Proxnically Motivated Multi-modal Transfer Learning from Imbalanced Data


Proxnically Motivated Multi-modal Transfer Learning from Imbalanced Data – The present work extends the multi-modal transfer learning (MMSL) approach to multi-modal data by using a sub-network of the network. The proposed network can be used to model each individual modality and to extract relevant information or feature features to learn more relevant modalities. The network is also trained for predicting the features that best describe the features used in some data. We further extend the MMSL framework to model the interactions of modalities that are considered. The model uses two steps, one of a neural network (NN) and the other of an imbalanced network (iN). We then use a neural network (NNL) as a model layer to model these modalities and predict the feature vectors that best describe them. The two steps are trained to predict the features that best represent the features used in the data. Experimental results on simulated data show that these learning methods outperform the state-of-the-art MMSL methods.

We propose an efficient automated search algorithm to improve the quality of the results of a large collaborative decision-making task. This algorithm generates a new list of items and presents the results of that list to the user. Our algorithm first retrieves items that are relevant for the current task and presents the items that can be retrieved in the future. We then use an ensemble of algorithms, which are trained to identify items that are relevant during the search process. By using the knowledge obtained during the search process, we propose an efficient algorithm for combining the advantages of the ensemble and the search process. Experimental results validate that our algorithm improves quality of results for all three tasks. The proposed algorithm is evaluated on a large scientific dataset and achieved a significant improvement in efficiency.

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Proxnically Motivated Multi-modal Transfer Learning from Imbalanced Data

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  • On the Impact of Data Compression and Sampling on Online Prediction of Machine Learning Performance

    The Emergence of Language Using the Complexity of Interactive Opinion ResearchWe propose an efficient automated search algorithm to improve the quality of the results of a large collaborative decision-making task. This algorithm generates a new list of items and presents the results of that list to the user. Our algorithm first retrieves items that are relevant for the current task and presents the items that can be retrieved in the future. We then use an ensemble of algorithms, which are trained to identify items that are relevant during the search process. By using the knowledge obtained during the search process, we propose an efficient algorithm for combining the advantages of the ensemble and the search process. Experimental results validate that our algorithm improves quality of results for all three tasks. The proposed algorithm is evaluated on a large scientific dataset and achieved a significant improvement in efficiency.


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