artificial intelligence technologies, until the rights of humans can be safeguarded. And many of those inside the industry agree that the technology is taking off too fast for our legal system to keep up. The commission wants to better regulate artificial intelligence like facial recognition to protect people's privacy and to prevent society's most vulnerable from being further disadvantaged.

Every time some genius decides to apply AI where it doesn't belong, the world collectively rolls its eyes and puts another ballot in the AI-Is-A-Fad box. If your dictionary defines AI as magic or robots (or magical robots), of course you'll be disappointed when it doesn't deliver the cure to all that ails you. Let's look at three common gripes using simple examples everyone can grasp. A respectable software engineer once asked me with a straight face, "Can AI know that Canada is a country?" Hold your horses there, cowboy.