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Found 12 papers, 5 papers with code
Multimodal 3D Brain Tumor Segmentation with Adversarial Training and Conditional Random Field
Accurate brain tumor segmentation remains a challenging task due to structural complexity and great individual differences of gliomas.
One-stop Training of Multiple Capacity Models
Notably, the analysis demonstrates that our method significantly influences the initial training process, leading to more efficient convergence and superior solutions.
CoLa-Diff: Conditional Latent Diffusion Model for Multi-Modal MRI Synthesis
Diffusion model has emerged as an effective technique for image synthesis by modelling complex and variable data distributions.
DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-Resolution
Moreover, DisC-Diff leverages a disentangled multi-stream network to fully exploit complementary information from multi-contrast MRI, improving model interpretation under multiple conditions of multi-contrast inputs.
ROSE: Robust Selective Fine-tuning for Pre-trained Language Models
Even though the large-scale language models have achieved excellent performances, they suffer from various adversarial attacks.
Detecting Layout Templates in Complex Multiregion Files
We present the Mondrian approach to automatically identify layout templates across multiple files and systematically extract the corresponding regions.
Critical Role of Simultaneous Reduction of Atmospheric Odd Oxygen for Winter Haze Mitigation
The lockdown due to COVID-19 created a rare opportunity to examine the nonlinear responses of secondary aerosols, which are formed through atmospheric oxidation of gaseous precursors, to intensive precursor emission reductions.
On Length Divergence Bias in Textual Matching Models
To determine whether TM models have adopted such heuristic, we introduce an adversarial evaluation scheme which invalidates the heuristic.
MAssistant: A Personal Knowledge Assistant for MOOC Learners
Massive Open Online Courses (MOOCs) have developed rapidly and attracted large number of learners.
Improving Neural Language Models by Segmenting, Attending, and Predicting the Future
In this work, we propose a method that improves language modeling by learning to align the given context and the following phrase.
Ranked #24 on
Language Modelling
on WikiText-103
Tracking multiple moving objects in images using Markov Chain Monte Carlo
A new Bayesian state and parameter learning algorithm for multiple target tracking (MTT) models with image observations is proposed.
Bayesian tracking and parameter learning for non-linear multiple target tracking models
We propose a new Bayesian tracking and parameter learning algorithm for non-linear non-Gaussian multiple target tracking (MTT) models.
