1 citations · 1 across the 4 of their papers we have counts for
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Using Early Readouts to Mediate Featural Bias in Distillation
Rishabh Tiwari, Durga Sivasubramanian, Anmol Mekala +2
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…
STREAMLINE: Streaming Active Learning for Realistic Multi-Distributional Settings
Nathan Beck, Suraj Kothawade, Pradeep Shenoy +1
Deep neural networks have consistently shown great performance in several real-world use cases like autonomous vehicles, satellite imaging, etc., effectively leveraging large corpo…
Overcoming Simplicity Bias in Deep Networks using a Feature Sieve
Rishabh Tiwari, Pradeep Shenoy
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 exace…
Interactive Concept Bottleneck Models
Kushal Chauhan, Rishabh Tiwari, Jan Freyberg +2
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…
GCR: Gradient Coreset Based Replay Buffer Selection For Continual Learning
Rishabh Tiwari, Krishnateja Killamsetty, Rishabh Iyer +1
Continual learning (CL) aims to develop techniques by which a single model adapts to an increasing number of tasks encountered sequentially, thereby potentially leveraging learning…