I am truly excited to share our latest work with @mscherbela.bsky.social, Philipp Grohs, and @guennemann on "Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems"!
arxiv.org/abs/2504.06087
Real data is noisy but HiPPO assumes it's clean. Our UnHiPPO initialization resists noise with implicit Kalman filtering and makes SSMs robust without architecture changes.
Learn more at our #ICML poster: Thu 11am E-2409
Paper: openreview.net/forum?id=U8G...
Code: github.com/martenlienen...
We present finite-range embeddings (FiRE), a novel wave function ansatz for accurate large-scale ab-initio electronic structure calculations. Compared to contemporary neural-network wave functions, Fi...
Excited to announce our #ICLR2025 spotlight work deriving the first exact certificates for neural networks against label poisoning 🎉. Joint work with @maha-saba.bsky.social, Stephan Günnemann & Debarghya Ghoshdastidar. For details, check out the thread below👇 or our paper arxiv.org/abs/2412.00537.
Nicholas Gao
Marten Lienen
Machine learning models are highly vulnerable to label flipping, i.e., the adversarial modification (poisoning) of training labels to compromise performance. Thus, deriving robustness certificates is ...
Super happy & honored that our work on certifying NNs against poisoning won the Best Paper Award at AdvML-Frontiers@ #NeurIPS2024. Come by our poster 10:40am-12&4-5pm (or talk) today :)
Joint work w/ Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar & Stephan Günnemann
L: arxiv.org/pdf/2407.10867
Excited to present our work on Neural Pfaffians at #NeurIPS.
🗣️ Oral: Friday 3:30pm, East Ballroom A, B
📊 Post: Friday 4:30pm - 7:30pm, East Exhibit Hall A-C #3600
📝 Paper: openreview.net/forum?id=HRk...
Happy to chat!
Lukas Gosch
Nicholas Gao
🎉Excited to announce our #ICLR2025 Spotlight!
🚀 @lukasgosch.bsky.social and I will be presenting our paper on the first exact certificate against label poisoning for neural nets and graph neural nets. Joint work with Stephan Guennemann and Debarghya Ghoshdastidar.
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