Sameer Singh
Department of Computer Science, University of California at Irvine, Irvine, CA, USA
- Publications
- Influence
Despite widespread adoption, machine learning models remain mostly black boxes. Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if… Expand
We introduce a novel model-agnostic system that explains the behavior of complex models with high-precision rules called anchors, representing local, “sufficient” conditions for predictions. We… Expand
Reading comprehension has recently seen rapid progress, with systems matching humans on the most popular datasets for the task. However, a large body of work has highlighted the brittleness of these… Expand
Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed. Recent work on adversarial examples, i.e. inputs with… Expand
Transactions of the Association for Computational…
Recent research on entity linking (EL) has introduced a plethora of promising techniques, ranging from deep neural networks to joint inference. But despite numerous papers there is surprisingly… Expand
Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to trust and act upon the… Expand
Cross-document coreference resolution is the task of grouping the entity mentions in a collection of documents into sets that each represent a distinct entity. It is central to knowledge base… Expand
Discriminatively trained undirected graphical models have had wide empirical success, and there has been increasing interest in toolkits that ease their application to complex relational data. The… Expand
Cross-document coreference, the task of grouping all the mentions of each entity in a document collection, arises in information extraction and automated knowledge base construction. For large… Expand
Contextual word representations, typically trained on unstructured, unlabeled text, do not contain any explicit grounding to real world entities and are often unable to remember facts about those… Expand