Ruby Sedgwick
@ruby-sedgwick.bsky.social
Research Scientist at Xyme interested in Bayesian machine learning for biotechnology applications & causality. Previously a postdoc at Imperial College London.
Reposted by Ruby Sedgwick
Trying to train RNNs in a biol plausible (local) way? Well, try our new method using predictive alignment. Paper just out in Nat. Com. Toshitake Asabuki deserves all the credit!
www.nature.com/articles/s41...
www.nature.com/articles/s41...
www.nature.com
July 23, 2025 at 12:10 PM
Trying to train RNNs in a biol plausible (local) way? Well, try our new method using predictive alignment. Paper just out in Nat. Com. Toshitake Asabuki deserves all the credit!
www.nature.com/articles/s41...
www.nature.com/articles/s41...
Excited to be presenting this work at ICML!
Our Bayesian method allows for causal discovery using more flexible assumptions that better reflect real-world data.
Come chat to us: Tues 4:30pm East Exhibition Hall A-B E-1912
Paper: arxiv.org/abs/2411.10154
Our Bayesian method allows for causal discovery using more flexible assumptions that better reflect real-world data.
Come chat to us: Tues 4:30pm East Exhibition Hall A-B E-1912
Paper: arxiv.org/abs/2411.10154
July 11, 2025 at 9:24 AM
Excited to be presenting this work at ICML!
Our Bayesian method allows for causal discovery using more flexible assumptions that better reflect real-world data.
Come chat to us: Tues 4:30pm East Exhibition Hall A-B E-1912
Paper: arxiv.org/abs/2411.10154
Our Bayesian method allows for causal discovery using more flexible assumptions that better reflect real-world data.
Come chat to us: Tues 4:30pm East Exhibition Hall A-B E-1912
Paper: arxiv.org/abs/2411.10154
New paper at #AISTATS2025: Weighted-Sum of Gaussian Process Latent Variable Models (WS-GPLVM)
We tackle a core challenge in signal separation where pure components vary nonlinearly across samples using latent variable Gaussian processes. (1/8)
We tackle a core challenge in signal separation where pure components vary nonlinearly across samples using latent variable Gaussian processes. (1/8)
May 5, 2025 at 4:06 PM
New paper at #AISTATS2025: Weighted-Sum of Gaussian Process Latent Variable Models (WS-GPLVM)
We tackle a core challenge in signal separation where pure components vary nonlinearly across samples using latent variable Gaussian processes. (1/8)
We tackle a core challenge in signal separation where pure components vary nonlinearly across samples using latent variable Gaussian processes. (1/8)