Videos

See AI2's full collection of videos on our YouTube channel.
Viewing 131-140 of 251 videos
  • Submodular Optimization and Data Summarization with Applications to Computer Vision Thumbnail

    Submodular Optimization and Data Summarization with Applications to Computer Vision

    October 17, 2018  |  Rishabh Iyer
    Visual Data in the form of Images and Videos have been growing at an unprecedented rate in the last few years. While this massive data is a blessing to data science by helping improve predictive accuracy, it is also a curse since humans are unable to consume this large amount of data. Moreover, today, machine…
  • Learning from Biomedical Data Thumbnail

    Learning from Biomedical Data

    October 10, 2018  |  Lucy Wang
    Human interpretability is essential in biomedicine, because information flow between computational platforms and human stakeholders is crucial to the proper management and care of disease. Biomedical data is abundant, but do not lend themselves to easy summary and interpretation. Luckily, there are many…
  • Resolving Abstract Anaphors in Discourse—Uphill Battles with Neural Ranking Models and Automatic Data Extraction Thumbnail

    Resolving Abstract Anaphors in Discourse—Uphill Battles with Neural Ranking Models and Automatic Data Extraction

    October 1, 2018  |  Ana Marasovic
    Abstract Anaphora Resolution (AAR) is a challenging task of finding a (typically) non-nominal antecedent of pronouns and noun phrases that refer to abstract objects like facts, events, actions or situations, in the (typically) preceding discourse. An example is given below. Our intuition is that we can learn…
  • The relevance search in PubMed Thumbnail

    The relevance search in PubMed

    September 27, 2018  |  Nicolas Fiorini
    PubMed is a free search engine for the biomedical literature accessed by millions of users from around the world each day. With the rapid growth of biomedical literature, finding and retrieving the most relevant papers for a given query is increasingly challenging. I will introduce Best Match, the new relevance…
  • From Paraphrase Modeling to Controlled Generation Thumbnail

    From Paraphrase Modeling to Controlled Generation

    September 19, 2018  |  Kevin Gimpel
    A key challenge in natural language understanding is recognizing when two sentences have the same meaning. I'll discuss our work on this problem over the past few years, including the exploration of compositional functional architectures, learning criteria, and naturally-occurring sources of training data. The…
  • Exposing Brittleness in Reading Comprehension Systems Thumbnail

    Exposing Brittleness in Reading Comprehension Systems

    August 29, 2018  |  Robin Jia
    Reading comprehension systems that answer questions over a context passage can often achieve high test accuracy, but they are frustratingly brittle: they often rely heavily on superficial cues, and therefore struggle on out-of-domain inputs. In this talk, I will describe our work on understanding and challenging…
  • Crafting Intelligible Intelligence Thumbnail

    Crafting Intelligible Intelligence

    August 28, 2018  |  Dan Weld
    Since AI software uses techniques like deep lookahead search and stochastic optimization of huge neural networks, it often results in complex behavior that is difficult for people to understand. Yet organizations are deploying AI algorithms in many mission-critical settings. To trust their behavior, we must make…
  • Neural Semi-supervised Learning under Domain Shift Thumbnail

    Neural Semi-supervised Learning under Domain Shift

    August 24, 2018  |  Sebastian Ruder
    Deep neural networks excel at learning from labeled data. In contrast, learning from unlabeled data, especially under domain shift, which is common in many real-world applications, remains a challenge. In this talk, I will touch on three aspects of learning under domain shift: First I will discuss an approach to…
  • Neural Symbolic Machines: Efficient Reinforcement Learning for Semantic Parsing and Program Synthesis Thumbnail

    Neural Symbolic Machines: Efficient Reinforcement Learning for Semantic Parsing and Program Synthesis

    August 21, 2018  |  Chen Liang
    Learning to generate programs from natural language can support a wide range of applications including question answering, virtual assistant, AutoML, etc. It is natural to apply reinforcement learning to directly optimize the task reward, and generalization to new unseen inputs is crucial. However, three…
  • Knowledge-Aware Natural Language Understanding Thumbnail

    Knowledge-Aware Natural Language Understanding

    August 6, 2018  |  Pradeep Dasigi
    Natural Language Understanding systems typically involve encoding and reasoning components that are trained end-to-end to produce task-specific outputs given human utterances as inputs. I will talk about the role of external knowledge in making both these components better, and describe NLU systems that benefit…