Multi-Context Reasoning for Question Answering


Multi-Context Reasoning for Question Answering – This paper deals with the problem of Answer Processing (AP) in a context-aware setting. In this context we refer to the context-aware semantic processing task which involves inferring the relevant information from a sentence. However, there are few clear criteria that can achieve the best scores for an appropriate task without the knowledge or ability of the human reader. To address this, this paper presents a new framework to model task-specific semantic information from a corpus using multi-scale attention mechanism. The framework is based on a novel method that we call Multi-Selection-Context Multiparameter Attention (M-CEAM). Our system generates sentences in a high dimensional context with multi-scale attention mechanism, but the task is different from typical human-authored text. We provide an efficient implementation of our framework by means of a supervised training and annotation pipeline for our system. In our experimental results, we show that M-CEAM outperforms state-of-the-art semantic and inference-based approaches on several tasks.

We present a method for image denoising with two fundamental components: a global and a local model. Compared to previous methods which have been presented on this problem, we show that it is possible to extend such a representation to new problems and still achieve satisfactory results without resorting to expensive dictionary-based denoising techniques. Here we show how the deep learning method can be used to encode the global model to represent the image. Since the global model is not directly present in the denoised data, this new representation is robust to noise and can be easily learnt without expensive dictionary-based denoising. Our main experimental and theoretical results demonstrate that the proposed method outperformed the existing methods on datasets containing only noise. Finally, we show that the proposed loss function is equivalent to the full loss, even when the image is cropped only from the global model. Our experiments demonstrate the effectiveness of the proposed network for image denoising.

Dictionary Learning for Scalable Image Classification

Deep Feature Fusion for Object Classification

Multi-Context Reasoning for Question Answering

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  • A Comparative Study between Convolutional Neural Networks for Image Recognition, Predictive Modeling and Clustering

    Image denoising by additive fog light using a deep dictionaryWe present a method for image denoising with two fundamental components: a global and a local model. Compared to previous methods which have been presented on this problem, we show that it is possible to extend such a representation to new problems and still achieve satisfactory results without resorting to expensive dictionary-based denoising techniques. Here we show how the deep learning method can be used to encode the global model to represent the image. Since the global model is not directly present in the denoised data, this new representation is robust to noise and can be easily learnt without expensive dictionary-based denoising. Our main experimental and theoretical results demonstrate that the proposed method outperformed the existing methods on datasets containing only noise. Finally, we show that the proposed loss function is equivalent to the full loss, even when the image is cropped only from the global model. Our experiments demonstrate the effectiveness of the proposed network for image denoising.


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