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Multi-label classification (MLC), which assigns multiple labels to each instance, is crucial to domains from computer vision to text mining. Conventional methods for MLC require huge amounts of labeled data to capture complex dependencies between labels. However, such labeled datasets are expensive, or even impossible, to acquire. Worse yet, these pre-trained MLC models can only be used for the particular label set covered in the training data. Despite this severe limitation, few methods exist for expanding the set of labels predicted by pre-trained models. Instead, we acquire vast amounts of new labeled data and retrain a new model from scratch. Here, we propose combining the knowledge from multiple pre-trained models (teachers) to train a new student model that covers the union of the labels predicted by this set of teachers. This student supports a broader label set than any one of its teachers without using labeled data. We call this new problem knowledge amalgamation for multi-label classification. Our new method, Adaptive KNowledge Transfer (ANT), trains a student by learning from each teacher’s partial knowledge of label dependencies to infer the global dependencies between all labels across the teachers. We show that ANT succeeds in unifying label dependencies among teachers, outperforming five state-of-the-art methods on eight real-world datasets. 

Explainability helps users trust deep learning solutions for time series classification. However, existing explainability methods for multi-class time series classifiers focus on one class at a time, ignoring relationships between the classes. Instead, when a classifier is choosing between many classes, an effective explanation must show what sets the chosen class apart from the rest. We now formalize this notion, studying the open problem of class-specific explainability for deep time series classifiers, a challenging and impactful problem setting. We design a novel explainability method, DEMUX, which learns saliency maps for explaining deep multi-class time series classifiers by adaptively ensuring that its explanation spotlights the regions in an input time series that a model uses specifically to its predicted class. DEMUX adopts a gradient-based approach composed of three interdependent modules that combine to generate consistent, class-specific saliency maps that remain faithful to the classifier’s behavior yet are easily understood by end users. Our experimental study demonstrates that DEMUX outperforms nine state-of-the-art alternatives on five popular datasets when explaining two types of deep time series classifiers. Further, through a case study, we demonstrate that DEMUX’s explanations indeed highlight what separates the predicted class from the others in the eyes of the classifier. 

Recurrent Classifier Chains (RCCs) are a leading approach for multi-label classification as they directly model the interdependencies between classes. Unfortunately, existing RCCs assume that every training instance is completely labeled with all its ground truth classes. In practice often only a subset of an instance's labels are annotated, while the annotations for other classes are missing. RCCs fail in this missing label scenario, predicting many false negatives and potentially missing important classes. In this work, we propose Robust-RCC, the first strategy for tackling this open problem of RCCs failing for multi-label missing-label data. Robust-RCC is a new type of deep recurrent classifier chain empowered to model inter-class relationships essential for predicting the complete label set most likely to match the ground truth. The key to Robust-RCC is the design of the Multi Incomplete Label Risk (MILR) function, which we prove to be equal in expectation to the true risk of the ground truth full label set despite being computed from incompletely labeled data. Our experimental study demonstrates that Robust-RCC consistently beats six state-of-of-the-art methods by as much as 30% in predicting the true labels. 

Spike train classification is an important problem in many areas such as healthcare and mobile sensing, where each spike train is a high-dimensional time series of binary values. Conventional re- search on spike train classification mainly focus on developing Spiking Neural Networks (SNNs) under resource-sufficient settings (e.g., on GPU servers). The neurons of the SNNs are usually densely connected in each layer. However, in many real-world applications, we often need to deploy the SNN models on resource-constrained platforms (e.g., mobile devices) to analyze high-dimensional spike train data. The high resource requirement of the densely-connected SNNs can make them hard to deploy on mobile devices. In this paper, we study the problem of energy-efficient SNNs with sparsely- connected neurons. We propose an SNN model with sparse spatiotemporal coding. Our solution is based on the re-parameterization of weights in an SNN and the application of sparsity regularization during optimization. We compare our work with the state-of-the-art SNNs and demonstrate that our sparse SNNs achieve significantly better computational efficiency on both neuromorphic and standard datasets with comparable classification accuracy. Furthermore, com- pared with densely-connected SNNs, we show that our method has a better capability of generalization on small-size datasets through extensive experiments. 

Spike train classification is an important problem in many areas such as healthcare and mobile sensing, where each spike train is a high-dimensional time series of binary values. Conventional re- search on spike train classification mainly focus on developing Spiking Neural Networks (SNNs) under resource-sufficient settings (e.g., on GPU servers). The neurons of the SNNs are usually densely connected in each layer. However, in many real-world applications, we often need to deploy the SNN models on resource-constrained platforms (e.g., mobile devices) to analyze high-dimensional spike train data. The high resource requirement of the densely-connected SNNs can make them hard to deploy on mobile devices. In this paper, we study the problem of energy-efficient SNNs with sparsely- connected neurons. We propose an SNN model with sparse spatio-temporal coding. Our solution is based on the re-parameterization of weights in an SNN and the application of sparsity regularization during optimization. We compare our work with the state-of-the-art SNNs and demonstrate that our sparse SNNs achieve significantly better computational efficiency on both neuromorphic and standard datasets with comparable classification accuracy. Furthermore, com- pared with densely-connected SNNs, we show that our method has a better capability of generalization on small-size datasets through extensive experiments.