4 citations · 5 across the 7 of their papers we have counts for
9 papers
RhinoNet: Unsupervised Learning for Phenoscaping and Generation of High Dimensional Point Clouds
Siddharth Viswanath, Hiren Madhu, Eddie Cavallin +7
We present RhinoNet, a simplicial complex-based point cloud neural network, that creates a geometry-preserving embedding of high-dimensional point clouds. RhinoNet takes collection…
CoPersona: Collaborative Persona Graphs for Robust LLM Personalization
Yangtian Zhang, Leyao Wang, Hiren Madhu +3
Real-world LLM personalization is often constrained by sparse and skewed user histories: most users provide only a handful of interactions, while even frequent users' logs capture…
HypRAG: Hyperbolic Dense Retrieval for Retrieval Augmented Generation
Hiren Madhu, Ngoc Bui, Ali Maatouk +6
Embedding geometry plays a fundamental role in retrieval quality, yet dense retrievers for retrieval-augmented generation (RAG) remain largely confined to Euclidean space. However,…
Embedding Alignment in Code Generation for Audio
Sam Kouteili, Hiren Madhu, George Typaldos +1
LLM-powered code generation has the potential to revolutionize creative coding endeavors, such as live-coding, by enabling users to focus on structural motifs over syntactic detail…
A Graph Laplacian Eigenvector-based Pre-training Method for Graph Neural Networks
Howard Dai, Nyambura Njenga, Hiren Madhu +4
The development of self-supervised graph pre-training methods is a crucial ingredient in recent efforts to design robust graph foundation models (GFMs). Structure-based pre-trainin…
Hyperbolic Deep Learning for Foundation Models: A Survey
Neil He, Hiren Madhu, Ngoc Bui +2
Foundation models pre-trained on massive datasets, including large language models (LLMs), vision-language models (VLMs), and large multimodal models, have demonstrated remarkable…
HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data
Hiren Madhu, João Felipe Rocha, Tinglin Huang +3
Single-cell transcriptomics and proteomics have become a great source for data-driven insights into biology, enabling the use of advanced deep learning methods to understand cellul…
HELM: Hyperbolic Large Language Models via Mixture-of-Curvature Experts
Neil He, Rishabh Anand, Hiren Madhu +5
Large language models (LLMs) have shown great success in text modeling tasks across domains. However, natural language exhibits inherent semantic hierarchies and nuanced geometric…
HiPoNet: A Multi-View Simplicial Complex Network for High Dimensional Point-Cloud and Single-Cell Data
Siddharth Viswanath, Hiren Madhu, Dhananjay Bhaskar +7
In this paper, we propose HiPoNet, an end-to-end differentiable neural network for regression, classification, and representation learning on high-dimensional point clouds. Our wor…