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Found 14 papers, 8 papers with code
Multi-Time Attention Networks for Irregularly Sampled Time Series
Irregular sampling occurs in many time series modeling applications where it presents a significant challenge to standard deep learning models.
The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants
We use this dataset to evaluate the capabilities of multilingual masked language models (MLMs) and large language models (LLMs).
Modeling Irregularly Sampled Clinical Time Series
In this paper, we present a new deep learning architecture for addressing this problem based on the use of a semi-parametric interpolation network followed by the application of a prediction network.
Interpolation-Prediction Networks for Irregularly Sampled Time Series
The interpolation network allows for information to be shared across multiple dimensions of a multivariate time series during the interpolation stage, while any standard deep learning model can be used for the prediction network.
Black-box Adversarial Attacks with Bayesian Optimization
We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples using information limited to loss function evaluations of input-output pairs.
Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series
Irregularly sampled time series commonly occur in several domains where they present a significant challenge to standard deep learning models.
Simple and Efficient Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes
We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples for deep learning models solely based on information limited to output label~(hard label) to a queried data input.
Gaussian MRF Covariance Modeling for Efficient Black-Box Adversarial Attacks
We study the problem of generating adversarial examples in a black-box setting, where we only have access to a zeroth order oracle, providing us with loss function evaluations.
Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification
In this paper, we consider the problem of assessing the adversarial robustness of deep neural network models under both Markov chain Monte Carlo (MCMC) and Bayesian Dark Knowledge (BDK) inference approximations.
Integrating Physiological Time Series and Clinical Notes with Deep Learning for Improved ICU Mortality Prediction
Intensive Care Unit Electronic Health Records (ICU EHRs) store multimodal data about patients including clinical notes, sparse and irregularly sampled physiological time series, lab results, and more.
A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series
Such data represent fundamental challenges to many classical models from machine learning and statistics due to the presence of non-uniform intervals between observations.
Revisiting Kernel Temporal Segmentation as an Adaptive Tokenizer for Long-form Video Understanding
While most modern video understanding models operate on short-range clips, real-world videos are often several minutes long with semantically consistent segments of variable length.
Universal Pyramid Adversarial Training for Improved ViT Performance
Recently, Pyramid Adversarial training (Herrmann et al., 2022) has been shown to be very effective for improving clean accuracy and distribution-shift robustness of vision transformers.
Learning to Localize Objects Improves Spatial Reasoning in Visual-LLMs
Integration of Large Language Models (LLMs) into visual domain tasks, resulting in visual-LLMs (V-LLMs), has enabled exceptional performance in vision-language tasks, particularly for visual question answering (VQA).
