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Found 6 papers, 1 papers with code
Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management
Financial portfolio management describes the task of distributing funds and conducting trading operations on a set of financial assets, such as stocks, index funds, foreign exchange or cryptocurrencies, aiming to maximize the profit while minimizing the loss incurred by said operations.
Deep Learning for Energy Time-Series Analysis and Forecasting
Energy time-series analysis describes the process of analyzing past energy observations and possibly external factors so as to predict the future.
Efficient training of lightweight neural networks using Online Self-Acquired Knowledge Distillation
Knowledge Distillation has been established as a highly promising approach for training compact and faster models by transferring knowledge from heavyweight and powerful models.
Quadratic mutual information regularization in real-time deep CNN models
In this paper, regularized lightweight deep convolutional neural network models, capable of effectively operating in real-time on devices with restricted computational power for high-resolution video input are proposed.
Semantic Scene Segmentation for Robotics Applications
Semantic scene segmentation plays a critical role in a wide range of robotics applications, e. g., autonomous navigation.
Heterogeneous Knowledge Distillation using Information Flow Modeling
The proposed method is capable of overcoming the aforementioned limitations by using an appropriate supervision scheme during the different phases of the training process, as well as by designing and training an appropriate auxiliary teacher model that acts as a proxy model capable of "explaining" the way the teacher works to the student.
