Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations

Neural networks are among the most accurate supervised learning methods in use today, but their opacity makes them difficult to trust in critical applications, especially when conditions in training differ from those in test. Recent work on explanations for black-box models has produced tools (e.g. LIME) to show the implicit rules behind predictions, which can help us identify when models are right for the wrong reasons. However, these methods do not scale to explaining entire datasets and cannot correct the problems they reveal. We introduce a method for efficiently explaining and regularizing differentiable models by examining and selectively penalizing their input gradients, which provide a normal to the decision boundary. We apply these penalties both based on expert annotation and in an unsupervised fashion that encourages diverse models with qualitatively different decision boundaries for the same classification problem. On multiple datasets, we show our approach generates faithful explanations and models that generalize much better when conditions differ between training and test

Managing the managers managing people: Lessons for recreation and water management in protected areas

Many of Australia’s critical urban water resources are located within protected areas, originally reserved for their timber production, recreation and aesthetic values. Later, these areas were also recognised for their conservation value and as reliable, potable water supplies. This paper presents a case study of water source protection planning in urban water catchments and impoundments in the south west of Western Australia and the impacts on recreation and tourism access in protected areas. Inland water catchments in the Southwest of Western Australia have historically been, and are currently, popular resources for public recreation. Recreation includes a broad range of leisure, pastime and entertainment activities ranging from passive through to active pursuits that vary in their character and potential for environmental impacts

SINCERE: Supervised Information Noise-Contrastive Estimation REvisited

The information noise-contrastive estimation (InfoNCE) loss function provides the basis of many self-supervised deep learning methods due to its strong empirical results and theoretic motivation. Previous work suggests a supervised contrastive (SupCon) loss to extend InfoNCE to learn from available class labels. This SupCon loss has been widely-used due to reports of good empirical performance. However, in this work we suggest that the specific SupCon loss formulated by prior work has questionable theoretic justification, because it can encourage images from the same class to repel one another in the learned embedding space. This problematic behavior gets worse as the number of inputs sharing one class label increases. We propose the Supervised InfoNCE REvisited (SINCERE) loss as a remedy. SINCERE is a theoretically justified solution for a supervised extension of InfoNCE that never causes images from the same class to repel one another. We further show that minimizing our new loss is equivalent to maximizing a bound on the KL divergence between class conditional embedding distributions. We compare SINCERE and SupCon losses in terms of learning trajectories during pretraining and in ultimate linear classifier performance after finetuning. Our proposed SINCERE loss better separates embeddings from different classes during pretraining while delivering competitive accuracy

Beyond Sparsity: Tree Regularization of Deep Models for Interpretability

The lack of interpretability remains a key barrier to the adoption of deep models in many applications. In this work, we explicitly regularize deep models so human users might step through the process behind their predictions in little time. Specifically, we train deep time-series models so their class-probability predictions have high accuracy while being closely modeled by decision trees with few nodes. Using intuitive toy examples as well as medical tasks for treating sepsis and HIV, we demonstrate that this new tree regularization yields models that are easier for humans to simulate than simpler L1 or L2 penalties without sacrificing predictive power.Comment: To appear in AAAI 2018. Contains 9-page main paper and appendix with supplementary materia

Reconciling diverse lacustrine and terrestrial system response to penultimate deglacial warming in southern Europe

Unlike the most recent deglaciation, the regional expression of climate changes during the penultimate deglaciation remains understudied, even though it led into a period of excess warmth with estimates of global average temperature 1–2 °C, and sea level ∼6 m, above pre-industrial values. We present the first complete high-resolution southern European diatom record capturing the penultimate glacial-interglacial transition, from Lake Ioannina (northwest Greece). It forms part of a suite of proxies selected to assess the character and phase relationships of terrestrial and aquatic ecosystem response to rapid climate warming, and to resolve apparent conflicts in proxy evidence for regional paleohydrology. The diatom data suggest a complex penultimate deglaciation driven primarily by multiple oscillations in lake level, and provide firm evidence for the regional influence of abrupt changes in North Atlantic conditions. There is diachroneity in lake and terrestrial ecosystem response to warming at the onset of the last interglacial, with an abrupt increase in lake level occurring ∼2.7 k.y. prior to sustained forest expansion with peak precipitation. We identify the potentially important role of direct input of snow melt and glacial meltwater transfer to the subterranean karst system in response to warming, which would cause rising regional groundwater levels. This explanation, and the greater sensitivity of diatoms to subtle changes in temperature, reconciles the divergent lacustrine and terrestrial proxy evidence and highlights the sensitivity of lakes situated in mountainous karstic environments to past climate warming

Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning

Aortic stenosis (AS) is a degenerative valve condition that causes substantial morbidity and mortality. This condition is under-diagnosed and under-treated. In clinical practice, AS is diagnosed with expert review of transthoracic echocardiography, which produces dozens of ultrasound images of the heart. Only some of these views show the aortic valve. To automate screening for AS, deep networks must learn to mimic a human expert's ability to identify views of the aortic valve then aggregate across these relevant images to produce a study-level diagnosis. We find previous approaches to AS detection yield insufficient accuracy due to relying on inflexible averages across images. We further find that off-the-shelf attention-based multiple instance learning (MIL) performs poorly. We contribute a new end-to-end MIL approach with two key methodological innovations. First, a supervised attention technique guides the learned attention mechanism to favor relevant views. Second, a novel self-supervised pretraining strategy applies contrastive learning on the representation of the whole study instead of individual images as commonly done in prior literature. Experiments on an open-access dataset and an external validation set show that our approach yields higher accuracy while reducing model size.Comment: multiple-instance learning; self-supervised learning; semi-supervised learning; medical imagin