Search Results for author: Alykhan Tejani

Found 9 papers, 3 papers with code

Deep Bayesian Bandits: Exploring in Online Personalized Recommendations

no code implementations3 Aug 2020 Dalin Guo, Sofia Ira Ktena, Ferenc Huszar, Pranay Kumar Myana, Wenzhe Shi, Alykhan Tejani

Recommender systems trained in a continuous learning fashion are plagued by the feedback loop problem, also known as algorithmic bias.

Recommendation Systems

Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction

no code implementations15 Jul 2019 Sofia Ira Ktena, Alykhan Tejani, Lucas Theis, Pranay Kumar Myana, Deepak Dilipkumar, Ferenc Huszar, Steven Yoo, Wenzhe Shi

The focus of this paper is to identify the best combination of loss functions and models that enable large-scale learning from a continuous stream of data in the presence of delayed labels.

Click-Through Rate Prediction

Faster gaze prediction with dense networks and Fisher pruning

2 code implementations Twitter 2018 Lucas Theis, Iryna Korshunova, Alykhan Tejani, Ferenc Huszár

Predicting human fixations from images has recently seen large improvements by leveraging deep representations which were pretrained for object recognition.

Gaze Estimation Gaze Prediction +3

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

126 code implementations CVPR 2017 Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi

The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images.

Image Super-Resolution

Latent-Class Hough Forests for 6 DoF Object Pose Estimation

no code implementations3 Feb 2016 Rigas Kouskouridas, Alykhan Tejani, Andreas Doumanoglou, Danhang Tang, Tae-Kyun Kim

In this paper we present Latent-Class Hough Forests, a method for object detection and 6 DoF pose estimation in heavily cluttered and occluded scenarios.

Object Detection Pose Estimation +1

Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture

no code implementations CVPR 2014 Danhang Tang, Hyung Jin Chang, Alykhan Tejani, Tae-Kyun Kim

In contrast to prior forest-based methods, which take dense pixels as input, classify them independently and then estimate joint positions afterwards; our method can be considered as a structured coarse-to-fine search, starting from the centre of mass of a point cloud until locating all the skeletal joints.

3D Hand Pose Estimation

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