Selle (équitation)thumb|Cheval sellé. La selle est un objet, en cuir ou synthétique, placé sur le dos d'un cheval (ou de toute autre monture : autruche, chameau, ...) et sur lequel le cavalier se place. En fonction de l'équitation pratiquée, les selles sont de formes variées mais adaptées au dos du cheval et au cavalier. Certaines selles sont dites « mixtes » car elles conviennent à plusieurs disciplines. Les centres équestres enseignant plusieurs disciplines parmi le dressage, le saut d'obstacles, le concours complet, les pony games, le horse-ball, sont en général pourvus de ce type de selle.
Apprentissage par renforcement profondL'apprentissage par renforcement profond (en anglais : deep reinforcement learning ou deep RL) est un sous-domaine de l'apprentissage automatique (en anglais : machine learning) qui combine l'apprentissage par renforcement et l'apprentissage profond (en anglais : deep learning). L'apprentissage par renforcement considère le problème d'un agent informatique (par exemple, un robot, un agent conversationnel, un personnage dans un jeu vidéo, etc.) qui apprend à prendre des décisions par essais et erreurs.
SidesaddleSidesaddle riding is a form of equestrianism that uses a type of saddle which allows female riders to sit aside rather than astride an equine. Sitting aside dates back to antiquity and developed in European countries in the Middle Ages as a way for women in skirts to ride a horse in a modest fashion while also wearing fine clothing. It has retained a specialty niche even in the modern world. The earliest depictions of women riding with both legs on the same side of the horse can be seen in Greek vases, sculptures, and Celtic stones.
Catastrophic interferenceCatastrophic interference, also known as catastrophic forgetting, is the tendency of an artificial neural network to abruptly and drastically forget previously learned information upon learning new information. Neural networks are an important part of the network approach and connectionist approach to cognitive science. With these networks, human capabilities such as memory and learning can be modeled using computer simulations. Catastrophic interference is an important issue to consider when creating connectionist models of memory.
Deep inferenceDeep inference names a general idea in structural proof theory that breaks with the classical sequent calculus by generalising the notion of structure to permit inference to occur in contexts of high structural complexity. The term deep inference is generally reserved for proof calculi where the structural complexity is unbounded; in this article we will use non-shallow inference to refer to calculi that have structural complexity greater than the sequent calculus, but not unboundedly so, although this is not at present established terminology.
