Search Results for author: Puja Trivedi

Found 15 papers, 4 papers with code

Forward Learning of Graph Neural Networks

no code implementations16 Mar 2024 Namyong Park, Xing Wang, Antoine Simoulin, Shuai Yang, Grey Yang, Ryan Rossi, Puja Trivedi, Nesreen Ahmed

To address these limitations, the forward-forward algorithm (FF) was recently proposed as an alternative to BP in the image classification domain, which trains NNs by performing two forward passes over positive and negative data.

Drug Discovery Graph Learning +2

Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks

no code implementations7 Jan 2024 Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan

While graph neural networks (GNNs) are widely used for node and graph representation learning tasks, the reliability of GNN uncertainty estimates under distribution shifts remains relatively under-explored.

Graph Classification Graph Representation Learning +1

PAGER: A Framework for Failure Analysis of Deep Regression Models

no code implementations20 Sep 2023 Jayaraman J. Thiagarajan, Vivek Narayanaswamy, Puja Trivedi, Rushil Anirudh

In this paper, we propose PAGER (Principled Analysis of Generalization Errors in Regressors), a framework to systematically detect and characterize failures in deep regression models.

regression

Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks

no code implementations20 Sep 2023 Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan

Safe deployment of graph neural networks (GNNs) under distribution shift requires models to provide accurate confidence indicators (CI).

Uncertainty Quantification

A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias

no code implementations23 Mar 2023 Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan

Advances in the expressivity of pretrained models have increased interest in the design of adaptation protocols which enable safe and effective transfer learning.

Out-of-Distribution Generalization Transfer Learning

On the Efficacy of Generalization Error Prediction Scoring Functions

no code implementations23 Mar 2023 Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan

Overall, our work carefully studies the effectiveness of popular scoring functions in realistic settings and helps to better understand their limitations.

Analyzing Data-Centric Properties for Graph Contrastive Learning

1 code implementation4 Aug 2022 Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan

Overall, our work rigorously contextualizes, both empirically and theoretically, the effects of data-centric properties on augmentation strategies and learning paradigms for graph SSL.

Contrastive Learning Self-Supervised Learning +1

Exploring the Design of Adaptation Protocols for Improved Generalization and Machine Learning Safety

no code implementations26 Jul 2022 Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan

While directly fine-tuning (FT) large-scale, pretrained models on task-specific data is well-known to induce strong in-distribution task performance, recent works have demonstrated that different adaptation protocols, such as linear probing (LP) prior to FT, can improve out-of-distribution generalization.

Anomaly Detection BIG-bench Machine Learning +2

Leveraging the Graph Structure of Neural Network Training Dynamics

1 code implementation9 Nov 2021 Fatemeh Vahedian, Ruiyu Li, Puja Trivedi, Di Jin, Danai Koutra

Understanding the training dynamics of deep neural networks (DNNs) is important as it can lead to improved training efficiency and task performance.

Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices

no code implementations5 Nov 2021 Puja Trivedi, Ekdeep Singh Lubana, Yujun Yan, Yaoqing Yang, Danai Koutra

Unsupervised graph representation learning is critical to a wide range of applications where labels may be scarce or expensive to procure.

Contrastive Learning Data Augmentation +5

Interrogating Paradigms in Self-supervised Graph Representation Learning

no code implementations29 Sep 2021 Puja Trivedi, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan

Using the recent population augmentation graph-based analysis of self-supervised learning, we show theoretically that the success of GCL with popular augmentations is bounded by the graph edit distance between different classes.

Contrastive Learning Graph Representation Learning +2

How do Quadratic Regularizers Prevent Catastrophic Forgetting: The Role of Interpolation

2 code implementations4 Feb 2021 Ekdeep Singh Lubana, Puja Trivedi, Danai Koutra, Robert P. Dick

Catastrophic forgetting undermines the effectiveness of deep neural networks (DNNs) in scenarios such as continual learning and lifelong learning.

Continual Learning

OrthoReg: Robust Network Pruning Using Orthonormality Regularization

1 code implementation10 Sep 2020 Ekdeep Singh Lubana, Puja Trivedi, Conrad Hougen, Robert P. Dick, Alfred O. Hero

To address this issue, we propose OrthoReg, a principled regularization strategy that enforces orthonormality on a network's filters to reduce inter-filter correlation, thereby allowing reliable, efficient determination of group importance estimates, improved trainability of pruned networks, and efficient, simultaneous pruning of large groups of filters.

Network Pruning

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