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Found 11 papers, 7 papers with code
A Transformer-based deep neural network model for SSVEP classification
The proposed model validates the feasibility of deep learning models based on Transformer structure for SSVEP classification task, and could serve as a potential model to alleviate the calibration procedure in the practical application of SSVEP-based BCI systems.
QuTE: decentralized multiple testing on sensor networks with false discovery rate control
We consider the setting where distinct agents reside on the nodes of an undirected graph, and each agent possesses p-values corresponding to one or more hypotheses local to its node.
ML-LOO: Detecting Adversarial Examples with Feature Attribution
Furthermore, we extend our method to include multi-layer feature attributions in order to tackle the attacks with mixed confidence levels.
HopSkipJumpAttack: A Query-Efficient Decision-Based Attack
We develop HopSkipJumpAttack, a family of algorithms based on a novel estimate of the gradient direction using binary information at the decision boundary.
LS-Tree: Model Interpretation When the Data Are Linguistic
We study the problem of interpreting trained classification models in the setting of linguistic data sets.
L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data
We study instancewise feature importance scoring as a method for model interpretation.
Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data
We present a probabilistic framework for studying adversarial attacks on discrete data.
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation
We introduce instancewise feature selection as a methodology for model interpretation.
Language-Based Image Editing with Recurrent Attentive Models
First, we introduce a synthetic dataset, called CoSaL, to evaluate the end-to-end performance of our LBIE system.
DAGGER: A sequential algorithm for FDR control on DAGs
We propose a linear-time, single-pass, top-down algorithm for multiple testing on directed acyclic graphs (DAGs), where nodes represent hypotheses and edges specify a partial ordering in which hypotheses must be tested.
Kernel Feature Selection via Conditional Covariance Minimization
We propose a method for feature selection that employs kernel-based measures of independence to find a subset of covariates that is maximally predictive of the response.
