Brooks Paige - RankMe

77 papers A* 15A 7B 1Journal 45Unranked 9

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	CACHE Challenge #3: Targeting the Nsp3 Macrodomain of SARS-CoV-2. J. Chem. Inf. Model. Oleksandra Herasymenko, Madhushika Silva, Galen J. Correy, Abd Al-Aziz A. Abu-Saleh, Suzanne Ackloo, Cheryl H. Arrowsmith, Alan Ashworth, Fuqiang Ban, Hartmut Beck, Kevin P. Bishop, Hugo J. Bohórquez, Albina Bolotokova, Marko Breznik, Irene Chau, Yu Chen, Artem Cherkasov, Wim Dehaen, Dennis Della Corte, Katrin Denzinger, Niklas Piet Doering, Kristina Edfeldt, Aled M. Edwards, Darren Fayne, Francesco Gentile, Elisa Gibson, Ozan Gökdemir, Anders Gunnarsson, Judith Guenther, John J. Irwin, Jan Halborg Jensen, Rachel J. Harding, Alexander Hillisch, Laurent Hoffer, Anders Hogner, Ashley Hutchinson, Shubhangi Kandwal, Andrea Karlova, Kushal Koirala, Sergei Kotelnikov, Dima Kozakov, Juyong Lee, Soowon Lee, Uta F. Lessel, Sijie Liu, Xuefeng Liu, Peter Loppnau, Jens Meiler, Rocco Moretti, Yurii S. Moroz, Charuvaka Muvva, Tudor I. Oprea, Brooks Paige, Amit Pandit, Keunwan Park, Gennady Poda, Mykola V. Protopopov, Vera Pütter, Rahul Ravichandran, Didier Rognan, Edina Rosta, Yogesh Sabnis, Thomas Scott, Almagul Seitova, Purshotam Sharma, François Sindt, Minghu Song, Casper Steinmann, Rick Stevens, Valerij Talagayev, Valentyna V. Tararina, Olga O. Tarkhanova, Damon Tingey, John F. Trant, Dakota Treleaven, Alexander Tropsha, W. Patrick Walters, Jude Wells, Yvonne Westermaier, Gerhard Wolber, Lars Wortmann, Shuangjia Zheng, James S. Fraser, Matthieu Schapira
2025	J	`jnl`	AbRank: A Benchmark Dataset and Metric-Learning Framework for Antibody-Antigen Affinity Ranking. CoRR Chunan Liu, Aurélien Pélissier, Yanjun Shao, Lilian Denzler, Andrew C. R. Martin, Brooks Paige, Mariia Rodriguez Martinez
2025	J	`jnl`	Bayesian Optimization for Molecules Should Be Pareto-Aware. CoRR Anabel Yong, Austin Tripp, Layla Hosseini-Gerami, Brooks Paige
2025	J	`jnl`	CACHE Challenge #2: Targeting the RNA Site of the SARS-CoV-2 Helicase Nsp13. J. Chem. Inf. Model. Oleksandra Herasymenko, Madhushika Silva, Abd Al-Aziz A. Abu-Saleh, Ayaz Ahmad, Jesus Alvarado-Huayhuaz, Oscar Emilio Arrúa Arce, Roly J. Armstrong, Cheryl H. Arrowsmith, Kelly E. Bachta, Hartmut Beck, Dénes Berta, Mateusz K. Bieniek, Vincent Blay, Albina Bolotokova, Philip E. Bourne, Marko Breznik, Peter J. Brown, Aaron D. G. Campbell, Emanuele Carosati, Irene Chau, Daniel J. Cole, Ben Cree, Wim Dehaen, Katrin Denzinger, Karina dos Santos Machado, Ian Dunn, Prasannavenkatesh Durai, Kristina Edfeldt, Aled M. Edwards, Darren Fayne, Daniel Felfoldi, Kallie Friston, Pegah Ghiabi, Elisa Gibson, Judith Guenther, Anders Gunnarsson, Alexander Hillisch, Douglas R. Houston, Jan Halborg Jensen, Rachel J. Harding, Kate S. Harris, Laurent Hoffer, Anders Hogner, Joshua T. Horton, Scott Houliston, Judd F. Hultquist, Ashley Hutchinson, John J. Irwin, Marko Jukic, Shubhangi Kandwal, Andrea Karlova, Vittorio L. Katis, Ryan P. Kich, Dmitri Kireev, David Koes, Nicole L. Inniss, Uta F. Lessel, Sijie Liu, Peter Loppnau, Wei Lu, Sam Alexander Martino, Miles McGibbon, Jens Meiler, Akhila Mettu, Sam Money-Kyrle, Rocco Moretti, Yurii S. Moroz, Charuvaka Muvva, Joseph A. Newman, Leon Obendorf, Brooks Paige, Amit Pandit, Keunwan Park, Sumera Perveen, Rachael Pirie, Gennady Poda, Mykola V. Protopopov, Vera Pütter, Federico Ricci, Natalie J. Roper, Edina Rosta, Margarita Rzhetskaya, Yogesh Sabnis, Karla J. F. Satchell, Frederico Schmitt Kremer, Thomas Scott, Almagul Seitova, Casper Steinmann, Valerij Talagayev, Olga O. Tarkhanova, Natalie J. Tatum, Dakota Treleaven, Adriano Velasque Werhli, W. Patrick Walters, Xiaowen Wang, Jude Wells, Geoffrey Wells, Yvonne Westermaier, Gerhard Wolber, Lars Wortmann, Jixian Zhang, Zheng Zhao, Shuangjia Zheng, Matthieu Schapira
2025	J	`jnl`	Effects of Random Edge-Dropping on Over-Squashing in Graph Neural Networks. CoRR Jasraj Singh, Keyue Jiang, Brooks Paige, Laura Toni
2025	A*	`conf`	Right Now, Wrong Then: Non-Stationary Direct Preference Optimization under Preference Drift. ICML Seongho Son, William Bankes, Sayak Ray Chowdhury, Brooks Paige, Ilija Bogunovic
2025	J	`jnl`	SmilesT5: Domain-specific pretraining for molecular language models. CoRR Philip Spence, Brooks Paige, Anne Osbourn
2025	J	`jnl`	Time-varying Factor Augmented Vector Autoregression with Grouped Sparse Autoencoder. CoRR Yiyong Luo, Brooks Paige, Jim Griffin
2024	A*	`conf`	Analysing the Generalisation and Reliability of Steering Vectors. NeurIPS Daniel Tan, David Chanin, Aengus Lynch, Brooks Paige, Dimitrios Kanoulas, Adrià Garriga-Alonso, Robert Kirk
2024	J	`jnl`	Analyzing the Generalization and Reliability of Steering Vectors. CoRR Daniel Tan, David Chanin, Aengus Lynch, Dimitrios Kanoulas, Brooks Paige, Adrià Garriga-Alonso, Robert Kirk
2024	A*	`conf`	AsEP: Benchmarking Deep Learning Methods for Antibody-specific Epitope Prediction. NeurIPS Chunan Liu, Lilian Denzler, Yihong Chen, Andrew C. R. Martin, Brooks Paige
2024	J	`jnl`	AsEP: Benchmarking Deep Learning Methods for Antibody-specific Epitope Prediction. CoRR Chunan Liu, Lilian Denzler, Yihong Chen, Andrew C. R. Martin, Brooks Paige
2024	A	`conf`	Balancing Calibration and Performance: Stochastic Depth in Segmentation BNNs. BMVC Linghong Yao, Denis Hadjivelichkov, Andromachi Maria Delfaki, Yuanchang Liu, Brooks Paige, Dimitrios Kanoulas
2024	J	`jnl`	Can a Confident Prior Replace a Cold Posterior? CoRR Martin Marek, Brooks Paige, Pavel Izmailov
2024	J	`jnl`	Chainsaw: protein domain segmentation with fully convolutional neural networks. Bioinform. Jude Wells, Alex Hawkins-Hooker, Nicola Bordin, Ian Sillitoe, Brooks Paige, Christine A. Orengo
2024	A*	`conf`	Diffusive Gibbs Sampling. ICML Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber
2024	J	`jnl`	Diffusive Gibbs Sampling. CoRR Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber
2024	A*	`conf`	Gaussian Processes on Cellular Complexes. ICML Mathieu Alain, So Takao, Brooks Paige, Marc Peter Deisenroth
2024	J	`jnl`	Generative Active Learning for the Search of Small-molecule Protein Binders. CoRR Maksym Korablyov, Cheng-Hao Liu, Moksh Jain, Almer M. van der Sloot, Eric Jolicoeur, Edward Ruediger, Andrei Cristian Nica, Emmanuel Bengio, Kostiantyn Lapchevskyi, Daniel St-Cyr, Doris Alexandra Schuetz, Victor Ion Butoi, Jarrid Rector-Brooks, Simon Blackburn, Leo Feng, Hadi Nekoei, Sai Krishna Gottipati, Priyesh Vijayan, Prateek Gupta, Ladislav Rampásek, Sasikanth Avancha, Pierre-Luc Bacon, William L. Hamilton, Brooks Paige, Sanchit Misra, Stanislaw Kamil Jastrzebski, Bharat Kaul, Doina Precup, José Miguel Hernández-Lobato, Marwin H. S. Segler, Michael M. Bronstein, Anne Marinier, Mike Tyers, Yoshua Bengio
2024	J	`jnl`	Right Now, Wrong Then: Non-Stationary Direct Preference Optimization under Preference Drift. CoRR Seongho Son, William Bankes, Sayak Ray Chowdhury, Brooks Paige, Ilija Bogunovic
2023	J	`jnl`	Explaining holistic image regressors and classifiers in urban analytics with plausible counterfactuals. Int. J. Geogr. Inf. Sci. Stephen Law, Rikuo Hasegawa, Brooks Paige, Chris Russell, Andrew Elliott
2023	J	`jnl`	Gaussian Processes on Cellular Complexes. CoRR Mathieu Alain, So Takao, Brooks Paige, Marc Peter Deisenroth
2023	A*	`conf`	Moment Matching Denoising Gibbs Sampling. NeurIPS Mingtian Zhang, Alex Hawkins-Hooker, Brooks Paige, David Barber
2023	J	`jnl`	Moment Matching Denoising Gibbs Sampling. CoRR Mingtian Zhang, Alex Hawkins-Hooker, Brooks Paige, David Barber
2022	A	`conf`	Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes. AISTATS Hugh Dance, Brooks Paige
2022	J	`jnl`	Improving VAE-based Representation Learning. CoRR Mingtian Zhang, Tim Z. Xiao, Brooks Paige, David Barber
2022	J	`jnl`	Towards Healing the Blindness of Score Matching. CoRR Mingtian Zhang, Oscar Key, Peter Hayes, David Barber, Brooks Paige, François-Xavier Briol
2021	J	`jnl`	Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes. CoRR Hugh Dance, Brooks Paige
2021	J	`jnl`	I Don't Need u: Identifiable Non-Linear ICA Without Side Information. CoRR Matthew Willetts, Brooks Paige
2021	A	`conf`	Learning Bijective Feature Maps for Linear ICA. AISTATS Alexander Camuto, Matthew Willetts, Chris C. Holmes, Brooks Paige, Stephen J. Roberts
2021	J	`jnl`	Reconstructing Genotypes in Private Genomic Databases from Genetic Risk Scores. J. Comput. Biol. Brooks Paige, James Bell, Aurélien Bellet, Adrià Gascón, Daphne Ezer
2021	A*	`conf`	Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models. ICLR Yuge Shi, Brooks Paige, Philip H. S. Torr, N. Siddharth
2021	J	`jnl`	Simulation Intelligence: Towards a New Generation of Scientific Methods. CoRR Alexander Lavin, Hector Zenil, Brooks Paige, David Krakauer, Justin Gottschlich, Tim Mattson, Anima Anandkumar, Sanjay Choudry, Kamil Rocki, Atilim Günes Baydin, Carina Prunkl, Olexandr Isayev, Erik Peterson, Peter L. McMahon, Jakob H. Macke, Kyle Cranmer, Jiaxin Zhang, Haruko M. Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfeffer
2020	A*	`conf`	Barking up the right tree: an approach to search over molecule synthesis DAGs. NeurIPS John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
2020	J	`jnl`	Barking up the right tree: an approach to search over molecule synthesis DAGs. CoRR John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
2020	J	`jnl`	Bayesian Graph Neural Networks for Molecular Property Prediction. CoRR George Lamb, Brooks Paige
2020	A	`conf`	Data Generation for Neural Programming by Example. AISTATS Judith Clymo, Adrià Gascón, Brooks Paige, Nathanaël Fijalkow, Haik Manukian
2020	A*	`conf`	Goal-directed Generation of Discrete Structures with Conditional Generative Models. NeurIPS Amina Mollaysa, Brooks Paige, Alexandros Kalousis
2020	J	`jnl`	Goal-directed Generation of Discrete Structures with Conditional Generative Models. CoRR Amina Mollaysa, Brooks Paige, Alexandros Kalousis
2020	J	`jnl`	Learning Bijective Feature Maps for Linear ICA. CoRR Alexander Camuto, Matthew Willetts, Brooks Paige, Chris C. Holmes, Stephen J. Roberts
2020	J	`jnl`	Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning. CoRR Aneesh Pappu, Brooks Paige
2020	B	`conf`	Reconstructing Genotypes in Private Genomic Databases from Genetic Risk Scores. RECOMB Brooks Paige, James Bell, Aurélien Bellet, Adrià Gascón, Daphne Ezer
2020	J	`jnl`	Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models. CoRR Yuge Shi, Brooks Paige, Philip H. S. Torr, N. Siddharth
2019	—	`conf`	A Generative Model For Electron Paths. ICLR (Poster) John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato
2019	A*	`conf`	A Model to Search for Synthesizable Molecules. NeurIPS John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
2019	J	`jnl`	A Model to Search for Synthesizable Molecules. CoRR John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
2019	J	`jnl`	Data Generation for Neural Programming by Example. CoRR Judith Clymo, Haik Manukian, Nathanaël Fijalkow, Adrià Gascón, Brooks Paige
2019	—	`conf`	Generating Molecules via Chemical Reactions. DGS@ICLR John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato
2019	A	`conf`	Structured Disentangled Representations. AISTATS Babak Esmaeili, Hao Wu, Sarthak Jain, Alican Bozkurt, N. Siddharth, Brooks Paige, Dana H. Brooks, Jennifer G. Dy, Jan-Willem van de Meent
2019	J	`jnl`	Take a Look Around: Using Street View and Satellite Images to Estimate House Prices. ACM Trans. Intell. Syst. Technol. Stephen Law, Brooks Paige, Chris Russell
2019	—	`conf`	Usability of Probabilistic Programming Languages. PPIG Alan F. Blackwell, Luke Church, Martin Erwig, James Geddes, Andy Gordon, Maria I. Gorinova, Atilim Gunes Baydin, Bradley Gram-Hansen, Tobias Kohn, Neil D. Lawrence, Vikash Mansinghka, Brooks Paige, Tomas Petricek, Diana Robinson, Advait Sarkar, Oliver Strickson
2019	A*	`conf`	Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models. NeurIPS Yuge Shi, Siddharth Narayanaswamy, Brooks Paige, Philip H. S. Torr
2019	J	`jnl`	Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models. CoRR Yuge Shi, N. Siddharth, Brooks Paige, Philip H. S. Torr
2018	J	`jnl`	An Introduction to Probabilistic Programming. CoRR Jan-Willem van de Meent, Brooks Paige, Hongseok Yang, Frank Wood
2018	J	`jnl`	Hierarchical Disentangled Representations. CoRR Babak Esmaeili, Hao Wu, Sarthak Jain, N. Siddharth, Brooks Paige, Jan-Willem van de Meent
2018	—	`conf`	Learning a Generative Model for Validity in Complex Discrete Structures. ICLR (Poster) David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato
2018	J	`jnl`	Predicting Electron Paths. CoRR John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato
2018	J	`jnl`	Take a Look Around: Using Street View and Satellite Images to Estimate House Prices. CoRR Stephen Law, Brooks Paige, Chris Russell
2017	A*	`conf`	Grammar Variational Autoencoder. ICML Matt J. Kusner, Brooks Paige, José Miguel Hernández-Lobato
2017	—	`conf`	Kernel Sequential Monte Carlo. ECML/PKDD (1) Ingmar Schuster, Heiko Strathmann, Brooks Paige, Dino Sejdinovic
2017	—	`conf`	Learning Disentangled Representations with Semi-Supervised Deep Generative Models. NIPS Siddharth Narayanaswamy, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah D. Goodman, Pushmeet Kohli, Frank D. Wood, Philip H. S. Torr
2017	J	`jnl`	Learning Disentangled Representations with Semi-Supervised Deep Generative Models. CoRR N. Siddharth, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Frank D. Wood, Noah D. Goodman, Pushmeet Kohli, Philip H. S. Torr
2017	J	`jnl`	Learning a Generative Model for Validity in Complex Discrete Structures. CoRR David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato
2016	A	`conf`	Black-Box Policy Search with Probabilistic Programs. AISTATS Jan-Willem van de Meent, Brooks Paige, David Tolpin, Frank D. Wood
2016	J	`jnl`	Inducing Interpretable Representations with Variational Autoencoders. CoRR N. Siddharth, Brooks Paige, Alban Desmaison, Jan-Willem van de Meent, Frank D. Wood, Noah D. Goodman, Pushmeet Kohli, Philip H. S. Torr
2016	A*	`conf`	Inference Networks for Sequential Monte Carlo in Graphical Models. ICML Brooks Paige, Frank D. Wood
2016	A*	`conf`	Interacting Particle Markov Chain Monte Carlo. ICML Tom Rainforth, Christian A. Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem van de Meent, Arnaud Doucet, Frank D. Wood
2016	J	`jnl`	Probabilistic structure discovery in time series data. CoRR David Janz, Brooks Paige, Tom Rainforth, Jan-Willem van de Meent, Frank D. Wood
2016	A	`conf`	Super-Sampling with a Reservoir. UAI Brooks Paige, Dino Sejdinovic, Frank D. Wood
2015	J	`jnl`	Adaptive Scheduling in MCMC and Probabilistic Programming. CoRR David Tolpin, Jan-Willem van de Meent, Brooks Paige, Frank D. Wood
2015	J	`jnl`	Black-Box Policy Search with Probabilistic Programs. CoRR Jan-Willem van de Meent, David Tolpin, Brooks Paige, Frank D. Wood
2015	—	`conf`	Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs. ECML/PKDD (2) David Tolpin, Jan-Willem van de Meent, Brooks Paige, Frank D. Wood
2015	J	`jnl`	Path Finding under Uncertainty through Probabilistic Inference. CoRR David Tolpin, Brooks Paige, Frank D. Wood
2014	A*	`conf`	A Compilation Target for Probabilistic Programming Languages. ICML Brooks Paige, Frank D. Wood
2014	J	`jnl`	A Compilation Target for Probabilistic Programming Languages. CoRR Brooks Paige, Frank D. Wood
2014	—	`conf`	Asynchronous Anytime Sequential Monte Carlo. NIPS Brooks Paige, Frank D. Wood, Arnaud Doucet, Yee Whye Teh
2013	—	`conf`	Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits. NIPS Benjamin Shababo, Brooks Paige, Ari Pakman, Liam Paninski