Search Results for author:
Found 14 papers, 7 papers with code
Smooth Deep Saliency
In this work, we investigate methods to reduce the noise in deep saliency maps coming from convolutional downsampling, with the purpose of explaining how a deep learning model detects tumors in scanned histological tissue samples.
Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation
Within our methodology, in order to quantify the sustainability awareness of these AI models, we propose a definition of the code's "green capacity", based on certain sustainability metrics.
Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time
In this work, we propose a fast and accurate method to reconstruct activations of classification and semantic segmentation networks by stitching them with a GAN generator utilizing a 1x1 convolution.
Improving Low-Resource Question Answering using Active Learning in Multiple Stages
Furthermore, they often yield very good performance but only in the domain they were trained on.
An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstruction
Deep image prior (DIP) was recently introduced as an effective unsupervised approach for image restoration tasks.
Conditional Invertible Neural Networks for Medical Imaging
Over the last years, deep learning methods have become an increasingly popular choice to solve tasks from the field of inverse problems.
Evolving Neuronal Plasticity Rules using Cartesian Genetic Programming
We formulate the search for phenomenological models of synaptic plasticity as an optimization problem.
Evolving to learn: discovering interpretable plasticity rules for spiking networks
Continuous adaptation allows survival in an ever-changing world.
Neurons and Cognition
ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents
We present ADVISER - an open-source, multi-domain dialog system toolkit that enables the development of multi-modal (incorporating speech, text and vision), socially-engaged (e. g. emotion recognition, engagement level prediction and backchanneling) conversational agents.
Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods
In this work, we investigate the application of deep learning methods for computed tomography in the context of having a low-data regime.
Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data
Neural networks have recently been established as a viable classification method for imaging mass spectrometry data for tumor typing.
The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods
Deep Learning approaches for solving Inverse Problems in imaging have become very effective and are demonstrated to be quite competitive in the field.
ADVISER: A Dialog System Framework for Education \& Research
In this paper, we present ADVISER - an open source dialog system framework for education and research purposes.
Normalizing flows for novelty detection in industrial time series data
Flow-based deep generative models learn data distributions by transforming a simple base distribution into a complex distribution via a set of invertible transformations.
