Videos

See AI2's full collection of videos on our YouTube channel.
Viewing 201-210 of 249 videos
  • AI for the Common Good

    May 23, 2016  |  Oren Etzioni
    Oren Etzioni, CEO of the Allen Institute for AI, shares his vision for deploying AI technologies for the common good.
  • Efficiently Learning and Applying Dense Feature Representations for NLP Thumbnail

    Efficiently Learning and Applying Dense Feature Representations for NLP

    May 17, 2016  |  Yi Yang
    With the resurgence of neural networks, low-dimensional dense features have been used in a wide range of natural language processing problems. Specifically, tasks like part-of-speech tagging, dependency parsing and entity linking have been shown to benefit from dense feature representations from both efficiency…
  • Predicting Human Visual Memory using Deep Learning Thumbnail

    Predicting Human Visual Memory using Deep Learning

    May 9, 2016  |  Aditya Khosla
    When glancing at a magazine or browsing the Internet, we are continuously exposed to photographs and images. While some images stick in our minds, others are ignored or quickly forgotten. Artists, advertisers and educators are routinely challenged by the question "what makes a picture memorable?" and must then…
  • Scene Understanding from RGB-D Images Thumbnail

    Scene Understanding from RGB-D Images

    May 3, 2016  |  Saurabh Gupta
    In this talk, I will talk about detailed scene understanding from RGB-D images. We approach this problem by studying central computer vision problems like bottom-up grouping, object detection, instance segmentation, pose estimation in context of RGB-D images, and finally aligning CAD models to objects in the…
  • Deep Canonical Correlation Analysis Thumbnail

    Deep Canonical Correlation Analysis

    April 26, 2016  |  
    The successes of deep learning in the past decade on difficult tasks ranging from image processing to speech recognition to game playing is strong evidence for the utility of abstract representations of complex natural sensory data. In this talk I will present the deep canonical correlation analysis (DCCA) model…
  • Learning from Zero Thumbnail

    Learning from Zero

    April 12, 2016  |  Percy Liang
    Can we learn if we start with zero examples, either labeled or unlabeled? This scenario arises in new user-facing systems (such as virtual assistants for new domains), where inputs should come from users, but no users exist until we have a working system, which depends on having training data. I will discuss…
  • Leveraging Human Insights into Problem Structure for Scientific Discovery Thumbnail

    Leveraging Human Insights into Problem Structure for Scientific Discovery

    April 6, 2016  |  Ronan Le Bras
    Most problems, from theoretical problems in combinatorics to real-world applications, comprise hidden structural properties not directly captured by the problem definition. A key to the recent progress in automated reasoning and combinatorial optimization has been to automatically uncover and exploit this hidden…
  • Predictive Interaction Thumbnail

    Predictive Interaction

    April 4, 2016  |  Jeffrey Heer
    How might we architect interactive systems that have better models of the tasks we're trying to perform, learn over time, help refine ambiguous user intents, and scale to large or repetitive workloads? In this talk I will present Predictive Interaction, a framework for interactive systems that shifts some of the…
  • Improving Structured Prediction With Locally Normalized Models Thumbnail

    Improving Structured Prediction With Locally Normalized Models

    March 25, 2016  |  Ashish Vaswani
    Locally normalized approaches for structured prediction, such as left-to-right parsing and sequence labeling, are attractive because of their simplicity, ease of training, and the flexibility in choosing features from observations. Combined with the power of neural networks, they have been widely adopted for NLP…
  • Beyond the Distributional Hypothesis: Learning Better Word Representations Thumbnail

    Beyond the Distributional Hypothesis: Learning Better Word Representations

    March 9, 2016  |  Manaal Faruqui
    Unsupervised learning of word representations have proven to provide exceptionally effective features in many NLP tasks. Traditionally, construction of word representations relies on the distributional hypothesis, which posits that the meaning of words is evidenced by the contextual words they occur with (Harris…