Decreasing Model Stealing Querying for Black Box Adversarial Attacks

Empirical Investigation of Learning Curves: Assessing Convexity Characteristics

Microtubule Detection and Instance Segmentation using Deep Convolutional Neural Networks

Deep Learning the Dynamics of Mechanical Systems

Data-Driven Empirical Analysis of Correlation-Based Feature Selection Techniques

Loss functions and neural networks: Comparing different loss functions for NLP neural networks

Who Cares About Fairness: How Background Influences the Way Practitioners Consider Machine Learning Harms

Estimatic

Learning a Latent Representation of the Opponent in Automated Negotiation

Black-box Adversarial Attacks using Substitute models: Effects of Data Distributions on Sample Transferability

Optimize the indescribable: A Look at the Unification between Machine Learning and Optimization

Bridging the Knowledge Gap: Identifying Essential Machine Learning Concepts for Effective Progression in Follow-Up Courses

Tailoring Attacks To Federated Continual Learning Models

Can Invariant Risk Minimization resist the temptation of learning spurious correlations?

Comparison of the usage of Fairness Toolkits amongst practitioners:AIF360 and Fairlearn

Algal Bloom Forecasting using Remote Sensing: Discovering the most predictive data modalities for Algal Bloom Forecasting

Transferring a Segmentation Task Between Real and Synthetic Data

Parallel cost-aware optimization of multidimensional black-box functions

Identifying biological markers in the gut microbiome associated with celiac disease using machine learning

Implementing beam shape variations in a deep learning based proton dose calculation algorithm