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pro vyhledávání: '"Tiwari, Rishabh"'
Autor:
Tiwari, Rishabh, Sivasubramanian, Durga, Mekala, Anmol, Ramakrishnan, Ganesh, Shenoy, Pradeep
Deep networks tend to learn spurious feature-label correlations in real-world supervised learning tasks. This vulnerability is aggravated in distillation, where a student model may have lesser representational capacity than the corresponding teacher
Externí odkaz:
http://arxiv.org/abs/2310.18590
Autor:
Tiwari, Rishabh, Shenoy, Pradeep
Simplicity bias is the concerning tendency of deep networks to over-depend on simple, weakly predictive features, to the exclusion of stronger, more complex features. This is exacerbated in real-world applications by limited training data and spuriou
Externí odkaz:
http://arxiv.org/abs/2301.13293
Concept bottleneck models (CBMs) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and then predict the final label based on the concept label predictions. We extend CBMs to
Externí odkaz:
http://arxiv.org/abs/2212.07430
Autor:
Aggarwal, Saksham, Gupta, Taneesh, Sahu, Pawan Kumar, Chavan, Arnav, Tiwari, Rishabh, Prasad, Dilip K., Gupta, Deepak K.
Object trackers deployed on low-power devices need to be light-weight, however, most of the current state-of-the-art (SOTA) methods rely on using compute-heavy backbones built using CNNs or transformers. Large sizes of such models do not allow their
Externí odkaz:
http://arxiv.org/abs/2211.13769
Gradient based meta-learning methods are prone to overfit on the meta-training set, and this behaviour is more prominent with large and complex networks. Moreover, large networks restrict the application of meta-learning models on low-power edge devi
Externí odkaz:
http://arxiv.org/abs/2206.01690
Continual learning (CL) aims to develop techniques by which a single model adapts to an increasing number of tasks encountered sequentially, thereby potentially leveraging learnings across tasks in a resource-efficient manner. A major challenge for C
Externí odkaz:
http://arxiv.org/abs/2111.11210
Structured pruning methods are among the effective strategies for extracting small resource-efficient convolutional neural networks from their dense counterparts with minimal loss in accuracy. However, most existing methods still suffer from one or m
Externí odkaz:
http://arxiv.org/abs/2102.07156
Deeper and wider CNNs are known to provide improved performance for deep learning tasks. However, most such networks have poor performance gain per parameter increase. In this paper, we investigate whether the gain observed in deeper models is purely
Externí odkaz:
http://arxiv.org/abs/2101.05650
Endoscopic artefact detection challenge consists of 1) Artefact detection, 2) Semantic segmentation, and 3) Out-of-sample generalisation. For Semantic segmentation task, we propose a multi-plateau ensemble of FPN (Feature Pyramid Network) with Effici
Externí odkaz:
http://arxiv.org/abs/2003.10129
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