Search Results for author:
Found 11 papers, 4 papers with code
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.
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.
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
MAssistant: A Personal Knowledge Assistant for MOOC Learners
Massive Open Online Courses (MOOCs) have developed rapidly and attracted large number of learners.
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.
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.
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.
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.
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.
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.
