Sam Duffield @Sam_Duffield
London, England Joined January 2012-
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One area where I think LLMs are super useful is as a learning assistant. I am currently reading a technical book on a topic I am not well versed. I can ask all the dumb questions I wouldn't dare to ask humans and get immediate answers. It really speeds up my learning.
One fun bonus part of this work was that we needed to rewrite the flow model Stable Diffusion 3.5 (typically an ODE) as an SDE in order to use it. This conversion is excellently described in arxiv.org/abs/2206.00364 but still nice to have it here in one page
One fun bonus part of this work was that we needed to rewrite the flow model Stable Diffusion 3.5 (typically an ODE) as an SDE in order to use it. This conversion is excellently described in arxiv.org/abs/2206.00364 but still nice to have it here in one page https://t.co/ottszpL8ob
We are excited to announce our new paper on arXiv: Lattice Random Walk (LRW) discretisations of stochastic differential equations (SDEs) by Samuel Duffield (@Sam_Duffield), Maxwell Aifer (@MaxAifer), Denis Melanson, Zach Belateche (@blip_tm), and Patrick J. Coles…
Lattice Random Walk Discretisations of Stochastic Differential Equations Stochastic differential equations (SDEs) are foundational in statistical physics, finance, and generative modeling. Our paper introduces a new method of solving SDEs, where both time and space are…
check out the Duffield discretisation for SDEs before it turns the stochastic computing game upside down :O
check out the Duffield discretisation for SDEs before it turns the stochastic computing game upside down :O
The era of Physics-based ASICs has arrived. On the surface, computing infrastructure seems unready to meet the demands of AI. But underneath, a Cambrian explosion of new computer architectures is rising to meet this challenge, with the goal of using physical processes as a…
VerilogEval is an odd coding dataset - it gives the agent access to the golden testbench, which you will use to evaluate the final design from the model. I'm honestly surprised how papers like VerilogCoder or ChipAgents only get ~95% accuracy in this setting... That's what…
Normalizing Flows (NFs) check all the boxes for RL: exact likelihoods (imitation learning), efficient sampling (real-time control), and variational inference (Q-learning)! Yet they are overlooked over more expensive and less flexible contemporaries like diffusion models. Are NFs…
You may think it’s “I use AI” but really it’s “AI uses me”
You may think it’s “I use AI” but really it’s “AI uses me”
@LucaAmb @Cohere_Labs @ml_collective Hmm. To me it makes sense that first-order methods are ones that use first derivatives and an analytical model of second-order structure. Second-order methods should use first *and* second-derivatives (and ideally an analytical model of third-order structure)
@xidulu Always a bit funny to me how the interpretation of "second-order optimisation" is not as narrowly-defined as one might expect. My experience is that operationally, it means something like 'a matrix is involved', which is a pretty wide net to cast!
posteriors 𝞡, our open source Python library for Bayesian computation, will be presented at #ICLR2025! posteriors provides essential tools for uncertainty quantification and reliable AI training. Join @Sam_Duffield at his poster session 'Scalable Bayesian Learning with…
Come out and join us, so we can talk about physical world AI and thermodynamic chips. Looking forward to hosting with @nathanbenaich.
Come out and join us, so we can talk about physical world AI and thermodynamic chips. Looking forward to hosting with @nathanbenaich.
Really exciting progress in silicon happening now at @NormalComputing. Check out Zach's blog post to read more about what we're building. 👇
Really exciting progress in silicon happening now at @NormalComputing. Check out Zach's blog post to read more about what we're building. 👇
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Rafe Meager (they/the... @economeager
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