[PA51] Input Data Transformation Framework for Low-Voltage Model
[PA50] Spectral Data Augmentation for Single Domain Generalization
[PA49] Generating a Test Diffusion Model
[PA48] Composite Adversarial Attack Model Training for Neural Networks
[PA47] Joint Input Perturbation and Temperature Scaling for Neural Network Calibration
[PA46] Training a Pose Estimation Model to Determine Anatomy Keypoints in Images
[PA45] Amino Acid Sequence Infilling
[PA44] Detecting Actions in Video using Machine Learning and based on Bidirectional Feedback between Predicted Type and Predicted Extent
[PA43] Reprogrammable Federated Learning
[PA42] Certification-based Robust Training by Refining Decision Boundary
[PA41] Temporal Action Localization with Mutual Task Guidance
[PA40] Self-supervised semantic shift detection and alignment
[PA39] Neural capacitance: neural network selection via edge dynamics
[PA38] Protein Structure Prediction using Machine Learning
[PA37] Counterfactual Debiasing Inference for Compositional Action Recognition
[PA36] Image Grounding with Modularized Graph Attention Networks
[PA35] Compositional Action Machine Learning Mechanisms
[PA34] Determining Analytical Model Accuracy with Perturbation Response
[PA33] Model-Agnostic Input Transformation for Neural Networks
[PA32] Decentralized Policy Gradient Descent and Ascent for Safe Multi-agent Reinforcement Learning
[PA31] Embedding-Based Generative Model for Protein Design
[PA30] Distributed Adversarial Training for Robust Deep Neural Networks
[PA29] Generating Unsupervised Adversarial Examples for Machine Learning
[PA28] Self-supervised semantic shift detection and alignment
[PA27] Transfer learning with machine learning systems
[PA26] Summarizing Videos Via Side Information
[PA25] Detecting Trojan Neural Networks
[PA24] State-augmented Reinforcement Learning
[PA23] Query-based Molecule Optimization and Applications to Functional Molecule Discovery
[PA22] Efficient Search of Robust Accurate Neural Networks
[PA21] Arranging content on a user interface of a computing device
[PA20] Filtering artificial intelligence designed molecules for laboratory testing
[PA19] Training robust machine learning models
[PA18] Robustness-aware quantization for neural networks against weight perturbations
[PA17] Inducing Creativity in an Artificial Neural Network
[PA16] Interpretability-Aware Adversarial Attack and Defense Method for Deep Learnings
[PA15] Mitigating adversarial effects in machine learning systems
[PA14] Designing and folding structural proteins from the primary amino acid sequence
[PA13] Contrastive explanations for images with monotonic attribute functions
[PA12] Efficient and secure gradient-free black box optimization
[PA11] Explainable machine learning based on heterogeneous data
[PA10] Computational creativity based on a tunable creativity control function of a model
[PA9] Integrated noise generation for adversarial training
[PA8] Framework for Certifying a lower bound on a robustness level of convolutional neural networks
[PA7] Adversarial input identification using reduced precision deep neural networks
[PA6] Model agnostic contrastive explanations for structured data
[PA5] Contrastive explanations for interpreting deep neural networks
[PA4] Computational Efficiency in Symbolic Sequence Analytics Using Random Sequence Embeddings
[PA3] Graph similarity analytics
[PA2] Testing adversarial robustness of systems with limited access
[PA1] System and methods for automated detection, reasoning and recommendations for resilient cyber systems