Search Results for author: Cedric De Boom

Found 11 papers, 4 papers with code

A learning gap between neuroscience and reinforcement learning

1 code implementation22 Apr 2021 Samuel T. Wauthier, Pietro Mazzaglia, Ozan Çatal, Cedric De Boom, Tim Verbelen, Bart Dhoedt

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

no code implementations24 Mar 2020 Cedric De Boom, Samuel Wauthier, Tim Verbelen, Bart Dhoedt

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

no code implementations6 Mar 2020 Ozan Çatal, Samuel Wauthier, Tim Verbelen, Cedric De Boom, Bart Dhoedt

Active inference is a theory that underpins the way biological agent's perceive and act in the real world.

Bayesian Inference Robot Navigation

Rhythm, Chord and Melody Generation for Lead Sheets using Recurrent Neural Networks

no code implementations21 Feb 2020 Cedric De Boom, Stephanie Van Laere, Tim Verbelen, Bart Dhoedt

Music that is generated by recurrent neural networks often lacks a sense of direction and coherence.

Learning Perception and Planning with Deep Active Inference

no code implementations30 Jan 2020 Ozan Çatal, Tim Verbelen, Johannes Nauta, Cedric De Boom, Bart Dhoedt

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

no code implementations9 Jun 2018 Pieter Van Molle, Tim Verbelen, Elias De Coninck, Cedric De Boom, Pieter Simoens, Bart Dhoedt

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.

Robotic Grasping

Character-level Recurrent Neural Networks in Practice: Comparing Training and Sampling Schemes

2 code implementations2 Jan 2018 Cedric De Boom, Thomas Demeester, Bart Dhoedt

Recurrent neural networks are nowadays successfully used in an abundance of applications, going from text, speech and image processing to recommender systems.

Recommendation Systems

Representation learning for very short texts using weighted word embedding aggregation

1 code implementation2 Jul 2016 Cedric De Boom, Steven Van Canneyt, Thomas Demeester, Bart Dhoedt

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.

Event Detection News Recommendation +4

Lazy Evaluation of Convolutional Filters

no code implementations27 May 2016 Sam Leroux, Steven Bohez, Cedric De Boom, Elias De Coninck, Tim Verbelen, Bert Vankeirsbilck, Pieter Simoens, Bart Dhoedt

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

1 code implementation9 May 2016 Cedric De Boom, Sam Leroux, Steven Bohez, Pieter Simoens, Thomas Demeester, Bart Dhoedt

We present four training and prediction schedules from the same character-level recurrent neural network.

Learning Semantic Similarity for Very Short Texts

no code implementations2 Dec 2015 Cedric De Boom, Steven Van Canneyt, Steven Bohez, Thomas Demeester, Bart Dhoedt

We therefore investigated several text representations as a combination of word embeddings in the context of semantic pair matching.

Information Retrieval Semantic Similarity +2

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