259 citations · 803 across the 48 of their papers we have counts for
7 papers · 1 filter
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.…
Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks
Amir Rahimi, Amirreza Shaban, Ching-An Cheng +2
Predicting calibrated confidence scores for multi-class deep networks is important for avoiding rare but costly mistakes. A common approach is to learn a post-hoc calibration funct…
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…
Deep Declarative Networks: A New Hope
Stephen Gould, Richard Hartley, Dylan Campbell
We explore a new class of end-to-end learnable models wherein data processing nodes (or network layers) are defined in terms of desired behavior rather than an explicit forward fun…
Block Mean Approximation for Efficient Second Order Optimization
Yao Lu, Mehrtash Harandi, Richard Hartley +1
Advanced optimization algorithms such as Newton method and AdaGrad benefit from second order derivative or second order statistics to achieve better descent directions and faster c…
Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds
Mehrtash Harandi, Richard Hartley, Chunhua Shen +2
Sparsity-based representations have recently led to notable results in various visual recognition tasks. In a separate line of research, Riemannian manifolds have been shown useful…
Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach
Mehrtash T. Harandi, Conrad Sanderson, Richard Hartley +1
Recent advances suggest that a wide range of computer vision problems can be addressed more appropriately by considering non-Euclidean geometry. This paper tackles the problem of s…