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Interpretable Local Concept-based Explanation with Human Feedback to Predict All-cause Mortality

Journal of Artificial Intelligence Research

Machine learning models are incorporated in different fields and disciplines in which some of them require a high level of accountability and transparency, for example, the healthcare sector. With the General Data Protection Regulation (GDPR), the importance for plausibility and verifiability of the predictions made by machine learning models has become essential. A widely used category of explanation techniques attempts to explain models’ predictions by quantifying the importance score of each input feature. However, summarizing such scores to provide human-interpretable explanations is challenging. Another category of explanation techniques focuses on learning a domain representation in terms of high-level human-understandable concepts and then utilizing them to explain predictions. These explanations are hampered by how concepts are constructed, which is not intrinsically interpretable. To this end, we propose Concept-based Local Explanations with Feedback (CLEF), a novel local model agnostic explanation framework for learning a set of high-level transparent concept definitions in high-dimensional tabular data that uses clinician-labeled concepts rather than raw features. CLEF maps the raw input features to high-level intuitive concepts and then decompose the evidence of prediction of the instance being explained into concepts. In addition, the proposed framework generates counterfactual explanations, suggesting the minimum changes in the instance’s concept based explanation that will lead to a different prediction. We demonstrate with simulated user feedback on predicting the risk of mortality. Such direct feedback is more effective than other techniques, that rely on hand-labelled or automatically extracted concepts, in learning concepts that align with ground truth concept definitions.



Top Computer Vision Applications and Opportunities

#artificialintelligence

Artificial intelligence (AI) is a term you must have heard, even if you are from the IT world. AI is when machines and computer systems simulate human intelligence processes. Right now, AI is literally taking over the world – at least 90% of tech giants invest in it. According to the Data and AI Leadership Executive Survey, the number of AI-friendly companies participating in the survey has doubled in one year. Another survey states that half of the interviewed companies use AI. Some more specific applications of AI include expert systems, natural language processing, speech recognition, and machine (computer) vision. The latter type of AI – computer vision – has already been integrated into road traffic, bank payments, and social networks. For the last decades, AI vision has learned to solve many tasks with an accuracy reaching the human one. "As many others have noticed and pointed out, the neocortex has a highly uniform architecture too across all of its input modalities. Perhaps nature has stumbled by a very similar powerful architecture and replicated it in a similar fashion, varying only some of the details. This consolidation in architecture will in turn focus and concentrate software, hardware, and infrastructure, further speeding up progress across AI. […] Anyway, exciting times." Many companies have started using computer vision in artificial intelligence tasks.


Using Time Series Analysis to Forecast Close Approaches to the Earth by Near-Earth Objects

#artificialintelligence

If we are to be struck by an impact event resulting in human extinction, it would most likely occur in the Spring or Fall. If you were to ask 100 people what they believed the greatest risk to human civilization is I would bet the top 3 answers would be nuclear war, global pandemic and global warming/climate change. However, less than 10 years ago a meteor with a diameter of approximately 20 meters and a mass of 10,000 tons exploded 30 km over the city Chelyabinsk in Russia. Although there were no fatalities, the blast was estimated to have resulted in $30 million worth of damages and injured 1,500 people. About 100 years previously, in 1908, a meteor 50–60 meters in size exploded over Siberia with the power of a 12 megaton explosion which destroyed about 2,200 squared kilometers of forest.


Complete 2022 Data Science & Machine Learning Bootcamp

#artificialintelligence

Welcome to the Complete Data Science and Machine Learning Bootcamp, the only course you need to learn Python and get into data science.


Fine-grained Prediction of Political Leaning on Social Media with Unsupervised Deep Learning

Journal of Artificial Intelligence Research

Predicting the political leaning of social media users is an increasingly popular task, given its usefulness for electoral forecasts, opinion dynamics models and for studying the political dimension of polarization and disinformation. Here, we propose a novel unsupervised technique for learning fine-grained political leaning from the textual content of social media posts. Our technique leverages a deep neural network for learning latent political ideologies in a representation learning task. Then, users are projected in a low-dimensional ideology space where they are subsequently clustered. The political leaning of a user is automatically derived from the cluster to which the user is assigned. We evaluated our technique in two challenging classification tasks and we compared it to baselines and other state-of-the-art approaches. Our technique obtains the best results among all unsupervised techniques, with micro F1 = 0.426 in the 8-class task and micro F1 = 0.772 in the 3-class task. Other than being interesting on their own, our results also pave the way for the development of new and better unsupervised approaches for the detection of fine-grained political leaning.


Adversarial Attacks and Defense Methods for Power Quality Recognition

arXiv.org Artificial Intelligence

Vulnerability of various machine learning methods to adversarial examples has been recently explored in the literature. Power systems which use these vulnerable methods face a huge threat against adversarial examples. To this end, we first propose a signal-specific method and a universal signal-agnostic method to attack power systems using generated adversarial examples. Black-box attacks based on transferable characteristics and the above two methods are also proposed and evaluated. We then adopt adversarial training to defend systems against adversarial attacks. Experimental analyses demonstrate that our signal-specific attack method provides less perturbation compared to the FGSM (Fast Gradient Sign Method), and our signal-agnostic attack method can generate perturbations fooling most natural signals with high probability. What's more, the attack method based on the universal signal-agnostic algorithm has a higher transfer rate of black-box attacks than the attack method based on the signal-specific algorithm. In addition, the results show that the proposed adversarial training improves robustness of power systems to adversarial examples. OWER quality refers to a variety of electromagnetic phenomena that characterize voltage and current measured at a given time instance and location in a power system [2]. Disturbance of power quality (PQ) signals can cause severe problems in electrical grids [3].


Improving performance of aircraft detection in satellite imagery while limiting the labelling effort: Hybrid active learning

arXiv.org Artificial Intelligence

The earth observation industry provides satellite imagery with high spatial resolution and short revisit time. To allow efficient operational employment of these images, automating certain tasks has become necessary. In the defense domain, aircraft detection on satellite imagery is a valuable tool for analysts. Obtaining high performance detectors on such a task can only be achieved by leveraging deep learning and thus us-ing a large amount of labeled data. To obtain labels of a high enough quality, the knowledge of military experts is needed.We propose a hybrid clustering active learning method to select the most relevant data to label, thus limiting the amount of data required and further improving the performances. It combines diversity- and uncertainty-based active learning selection methods. For aircraft detection by segmentation, we show that this method can provide better or competitive results compared to other active learning methods.


A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems

arXiv.org Machine Learning

Constructing surrogate models for uncertainty quantification (UQ) on complex partial differential equations (PDEs) having inherently high-dimensional $\mathcal{O}(10^{\ge 2})$ stochastic inputs (e.g., forcing terms, boundary conditions, initial conditions) poses tremendous challenges. The curse of dimensionality can be addressed with suitable unsupervised learning techniques used as a pre-processing tool to encode inputs onto lower-dimensional subspaces while retaining its structural information and meaningful properties. In this work, we review and investigate thirteen dimension reduction methods including linear and nonlinear, spectral, blind source separation, convex and non-convex methods and utilize the resulting embeddings to construct a mapping to quantities of interest via polynomial chaos expansions (PCE). We refer to the general proposed approach as manifold PCE (m-PCE), where manifold corresponds to the latent space resulting from any of the studied dimension reduction methods. To investigate the capabilities and limitations of these methods we conduct numerical tests for three physics-based systems (treated as black-boxes) having high-dimensional stochastic inputs of varying complexity modeled as both Gaussian and non-Gaussian random fields to investigate the effect of the intrinsic dimensionality of input data. We demonstrate both the advantages and limitations of the unsupervised learning methods and we conclude that a suitable m-PCE model provides a cost-effective approach compared to alternative algorithms proposed in the literature, including recently proposed expensive deep neural network-based surrogates and can be readily applied for high-dimensional UQ in stochastic PDEs.


Impact of Parameter Sparsity on Stochastic Gradient MCMC Methods for Bayesian Deep Learning

arXiv.org Artificial Intelligence

Bayesian methods hold significant promise for improving the uncertainty quantification ability and robustness of deep neural network models. Recent research has seen the investigation of a number of approximate Bayesian inference methods for deep neural networks, building on both the variational Bayesian and Markov chain Monte Carlo (MCMC) frameworks. A fundamental issue with MCMC methods is that the improvements they enable are obtained at the expense of increased computation time and model storage costs. In this paper, we investigate the potential of sparse network structures to flexibly trade-off model storage costs and inference run time against predictive performance and uncertainty quantification ability. We use stochastic gradient MCMC methods as the core Bayesian inference method and consider a variety of approaches for selecting sparse network structures. Surprisingly, our results show that certain classes of randomly selected substructures can perform as well as substructures derived from state-of-the-art iterative pruning methods while drastically reducing model training times.