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.
Papers
More filters
Proceedings Article
Using Classification to Protect the Integrity of Spectrum Measurements in White Space Networks
TL;DR: This paper proposes CUSP, a new technique based on machine learning that uses a trusted initial set of signal propagation data in a region as input to build a classifier using Support Vector Machines, subsequently used to detect integrity violations.
Proceedings ArticleDOI
Incorporating Scene Context and Object Layout into Appearance Modeling
TL;DR: This paper proposes a method to learn scene structures that can encode three main interlacing components of a scene: the scene category, the context-specific appearance of objects, and their layout.
Posted Content
Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning
TL;DR: In this article, a meta-reinforcement learning approach is proposed to encourage effective visual navigation in unseen scenes, where an agent learns a self-supervised interaction loss that encourages effective navigation.
Proceedings Article
Discovering Neural Wirings
TL;DR: In this paper, the authors relax the typical notion of layers and instead enable channels to form connections independent of each other, which allows for a much larger space of possible networks and shows that learning the learned connectivity outperforms hand engineered and randomly wired networks.
Journal ArticleDOI
PhotoShape: Photorealistic Materials for Large-Scale Shape Collections
TL;DR: This work presents an approach to automatically assign high-quality, realistic appearance models to large scale 3D shape collections using three types of online data -- shape collections, material collections, and photo collections, using the photos as reference to guide assignment of materials to shapes.