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Viewing 22 papers from 2016 in Aristo
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    • 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)
    • 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)
    • 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)
    • 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)
    • AKBC 2016
      Bhavana Dalvi, Sumithra Bhakthavatsalam, Chris Clark, Peter Clark, Oren Etzioni, Anthony Fader, and Dirk Groeneveld
      Recent work on information extraction has suggested that fast, interactive tools can be highly effective; however, creating a usable system is challenging, and few publicly available tools exist. In this paper we present IKE, a new extraction tool that performs fast, interactive bootstrapping to…  (More)
    • CACM 2016 Video
      Carissa Schoenick, Peter Clark, Oyvind Tafjord, Peter Turney, and Oren Etzioni
      The field of Artificial Intelligence has made great strides forward recently, for example AlphaGo's recent victory against the world champion Lee Sedol in the game of Go, leading to great optimism about the field. But are we really moving towards smarter machines, or are these successes restricted…  (More)
    • SEM 2016
      Saif M. Mohammad, Ekaterina Shutova, and Peter D. Turney
      It is generally believed that a metaphor tends to have a stronger emotional impact than a literal statement; however, there is no quantitative study establishing the extent to which this is true. Further, the mechanisms through which metaphors convey emotions are not well understood. We present the…  (More)
    • ICML 2016
      Tudor Achim, Ashish Sabharwal, and Stefano Ermon
      Random projections have played an important role in scaling up machine learning and data mining algorithms. Recently they have also been applied to probabilistic inference to estimate properties of high-dimensional distributions; however , they all rely on the same class of projections based on…  (More)
    • EMNLP 2016
      Samuel Louvan, Chetan Naik, Sadhana Kumaravel, Heeyoung Kwon, Niranjan Balasubramanian, and Peter Clark
      For AI systems to reason about real world situations, they need to recognize which processes are at play and which entities play key roles in them. Our goal is to extract this kind of rolebased knowledge about processes, from multiple sentence-level descriptions. This knowledge is hard to acquire…  (More)
    • EMNLP 2016
      Rebecca Sharp, Mihai Surdeanu, Peter Jansen, and Peter Clark
      A common model for question answering (QA) is that a good answer is one that is closely related to the question, where relatedness is often determined using generalpurpose lexical models such as word embeddings. We argue that a better approach is to look for answers that are related to the question…  (More)
    • EMNLP 2016
      Jayant Krishnamurthy, Oyvind Tafjord, and Aniruddha Kembhavi
      Situated question answering is the problem of answering questions about an environment such as an image or diagram. This problem requires jointly interpreting a question and an environment using background knowledge to select the correct answer. We present Parsing to Probabilistic Programs (P3), a…  (More)
    • COLING 2016
      Peter Jansen, Niranjan Balasubramanian, Mihai Surdeanu, and Peter Clark
      QA systems have been making steady advances in the challenging elementary science exam domain. In this work, we develop an explanation-based analysis of knowledge and inference requirements, which supports a fine-grained characterization of the challenges. In particular, we model the requirements…  (More)
    • NIPS 2016
      Been Kim, Sanmi Koyejo and Rajiv Khanna
      Example-based explanations are widely used in the effort to improve the interpretability of highly complex distributions. However, prototypes alone are rarely sufficient to represent the gist of the complexity. In order for users to construct better mental models and understand complex data…  (More)
    • NIPS 2016
      Shengjia Zhao, Enze Zhou, Ashish Sabharwal, and Stefano Ermon
      A key challenge in sequential decision problems is to determine how many samples are needed for an agent to make reliable decisions with good probabilistic guarantees. We introduce Hoeffding-like concentration inequalities that hold for a random, adaptively chosen number of samples. Our…  (More)
    • AI Magazine 2016
      Peter Clark and Oren Etzioni
      Given the well-known limitations of the Turing Test, there is a need for objective tests to both focus attention on, and measure progress towards, the goals of AI. In this paper we argue that machine performance on standardized tests should be a key component of any new measure of AI, because…  (More)
    • AAAI 2016
      Ashish Sabharwal, Horst Samulowitz, and Gerald Tesauro
      We study a novel machine learning (ML) problem setting of sequentially allocating small subsets of training data amongst a large set of classifiers. The goal is to select a classifier that will give near-optimal accuracy when trained on all data, while also minimizing the cost of misallocated…  (More)
    • AAAI 2016
      Carolyn Kim, Ashish Sabharwal, and Stefano Ermon
      We consider the problem of sampling from a discrete probability distribution specified by a graphical model. Exact samples can, in principle, be obtained by computing the mode of the original model perturbed with an exponentially many i.i.d. random variables. We propose a novel algorithm that views…  (More)
    • AAAI 2016
      Shengjia Zhao, Sorathan Chaturapruek, Ashish Sabharwal, and Stefano Ermon
      Many recent algorithms for approximate model counting are based on a reduction to combinatorial searches over random subsets of the space defined by parity or XOR constraints. Long parity constraints (involving many variables) provide strong theoretical guarantees but are computationally difficult…  (More)
    • AAAI 2016
      Shuo Yang, Tushar Khot, Kristian Kersting, and Sriraam Natarajan
      Many real world applications in medicine, biology, communication networks, web mining, and economics, among others, involve modeling and learning structured stochastic processes that evolve over continuous time. Existing approaches, however, have focused on propositional domains only. Without…  (More)
    • AAAI 2016
      Peter Clark, Oren Etzioni, Daniel Khashabi, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, and Peter Turney
      What capabilities are required for an AI system to pass standard 4th Grade Science Tests? Previous work has examined the use of Markov Logic Networks (MLNs) to represent the requisite background knowledge and interpret test questions, but did not improve upon an information retrieval (IR) baseline…  (More)
    • WSDM 2016
      Bhavana Dalvi, Aditya Mishra, and William W. Cohen
      In an entity classification task, topic or concept hierarchies are often incomplete. Previous work by Dalvi et al. has shown that in non-hierarchical semi-supervised classification tasks, the presence of such unanticipated classes can cause semantic drift for seeded classes. The Exploratory…  (More)