3.6k citations · 5.8k across the 64 of their papers we have counts for
6 papers · 1 filter
Robust normalizing flows using Bernstein-type polynomials
Sameera Ramasinghe, Kasun Fernando, Salman Khan +1
Modeling real-world distributions can often be challenging due to sample data that are subjected to perturbations, e.g., instrumentation errors, or added random noise. Since flow m…
Meta-learning the Learning Trends Shared Across Tasks
Jathushan Rajasegaran, Salman Khan, Munawar Hayat +2
Meta-learning stands for 'learning to learn' such that generalization to new tasks is achieved. Among these methods, Gradient-based meta-learning algorithms are a specific sub-clas…
Conditional Generative Modeling via Learning the Latent Space
Sameera Ramasinghe, Kanchana Ranasinghe, Salman Khan +2
Although deep learning has achieved appealing results on several machine learning tasks, most of the models are deterministic at inference, limiting their application to single-mod…
iTAML: An Incremental Task-Agnostic Meta-learning Approach
Jathushan Rajasegaran, Salman Khan, Munawar Hayat +2
Humans can continuously learn new knowledge as their experience grows. In contrast, previous learning in deep neural networks can quickly fade out when they are trained on a new ta…
Blended Convolution and Synthesis for Efficient Discrimination of 3D Shapes
Sameera Ramasinghe, Salman Khan, Nick Barnes +1
Existing networks directly learn feature representations on 3D point clouds for shape analysis. We argue that 3D point clouds are highly redundant and hold irregular (permutation-i…
Volumetric Convolution: Automatic Representation Learning in Unit Ball
Sameera Ramasinghe, Salman Khan, Nick Barnes
Convolution is an efficient technique to obtain abstract feature representations using hierarchical layers in deep networks. Although performing convolution in Euclidean geometries…