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Found 14 papers, 5 papers with code
Changing Data Sources in the Age of Machine Learning for Official Statistics
However, the quality and integrity of data-science-driven statistics rely on the accuracy and reliability of the data sources and the machine learning techniques that support them.
Neural Bayesian Network Understudy
Bayesian Networks may be appealing for clinical decision-making due to their inclusion of causal knowledge, but their practical adoption remains limited as a result of their inability to deal with unstructured data.
Audio-guided Album Cover Art Generation with Genetic Algorithms
We find that our framework can generate suitable cover art for most genres, and that the visual features adapt themselves to audio feature changes.
A learning gap between neuroscience and reinforcement learning
Historically, artificial intelligence has drawn much inspiration from neuroscience to fuel advances in the field.
Dynamic Narrowing of VAE Bottlenecks Using GECO and L0 Regularization
In case the dimensionality is not predefined, this parameter is usually determined using time- and resource-consuming cross-validation.
Deep Active Inference for Autonomous Robot Navigation
Active inference is a theory that underpins the way biological agent's perceive and act in the real world.
Rhythm, Chord and Melody Generation for Lead Sheets using Recurrent Neural Networks
Music that is generated by recurrent neural networks often lacks a sense of direction and coherence.
Learning Perception and Planning with Deep Active Inference
Active inference is a process theory of the brain that states that all living organisms infer actions in order to minimize their (expected) free energy.
Learning to Grasp from a Single Demonstration
Learning-based approaches for robotic grasping using visual sensors typically require collecting a large size dataset, either manually labeled or by many trial and errors of a robotic manipulator in the real or simulated world.
Character-level Recurrent Neural Networks in Practice: Comparing Training and Sampling Schemes
Recurrent neural networks are nowadays successfully used in an abundance of applications, going from text, speech and image processing to recommender systems.
Representation learning for very short texts using weighted word embedding aggregation
Traditional textual representations, such as tf-idf, have difficulty grasping the semantic meaning of such texts, which is important in applications such as event detection, opinion mining, news recommendation, etc.
Lazy Evaluation of Convolutional Filters
In this paper we propose a technique which avoids the evaluation of certain convolutional filters in a deep neural network.
Efficiency Evaluation of Character-level RNN Training Schedules
We present four training and prediction schedules from the same character-level recurrent neural network.
Learning Semantic Similarity for Very Short Texts
We therefore investigated several text representations as a combination of word embeddings in the context of semantic pair matching.
