205 citations · 440 across the 14 of their papers we have counts for
14 papers
The impact of the AI revolution on asset management
Michael Kopp
Recent progress in deep learning, a special form of machine learning, has led to remarkable capabilities machines can now be endowed with: they can read and understand free flowing…
Dsfer-Net: A Deep Supervision and Feature Retrieval Network for Bitemporal Change Detection Using Modern Hopfield Networks
Shizhen Chang, Michael Kopp, Pedram Ghamisi +1
Change detection, an essential application for high-resolution remote sensing images, aims to monitor and analyze changes in the land surface over time. Due to the rapid increase i…
Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors
Moritz Neun, Christian Eichenberger, Henry Martin +27
The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem…
Metropolitan Segment Traffic Speeds from Massive Floating Car Data in 10 Cities
Moritz Neun, Christian Eichenberger, Yanan Xin +7
Traffic analysis is crucial for urban operations and planning, while the availability of dense urban traffic data beyond loop detectors is still scarce. We present a large-scale fl…
Profiling and Improving the PyTorch Dataloader for high-latency Storage: A Technical Report
Ivan Svogor, Christian Eichenberger, Markus Spanring +2
A growing number of Machine Learning Frameworks recently made Deep Learning accessible to a wider audience of engineers, scientists, and practitioners, by allowing straightforward…
Sketched Multi-view Subspace Learning for Hyperspectral Anomalous Change Detection
Shizhen Chang, Michael Kopp, Pedram Ghamisi
In recent years, multi-view subspace learning has been garnering increasing attention. It aims to capture the inner relationships of the data that are collected from multiple sourc…
Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network
Mario Krenn, Lorenzo Buffoni, Bruno Coutinho +13
A tool that could suggest new personalized research directions and ideas by taking insights from the scientific literature could significantly accelerate the progress of science. A…
Txt2Img-MHN: Remote Sensing Image Generation from Text Using Modern Hopfield Networks
Yonghao Xu, Weikang Yu, Pedram Ghamisi +2
The synthesis of high-resolution remote sensing images based on text descriptions has great potential in many practical application scenarios. Although deep neural networks have ac…
Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection
Omid Ghorbanzadeh, Yonghao Xu, Pedram Ghamisi +2
This study introduces \textit{Landslide4Sense}, a reference benchmark for landslide detection from remote sensing. The repository features 3,799 image patches fusing optical layers…
Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes
Christian Eichenberger, Moritz Neun, Henry Martin +34
The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggrega…
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP
Andreas Fürst, Elisabeth Rumetshofer, Johannes Lehner +8
CLIP yielded impressive results on zero-shot transfer learning tasks and is considered as a foundation model like BERT or GPT3. CLIP vision models that have a rich representation a…
A remark on a paper of Krotov and Hopfield [arXiv:2008.06996]
Fei Tang, Michael Kopp
In their recent paper titled "Large Associative Memory Problem in Neurobiology and Machine Learning" [arXiv:2008.06996] the authors gave a biologically plausible microscopic theory…
Cross-Domain Few-Shot Learning by Representation Fusion
Thomas Adler, Johannes Brandstetter, Michael Widrich +5
In order to quickly adapt to new data, few-shot learning aims at learning from few examples, often by using already acquired knowledge. The new data often differs from the previous…
A physical protocol for observers near the boundary to obtain bulk information in quantum gravity
Chandramouli Chowdhury, Olga Papadoulaki, Suvrat Raju
We consider a set of observers who live near the boundary of global AdS, and are allowed to act only with simple low-energy unitaries and make measurements in a small interval of t…