Frederik Wenkel

25 papers A* 5Misc 1Journal 16Unranked 3
YearRankTypeTitle / Venue / Authors
2026 J jnl
Can Computational Reducibility Lead to Transferable Models for Graph Combinatorial Optimization?
CoRR
Semih Cantürk, Thomas Sabourin, Frederik Wenkel, Michael Perlmutter, Guy Wolf
2025 J jnl
TxPert: Leveraging Biochemical Relationships for Out-of-Distribution Transcriptomic Perturbation Prediction.
CoRR
Frederik Wenkel, Wilson Tu, Cassandra Masschelein, Hamed Shirzad, Cian Eastwood, Shawn Whitfield, Ihab Bendidi, Craig Russell, Liam Hodgson, Yassir El Mesbahi, Jiarui Ding, Marta M. Fay, Berton Earnshaw, Emmanuel Noutahi, Alisandra Kaye Denton
2024 A* conf
How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval.
NeurIPS
Philip Fradkin, Puria Azadi Moghadam, Karush Suri, Frederik Wenkel, Ali Bashashati, Maciej Sypetkowski, Dominique Beaini
2024 J jnl
How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval.
CoRR
Philip Fradkin, Puria Azadi, Karush Suri, Frederik Wenkel, Ali Bashashati, Maciej Sypetkowski, Dominique Beaini
2024 J jnl
Learnable Filters for Geometric Scattering Modules.
IEEE Trans. Signal Process.
Alexander Tong, Frederik Wenkel, Dhananjay Bhaskar, Kincaid MacDonald, Jackson D. Grady, Michael Perlmutter, Smita Krishnaswamy, Guy Wolf
2024 A* conf
On the Scalability of GNNs for Molecular Graphs.
NeurIPS
Maciej Sypetkowski, Frederik Wenkel, Farimah Poursafaei, Nia Dickson, Karush Suri, Philip Fradkin, Dominique Beaini
2024 J jnl
On the Scalability of GNNs for Molecular Graphs.
CoRR
Maciej Sypetkowski, Frederik Wenkel, Farimah Poursafaei, Nia Dickson, Karush Suri, Philip Fradkin, Dominique Beaini
2024 A* conf
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets.
ICLR
Dominique Beaini, Shenyang Huang, Joao Alex Cunha, Zhiyi Li, Gabriela Moisescu-Pareja, Oleksandr Dymov, Samuel Maddrell-Mander, Callum McLean, Frederik Wenkel, Luis Müller, Jama Hussein Mohamud, Ali Parviz, Michael Craig, Michal Koziarski, Jiarui Lu, Zhaocheng Zhu, Cristian Gabellini, Kerstin Klaser, Josef Dean, Cas Wognum, Maciej Sypetkowski, Guillaume Rabusseau, Reihaneh Rabbany, Jian Tang, Christopher Morris, Mirco Ravanelli, Guy Wolf, Prudencio Tossou, Hadrien Mary, Therence Bois, Andrew W. Fitzgibbon, Blazej Banaszewski, Chad Martin, Dominic Masters
2024 J jnl
Towards a General GNN Framework for Combinatorial Optimization.
CoRR
Frederik Wenkel, Semih Cantürk, Michael Perlmutter, Guy Wolf
2024 — conf
Towards a General Recipe for Combinatorial Optimization With Multi-Filter GNNs.
LoG
Frederik Wenkel, Semih Cantürk, Stefan Horoi, Michael Perlmutter, Guy Wolf
2023 — conf
Inferring Dynamic Regulatory Interaction Graphs From Time Series Data With Perturbations.
LoG
Dhananjay Bhaskar, Daniel Sumner Magruder, Matheo Morales, Edward De Brouwer, Aarthi Venkat, Frederik Wenkel, Guy Wolf, Smita Krishnaswamy
2023 J jnl
Inferring dynamic regulatory interaction graphs from time series data with perturbations.
CoRR
Dhananjay Bhaskar, Daniel Sumner Magruder, Edward De Brouwer, Aarthi Venkat, Frederik Wenkel, Guy Wolf, Smita Krishnaswamy
2023 J jnl
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets.
CoRR
Dominique Beaini, Shenyang Huang, Joao Alex Cunha, Zhiyi Li, Gabriela Moisescu-Pareja, Oleksandr Dymov, Samuel Maddrell-Mander, Callum McLean, Frederik Wenkel, Luis Müller, Jama Hussein Mohamud, Ali Parviz, Michael Craig, Michal Koziarski, Jiarui Lu, Zhaocheng Zhu, Cristian Gabellini, Kerstin Klaser, Josef Dean, Cas Wognum, Maciej Sypetkowski, Guillaume Rabusseau, Reihaneh Rabbany, Jian Tang, Christopher Morris, Ioannis Koutis, Mirco Ravanelli, Guy Wolf, Prudencio Tossou, Hadrien Mary, Therence Bois, Andrew W. Fitzgibbon, Blazej Banaszewski, Chad Martin, Dominic Masters
2022 J jnl
Can Hybrid Geometric Scattering Networks Help Solve the Maximal Clique Problem?
CoRR
Yimeng Min, Frederik Wenkel, Michael Perlmutter, Guy Wolf
2022 A* conf
Can Hybrid Geometric Scattering Networks Help Solve the Maximum Clique Problem?
NeurIPS
Yimeng Min, Frederik Wenkel, Michael Perlmutter, Guy Wolf
2022 J jnl
Learnable Filters for Geometric Scattering Modules.
CoRR
Alexander Tong, Frederik Wenkel, Dhananjay Bhaskar, Kincaid MacDonald, Jackson D. Grady, Michael Perlmutter, Smita Krishnaswamy, Guy Wolf
2022 J jnl
Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks.
CoRR
Frederik Wenkel, Yimeng Min, Matthew J. Hirn, Michael Perlmutter, Guy Wolf
2022 — conf
Taxonomy of Benchmarks in Graph Representation Learning.
LoG
Renming Liu, Semih Cantürk, Frederik Wenkel, Sarah McGuire, Xinyi Wang, Anna Little, Leslie O'Bray, Michael Perlmutter, Bastian Rieck, Matthew J. Hirn, Guy Wolf, Ladislav Rampásek
2022 J jnl
Taxonomy of Benchmarks in Graph Representation Learning.
CoRR
Renming Liu, Semih Cantürk, Frederik Wenkel, Dylan Sandfelder, Devin Kreuzer, Anna Little, Sarah McGuire, Leslie O'Bray, Michael Perlmutter, Bastian Rieck, Matthew J. Hirn, Guy Wolf, Ladislav Rampásek
2021 Misc conf
Geometric Scattering Attention Networks.
ICASSP
Yimeng Min, Frederik Wenkel, Guy Wolf
2021 J jnl
Towards a Taxonomy of Graph Learning Datasets.
CoRR
Renming Liu, Semih Cantürk, Frederik Wenkel, Dylan Sandfelder, Devin Kreuzer, Anna Little, Sarah McGuire, Leslie O'Bray, Michael Perlmutter, Bastian Rieck, Matthew J. Hirn, Guy Wolf, Ladislav Rampásek
2020 J jnl
Data-Driven Learning of Geometric Scattering Networks.
CoRR
Alexander Tong, Frederik Wenkel, Kincaid MacDonald, Smita Krishnaswamy, Guy Wolf
2020 J jnl
Geometric Scattering Attention Networks.
CoRR
Yimeng Min, Frederik Wenkel, Guy Wolf
2020 A* conf
Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks.
NeurIPS
Yimeng Min, Frederik Wenkel, Guy Wolf
2020 J jnl
Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks.
CoRR
Yimeng Min, Frederik Wenkel, Guy Wolf