Search Results for author:
Found 12 papers, 6 papers with code
Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation
We introduce a conditional pseudo-reversible normalizing flow for constructing surrogate models of a physical model polluted by additive noise to efficiently quantify forward and inverse uncertainty propagation.
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences.
BDMMT: Backdoor Sample Detection for Language Models through Model Mutation Testing
We also make the first attempt to defend against the latest style-level backdoor attacks.
Rényi Divergence Deep Mutual Learning
This paper revisits Deep Mutual Learning (DML), a simple yet effective computing paradigm.
Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL
However, we identify two biases in existing datasets for XDTS: (1) a high proportion of context-independent questions and (2) a high proportion of easy SQL queries.
Can We Trust Your Explanations? Sanity Checks for Interpreters in Android Malware Analysis
For this reason, a variety of explanation approaches are proposed to interpret predictions by providing important features.
Cryptography and Security Software Engineering
Can We Mitigate Backdoor Attack Using Adversarial Detection Methods?
It is unknown whether there are any connections and common characteristics between the defenses against these two attacks.
DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy
Our method combines the strength of deep learning and statistical inference, where deep learning captures the underlying distribution of the fluorophores that are consistent with the observed time-series fluorescent images by exploring local features and correlation along time-axis, and statistical inference further refines the ultrastructure extracted by deep learning and endues physical meaning to the final image.
Representation Learning with Deconvolution for Multivariate Time Series Classification and Visualization
We propose a new model based on the deconvolutional networks and SAX discretization to learn the representation for multivariate time series.
Empirical Studies on Symbolic Aggregation Approximation Under Statistical Perspectives for Knowledge Discovery in Time Series
Our work provides an analytical framework with several statistical tools to analyze, evaluate and further improve the symbolic dynamics for knowledge discovery in time series.
