Search Results for author:
Found 18 papers, 7 papers with code
Improved motif-scaffolding with SE(3) flow matching
Protein design often begins with the knowledge of a desired function from a motif which motif-scaffolding aims to construct a functional protein around.
Fast protein backbone generation with SE(3) flow matching
We present FrameFlow, a method for fast protein backbone generation using SE(3) flow matching.
Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck
Markov processes are widely used mathematical models for describing dynamic systems in various fields.
Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities
Relevantly, the non-magnitude-based nature of DPP allows for joint optimization of pruning and weight quantization in order to even further compress the network, which we show as well.
Dynamic Probabilistic Pruning: Training sparse networks based on stochastic and dynamic masking
Deep Learning (DL) models are known to be heavily over-parametrized, resulting in a large memory footprint and power consumption.
Active Deep Probabilistic Subsampling
Subsampling a signal of interest can reduce costly data transfer, battery drain, radiation exposure and acquisition time in a wide range of problems.
Deep probabilistic subsampling for task-adaptive compressed sensing
The field of deep learning is commonly concerned with optimizing predictive models using large pre-acquired datasets of densely sampled datapoints or signals.
Learning Sampling and Model-Based Signal Recovery for Compressed Sensing MRI
Compressed sensing (CS) MRI relies on adequate undersampling of the k-space to accelerate the acquisition without compromising image quality.
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Variational Bayesian Inference is a popular methodology for approximating posterior distributions over Bayesian neural network weights.
How Good is the Bayes Posterior in Deep Neural Networks Really?
In this work we cast doubt on the current understanding of Bayes posteriors in popular deep neural networks: we demonstrate through careful MCMC sampling that the posterior predictive induced by the Bayes posterior yields systematically worse predictions compared to simpler methods including point estimates obtained from SGD.
Hydra: Preserving Ensemble Diversity for Model Distillation
As a result, the diversity of the ensemble predictions, stemming from each member, is lost.
On the Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Variational Bayesian Inference is a popular methodology for approximating posterior distributions in Bayesian neural networks.
Learning Sub-Sampling and Signal Recovery with Applications in Ultrasound Imaging
Compressed sensing emerged as a popular framework for sparse signal reconstruction from a small set of compressed measurements.
Putting An End to End-to-End: Gradient-Isolated Learning of Representations
We propose a novel deep learning method for local self-supervised representation learning that does not require labels nor end-to-end backpropagation but exploits the natural order in data instead.
Ranked #59 on
Image Classification
on STL-10
Representation Learning
Self-Supervised Audio Classification
+2
Predictive Uncertainty through Quantization
High-risk domains require reliable confidence estimates from predictive models.
Sample Efficient Semantic Segmentation using Rotation Equivariant Convolutional Networks
The group equivariant CNN framework is extended for segmentation by introducing a new equivariant (G->Z2)-convolution that transforms feature maps on a group to planar feature maps.
Rotation Equivariant CNNs for Digital Pathology
We propose a new model for digital pathology segmentation, based on the observation that histopathology images are inherently symmetric under rotation and reflection.
Ranked #7 on
Breast Tumour Classification
on PCam
Towards radiologist-level cancer risk assessment in CT lung screening using deep learning
Importance: Lung cancer is the leading cause of cancer mortality in the US, responsible for more deaths than breast, prostate, colon and pancreas cancer combined and it has been recently demonstrated that low-dose computed tomography (CT) screening of the chest can significantly reduce this death rate.
