Search Results for author:
Found 13 papers, 3 papers with code
Serpent: Scalable and Efficient Image Restoration via Multi-scale Structured State Space Models
The landscape of computational building blocks of efficient image restoration architectures is dominated by a combination of convolutional processing and various attention mechanisms.
Multimodal Foundation Models for Zero-shot Animal Species Recognition in Camera Trap Images
In particular, we instruction tune vision-language models to generate detailed visual descriptions of camera trap images using similar terminology to experts.
Adapt and Diffuse: Sample-adaptive Reconstruction via Latent Diffusion Models
Our framework acts as a wrapper that can be combined with any latent diffusion-based baseline solver, imbuing it with sample-adaptivity and acceleration.
mL-BFGS: A Momentum-based L-BFGS for Distributed Large-Scale Neural Network Optimization
Quasi-Newton methods still face significant challenges in training large-scale neural networks due to additional compute costs in the Hessian related computations and instability issues in stochastic training.
Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory
Deep learning models are prone to forgetting information learned in the past when trained on new data.
DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-Consistency
In this work, we propose a novel framework for inverse problem solving, namely we assume that the observation comes from a stochastic degradation process that gradually degrades and noises the original clean image.
HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction
These models split input images into non-overlapping patches, embed the patches into lower-dimensional tokens and utilize a self-attention mechanism that does not suffer from the aforementioned weaknesses of convolutional architectures.
Ranked #1 on
MRI Reconstruction
on fastMRI Knee 8x
(using extra training data)
SLIM-QN: A Stochastic, Light, Momentumized Quasi-Newton Optimizer for Deep Neural Networks
SLIM-QN addresses two key barriers in existing second-order methods for large-scale DNNs: 1) the high computational cost of obtaining the Hessian matrix and its inverse in every iteration (e. g. KFAC); 2) convergence instability due to stochastic training (e. g. L-BFGS).
Data augmentation for deep learning based accelerated MRI reconstruction with limited data
Deep neural networks have emerged as very successful tools for image restoration and reconstruction tasks.
Data augmentation for deep learning based accelerated MRI reconstruction
Inspired by the success of Data Augmentation (DA) for classification problems, in this paper, we propose a pipeline for data augmentation for image reconstruction tasks arising in medical imaging and explore its effectiveness at reducing the required training data in a variety of settings.
Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks
In this approach a model trained for a source task, where plenty of labeled training data is available, is used as a starting point for training a model on a related target task with only few labeled training data.
GENERALIZATION GUARANTEES FOR NEURAL NETS VIA HARNESSING THE LOW-RANKNESS OF JACOBIAN
We show that over the information space learning is fast and one can quickly train a model with zero training loss that can also generalize well.
Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian
We show that over the information space learning is fast and one can quickly train a model with zero training loss that can also generalize well.
