A Novel Progressive Multi-label Classifier for Classincremental Data

In this paper, a progressive learning algorithm for multi-label classification to learn new labels while retaining the knowledge of previous labels is designed. New output neurons corresponding to new labels are added and the neural network connections and parameters are automatically restructured as if the label has been introduced from the beginning. This work is the first of the kind in multi-label classifier for class-incremental learning. It is useful for real-world applications such as robotics where streaming data are available and the number of labels is often unknown. Based on the Extreme Learning Machine framework, a novel universal classifier with plug and play capabilities for progressive multi-label classification is developed. Experimental results on various benchmark synthetic and real datasets validate the efficiency and effectiveness of our proposed algorithm.Comment: 5 pages, 3 figures, 4 table

Multimodal machine translation

Over the past few years, there has been a lot of progress being made in machine translation through deep learning networks. But there is relatively lesser progress made in using images to catalyze the translation tasks. In this study, we explore various models to incorporate the image features in the machine translation models. We start with a monomodal translation model which uses only textual features. We extend this model to develop the multimodal system which incorporates the visual features related to the source sentence. We also propose a multitask system which uses image captioning task to aid the translation task. Our models are tested on multiple datasets using the automatic evaluation metrics like METEOR and BLEU. The experiments show that the proposed models outperform the text-only baseline model

Multimodal machine translation

Over the past few years, there has been a lot of progress being made in machine translation through deep learning networks. But there is relatively lesser progress made in using images to catalyze the translation tasks. In this study, we explore various models to incorporate the image features in the machine translation models. We start with a monomodal translation model which uses only textual features. We extend this model to develop the multimodal system which incorporates the visual features related to the source sentence. We also propose a multitask system which uses image captioning task to aid the translation task. Our models are tested on multiple datasets using the automatic evaluation metrics like METEOR and BLEU. The experiments show that the proposed models outperform the text-only baseline model.LimitedAuthor requested closed access (OA after 2yrs) in Vireo ETD syste

Architectural-Space Exploration of Heterogeneous Reliability and Checkpointing Modes for Out-of-Order Superscalar Processors

State-of-the-art reliability techniques and mechanisms deploy full-scale redundancy, like double or triple modular redundancy (DMR, TMR), on different layers of the computing stack to detect and/or correct such transient faults. However, the techniques relying on full-scale redundancy incur significant area, performance, and/or power overheads, which might not always be feasible/practical due to system constraints such as deadlines and available power budget for the full chip (or a processor core). In this work, we propose a novel design methodology to generate and explore the architectural-space of heterogeneous reliability modes for out-of-order superscalar multi-core processors. These heterogeneous modes enable varying reliability and power/area trade-offs, from which an optimal configuration can be chosen at run time to meet the reliability requirements of a given system while reducing the corresponding power overheads (or solving the inverse problem, i.e., maximizing the reliability under a given power constraint). Our experimental results show that a pareto-optimal heterogeneous reliability mode reduces the core vulnerability by 87%, on average, across multiple application workloads, with area and power overheads of 10% and 43%, respectively. To further enhance the design space of heterogeneous reliability modes, we investigate the effectiveness of combining different processor state compression techniques like Distributed Multi-threaded Checkpointing (DMTCP), Hash-based Incremental Checkpointing (HBICT) and GNU zip, such that the correct processor state can be recovered once a fault is detected. We reduced the checkpoint sizes by a factor of ~6× using a unique combination of different state compression techniques