Neural Network Accelerated Implicit Filtering: Integrating Neural Network Surrogates With Provably Convergent Derivative Free Optimization Methods
No confident code implementation found for this paper.
No confident code implementation found for this paper.
No confident code match yet
We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.
No code implementations found yet.
Know of an implementation? Let us know in the comments below!
Not All Tokens Matter All The Time: Dynamic Token Aggregation Towards Efficient Detection Transformers
Irregular Multivariate Time Series Forecasting: A Transformable Patching Graph Neural Networks Approach
Let Your Features Tell The Differences: Understanding Graph Convolution By Feature Splitting
Neural ePDOs: Spatially Adaptive Equivariant Partial Differential Operator Based Networks
Data Mixture Inference Attack: BPE Tokenizers Reveal Training Data Compositions
GReaTer: Gradients Over Reasoning Makes Smaller Language Models Strong Prompt Optimizers
@article{irwin2026neural,
title = {Neural Network Accelerated Implicit Filtering: Integrating Neural Network Surrogates With Provably Convergent Derivative Free Optimization Methods},
author = {Brian Irwin and E. Haber and Raviv Gal and A. Ziv},
year = {2026},
url = {https://api.semanticscholar.org/CorpusID:20860e8aac84c63127d4084f1e053ec9cc1d87e7},
journal = {ICML 2023 2023}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/20860e8aac84c63127d4084f1e053ec9cc1d87e7)