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Found 9 papers, 5 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.
Variational Voxel Pseudo Image Tracking
Uncertainty estimation is an important task for critical problems, such as robotics and autonomous driving, because it allows creating statistically better perception models and signaling the model's certainty in its predictions to the decision method or a human supervisor.
A Novel Dataset for Evaluating and Alleviating Domain Shift for Human Detection in Agricultural Fields
In this paper we evaluate the impact of domain shift on human detection models trained on well known object detection datasets when deployed on data outside the distribution of the training set, as well as propose methods to alleviate such phenomena based on the available annotations from the target domain.
MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge
The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 seconds with the inclusion of precession effects and higher order modes.
VPIT: Real-time Embedded Single Object 3D Tracking Using Voxel Pseudo Images
In this paper, we propose a novel voxel-based 3D single object tracking (3D SOT) method called Voxel Pseudo Image Tracking (VPIT).
Deep Residual Error and Bag-of-Tricks Learning for Gravitational Wave Surrogate Modeling
In several cases, small improvements can be observed, but the most significant improvement still comes from the addition of a second network that models the residual error.
Autoencoder-driven Spiral Representation Learning for Gravitational Wave Surrogate Modelling
Based on this finding, we design a spiral module with learnable parameters, that is used as the first layer in a neural network, which learns to map the input space to the coefficients.
Machine Learning for Forecasting Mid Price Movement using Limit Order Book Data
Forecasting the movements of stock prices is one the most challenging problems in financial markets analysis.
