research
∙
01/30/2023
An adversarial feature learning strategy for debiasing neural networks
Simplicity bias is the concerning tendency of deep networks to over-depe...
share
research
∙
12/14/2022
Interactive Concept Bottleneck Models
Concept bottleneck models (CBMs) (Koh et al. 2020) are interpretable neu...
research
∙
11/24/2022
On designing light-weight object trackers through network pruning: Use CNNs or transformers?
Object trackers deployed on low-power devices need to be light-weight, h...
research
∙
06/03/2022
Dynamic Kernel Selection for Improved Generalization and Memory Efficiency in Meta-learning
Gradient based meta-learning methods are prone to overfit on the meta-tr...
research
∙
11/18/2021
GCR: Gradient Coreset Based Replay Buffer Selection For Continual Learning
Continual learning (CL) aims to develop techniques by which a single mod...
research
∙
02/14/2021
ChipNet: Budget-Aware Pruning with Heaviside Continuous Approximations
Structured pruning methods are among the effective strategies for extrac...
research
∙
01/14/2021
Rescaling CNN through Learnable Repetition of Network Parameters
Deeper and wider CNNs are known to provide improved performance for deep...
research
∙
03/23/2020
