Grosse, Roger

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations

Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 2,412

Importance Weighted Autoencoders

Burda, Y., Grosse, R., & Salakhutdinov, R. (2015) · arXiv

Gaussian Processes and Bayesian InferenceTopic Modeling

Cited by 644

Discovering Language Model Behaviors with Model-Written Evaluations

Perez, E., Ringer, S., Lukosiute, K., Nguyen, K., Chen, E., Heiner, S., Pettit, C., Olsson, C., Kundu, S., Kadavath, S., et al. (2023) · Findings of the Association for Computational Linguistics: ACL 2023

Topic ModelingNatural Language Processing Techniques

Cited by 194

Discovering Language Model Behaviors with Model-Written Evaluations

Perez, E., Ringer, S., Lukošiūtė, K., Nguyen, K., Chen, E., Heiner, S., Pettit, C., Olsson, C., Kundu, S., Kadavath, S., et al. (2022) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 53

Toy Models of Superposition

Elhage, N., Hume, T., Olsson, C., Schiefer, N., Henighan, T., Kravec, S., Hatfield-Dodds, Z., Lasenby, R., Drain, D., Chen, C., et al. (2022) · arXiv

Neural Networks and ApplicationsAdvanced Neural Network Applications

Cited by 51

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training

Hubinger, E., Denison, C., Mu, J., Lambert, M., Tong, M., MacDiarmid, M., Lanham, T., Ziegler, D. M., Maxwell, T., Cheng, N., et al. (2024) · arXiv

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 46

INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving

Wu, Y., Jiang, A. Q., Ba, J., & Grosse, R. (2020) · arXiv

AI-based Problem Solving and PlanningLogic, programming, and type systems

Cited by 30

Studying Large Language Model Generalization with Influence Functions

Grosse, R., Bae, J., Anil, C., Elhage, N., Tamkin, A., Tajdini, A., Steiner, B., Li, D., Durmus, E., Perez, E., et al. (2023) · arXiv

Explainable Artificial Intelligence (XAI)Adversarial Robustness in Machine Learning

Cited by 26

Many-shot Jailbreaking

Anil, C., Durmus, E., Panickssery, N., Sharma, M., Benton, J., Kundu, S., Batson, J., Tong, M., Mu, J., Ford, D., et al. (2024) · Advances in Neural Information Processing Systems 37

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 25

Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data

Treutlein, J., Choi, D., Betley, J., Marks, S., Anil, C., Grosse, R., & Evans, O. (2024) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 6

Sabotage Evaluations for Frontier Models

Benton, J., Wagner, M., Christiansen, E., Anil, C., Perez, E., Srivastav, J., Durmus, E., Ganguli, D., Kravec, S., Shlegeris, B., et al. (2024) · arXiv

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 2

Learning to Give Checkable Answers with Prover-Verifier Games

Anil, C., Zhang, G., Wu, Y., & Grosse, R. (2021) · arXiv

Game Theory and ApplicationsExplainable Artificial Intelligence (XAI)

Cited by 0