Convolutional neural networks (CNNs) have demonstrated superior capability for extracting information from raw signals in computer vision. Recently, character-level and multi-channel CNNs have exhibited excellent performance for sentence classification tasks. We apply CNNs to large-scale authorship attribution, which aims to determine an unknown text's author among many candidate authors, motivated by their ability to process character-level signals and to differentiate between a large number of classes, while making fast predictions in comparison to state-of-the-art approaches. We extensively evaluate CNN-based approaches that leverage word and character channels and compare them against state-of-the-art methods for a large range of author numbers, shedding new light on traditional approaches. We show that character-level CNNs outperform the state-of-the-art on four out of five datasets in different domains. Additionally, we present the first application of authorship attribution to reddit.
are pervasive in domains where the input can naturally be separated into different channels, e.g. color channels in computer vision, wave lengths in speech recognition @cite_12 . Natural language input is typically single-channel in the form of tokens or characters. Kim observe that a static word channel is able to encode general semantic similarities, while a non-static channel can be fine-tuned to the task at hand and improves performance on some datasets.
@cite_12
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Detecting complex events in videos is intrinsically a multimodal problem since both audio and visual channels provide important clues. While conventional methods fuse both modalities at a superficial level, in this paper we propose a new representation--called bi-modal words--to explore representative joint audio---visual patterns. We first build a bipartite graph to model relation across the quantized words extracted from the visual and audio modalities. Partitioning over the bipartite graph is then applied to produce the bi-modal words that reveal the joint patterns across modalities. Different pooling strategies are then employed to re-quantize the visual and audio words into the bi-modal words and form bi-modal Bag-of-Words representations. Since it is difficult to predict the suitable number of bi-modal words, we generate bi-modal words at different levels (i.e., codebooks with different sizes), and use multiple kernel learning to combine the resulting multiple representations during event classifier learning. Experimental results on three popular datasets show that the proposed method achieves statistically significant performance gains over methods using individual visual and audio feature alone and existing popular multi-modal fusion methods. We also find that average pooling is particularly suitable for bi-modal representation, and using multiple kernel learning to combine multi-modal representations at various granularities is helpful. Solving the visual symbol grounding problem has long been a goal of artificial intelligence. The field appears to be advancing closer to this goal with recent breakthroughs in deep learning for natural language grounding in static images. In this paper, we propose to translate videos directly to sentences using a unified deep neural network with both convolutional and recurrent structure. Described video datasets are scarce, and most existing methods have been applied to toy domains with a small vocabulary of possible words. By transferring knowledge from 1.2M+ images with category labels and 100,000+ images with captions, our method is able to create sentence descriptions of open-domain videos with large vocabularies. We compare our approach with recent work using language generation metrics, subject, verb, and object prediction accuracy, and a human evaluation. Solving the visual symbol grounding problem has long been a goal of artificial intelligence. The field appears to be advancing closer to this goal with recent breakthroughs in deep learning for natural language grounding in static images. In this paper, we propose to translate videos directly to sentences using a unified deep neural network with both convolutional and recurrent structure. Described video datasets are scarce, and most existing methods have been applied to toy domains with a small vocabulary of possible words. By transferring knowledge from 1.2M+ images with category labels and 100,000+ images with captions, our method is able to create sentence descriptions of open-domain videos with large vocabularies. We compare our approach with recent work using language generation metrics, subject, verb, and object prediction accuracy, and a human evaluation. We address the problem of single-channel speech separation and recognition using loopy belief propagation in a way that enables efficient inference for an arbitrary number of speech sources. The graphical model consists of a set of N Markov chains, each of which represents a language model or grammar for a given speaker. A Gaussian mixture model with shared states is used to model the hidden acoustic signal for each grammar state of each source. The combination of sources is modeled in the log spectrum domain using non-linear interaction functions. Previously, temporal inference in such a model has been performed using an N-dimensional Viterbi algorithm that scales exponentially with the number of sources. In this paper, we describe a loopy message passing algorithm that scales linearly with language model size. The algorithm achieves human levels of performance, and is an order of magnitude faster than competitive systems for two speakers.