332 citations
- IBM (United States)US24 papers
- Massachusetts Institute of TechnologyUS5 papers
- IBM Research - ZurichCH3 papers
- University of California, Los AngelesUS3 papers
- IBM Research - Thomas J. Watson Research CenterUS2 papers
- University of California, BerkeleyUS2 papers
- Austrian Academy of SciencesAT1 paper
- Berkeley CollegeUS1 paper
- Binghamton UniversityUS1 paper
- Boston Scientific (United States)US1 paper
- Brown UniversityUS1 paper
- California Institute of TechnologyUS1 paper
8 papers · 1 filter
PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences
Payel Das, Kahini Wadhawan, Oscar Chang +6
Given the emerging global threat of antimicrobial resistance, new methods for next-generation antimicrobial design are urgently needed. We report a peptide generation framework Pep…
Efficient Neural Network Robustness Certification with General Activation Functions
Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen +2
Finding minimum distortion of adversarial examples and thus certifying robustness in neural network classifiers for given data points is known to be a challenging problem. Neverthe…
Workload-aware Automatic Parallelization for Multi-GPU DNN Training
Sungho Shin, Youngmin Jo, Jungwook Choi +3
Deep neural networks (DNNs) have emerged as successful solutions for variety of artificial intelligence applications, but their very large and deep models impose high computational…
On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm
Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen +3
CLEVER (Cross-Lipschitz Extreme Value for nEtwork Robustness) is an Extreme Value Theory (EVT) based robustness score for large-scale deep neural networks (DNNs). In this paper, we…
An Upper Bound for Random Measurement Error in Causal Discovery
Tineke Blom, Anna Klimovskaia, Sara Magliacane +1
Causal discovery algorithms infer causal relations from data based on several assumptions, including notably the absence of measurement error. However, this assumption is most like…
Stakeholders in Explainable AI
Alun Preece, Dan Harborne, Dave Braines +2
There is general consensus that it is important for artificial intelligence (AI) and machine learning systems to be explainable and/or interpretable. However, there is no general c…