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Found 14 papers, 8 papers with code
VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models
Correspondingly, we propose a novel VQAttack model, which can iteratively generate both image and text perturbations with the designed modules: the large language model (LLM)-enhanced image attack and the cross-modal joint attack module.
Recent Advances in Predictive Modeling with Electronic Health Records
This survey systematically reviews recent advances in deep learning-based predictive models using EHR data.
WISE: full-Waveform variational Inference via Subsurface Extensions
We introduce a probabilistic technique for full-waveform inversion, employing variational inference and conditional normalizing flows to quantify uncertainty in migration-velocity models and its impact on imaging.
Inference of CO2 flow patterns -- a feasibility study
To arrive at a formulation capable of inferring flow patterns for regular and irregular flow from well and seismic data, the performance of conditional normalizing flow will be analyzed on a series of carefully designed numerical experiments.
Hierarchical Pretraining on Multimodal Electronic Health Records
Pretraining has proven to be a powerful technique in natural language processing (NLP), exhibiting remarkable success in various NLP downstream tasks.
VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models
In this paper, we aim to investigate a new yet practical task to craft image and text perturbations using pre-trained VL models to attack black-box fine-tuned models on different downstream tasks.
MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation
Health risk prediction is one of the fundamental tasks under predictive modeling in the medical domain, which aims to forecast the potential health risks that patients may face in the future using their historical Electronic Health Records (EHR).
Solving multiphysics-based inverse problems with learned surrogates and constraints
Solving multiphysics-based inverse problems for geological carbon storage monitoring can be challenging when multimodal time-lapse data are expensive to collect and costly to simulate numerically.
Learned multiphysics inversion with differentiable programming and machine learning
We present the Seismic Laboratory for Imaging and Modeling/Monitoring (SLIM) open-source software framework for computational geophysics and, more generally, inverse problems involving the wave-equation (e. g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations.
De-risking Carbon Capture and Sequestration with Explainable CO2 Leakage Detection in Time-lapse Seismic Monitoring Images
With the growing global deployment of carbon capture and sequestration technology to combat climate change, monitoring and detection of potential CO2 leakage through existing or storage induced faults are critical to the safe and long-term viability of the technology.
De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection
Amongst the different monitoring modalities, seismic imaging stands out with its ability to attain high resolution and high fidelity images.
Model-Parallel Fourier Neural Operators as Learned Surrogates for Large-Scale Parametric PDEs
Fourier neural operators (FNOs) are a recently introduced neural network architecture for learning solution operators of partial differential equations (PDEs), which have been shown to perform significantly better than comparable deep learning approaches.
Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators
We show that we can accurately use a Fourier neural operator as a proxy for the fluid-flow simulator for a fraction of the computational cost.
Attention-Oriented Action Recognition for Real-Time Human-Robot Interaction
Despite the notable progress made in action recognition tasks, not much work has been done in action recognition specifically for human-robot interaction.
