Collaborating Authors

Towards a Universal Continuous Knowledge Base Artificial Intelligence

In artificial intelligence, knowledge is the information required by an intelligent system to accomplish tasks. While traditional knowledge bases use discrete, symbolic representations, detecting knowledge encoded in the continuous representations learned from data has received increasing attention recently. In this work, we propose a method for building a continuous knowledge base that can store knowledge imported from multiple, diverse neural networks. The key idea of our approach is to define an interface for each neural network and cast knowledge transferring as a function simulation problem. Preliminary experiments on text classification show promising results: we first import the knowledge encoded in an RNN model and a CNN model to the knowledge base, from which the fused knowledge is exported back to the RNN model, achieving a higher classification accuracy than the original RNN model. With the continuous knowledge base, it is also easy to achieve knowledge distillation and transfer learning. Our work opens the door to building a universal continuous knowledge base to collect, store, and organize all continuous knowledge encoded in different neural networks trained for different AI tasks.

Learning to Model the Tail

Neural Information Processing Systems

We describe an approach to learning from long-tailed, imbalanced datasets that are prevalent in real-world settings. Here, the challenge is to learn accurate "few-shot'' models for classes in the tail of the class distribution, for which little data is available. We cast this problem as transfer learning, where knowledge from the data-rich classes in the head of the distribution is transferred to the data-poor classes in the tail. Our key insights are as follows. First, we propose to transfer meta-knowledge about learning-to-learn from the head classes.

Generating Negative Commonsense Knowledge Artificial Intelligence

The acquisition of commonsense knowledge is an important open challenge in artificial intelligence. In this work-in-progress paper, we study the task of automatically augmenting commonsense knowledge bases (KBs) with novel statements. We show empirically that obtaining meaningful negative samples for the completion task is nontrivial, and propose NegatER, a framework for generating negative commonsense knowledge, to address this challenge. In our evaluation we demonstrate the intrinsic value and extrinsic utility of the knowledge generated by NegatER, opening up new avenues for future research in this direction.

Knowledge-Based Learning through Feature Generation Machine Learning

Machine learning algorithms have difficulties to generalize over a small set of examples. Humans can perform such a task by exploiting vast amount of background knowledge they possess. One method for enhancing learning algorithms with external knowledge is through feature generation. In this paper, we introduce a new algorithm for generating features based on a collection of auxiliary datasets. We assume that, in addition to the training set, we have access to additional datasets. Unlike the transfer learning setup, we do not assume that the auxiliary datasets represent learning tasks that are similar to our original one. The algorithm finds features that are common to the training set and the auxiliary datasets. Based on these features and examples from the auxiliary datasets, it induces predictors for new features from the auxiliary datasets. The induced predictors are then added to the original training set as generated features. Our method was tested on a variety of learning tasks, including text classification and medical prediction, and showed a significant improvement over using just the given features.

API Scraping - Building a Car Dealer Database in PostgreSQL


In this Project we will scrape data from an Car Dealer website, using the hidden API. Especially for people who work with data it is important to be able to create own datasets. Often we rely on datasets from someone else. This course should show all data enthusiasts how to scrape and store data in Excel Files and in the PostgreSQL database. The requirement for this course is basic knowledge of Python Programming.