26 accepted papers
Graph coarsening is a fundamental dimensionality reduction technique for scaling large graphs while preserving structural and feature information. However, most existing coarsening methods are designed for static graphs and do not extend well to dynamic settings where nodes, edges, and connectivity …
Graph structure learning is a core problem in graph-based machine learning, essential for uncovering latent relationships and ensuring model interpretability. However, most existing approaches are ill-suited for large-scale and dynamically evolving graphs, as they often require complete re-learning …
Graph Neural Networks (GNNs) are expressive architectures for learning from complex graph-structured data. However, their practical use is often limited by the high computational cost of neighborhood aggregation. Recent efforts have focused on knowledge distillation from GNNs to inference-efficient
EEG-based dementia classification often degrades under clinically realistic subject-wise evaluation due to non-stationarity and large inter-subject variability. A key modeling limitation is relation compression: many EEG-GNN pipelines encode functional connectivity as scalar edge weights and blur in
Pulse waveforms contain rich hemodynamic information, essential for real-time patient-specific diagnosis. While most research focuses on predicting central waveforms from peripheral waveforms and extracting hemodynamic data, we explore the potential of analyzing real-time pulse waveforms across the…
The widespread adoption of Large Language Models (LLMs) and publicly available ChatGPT have marked a significant turning point in the integration of Artificial Intelligence (AI) into people’s everyday lives. This study explores the capability of LLMs in generating novel research ideas based on infor
Automated evaluation is crucial for assessing the quality of natural language text, especially in open-ended generation tasks, given the costly and time-consuming nature of human evaluation. Existing automatic evaluation metrics like ROUGE and BLEU often show low correlation with human judgments. As…
Graph neural networks for hyperbolic space has emerged as a powerful tool for embedding datasets exhibiting a highly non-Euclidean latent anatomy e.g., graphs with hierarchical structures. While several Hyperbolic Graph Neural Networks (Hy-GNNs) have been developed to enhance the representation of h…
The linguistic diversity of India poses significant machine translation challenges, especially for underrepresented tribal languages like Bhili, which lack high-quality linguistic resources. This paper addresses the gap by introducing Bhili-Hindi-English Parallel Corpus (BHEPC), the first and larges
The growing use of large language models (LLMs) in academic peer review poses significant challenges, particularly in distinguishing AI-generated content from human-written feedback. This research addresses the problem of identifying AI-generated peer review comments, which are crucial to maintainin…
Science communication, in layperson’s terms, is essential to reach the general population and also maximize the impact of underlying scientific research. Hence, good science blogs and journalistic reviews of research articles are so well-read and critical to conveying science. Scientific blogging go…
Ensuring fairness in Recommendation Systems (RSs) across demographic groups is critical due to the increased integration of RSs in applications such as personalized healthcare, finance, and e-commerce. Graph-based RSs play a crucial role in capturing intricate higher-order interactions among entitie…
In data-intensive applications, graphs serve as foundational structures across various domains. However, the increasing size of datasets poses significant challenges to performing downstream tasks. To address this problem, techniques such as graph coarsening, condensation, and summarization have bee…
In the era of big data, graphs have emerged as a natural representation of intricate relationships. However, graph sizes often become unwieldy, leading to storage, computation, and analysis challenges. A crucial demand arises for methods that can effectively downsize large graphs while retaining vit…
The integrity of the peer-review process is vital for maintaining scientific rigor and trust within the academic community. With the steady increase in the usage of large language models (LLMs) like ChatGPT in academic writing, there is a growing concern that AI-generated texts could compromise the…
Gaussian Markov random fields, a class of graphical models, play an increasingly important role in real-world problems, where they are often applied to uncover conditional correlations between pairs of entities in a network. Motivated by recent applications of graphs in financial markets, we investi…
Graph coarsening is a dimensionality reduction technique that aims to learn a smaller-tractable graph while preserving the properties of the original input graph. However, many real-world graphs also have features or contexts associated with each node. The existing graph coarsening methods do not co…
The design automation of analog circuits poses significant challenges in terms of the large design space, complex interdependencies between circuit specifications, and resource-intensive simulations. To address these challenges, this paper presents an innovative framework called the Graph of Circuit…
This paper addresses the problem of robust and sparse PCA. We consider a formulation combining a M-estimation type robust subspace recovery term and a mixed norm that promotes structured sparsity in the basis vectors, which is especially interesting for joint dimension reduction and variable selecti…
To this date, the efficacy of the scientific publishing enterprise fundamentally rests on the strength of the peer review process. The journal editor or the conference chair primarily relies on the expert reviewers' assessment, $\textit{identify points of agreement and disagreement}$ and try to reac…
An activation function is a crucial component of a neural network that introduces non-linearity in the network. The state-of-the-art performance of a neural network depends also on the perfect choice of an activation function. We propose two novel non-monotonic smooth trainable activation functions,…
"Well-known activation functions like ReLU or Leaky ReLU are non-differentiable at the origin. Over the years, many smooth approximations of ReLU have been proposed using various smoothing techniques. We propose new smooth approximations of a non-differentiable activation function by convolving it w…
Deep learning researchers have a keen interest in proposing new novel activation functions that can boost neural network performance. A good choice of activation function can have a significant effect on improving network performance and training dynamics. Rectified Linear Unit (ReLU) is a popular h…
The vector autoregressive (VAR) models provide a significant tool for multivariate time series analysis. Most existing works on VAR modeling are based on the multivariate Gaussian distribution. However, heavy-tailed distributions are suggested more reasonable for capturing the real-world phenomena,…
Learning a graph with a specific structure is essential for interpretability and identification of the relationships among data. But structured graph learning from observed samples is an NP-hard combinatorial problem. In this paper, we first show, for a set of important graph families it is possible…
This paper proposes a novel and structured framework for parameter estimation from incomplete time series data under heavy-tailed random walk model. Traditionally, maximum likelihood estimation (MLE) for Gaussian random walk model from incomplete data has been considered. However, it is not applicab…