Domain invariant representation learning with domain density transformations by Nguyen, AT, Tran, T, Gal, Y, Baydin, AG

Toward 3D retrieval of exoplanet atmospheres: assessing thermochemical equilibrium estimation methods by Himes, MD, Harrington, J, Baydin, AG

KL guided domain adaptation by Nguyen, AT, Tran, T, Gal, Y, Torr, PHS, Baydin, AG

KL guided domain adaptation by Nguyen, AT, Tran, T, Gal, Y, Torr, P, Baydin, AG

Automatic differentiation in machine learning: A survey by Baydin, AG, Pearlmutter, BA, Radul, AA, Siskind, JM

Attention for inference compilation by Harvey, W, Munk, A, Baydin, AG, Bergholm, A, Wood, F

AutoSimulate: (Quickly) learning synthetic data generation by Behl, HS, Baydin, AG, Gal, R, Torr, PHS, Vineet, V

Estimating the impact of coordinated inauthentic behavior on content recommendations in social networks by Mehta, S, State, B, Bonneau, R, Nagler, J, Torr, P, Baydin, AG

Technology readiness levels for machine learning systems by Lavin, A, Gilligan-Lee, CM, Visnjic, A, Ganju, S, Newman, D, Ganguly, S, Lange, D, Baydin, AG, Baydin, AG, Sharma, A, Gibson, A, Zheng, S, Xing, EP, Mattmann, C, Parr, J, Gal, Y

Exploring the limits of synthetic creation of solar EUV images via image-to-image translation by Salvatelli, V, dos Santos, LFG, Bose, S, Neuberg, B, Cheung, MCM, Janvier, M, Jin, M, Gal, Y, Baydin, AG

Towards global flood mapping onboard low cost satellites with machine learning by Mateo-Garcia, G, Veitch-Michaelis, J, Smith, L, Oprea, SV, Schumann, G, Gal, Y, Baydin, AG, Backes, D

PyATMOS: a scalable grid of hypothetical planetary atmospheres by Chopra, A, Bell, AC, Fawcett, W, Talebi, R, Angerhausen, D, Baydin, AG, Berea, A, Cabrol, NA, Kempes, C, Mascaro, M

Multichannel autocalibration for the Atmospheric Imaging Assembly using machine learning by Dos Santos, LFG, Bose, S, Salvatelli, V, Neuberg, B, Cheung, MCM, Janvier, M, Jin, M, Gal, Y, Boerner, P, Baydin, AG

Accurate machine-learning atmospheric retrieval via a neural-network surrogate model for radiative transfer by Himes, MD, Harrington, J, Cobb, AD, Baydin, AG, Soboczenski, F, O'Beirne, MD, Zorzan, S, Wright, DC, Scheffer, Z, Domagal-Goldman, SD, Arney, GN

Technology readiness levels for machine learning systems by Lavin, A, Gilligan-Lee, CM, Visnjic, A, Ganju, S, Newman, D, Baydin, AG, Ganguly, S, Lange, D, Sharma, A, Zheng, S, Xing, EP, Gibson, A, Parr, J, Mattmann, C, Gal, Y

Efficient probabilistic inference in the quest for physics beyond the standard model by Baydin, AG, Heinrich, L, Bhimji, W, Shao, L, Naderiparizi, S, Munk, A, Liu, J, Gram-Hansen, B, Louppe, G, Meadows, L, Toor, P, Lee, V, Prabhat, Cranmer, K, Wood, F

Etalumis: bringing probabilistic programming to scientific simulators at scale by Baydin, AG, Shao, L, Bhimji, W, Heinrich, L, Meadows, L, Liu, J, Munk, A, Naderiparizi, S, Gram-Hansen, B, Louppe, G, Ma, M, Zhao, X, Torr, PHS, Lee, V, Cranmer, K, Prabhat, Wood, F

Managing extreme AI risks amid rapid progress by Bengio, Y, Hinton, G, Yao, A, Song, D, Abbeel, P, Darrell, T, Harari, YN, Zhang, Y-Q, Xue, L, Shalev-Shwartz, S, Hadfield, G, Clune, J, Maharaj, T, Hutter, F, Baydin, AG, McIlraith, S, Gao, Q, Acharya, A, Krueger, D, Dragan, A, Torr, P, Russell, S, Kahneman, D, Brauner, J, Mindermann, S

Toward the end-to-end optimization of particle physics instruments with differentiable programming by Dorigo, T, Giammanco, A, Vischia, P, Aehle, M, Bawaj, M, Boldyrev, A, de Castro Manzano, P, Derkach, D, Donini, J, Edelen, A, Fanzago, F, Gauger, NR, Glaser, C, Baydin, AG, Heinrich, L, Keidel, R, Kieseler, J, Krause, C, Lagrange, M, Lamparth, M, Layer, L, Maier, G, Nardi, F, Pettersen, HES, Ramos, A, Ratnikov, F, Röhrich, D, de Austri, RR, del Árbol, PMR, Savchenko, O, Simpson, N, Strong, GC, Taliercio, A, Tosi, M, Ustyuzhanin, A, Zaraket, H