Delve into the cutting edge of Artificial Intelligence with our dedicated Research Paper section.
Multilevel CNNs for Parametric PDEs Cosmas Heiß, Ingo Gühring, Martin Eigel; 24(373):1−42, 2023. Abstract We combine concepts from multilevel solvers for partial differential equations (PDEs) with neural network based deep learning and propose a new methodology for the efficient numerical…
![Most discriminative stimuli for functional cell type identification. (arXiv:2401.05342v1 [q-bio.NC])](http://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png "Most discriminative stimuli for functional cell type identification. (arXiv:2401.05342v1 [q-bio.NC])"){kind=logo}
Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure…
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Robustness certification, which aims to formally certify the predictions of neural networks against adversarial inputs, has become an integral part of important tool for safety-critical applications. Despite considerable progress, existing certification methods are limited to elementary architectures, such as convolutional…
Assessing student’s answers and in particular natural language answers is a crucial challenge in the field of education. Advances in machine learning, including transformer-based models such as Large Language Models(LLMs), have led to significant progress in various natural language tasks.…
In this work, we propose a denoising diffusion generative model (DDGM) trained with healthy electrocardiogram (ECG) data that focuses on ECG morphology and inter-lead dependence. Our results show that this innovative generative model can successfully generate realistic ECG signals. Furthermore,…
Densely Connected G-invariant Deep Neural Networks with Signed Permutation Representations Devanshu Agrawal, James Ostrowski; 24(370):1−40, 2023. Abstract We introduce and investigate, for finite groups $G$, $G$-invariant deep neural network ($G$-DNN) architectures with ReLU activation that are densely connected–i.e., include all…
[Submitted on 8 Jan 2024] Download a PDF of the paper titled Exploring Attack Resilience in Distributed Platoon Controllers with Model Predictive Control, by Tashfique Hasnine Choudhury Download PDF Abstract:The extensive use of distributed vehicle platoon controllers has resulted in…
Graph clustering algorithms with autoencoder structures have recently gained popularity due to their efficient performance and low training cost. However, for existing graph autoencoder clustering algorithms based on GCN or GAT, not only do they lack good generalization ability, but…
In recent years, the recognition of free-hand sketches has remained a popular task. However, in some special fields such as the military field, free-hand sketches are difficult to sample on a large scale. Common data augmentation and image generation techniques…
Prior work on English monolingual retrieval has shown that a cross-encoder trained using a large number of relevance judgments for query-document pairs can be used as a teacher to train more efficient, but similarly effective, dual-encoder student models. Applying a…
In this work, we provide a simulation algorithm to simulate from a (multivariate) characteristic function, which is only accessible in a black-box format. We construct a generative neural network, whose loss function exploits a specific representation of the Maximum-Mean-Discrepancy metric…