There are many classification tasks where we are given a large number of unlabeled examples in addition to only a few labeled training examples. For such scenario, it is important to include unlabeled examples during the training to generalize well to the unseen data, and thus avoid overfitting. Larochelle and Bengio (2008) proposed the semi-supervised training of the discriminative restricted ...

Supervised learning methods for WSD yield better performance than unsupervised methods. Yet the availability of clean training data for the former is still a severe challenge. In this paper, we present an unsupervised bootstrapping approach for WSD which exploits huge amounts of automatically generated noisy data for training within a supervised learning framework. The method is evaluated using...

In semi-supervised learning (SSL), we learn a predictive model from a collection of labeled data and a typically much larger collection of unlabeled data. These lecture notes present a framework called multi-view point cloud regularization (MVPCR) [5], which unifies and generalizes several semi-supervised kernel methods that are based on data-dependent regularization in reproducing kernel Hilbe...

The Universum sample, which is defined as the sample that doesn’t belong to any of the classes the learning task concerns, has been proved to be helpful in both supervised and semi-supervised settings. The former works treat the Universum samples equally. Our research found that not all the Universum samples are helpful, and we propose a method to pick the informative ones, i.e., inbetween Univ...

We introduce a new model for building conditional generative models in a semisupervised setting to conditionally generate data given attributes by adapting the GAN framework. The proposed semi-supervised GAN (SS-GAN) model uses a pair of stacked discriminators to learn the marginal distribution of the data, and the conditional distribution of the attributes given the data respectively. In the s...

This project studies semi-supervised discovery of named entities, relational entities and prepositional phrase attachments within a read-the-web framework. Meanings of an entity can be improvised and updated faster in the internet world than printed references. The main idea of this project is to study the feasibility of characterizing entities by web content directly. The approach is that cont...

In semi-supervised learning (SSL), we learn a predictive model from a collection of labeled data and a typically much larger collection of unlabeled data. These lecture notes present a framework called multi-view point cloud regularization (MVPCR) [5], which unifies and generalizes several semi-supervised kernel methods that are based on data-dependent regularization in reproducing kernel Hilbe...

OBJECTIVE Most BCIs have to undergo a calibration session in which data is recorded to train decoders with machine learning. Only recently zero-training methods have become a subject of study. This work proposes a probabilistic framework for BCI applications which exploit event-related potentials (ERPs). For the example of a visual P300 speller we show how the framework harvests the structure s...

The need to learn a good representation is core problem central AI. We present self-supervised learning framework and demonstrate its use for few-shot classification clustering. Our can be interpreted as repeatedly discovering new categories from learned embeddings training embedding function with signals differentiate the discovered categories. In our framework, we first discover unlabeled dat...