Search Results for author: Karim Ahmed

Found 6 papers, 1 papers with code

STAR-Caps: Capsule Networks with Straight-Through Attentive Routing

no code implementations NeurIPS 2019 Karim Ahmed, Lorenzo Torresani

Capsule networks have been shown to be powerful models for image classification, thanks to their ability to represent and capture viewpoint variations of an object.

Classification General Classification +1

Weighted Transformer Network for Machine Translation

5 code implementations ICLR 2018 Karim Ahmed, Nitish Shirish Keskar, Richard Socher

State-of-the-art results on neural machine translation often use attentional sequence-to-sequence models with some form of convolution or recursion.

Machine Translation Translation

Connectivity Learning in Multi-Branch Networks

no code implementations ICLR 2018 Karim Ahmed, Lorenzo Torresani

While much of the work in the design of convolutional networks over the last five years has revolved around the empirical investigation of the importance of depth, filter sizes, and number of feature channels, recent studies have shown that branching, i. e., splitting the computation along parallel but distinct threads and then aggregating their outputs, represents a new promising dimension for significant improvements in performance.

Image Classification

BranchConnect: Large-Scale Visual Recognition with Learned Branch Connections

no code implementations20 Apr 2017 Karim Ahmed, Lorenzo Torresani

We introduce an architecture for large-scale image categorization that enables the end-to-end learning of separate visual features for the different classes to distinguish.

Image Categorization Object Recognition

Network of Experts for Large-Scale Image Categorization

no code implementations20 Apr 2016 Karim Ahmed, Mohammad Haris Baig, Lorenzo Torresani

The training of our "network of experts" is completely end-to-end: the partition of categories into disjoint subsets is learned simultaneously with the parameters of the network trunk and the experts are trained jointly by minimizing a single learning objective over all classes.

Image Categorization Image Classification

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