2 citations · 2 across the 8 of their papers we have counts for
9 papers
Squeeze-Release: Iterative Pruning with Exact Structural Minimization
Roman Denkin, Ida Akerholm, Prashant Singh +1
Unstructured pruning produces sparse weight tensors, but the standard implementation keeps tensor shapes unchanged so the deployed model is no smaller than before pruning. We prese…
GeoFlowVLM: Geometry-Aware Joint Uncertainty for Frozen Vision-Language Embedding
Mayank Nautiyal, Li Ju, Andreas Hellander +2
Standard dual-encoder vision-language models that map images and text to deterministic points on a shared unit hypersphere through normalization typically expose neither \…
OneFlowSBI: One Model, Many Queries for Simulation-Based Inference
Mayank Nautiyal, Li Ju, Melker Ernfors +5
We introduce \textit{OneFlowSBI}, a unified framework for simulation-based inference that learns a single flow-matching generative model over the joint distribution of parameters a…
Epistemic Uncertainty Quantification for Pre-trained VLMs via Riemannian Flow Matching
Li Ju, Mayank Nautiyal, Andreas Hellander +2
Vision-Language Models (VLMs) are typically deterministic in nature and lack intrinsic mechanisms to quantify epistemic uncertainty, which reflects the model's lack of knowledge or…
Exploiting the Asymmetric Uncertainty Structure of Pre-trained VLMs on the Unit Hypersphere
Li Ju, Max Andersson, Stina Fredriksson +4
Vision-language models (VLMs) as foundation models have significantly enhanced performance across a wide range of visual and textual tasks, without requiring large-scale training f…
ConDiSim: Conditional Diffusion Models for Simulation Based Inference
Mayank Nautiyal, Andreas Hellander, Prashant Singh
We present a conditional diffusion model - ConDiSim, for simulation-based inference of complex systems with intractable likelihoods. ConDiSim leverages denoising diffusion probabil…
PARIC: Probabilistic Attention Regularization for Language Guided Image Classification from Pre-trained Vison Language Models
Mayank Nautiyal, Stela Arranz Gheorghe, Kristiana Stefa +3
Language-guided attention frameworks have significantly enhanced both interpretability and performance in image classification; however, the reliance on deterministic embeddings fr…
Variational Autoencoders for Efficient Simulation-Based Inference
Mayank Nautiyal, Andrey Shternshis, Andreas Hellander +1
We present a generative modeling approach based on the variational inference framework for likelihood-free simulation-based inference. The method leverages latent variables within…
EnterpriseEM: Fine-tuned Embeddings for Enterprise Semantic Search
Kamalkumar Rathinasamy, Jayarama Nettar, Amit Kumar +24
Enterprises grapple with the significant challenge of managing proprietary unstructured data, hindering efficient information retrieval. This has led to the emergence of AI-driven…