Videos

See AI2's full collection of videos on our YouTube channel.
Viewing 181-190 of 202 videos
  • Distantly Supervised Information Extraction Using Bootstrapped Patterns Thumbnail

    Distantly Supervised Information Extraction Using Bootstrapped Patterns

    March 27, 2015  |  Sonal Gupta
    Although most work in information extraction (IE) focuses on tasks that have abundant training data, in practice, many IE problems do not have any supervised training data. State-of-the-art supervised techniques like conditional random fields are impractical for such real world applications because: (1) they…
  • Exploiting Parallel News Streams for Relation Extraction Thumbnail

    Exploiting Parallel News Streams for Relation Extraction

    March 17, 2015  |  Congle Zhang
    Most approaches to relation extraction, the task of extracting ground facts from natural language text, are based on machine learning and thus starved by scarce training data. Manual annotation is too expensive to scale to a comprehensive set of relations. Distant supervision, which automatically creates training…
  • Language and Perceptual Categorization in Computer Vision Thumbnail

    Language and Perceptual Categorization in Computer Vision

    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…
  • Learning and Sampling Scalable Graph Models Thumbnail

    Learning and Sampling Scalable Graph Models

    March 11, 2015  |  Joel Pfeiffer
    Networks provide an effective representation to model many real-world domains, with edges (e.g., friendships, citations, hyperlinks) representing relationships between items (e.g., individuals, papers, webpages). By understanding common network features, we can develop models of the distribution from which the…
  • Spectral Probabilistic Modeling and Applications to Natural Language Processing Thumbnail

    Spectral Probabilistic Modeling and Applications to Natural Language Processing

    March 3, 2015  |  Ankur Parikh
    Being able to effectively model latent structure in data is a key challenge in modern AI research, particularly in Natural Language Processing (NLP) where it is crucial to discover and leverage syntactic and semantic relationships that may not be explicitly annotated in the training set. Unfortunately, while…
  • Multimodal Science Learning Thumbnail

    Multimodal Science Learning

    February 26, 2015  |  Ken Forbus
    Creating systems that can work with people, using natural modalities, as apprentices is a key step towards human-level AI. This talk will describe how my group is combining research on sketch understanding, natural language understanding, and analogical learning within the Companion cognitive architecture to…
  • Semi-Supervised Learning In Realistic Settings Thumbnail

    Semi-Supervised Learning In Realistic Settings

    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…
  • Bayesian Case Model — Generative Approach for Case-based Reasoning and Prototype Thumbnail

    Bayesian Case Model — Generative Approach for Case-based Reasoning and Prototype

    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…
  • Event Discovery, Content Models, and Relevance Thumbnail

    Event Discovery, Content Models, and Relevance

    December 4, 2014  |  Aria Haghigi
    I discuss three problems in applied natural language processing and machine learning: event discovery from distributed discourse, document content models for information extraction, and relevance engineering for a large-scale personalization engine. The first two are information extraction problems over social…
  • Toward Scene Understanding Thumbnail

    Toward Scene Understanding

    December 3, 2014  |  Roozbeh Mottaghi
    Scene understanding is one of the holy grails of computer vision, and despite decades of research, it is still considered an unsolved problem. In this talk, I will present a number of methods, which help us take a step further towards the ultimate goal of holistic scene understanding. In particular, I will talk…