Search Results - "Benton, Joe"

From denoising diffusions to denoising Markov models by Benton, Joe, Shi, Yuyang, De Bortoli, Valentin, Deligiannidis, George, Doucet, Arnaud

“…Abstract Denoising diffusions are state-of-the-art generative models exhibiting remarkable empirical performance. They work by diffusing the data distribution…”
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Authors’ reply to the Discussion of ‘From denoising diffusions to denoising Markov models’ at the Discussion Meeting on ‘Probabilistic and statistical aspects of machine learning by Benton, Joe, Shi, Yuyang, De Bortoli, Valentin, Deligiannidis, George, Doucet, Arnaud

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Measuring Feature Sparsity in Language Models by Deng, Mingyang, Tao, Lucas, Benton, Joe

“…Recent works have proposed that activations in language models can be modelled as sparse linear combinations of vectors corresponding to features of input…”
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Error Bounds for Flow Matching Methods by Benton, Joe, Deligiannidis, George, Doucet, Arnaud

“…Score-based generative models are a popular class of generative modelling techniques relying on stochastic differential equations (SDE). From their inception,…”
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Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization by Benton, Joe, De Bortoli, Valentin, Doucet, Arnaud, Deligiannidis, George

“…Denoising diffusions are a powerful method to generate approximate samples from high-dimensional data distributions. Recent results provide polynomial bounds…”
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Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics by Daudel, Kamélia, Benton, Joe, Shi, Yuyang, Doucet, Arnaud

“…Several algorithms involving the Variational R\'enyi (VR) bound have been proposed to minimize an alpha-divergence between a target posterior distribution and…”
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From Denoising Diffusions to Denoising Markov Models by Benton, Joe, Shi, Yuyang, De Bortoli, Valentin, Deligiannidis, George, Doucet, Arnaud

“…Denoising diffusions are state-of-the-art generative models exhibiting remarkable empirical performance. They work by diffusing the data distribution into a…”
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Polysemanticity and Capacity in Neural Networks by Scherlis, Adam, Sachan, Kshitij, Jermyn, Adam S, Benton, Joe, Shlegeris, Buck

“…Individual neurons in neural networks often represent a mixture of unrelated features. This phenomenon, called polysemanticity, can make interpreting neural…”
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A Continuous Time Framework for Discrete Denoising Models by Campbell, Andrew, Benton, Joe, De Bortoli, Valentin, Rainforth, Tom, Deligiannidis, George, Doucet, Arnaud

“…We provide the first complete continuous time framework for denoising diffusion models of discrete data. This is achieved by formulating the forward noising…”
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Sabotage Evaluations for Frontier Models by Benton, Joe, Wagner, Misha, Christiansen, Eric, Anil, Cem, Perez, Ethan, Srivastav, Jai, Durmus, Esin, Ganguli, Deep, Kravec, Shauna, Shlegeris, Buck, Kaplan, Jared, Karnofsky, Holden, Hubinger, Evan, Grosse, Roger, Bowman, Samuel R, Duvenaud, David

“…Sufficiently capable models could subvert human oversight and decision-making in important contexts. For example, in the context of AI development, models…”
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When Do Universal Image Jailbreaks Transfer Between Vision-Language Models? by Schaeffer, Rylan, Valentine, Dan, Bailey, Luke, Chua, James, Eyzaguirre, Cristóbal, Durante, Zane, Benton, Joe, Miranda, Brando, Sleight, Henry, Hughes, John, Agrawal, Rajashree, Sharma, Mrinank, Emmons, Scott, Koyejo, Sanmi, Perez, Ethan

“…The integration of new modalities into frontier AI systems offers exciting capabilities, but also increases the possibility such systems can be adversarially…”
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An examination of state level homeland security organizations by Davis, Joe Benton

“…The requirement for homeland security within the United States became apparent following the terrorist attacks on September 11, 2001. Prior to the attacks,…”
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A syllabus for a high school business correspondence course by Cox, Joe Benton

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