19 citations · 61 across the 10 of their papers we have counts for
6 papers · 1 filter
Understanding and Improving the Role of Projection Head in Self-Supervised Learning
Kartik Gupta, Thalaiyasingam Ajanthan, Anton van den Hengel +1
Self-supervised learning (SSL) aims to produce useful feature representations without access to any human-labeled data annotations. Due to the success of recent SSL methods based o…
Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices
Kartik Gupta, Marios Fournarakis, Matthias Reisser +2
Federated Learning (FL) is a machine learning paradigm to distributively learn machine learning models from decentralized data that remains on-device. Despite the success of standa…
Post-hoc Calibration of Neural Networks by g-Layers
Amir Rahimi, Thomas Mensink, Kartik Gupta +3
Calibration of neural networks is a critical aspect to consider when incorporating machine learning models in real-world decision-making systems where the confidence of decisions a…
Calibration of Neural Networks using Splines
Kartik Gupta, Amir Rahimi, Thalaiyasingam Ajanthan +3
Calibrating neural networks is of utmost importance when employing them in safety-critical applications where the downstream decision making depends on the predicted probabilities.…
Mirror Descent View for Neural Network Quantization
Thalaiyasingam Ajanthan, Kartik Gupta, Philip H. S. Torr +2
Quantizing large Neural Networks (NN) while maintaining the performance is highly desirable for resource-limited devices due to reduced memory and time complexity. It is usually fo…
Nearly-optimal Robust Matrix Completion
Yeshwanth Cherapanamjeri, Kartik Gupta, Prateek Jain
In this paper, we consider the problem of Robust Matrix Completion (RMC) where the goal is to recover a low-rank matrix by observing a small number of its entries out of which a fe…