The averaged-perceptron learning algorithm is simple, versatile and effective. However, when used in NLP settings it tends to produce very dense solutions, while much sparser ones are also possible. We present a simple modification to the perceptron algorithm which allows it to produce sparser solutions while remaining accurate and computationally efficient. We test the method on a multiclass c...

With the extensive applications of machine learning, the issue of private or sensitive data in the training examples becomes more and more serious: during the training process, personal information or habits may be disclosed to unexpected persons or organisations, which can cause serious privacy problems or even financial loss. In this paper, we present a quantum privacy-preserving algorithm fo...

We present a brief survey of existing mistake bounds and introduce novel bounds for the Perceptron or the kernel Perceptron algorithm. Our novel bounds generalize beyond standard margin-loss type bounds, allow for any convex and Lipschitz loss function, and admit a very simple proof.

How do we determine the mutational effects in exome sequencing data with little or no statistical evidence? Can protein structural information fill in the gap of not having enough statistical evidence? In this work, we answer the two questions with the goal towards determining pathogenic effects of rare variants in rare disease. We take the approach of determining the importance of point mutati...

We present a method to use multilayer perceptrons (MLPs) for a verification task, i.e. to verify whether two vectors are from the same class or not. In tests with synthetic data we could show that the verification MLPs are almost optimal from a Bayesian point of view. With speech data we have shown that verification MLPs generalize well such that they can be deployed as well for classes which w...

1 Abstract During the last decade, researchers have applied neural networks to a multitude of diicult tasks which would normally require human intelligence. In particular, percep-trons are used to classify patterns into diierent classes. Recently, several researchers introduced a novel class of artiicial neural networks, called morphological neural networks. In this new theory, the rst step in ...

Frank Rosenblatt invented the Perceptron algorithm in 1957 as part of an early attempt to build “brain models” – artificial neural networks. In this paper, we apply tools from symbolic logic – dependent type theory as implemented in the interactive theorem prover Coq – to prove that one-layer perceptrons for binary classification converge when trained on linearly separable datasets (the Percept...