12 citations · 41 across the 13 of their papers we have counts for
12 papers · 1 filter
A Survey of Deep Learning: From Activations to Transformers
Johannes Schneider, Michalis Vlachos
Deep learning has made tremendous progress in the last decade. A key success factor is the large amount of architectures, layers, objectives, and optimization techniques. They incl…
Concept-based Adversarial Attacks: Tricking Humans and Classifiers Alike
Johannes Schneider, Giovanni Apruzzese
We propose to generate adversarial samples by modifying activations of upper layers encoding semantically meaningful concepts. The original sample is shifted towards a target sampl…
The learning phases in NN: From Fitting the Majority to Fitting a Few
Johannes Schneider
The learning dynamics of deep neural networks are subject to controversy. Using the information bottleneck (IB) theory separate fitting and compression phases have been put forward…
Domain Transformer: Predicting Samples of Unseen, Future Domains
Johannes Schneider
The data distribution commonly evolves over time leading to problems such as concept drift that often decrease classifier performance. Current techniques are not adequate for this…
Reflective-Net: Learning from Explanations
Johannes Schneider, Michalis Vlachos
We examine whether data generated by explanation techniques, which promote a process of self-reflection, can improve classifier performance. Our work is based on the idea that huma…
Explaining Neural Networks by Decoding Layer Activations
Johannes Schneider, Michalis Vlachos
We present a `CLAssifier-DECoder' architecture (\emph{ClaDec}) which facilitates the comprehension of the output of an arbitrary layer in a neural network (NN). It uses a decoder t…
Correlated Initialization for Correlated Data
Johannes Schneider
Spatial data exhibits the property that nearby points are correlated. This also holds for learnt representations across layers, but not for commonly used weight initialization meth…
Deceptive AI Explanations: Creation and Detection
Johannes Schneider, Christian Meske, Michalis Vlachos
Artificial intelligence (AI) comes with great opportunities but can also pose significant risks. Automatically generated explanations for decisions can increase transparency and fo…
Human-to-AI Coach: Improving Human Inputs to AI Systems
Johannes Schneider
Humans increasingly interact with Artificial intelligence(AI) systems. AI systems are optimized for objectives such as minimum computation or minimum error rate in recognizing and…
Personalization of Deep Learning
Johannes Schneider, Michail Vlachos
We discuss training techniques, objectives and metrics toward personalization of deep learning models. In machine learning, personalization addresses the goal of a trained model to…
Locality-Promoting Representation Learning
Johannes Schneider
This work investigates fundamental questions related to learning features in convolutional neural networks (CNN). Empirical findings across multiple architectures such as VGG, ResN…
Personalized explanation in machine learning: A conceptualization
Johanes Schneider, Joshua Handali
Explanation in machine learning and related fields such as artificial intelligence aims at making machine learning models and their decisions understandable to humans. Existing wor…