Search Results for author: Bhavya Vasudeva

Found 9 papers, 4 papers with code

Simplicity Bias of Transformers to Learn Low Sensitivity Functions

no code implementations11 Mar 2024 Bhavya Vasudeva, Deqing Fu, Tianyi Zhou, Elliott Kau, Youqi Huang, Vatsal Sharan

Transformers achieve state-of-the-art accuracy and robustness across many tasks, but an understanding of the inductive biases that they have and how those biases are different from other neural network architectures remains elusive.

Implicit Bias and Fast Convergence Rates for Self-attention

no code implementations8 Feb 2024 Bhavya Vasudeva, Puneesh Deora, Christos Thrampoulidis

Self-attention, the core mechanism of transformers, distinguishes them from traditional neural networks and drives their outstanding performance.

Binary Classification Decoder +1

Mitigating Simplicity Bias in Deep Learning for Improved OOD Generalization and Robustness

1 code implementation9 Oct 2023 Bhavya Vasudeva, Kameron Shahabi, Vatsal Sharan

Neural networks (NNs) are known to exhibit simplicity bias where they tend to prefer learning 'simple' features over more 'complex' ones, even when the latter may be more informative.


GradML: A Gradient-based Loss for Deep Metric Learning

no code implementations NeurIPS Workshop ICBINB 2021 Bhavya Vasudeva, Puneesh Deora, Saumik Bhattacharya, Umapada Pal, Sukalpa Chanda

Deep metric learning (ML) uses a carefully designed loss function to learn distance metrics for improving the discriminatory ability for tasks like clustering and retrieval.

Metric Learning Retrieval

LoOp: Looking for Optimal Hard Negative Embeddings for Deep Metric Learning

1 code implementation ICCV 2021 Bhavya Vasudeva, Puneesh Deora, Saumik Bhattacharya, Umapada Pal, Sukalpa Chanda

Deep metric learning has been effectively used to learn distance metrics for different visual tasks like image retrieval, clustering, etc.

Image Retrieval Metric Learning +1

Multi-Phase Locking Value: A Generalized Method for Determining Instantaneous Multi-frequency Phase Coupling

no code implementations20 Feb 2021 Bhavya Vasudeva, Runfeng Tian, Dee H. Wu, Shirley A. James, Hazem H. Refai, Fei He, Yuan Yang

Methods such as $n:m$ phase locking value and bi-phase locking value have previously been proposed to quantify phase coupling between two resonant frequencies (e. g. $f$, $2f/3$) and across three frequencies (e. g. $f_1$, $f_2$, $f_1+f_2$), respectively.

Co-VeGAN: Complex-Valued Generative Adversarial Network for Compressive Sensing MR Image Reconstruction

no code implementations24 Feb 2020 Bhavya Vasudeva, Puneesh Deora, Saumik Bhattacharya, Pyari Mohan Pradhan

Although state-of-the-art deep learning based methods have been able to obtain fast, high-quality reconstruction of CS-MR images, their main drawback is that they treat complex-valued MRI data as real-valued entities.

Compressive Sensing Generative Adversarial Network +1

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