🏢 Rochester Institute of Technology

High-resolution datasets with diverse classes significantly improve the transferability of pretrained DNNs by reducing representation compression and mitigating the ’tunnel effect.'

Visual Fourier Prompt Tuning (VFPT) leverages the Fast Fourier Transform to seamlessly integrate spatial and frequency information for superior parameter-efficient vision model fine-tuning, even with …

Meta-learning solves hybrid deep generative model unidentifiability!

E-NSDE, a novel time-aware sequential recommendation model, integrates neural stochastic differential equations and evidential learning to improve recommendation accuracy by effectively handling varia…

Evidential Mixture Machines (EMM) enhances multi-label active learning by deciphering label correlations for improved accuracy and uncertainty quantification in large, sparse label spaces.

Diffusion-Inspired Truncated Sampler (DITS) revolutionizes text-video retrieval by progressively aligning embeddings and enhancing CLIP embedding space structure, achieving state-of-the-art results.

Federated Hardware-Prompt Learning (FedHP) enables robust cross-hardware SCI training by aligning inconsistent data distributions using a hardware-conditioned prompter, outperforming existing FL metho…

Bayesian-PEFT boosts vision model accuracy and confidence in few-shot learning by integrating Bayesian components into PEFT, solving the underconfidence problem.

AIRS framework, guided by Evidential Q-learning, dynamically balances exploration and exploitation to achieve superior dense object detection accuracy by adaptively selecting important regions.