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2. Rule-based machine learning - Wikipedia

   en.wikipedia.org/wiki/Rule-based_machine_learning

   Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply. [ 1 ] [ 2 ] [ 3 ] The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that ...

3. Adversarial machine learning - Wikipedia

   en.wikipedia.org/wiki/Adversarial_machine_learning

   Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. [1] A survey from May 2020 exposes the fact that practitioners report a dire need for better protecting machine learning systems in industrial applications.

4. Hyperparameter (machine learning) - Wikipedia

   en.wikipedia.org/wiki/Hyperparameter_(machine...

   In machine learning, a hyperparameter is a parameter, such as the learning rate or choice of optimizer, which specifies details of the learning process, hence the name hyperparameter. This is in contrast to parameters which determine the model itself.

5. Transformer (deep learning architecture) - Wikipedia

   en.wikipedia.org/wiki/Transformer_(deep_learning...

   For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...

6. Attention (machine learning) - Wikipedia

   en.wikipedia.org/wiki/Attention_(machine_learning)

   During the deep learning era, attention mechanism was developed to solve similar problems in encoding-decoding. [1]In machine translation, the seq2seq model, as it was proposed in 2014, [16] would encode an input text into a fixed-length vector, which would then be decoded into an output text.

7. Restricted Boltzmann machine - Wikipedia

   en.wikipedia.org/wiki/Restricted_Boltzmann_machine

   Diagram of a restricted Boltzmann machine with three visible units and four hidden units (no bias units) A restricted Boltzmann machine (RBM) (also called a restricted Sherrington–Kirkpatrick model with external field or restricted stochastic Ising–Lenz–Little model) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.