A
Ali Farhadi
Researcher at University of Washington
Publications - 247
Citations - 87076
Ali Farhadi is an academic researcher from University of Washington. The author has contributed to research in topics: Context (language use) & Question answering. The author has an hindex of 63, co-authored 234 publications receiving 57227 citations. Previous affiliations of Ali Farhadi include University of Illinois at Urbana–Champaign & Lorestan University of Medical Sciences.
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Object-Centric Anomaly Detection by Attribute-Based Reasoning
TL;DR: This paper introduces the abnormality detection as a recognition problem and shows how to model typicalities and, consequently, meaningful deviations from prototypical properties of categories.
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Unsupervised Deep Embedding for Clustering Analysis
TL;DR: Deep Embedded Clustering (DEC) as mentioned in this paper learns a mapping from the data space to a lower-dimensional feature space in which it iteratively optimizes a clustering objective.
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Discovering Neural Wirings
TL;DR: DNW provides an effective mechanism for discovering sparse subnetworks of predefined architectures in a single training run and is regarded as unifying core aspects of the neural architecture search problem with sparse neural network learning.
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"What happens if..." Learning to Predict the Effect of Forces in Images
TL;DR: In this article, a deep neural network model was proposed to predict long-term sequential dependencies of object movements while taking into account the geometry and appearance of the scene by combining Convolutional and Recurrent Neural Networks.
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LCNN: Lookup-based Convolutional Neural Network
TL;DR: This paper introduces LCNN, a lookup-based convolutional neural network that encodes convolutions by few lookups to a dictionary that is trained to cover the space of weights in CNNs and shows the benefits of LCNN in few-shot learning and few-iteration learning, two crucial aspects of on-device training of deep learning models.