Rajeev Ranjan Dwivedi

2024

CosFairNet: A Parameter-Space based Approach for Bias Free Learning

Rajeev Ranjan Dwivedi, Priyadarshni Kumari, Vinod K Kurmi

British Machine Vision Conference (BMVC), (2024)

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Deep neural networks trained on biased data often inadvertently learn unintended inference rules, particularly when labels are strongly correlated with biased features. Existing bias mitigation methods typically involve either a) predefining bias types and enforcing them as prior knowledge or b) reweighting training samples to emphasize bias-conflicting samples over bias-aligned samples. However, both strategies address bias indirectly in the feature or sample space, with no control over learned weights, making it difficult to control the bias propagation across different layers. Based on this observation, we introduce a novel approach to address bias directly in the model's parameter space, preventing its propagation across layers. Our method involves training two models: a bias model for biased features and a debias model for unbiased details, guided by the bias model. We enforce dissimilarity in the debias model's later layers and similarity in its initial layers with the bias model, ensuring it learns unbiased low-level features without adopting biased high-level abstractions. By incorporating this explicit constraint during training, our approach shows enhanced classification accuracy and debiasing effectiveness across various synthetic and real-world datasets of different sizes. Moreover, the proposed method demonstrates robustness across different bias types and percentages of biased samples in the training data.

Quantifying Uncertainty in Neural Networks through Residuals

Udhbav Dalavi, Rini Smita Thakur, Rajeev Ranjan Dwivedi, Vinod K Kurmi

33rd ACM International Conference on Information and Knowledge Management (CIKM), (2024)

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Regression models are of fundamental importance in explicitly explaining the response variable in terms of covariates. Heteroscedasticity is common in most real-world scenarios and is hard to model due to its randomness. The Gaussian process generally captures epistemic (model) uncertainty but fails to capture heteroscedastic aleatoric uncertainty. The framework of Heteroscedastic Gaussian Process (HetGP) inherently captures both epistemic and aleatoric by placing independent Gaussian processes on both mean function and error term. In this work, we propose the posthoc HetGP on the residuals of the trained neural network to obtain both epistemic and aleatoric uncertainty. The advantage of posthoc HetGP on residuals is that it can be extended to any type of model, since the model is assumed to be black-box that gives point predictions. We demonstrate our approach through simulation studies on UCI regression datasets.

2023

Predicting Missing Light Curves of Gamma-Ray Bursts with Bidirectional-LSTM: An Approach for Enhanced Analysis

Shashwat Sourav, Amit Shukla, Rajeev Ranjan Dwivedi, Kartikey Singh

Accepted at SPAICE,24 ECSAT, UK

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Gamma-ray bursts (GRB) are powerful transient events that emit a large output of gamma rays within a few seconds. Studying these short bursts is vital for cosmological research since they originate from sources observed at large redshifts. To effectively carry out these studies, it is crucial to establish a correlation between the observable features of GRBs while reducing their uncertainty. For these reasons, a comprehensive description of the general GRB light curve (LC) would be crucial for the studies. However, unevenly spaced observations and significant gaps in the LC, which are primarily unavoidable for various reasons, make it difficult to characterize GRBs. Therefore, the general classification of GRB LCs remains challenging. In this study, we present a novel approach to reconstruct gamma-ray burst (GRB) light curves using bidirectional Long Short-Term Memory (BiLSTM). Experimental results show that the BiLSTM approach performs better than traditional methods and produces smoother and more convincing reconstructions for GRBs.