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Viewing 44 papers from 2016
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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)
• 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)
• 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
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
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)
• 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)
• CVPR 2016
Roozbeh Mottaghi, Hannaneh Hajishirzi, and Ali Fahradi
With the recent progress in visual recognition, we have already started to see a surge of vision related real-world applications. These applications, unlike general scene understanding, are task oriented and require specific information from visual data. Considering the current growth in new…  (More)
• JCDL 2016
Christopher Clark and Santosh Divvala
Figures and tables are key sources of information in many scholarly documents. However, current academic search engines do not make use of figures and tables when semantically parsing documents or presenting document summaries to users. To facilitate these applications we develop an algorithm that…  (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
Junyuan Xie, Ross Girshick, and Ali Farhadi
Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms. Relatively little work has focused on learning representations for clustering. In this paper, we propose Deep Embedded Clustering (DEC), a method…  (More)
• ECCV 2016
Aniruddha Kembhavi, Mike Salvato, Eric Kolve, Minjoon Seo, Hannaneh Hajishirzi, and Ali Farhadi
Diagrams are common tools for representing complex concepts, relationships and events, often when it would be difficult to portray the same information with natural images. Understanding natural images has been extensively studied in computer vision, while diagram understanding has received little…  (More)
• ECCV 2016
What happens if one pushes a cup sitting on a table toward the edge of the table? How about pushing a desk against a wall? In this paper, we study the problem of understanding the movements of objects as a result of applying external forces to them. For a given force vector applied to a specific…  (More)
• ECCV 2016
We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-Weight-Networks, the filters are approximated with binary values resulting in $32\times$ memory saving. In XNOR-Networks, both the filters and the input to…  (More)
• ECCV 2016
Gunnar A. Sigurdsson, Gül Varol, Xiaolong Wang, Ali Farhadi, Ivan Laptev, and Abhinav Gupta
Computer vision has a great potential to help our daily lives by searching for lost keys, watering flowers or reminding us to take a pill. To succeed with such tasks, computer vision methods need to be trained from real and diverse examples of our daily dynamic scenes. While most of such scenes are…  (More)
• HCOMP 2016
Gunnar A. Sigurdsson, Olga Russakovsky, Ali Farhadi, Ivan Laptev, and Abhinav Gupta
Large-scale annotated datasets allow AI systems to learn from and build upon the knowledge of the crowd. Many crowdsourcing techniques have been developed for collecting image annotations. These techniques often implicitly rely on the fact that a new input image takes a negligible amount of time to…  (More)
• ECCV 2016
Noah Siegel, Zachary Horvitz, Roie Levin, Santosh Divvala, and Ali Farhadi
‘Which are the pedestrian detectors that yield a precision above 95% at 25% recall?’ Answering such a complex query involves identifying and analyzing the results reported in figures within several research papers. Despite the availability of excellent academic search engines, retrieving such…  (More)
• ECCV 2016
Junyuan Xie, Ross Girshick, and Ali Farhadi
We propose Deep3D, a fully automatic 2D-to-3D conversion algorithm that takes 2D images or video frames as input and outputs stereo 3D image pairs. The stereo images can be viewed with 3D glasses or head-mounted VR displays. Deep3D is trained directly on stereo pairs from a dataset of 3D movies to…  (More)
• CVPR 2016
Mahyar Najibi, Mohammad Rastegari, and Larry Davis
We introduce G-CNN, an object detection technique based on CNNs which works without proposal algorithms. G-CNN starts with a multi-scale grid of fixed bounding boxes. We train a regressor to move and scale elements of the grid towards objects iteratively. G-CNN models the problem of object…  (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)
• Ethics 2016
Amitai Etzioni and Oren Etzioni
The growing number of 'smart' instruments, those equipped with AI, has raised concerns because these instruments make autonomous decisions; that is, they act beyond the guidelines provided them by programmers. Hence, the question the makers and users of smart instrument (e.g., driver-less cars…  (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)
• Vanderbilt 2016
Amitai Etzioni and Oren Etzioni
AI programs make numerous decisions on their own, lack transparency, and may change frequently. Hence, the article shows, unassisted human agents — such as auditors, accountants, inspectors, and police — cannot ensure that AI guided instruments will abide by the law. Human agents need assistance of…  (More)