Viewing 6 videos from 2015 in Talks by AI2 Team Members See AI2’s full collection of videos on our YouTube channel.
    • December 10, 2015

      Chandra Bhagavatula

      In this talk, I will describe two systems designed to extract structured knowledge from unstructured and semi-structured data. First, I'll present an entity linking system for Web tables. Next, I'll talk about a key phrase extraction system that extracts a set of key concepts from a research article. Towards the end of the talk, I will briefly introduce an underlying common problem which connects these two seemingly distinct tasks. I will also present an approach, based on topic modeling, to solve this common underlying problem.

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    • November 3, 2015

      Hanie Sedghi

      Learning with big data is akin to finding a needle in a haystack: useful information is hidden in high dimensional data. Optimization methods, both convex and nonconvex, require new thinking when dealing with high dimensional data, and I present two novel solutions.

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    • July 30, 2015

      Matt Gardner

      A lot of attention has recently been given to the creation of large knowledge bases that contain millions of facts about people, things, and places in the world. In this talk I present methods for using these knowledge bases to generate features for machine learning models. These methods view the knowledge base as a graph which can be traversed to find potentially predictive information. I show how these methods can be applied to models of knowledge base completion, relation extraction, and question answering.

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    • March 12, 2015

      Vicente Ordonez

      Recently, there has been great progress in both computer vision and natural language processing in representing and recognizing semantic units like objects, attributes, named entities, or constituents. These advances provide opportunities to create systems able to interpret and describe the visual world using natural language. This is in contrast to traditional computer vision systems, which typically output a set of disconnected labels, object locations, or annotations for every pixel in an image. The rich visually descriptive language produced by people incorporates world knowledge and human intuition that often can not be captured by other types of annotations. In this talk, I will present several approaches that explore the connections between language, perception, and vision at three levels: learning how to name objects, generating referring expressions for objects in natural scenes, and producing general image descriptions. These methods provide a framework to augment computer vision systems with linguistic information and to take advantage of the vast amount of text associated with images on the web. I will also discuss some of the intuitions from linguistics and perception behind these efforts and how they potentially connect to the larger goal of creating visual systems that can better learn from and communicate with people.

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    • February 5, 2015

      Bhavana Dalvi

      Semi-supervised learning (SSL) has been widely used over a decade for various tasks -- including knowledge acquisition-- that lack large amount of training data. My research proposes a novel learning scenario in which the system knows a few categories in advance, but the rest of the categories are unanticipated and need to be discovered from the unlabeled data. With the availability of enormous unlabeled datasets at low cost, and difficulty of collecting labeled data for all possible categories, it becomes even more important to adapt traditional semi-supervised learning techniques to such realistic settings.

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    • January 7, 2015

      Been Kim

      I will present the Bayesian Case Model (BCM), a general framework for Bayesian case-based reasoning (CBR) and prototype classification and clustering. BCM brings the intuitive power of CBR to a Bayesian generative framework. The BCM learns prototypes, the ``quintessential" observations that best represent clusters in a data set, by performing joint inference on cluster labels, prototypes and important features. Simultaneously, BCM pursues sparsity by learning subspaces, the sets of features that play important roles in the characterization of the prototypes. The prototype and subspace representation provides quantitative benefits in interpretability while preserving classification accuracy. Human subject experiments verify statistically significant improvements to participants' understanding when using explanations produced by BCM, compared to those given by prior art.

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