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Deep Learning for Forecasting Stock Returns in the Cross-Section

, 2018
Many studies have been undertaken by using machine learning techniques, including neural networks, to predict stock returns. Recently, a method known as deep learning, which achieves high performance mainly in image recognition and speech recognition ...
A Subrahmanyam, AJ Smola, C Krauss, CR Harvey, D Olson, GS Atsalakis, I Goodfellow, L Breiman, L Kryzanowski, Q Cao, RD McLean, S Soni, Y LeCun   +12 more
core   +1 more source

Automatic generation of hardware Tree Classifiers [PDF]

, 2017
Machine Learning is growing in popularity and spreading across different fields for various applications. Due to this trend, machine learning algorithms use different hardware platforms and are being experimented to obtain high test accuracy and ...
Thanjavur Bhaaskar, Kiran Vishal
core  

Double/Debiased Machine Learning for Treatment and Structural Parameters

, 2017
We revisit the classic semiparametric problem of inference on a low dimensional parameter θ_0 in the presence of high-dimensional nuisance parameters η_0.
V. Chernozhukov, D. Chetverikov, Mert Demirer, E. Duflo, Christian Hansen, Whitney K. Newey, J. Robins   +6 more
semanticscholar   +1 more source

3D scattering transforms for disease classification in neuroimaging

NeuroImage: Clinical, 2017
Classifying neurodegenerative brain diseases in MRI aims at correctly assigning discrete labels to MRI scans. Such labels usually refer to a diagnostic decision a learner infers based on what it has learned from a training sample of MRI scans ...
Tameem Adel, Taco Cohen, Matthan Caan, Max Welling   +3 more
doaj   +1 more source

Accelerating crystal structure search through active learning with neural networks for rapid relaxations

npj Computational Materials
Global optimization of crystal compositions is a significant yet computationally intensive method to identify stable structures within chemical space.
Stefaan S. P. Hessmann, Kristof T. Schütt, Niklas W. A. Gebauer, Michael Gastegger, Tamio Oguchi, Tomoki Yamashita   +5 more
doaj   +1 more source

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

Nature Communications, 2019
Machine learning models can accurately predict atomistic chemical properties but do not provide access to the molecular electronic structure. Here the authors use a deep learning approach to predict the quantum mechanical wavefunction at high efficiency ...
K. T. Schütt, M. Gastegger, A. Tkatchenko, K.-R. Müller, R. J. Maurer   +4 more
doaj   +1 more source

Machine Learning Playground [PDF]

, 2018
Machine learning is a science that “learns” about the data by finding unique patterns and relations in the data. There are a lot of libraries or tools available for processing machine learning datasets.
Khan, Adil
core   +1 more source

A Probabilistic Adversarial Autoencoder for Novelty Detection: Leveraging Lightweight Design and Reconstruction Loss

IEEE Access
A novelty detection task involves identifying whether a data point is an outlier, given a training dataset that primarily captures the distribution of inliers. The novel class is usually absent, poorly sampled, or not well defined in the training data. A
Muhammad Asad, Ihsan Ullah, Muhammad Adeel Hafeez, Ganesh Sistu, Michael G. Madden   +4 more
doaj   +1 more source

Algorithms & Fiduciaries: Existing and Proposed Regulatory Approaches to Artificially Intelligent Financial Planners [PDF]

, 2017
Artificial intelligence is no longer solely in the realm of science fiction. Today, basic forms of machine learning algorithms are commonly used by a variety of companies.
Lightbourne, John
core   +1 more source

Machine Learning Proceedings 1988 [PDF]

, 1988

openalex   +2 more sources