324 citations · 1.4k across the 135 of their papers we have counts for
15 papers · 1 filter
Learning Interpretable Queries for Explainable Image Classification with Information Pursuit
Stefan Kolek, Aditya Chattopadhyay, Kwan Ho Ryan Chan +3
Information Pursuit (IP) is an explainable prediction algorithm that greedily selects a sequence of interpretable queries about the data in order of information gain, updating its…
SuperHF: Supervised Iterative Learning from Human Feedback
Gabriel Mukobi, Peter Chatain, Su Fong +4
While large language models demonstrate remarkable capabilities, they often present challenges in terms of safety, alignment with human values, and stability during training. Here,…
The First Pathloss Radio Map Prediction Challenge
Çağkan Yapar, Fabian Jaensch, Ron Levie +2
To foster research and facilitate fair comparisons among recently proposed pathloss radio map prediction methods, we have launched the ICASSP 2023 First Pathloss Radio Map Predicti…
Learning-based adaption of robotic friction models
Philipp Scholl, Maged Iskandar, Sebastian Wolf +5
In the Fourth Industrial Revolution, wherein artificial intelligence and the automation of machines occupy a central role, the deployment of robots is indispensable. However, the m…
ParFam -- (Neural Guided) Symbolic Regression Based on Continuous Global Optimization
Philipp Scholl, Katharina Bieker, Hillary Hauger +1
The problem of symbolic regression (SR) arises in many different applications, such as identifying physical laws or deriving mathematical equations describing the behavior of finan…
Expressivity of Spiking Neural Networks
Manjot Singh, Adalbert Fono, Gitta Kutyniok
The synergy between spiking neural networks and neuromorphic hardware holds promise for the development of energy-efficient AI applications. Inspired by this potential, we revisit…
Neural Poisson Surface Reconstruction: Resolution-Agnostic Shape Reconstruction from Point Clouds
Hector Andrade-Loarca, Julius Hege, Daniel Cremers +1
We introduce Neural Poisson Surface Reconstruction (nPSR), an architecture for shape reconstruction that addresses the challenge of recovering 3D shapes from points. Traditional de…
Reliable AI: Does the Next Generation Require Quantum Computing?
Aras Bacho, Holger Boche, Gitta Kutyniok
In this survey, we aim to explore the fundamental question of whether the next generation of artificial intelligence requires quantum computing. Artificial intelligence is increasi…
Sumformer: Universal Approximation for Efficient Transformers
Silas Alberti, Niclas Dern, Laura Thesing +1
Natural language processing (NLP) made an impressive jump with the introduction of Transformers. ChatGPT is one of the most famous examples, changing the perception of the possibil…
On the Interplay of Subset Selection and Informed Graph Neural Networks
Niklas Breustedt, Paolo Climaco, Jochen Garcke +5
Machine learning techniques paired with the availability of massive datasets dramatically enhance our ability to explore the chemical compound space by providing fast and accurate…
Learning optimal controllers: a dynamical motion primitive approach
Hugo T. M. Kussaba, Abdalla Swikir, Fan Wu +3
Real-time computation of optimal control is a challenging problem and, to solve this difficulty, many frameworks proposed to use learning techniques to learn (possibly sub-optimal)…
Neural (Tangent Kernel) Collapse
Mariia Seleznova, Dana Weitzner, Raja Giryes +2
This work bridges two important concepts: the Neural Tangent Kernel (NTK), which captures the evolution of deep neural networks (DNNs) during training, and the Neural Collapse (NC)…
A Fractional Graph Laplacian Approach to Oversmoothing
Sohir Maskey, Raffaele Paolino, Aras Bacho +1
Graph neural networks (GNNs) have shown state-of-the-art performances in various applications. However, GNNs often struggle to capture long-range dependencies in graphs due to over…
Graph Scattering beyond Wavelet Shackles
Christian Koke, Gitta Kutyniok
This work develops a flexible and mathematically sound framework for the design and analysis of graph scattering networks with variable branching ratios and generic functional calc…
Computability of Optimizers
Yunseok Lee, Holger Boche, Gitta Kutyniok
Optimization problems are a staple of today's scientific and technical landscape. However, at present, solvers of such problems are almost exclusively run on digital hardware. Usin…