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    • 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
      Roozbeh Mottaghi, Mohammad Rastegari, Abhinav Gupta, and Ali Farhadi
      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
      Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi
      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)