We're excited to have a number of papers by co-authors at Prescient and @genentech Research Bio AI/ML at #ICLR2023, #ICML23, and #AISTATS2023 spanning topics from basic ML to applications in drug discovery and even on the properties of black holes! 🌌 Check out below 👇 1/9
"Towards Understanding and Improving GFlowNet Training" #ICML23
@maxwshen, @folinoid, @EhsanHRA, @loukasa_tweet, @kchonyc, @tbyanc
2/9
"Few-shot Learning of Abstract Geometric Reasoning by Infusing Lattice Symmetry Priors in Attention Mechanisms" #ICML23
Matti Atzeni, @mrinmayasachan, @loukasa_tweet
3/9
"Bayesian Optimization with Conformal Prediction Sets" #AISTATS2023
arxiv.org/abs/2210.12496
@samuel_stanton_, Wesley Maddox, @andrewgwils
4/9
"Retrospective Uncertainties for Deep Models using Vine Copulas" #AISTATS2023
arxiv.org/abs/2302.12606
@tagasovska, @Firat_Ozdemir, @AxelBrando_
5/9
"Latent SDEs for Modelling Quasar Variability and Inferring Black Hole Properties" (Spotlight) #ICLR2023 #Physics4ML
openreview.net/pdf?id=x2NOLHC…
@joshua_fagin, @jiwoncpark, Henry Best, @saavikford, Matthew J Graham, Ashley Villar, @cosmo_shirley, James Chan, @matt_of_earth
6/9
"Learning protein family manifolds with smoothed energy-based models" (Spotlight) #ICLR2023 #Physics4ML and @MLDD_Workshop
openreview.net/pdf?id=IilnB8j…
@nc_frey, @dabkiel1, Joseph Kleinhenz, et al.
7/9
"SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers" #ICLR2023 @MLDD_Workshop
arxiv.org/abs/2302.07754
Michael Maser, @jiwoncpark, @joshualin24, @jaehyeon_lee_ml, @nc_frey, @amw_stanford
8/9
"Improving Graph Generation by Restricting Graph Bandwidth" #ICLR2023 @MLDD_Workshop
arxiv.org/abs/2301.10857
Nathaniel Diamant, @alexmtseng, @kangway, @tbyanc, @gabo_scalia
9/9
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