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06/17/2023
Understanding Certified Training with Interval Bound Propagation
As robustness verification methods are becoming more precise, training c...
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05/18/2023
The Graph Database Interface: Scaling Online Transactional and Analytical Graph Workloads to Hundreds of Thousands of Cores
Graph databases (GDBs) are crucial in academic and industry applications...
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05/08/2023
TAPS: Connecting Certified and Adversarial Training
Training certifiably robust neural networks remains a notoriously hard p...
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03/09/2023
Efficient Certified Training and Robustness Verification of Neural ODEs
Neural Ordinary Differential Equations (NODEs) are a novel neural archit...
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12/12/2022
Prompting Is Programming: A Query Language For Large Language Models
Large language models have demonstrated outstanding performance on a wid...
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11/16/2022
Prompt Tuning for Parameter-efficient Medical Image Segmentation
Neural networks pre-trained on a self-supervision scheme have become the...
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10/27/2022
Private and Reliable Neural Network Inference
Reliable neural networks (NNs) provide important inference-time reliabil...
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10/10/2022
Certified Training: Small Boxes are All You Need
We propose the novel certified training method, SABR, which outperforms ...
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05/27/2022
(De-)Randomized Smoothing for Decision Stump Ensembles
Tree-based models are used in many high-stakes application domains such ...
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04/01/2022
Robust and Accurate – Compositional Architectures for Randomized Smoothing
Randomized Smoothing (RS) is considered the state-of-the-art approach to...
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10/14/2021
Effective Certification of Monotone Deep Equilibrium Models
Monotone Operator Equilibrium Models (monDEQs) represent a class of mode...
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09/01/2021
Shared Certificates for Neural Network Verification
Existing neural network verifiers compute a proof that each input is han...
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07/01/2021
Scalable Certified Segmentation via Randomized Smoothing
We present a new certification method for image and point cloud segmenta...
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06/13/2021
Boosting Randomized Smoothing with Variance Reduced Classifiers
Randomized Smoothing (RS) is a promising method for obtaining robustness...
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10/28/2020
Substream-Centric Maximum Matchings on FPGA
Developing high-performance and energy-efficient algorithms for maximum ...
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08/05/2020
Fully Automated and Standardized Segmentation of Adipose Tissue Compartments by Deep Learning in Three-dimensional Whole-body MRI of Epidemiological Cohort Studies
Purpose: To enable fast and reliable assessment of subcutaneous and visc...
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02/27/2020
Certification of Semantic Perturbations via Randomized Smoothing
We introduce a novel certification method for parametrized perturbations...
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02/24/2020
Learning Certified Individually Fair Representations
To effectively enforce fairness constraints one needs to define an appro...
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12/29/2019
Practice of Streaming and Dynamic Graphs: Concepts, Models, Systems, and Parallelism
Graph processing has become an important part of various areas of comput...
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11/03/2019
Online Robustness Training for Deep Reinforcement Learning
In deep reinforcement learning (RL), adversarial attacks can trick an ag...
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10/20/2019
Demystifying Graph Databases: Analysis and Taxonomy of Data Organization, System Designs, and Graph Queries
Graph processing has become an important part of multiple areas of compu...
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06/17/2018