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    • EMNLP 2017
      Aaron Sarnat, Vidur Joshi, Cristian Petrescu-Prahova, Alvaro Herrasti, Brandon Stilson, and Mark Hopkins
      We provide a visualization library and web interface for interactively exploring a parse tree or a forest of parses. The library is not tied to any particular linguistic representation, but provides a generalpurpose API for the interactive exploration of hierarchical linguistic structure. To…  (More)
    • Military Review 2017
      Amitai Etzioni and Oren Etzioni
      Autonomous weapons systems and military robots are progressing from science fiction movies to designers' drawing boards, to engineering laboratories, and to the battlefield. These machines have prompted a debate among military planners, roboticists, and ethicists about the development and…  (More)
    • ICCV 2017
      Roozbeh Mottaghi, Connor Schenck, Dieter Fox, Ali Farhadi
      Humans have rich understanding of liquid containers and their contents; for example, we can effortlessly pour water from a pitcher to a cup. Doing so requires estimating the volume of the cup, approximating the amount of water in the pitcher, and predicting the behavior of water when we tilt the…  (More)
    • ICCV 2017
      Yuke Zhu, Daniel Gordon, Eric Kolve, Dieter Fox, Li Fei-Fei, Abhinav Gupta, Roozbeh Mottaghi, Ali Farhadi
      A crucial capability of real-world intelligent agents is their ability to plan a sequence of actions to achieve their goals in the visual world. In this work, we address the problem of visual semantic planning: the task of predicting a sequence of actions from visual observations that transform a…  (More)
    • ACL 2017
      Luheng He, Kenton Lee, Mike Lewis, Luke S. Zettlemoyer
      We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations. We use a deep highway BiLSTM architecture with constrained decoding, while observing a number of recent…  (More)
    • AAAI 2017
      Matt Gardner and Jayant Krishnamurthy
      Traditional semantic parsers map language onto compositional, executable queries in a fixed schema. This map- ping allows them to effectively leverage the information con- tained in large, formal knowledge bases (KBs, e.g., Freebase) to answer questions, but it is also fundamentally limiting…  (More)
    • NIPS • NAMPI Workshop 2016
      Kenton W. Murray and Jayant Krishnamurthy
      We present probabilistic neural programs, a framework for program induction that permits flexible specification of both a computational model and inference algorithm while simultaneously enabling the use of deep neural networks. Probabilistic neural programs combine a computation graph for…  (More)
    • CACM 2016
      Amitai Etzioni and Oren Etzioni
      Operational AI systems (for example, self-driving cars) need to obey both the law of the land and our values. We propose AI oversight systems ("AI Guardians") as an approach to addressing this challenge, and to respond to the potential risks associated with increasingly autonomous AI systems. These…  (More)
    • ACL 2016
      Sujay Kumar Jauhar, Peter D. Turney, Eduard Hovy
      Question answering requires access to a knowledge base to check facts and reason about information. Knowledge in the form of natural language text is easy to acquire, but difficult for automated reasoning. Highly-structured knowledge bases can facilitate reasoning, but are difficult to acquire. In…  (More)
    • CSCW 2016
      Shih-Wen Huang, Jonathan Bragg, Isaac Cowhey, Oren Etzioni, and Daniel S. Weld
      Successful online communities (e.g., Wikipedia, Yelp, and StackOverflow) can produce valuable content. However, many communities fail in their initial stages. Starting an online community is challenging because there is not enough content to attract a critical mass of active members. This paper…  (More)
    • AAAI 2016
      Amos Azaria, Jayant Krishnamurthy, and Tom M. Mitchell
      Unlike traditional machine learning methods, humans often learn from natural language instruction. As users become increasingly accustomed to interacting with mobile devices using speech, their interest in instructing these devices in natural language is likely to grow. We introduce our Learning by…  (More)
    • Award Best Student Paper Award
      AAAI 2016
      Babak Saleh, Ahmed Elgammal, Jacob Feldman, and Ali Farhadi
      The human visual system can spot an abnormal image, and reason about what makes it strange. This task has not received enough attention in computer vision. In this paper we study various types of atypicalities in images in a more comprehensive way than has been done before. We propose a new dataset…  (More)
    • AAAI 2016
      Hessam Bagherinezhad, Hannaneh Hajishirzi, Yejin Choi, and Ali Farhadi
      Human vision greatly benefits from the information about sizes of objects. The role of size in several visual reasoning tasks has been thoroughly explored in human perception and cognition. However, the impact of the information about sizes of objects is yet to be determined in AI. We postulate…  (More)
    • NAACL 2016
      Mark Yatskar, Vicente Ordonez, and Ali Farhadi
      Obtaining common sense knowledge using current information extraction techniques is extremely challenging. In this work, we instead propose to derive simple common sense statements from fully annotated object detection corpora such as the Microsoft Common Objects in Context dataset. We show that…  (More)
    • NAACL 2016
      Jayant Krishnamurthy
      We introduce several probabilistic models for learning the lexicon of a semantic parser. Lexicon learning is the first step of training a semantic parser for a new application domain and the quality of the learned lexicon significantly affects both the accuracy and efficiency of the final semantic…  (More)
    • IJCAI 2016 Code Demo
      Daniel Khashabi, Tushar Khot, Ashish Sabharwal, Peter Clark, Oren Etzioni, and Dan Roth
      Answering science questions posed in natural language is an important AI challenge. Answering such questions often requires non-trivial inference and knowledge that goes beyond factoid retrieval. Yet, most systems for this task are based on relatively shallow Information Retrieval (IR) and…  (More)
    • CVPR 2016
      Mark Yatskar, Luke Zettlemoyer, and Ali Farhadi
      This paper introduces situation recognition, the problem of producing a concise summary of the situation an image depicts including: (1) the main activity (e.g., clipping), (2) the participating actors, objects, substances, and locations (e.g., man, shears, sheep, wool, and field) and most…  (More)
    • CVPR 2016
      Roozbeh Mottaghi, Hessam Bagherinezhad, Mohammad Rastegari, and Ali Farhadi
      In this paper, we study the challenging problem of predicting the dynamics of objects in static images. Given a query object in an image, our goal is to provide a physical understanding of the object in terms of the forces acting upon it and its long term motion as response to those forces. Direct…  (More)
    • Award OpenCV People's Choice Award
      CVPR 2016
      Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi
      We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network pre- dicts…  (More)
    • CVPR 2016
      Xiaolong Wang, Ali Farhadi, and Abhinav Gupta
      What defines an action like “kicking ball”? We argue that the true meaning of an action lies in the change or transformation an action brings to the environment. In this paper, we propose a novel representation for actions by modeling an action as a transformation which changes the state of the…  (More)