In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Q: Object recognition and localization are important tasks in computer vision. The focus of this work is the incorporation of contextual information in order to improve object recognition and localization. For instance, it is natural to expect not to see an elephant to appear in the middle of an ocean. We consider a simple approach to encapsulate such common sense knowledge using co-occurrence statistics from web documents. By merely counting the number of times nouns (such as elephants, sharks, oceans, etc.) co-occur in web documents, we obtain a good estimate of expected co-occurrences in visual data. We then cast the problem of combining textual co-occurrence statistics with the predictions of image-based classifiers as an optimization problem. The resulting optimization problem serves as a surrogate for our inference procedure. Albeit the simplicity of the resulting optimization problem, it is effective in improving both recognition and localization accuracy. Concretely, we observe significant improvements in recognition and localization rates for both ImageNet Detection 2012 and Sun 2012 datasets.

A: Using Web Co-occurrence Statistics for Improving Image Categorization
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Q: In this work, we propose a novel recurrent neural network (RNN) architecture. The proposed RNN, gated-feedback RNN (GF-RNN), extends the existing approach of stacking multiple recurrent layers by allowing and controlling signals flowing from upper recurrent layers to lower layers using a global gating unit for each pair of layers. The recurrent signals exchanged between layers are gated adaptively based on the previous hidden states and the current input. We evaluated the proposed GF-RNN with different types of recurrent units, such as tanh, long short-term memory and gated recurrent units, on the tasks of character-level language modeling and Python program evaluation. Our empirical evaluation of different RNN units, revealed that in both tasks, the GF-RNN outperforms the conventional approaches to build deep stacked RNNs. We suggest that the improvement arises because the GFRNN can adaptively assign different layers to different timescales and layer-to-layer interactions (including the top-down ones which are not usually present in a stacked RNN) by learning to gate these interactions.

A: Gated Feedback Recurrent Neural Networks
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Q: It was recently shown that the problem of de­<lb>coding messages transmitted through a noisy<lb>channel can be formulated as a belief up­<lb>dating task over a probabilistic network (14).<lb>Moreover, it was observed that iterative ap­<lb>plication of the (linear time) belief propa­<lb>gation algorithm designed for polytrees (15)<lb>outperformed state of the art decoding algo­<lb>rithms, even though the corresponding net­<lb>works may have many cycles.<lb>This paper demonstrates empirically that<lb>an approximation algorithm approx-mpe<lb>for solving the most probable explana­<lb>tion<lb>(MPE) problem, developed within the<lb>recently proposed mini-bucket elimination<lb>framework (4), outperforms iterative belief<lb>propagation on classes of coding networks<lb>that have bounded induced width.<lb>Our ex­<lb>periments suggest that approximate MPE de­<lb>coders can be good competitors to the ap­<lb>proximate belief updating decoders.

A:
Empirical Evaluation of Approximation Algorithms for Probabilistic Decoding
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