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Found 18 papers, 7 papers with code
State-driven Implicit Modeling for Sparsity and Robustness in Neural Networks
Implicit models are a general class of learning models that forgo the hierarchical layer structure typical in neural networks and instead define the internal states based on an ``equilibrium'' equation, offering competitive performance and reduced memory consumption.
Sparse Optimization for Unsupervised Extractive Summarization of Long Documents with the Frank-Wolfe Algorithm
To generate a summary with $k$ sentences, the algorithm only needs to execute $\approx k$ iterations, making it very efficient.
Data-Driven Reachability analysis and Support set Estimation with Christoffel Functions
In addition to applying classical Vapnik-Chervonenkis (VC) dimension bound arguments, we apply the PAC-Bayes theorem by leveraging a formal connection between kernelized empirical inverse Christoffel functions and Gaussian process regression models.
Recurrent Neural Network Controllers Synthesis with Stability Guarantees for Partially Observed Systems
Neural network controllers have become popular in control tasks thanks to their flexibility and expressivity.
Data-Driven Reachability Analysis with Christoffel Functions
We present an algorithm for data-driven reachability analysis that estimates finite-horizon forward reachable sets for general nonlinear systems using level sets of a certain class of polynomials known as Christoffel functions.
Text Analytics for Resilience-Enabled Extreme Events Reconnaissance
Post-hazard reconnaissance for natural disasters (e. g., earthquakes) is important for understanding the performance of the built environment, speeding up the recovery, enhancing resilience and making informed decisions related to current and future hazards.
Implicit Graph Neural Networks
Graph Neural Networks (GNNs) are widely used deep learning models that learn meaningful representations from graph-structured data.
FANOK: Knockoffs in Linear Time
We describe a series of algorithms that efficiently implement Gaussian model-X knockoffs to control the false discovery rate on large scale feature selection problems.
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization
We propose a novel Stochastic Frank-Wolfe (a. k. a.
Implicit Deep Learning
Implicit deep learning prediction rules generalize the recursive rules of feedforward neural networks.
Naive Feature Selection: Sparsity in Naive Bayes
We propose a sparse version of naive Bayes, which can be used for feature selection.
Theoretically Principled Trade-off between Robustness and Accuracy
We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples.
Ranked #3 on
Adversarial Attack
on CIFAR-10
Fenchel Lifted Networks: A Lagrange Relaxation of Neural Network Training
In this paper, we introduce a new class of lifted models, Fenchel lifted networks, that enjoy the same benefits as previous lifted models, without suffering a degradation in performance over classical networks.
Greedy Frank-Wolfe Algorithm for Exemplar Selection
In this paper, we consider the problem of selecting representatives from a data set for arbitrary supervised/unsupervised learning tasks.
Kernel-based Outlier Detection using the Inverse Christoffel Function
Outlier detection methods have become increasingly relevant in recent years due to increased security concerns and because of its vast application to different fields.
Lifted Neural Networks
We describe a novel family of models of multi- layer feedforward neural networks in which the activation functions are encoded via penalties in the training problem.
Robust sketching for multiple square-root LASSO problems
Many learning tasks, such as cross-validation, parameter search, or leave-one-out analysis, involve multiple instances of similar problems, each instance sharing a large part of learning data with the others.
Concise comparative summaries (CCS) of large text corpora with a human experiment
In this paper we propose a general framework for topic-specific summarization of large text corpora and illustrate how it can be used for the analysis of news databases.
