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Found 8 papers, 5 papers with code
The Fourth International Verification of Neural Networks Competition (VNN-COMP 2023): Summary and Results
This report summarizes the 4th International Verification of Neural Networks Competition (VNN-COMP 2023), held as a part of the 6th Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS), that was collocated with the 35th International Conference on Computer-Aided Verification (CAV).
Provably Bounding Neural Network Preimages
Most work on the formal verification of neural networks has focused on bounding the set of outputs that correspond to a given set of inputs (for example, bounded perturbations of a nominal input).
First Three Years of the International Verification of Neural Networks Competition (VNN-COMP)
This paper presents a summary and meta-analysis of the first three iterations of the annual International Verification of Neural Networks Competition (VNN-COMP) held in 2020, 2021, and 2022.
The Third International Verification of Neural Networks Competition (VNN-COMP 2022): Summary and Results
This report summarizes the 3rd International Verification of Neural Networks Competition (VNN-COMP 2022), held as a part of the 5th Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS), which was collocated with the 34th International Conference on Computer-Aided Verification (CAV).
Two-Way Neural Machine Translation: A Proof of Concept for Bidirectional Translation Modeling using a Two-Dimensional Grid
Neural translation models have proven to be effective in capturing sufficient information from a source sentence and generating a high-quality target sentence.
Debona: Decoupled Boundary Network Analysis for Tighter Bounds and Faster Adversarial Robustness Proofs
We provide proofs for tight upper and lower bounds on max-pooling layers in convolutional networks.
Successfully Applying the Stabilized Lottery Ticket Hypothesis to the Transformer Architecture
Sparse models require less memory for storage and enable a faster inference by reducing the necessary number of FLOPs.
Towards Two-Dimensional Sequence to Sequence Model in Neural Machine Translation
This work investigates an alternative model for neural machine translation (NMT) and proposes a novel architecture, where we employ a multi-dimensional long short-term memory (MDLSTM) for translation modeling.
