I exchange computer vision and machine learning for goods and services. Formerly of @OxfordTVG and @_FiveAI, now on the market.
Oxford, UK
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New blog post: many deep nets are *very* similar to one another, and we can demonstrate it with a cute transfer attack.
medium.com/fiveai/many-deep-…
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For that special person in your life who needs to understand adversarial examples but is always on the go: medium.com/@nick_62384/a-sim…
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Nicholas A. Lord retweeted
Check out our big news at Five on joining forces with Bosch linkedin.com/pulse/five-join…
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Hi. Do you care about adversarial attacks? Or not?
Either way, check out our #iclr2022 paper, "Attacking Deep Networks with Surrogate-Based Adversarial Black-Box Methods Is Easy".
Paper: arxiv.org/abs/2203.08725
Video: piped.video/gKzwEq6j5C8
More below. 👇 1/
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Median query counts in the single digits for successful attacks, from an algorithm that just alternates between a "harder" and "softer" form of gradient transfer within a standard optimiser. Works nearly every time. 👍 5/
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Work done with @RomainMueller and @lbertinetto.
(Really simple) code will be available shortly at github.com/fiveai/GFCS .
Use it on your own networks and tell us what you find. 🙏 6/6
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Nicholas A. Lord retweeted
Are you an undergrad/postgrad interested in robotics/computer vision? Applications currently open for an internship in the Scene Understanding team at Five:
apply.workable.com/five-ai-i…
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I'm not sure what my favourite part of the review process is, but I think it might be the part where the reviewer plugs their own paper.
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I'd been considering writing a blog post about adversarial examples, but then, in the spirit of quick and dirty first-order approximations that get the job done, I just did this instead:
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Just stumbled onto this old blog post: colah.github.io/posts/2015-0…
That's the best primer on information theory I've ever seen. There's a lot of insight in those deceptively simple visuals. I learned a few things.
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Nicholas A. Lord retweeted
My supervisor Sid has 2 PhD positions available at University of Edinburgh! A great learning opportunity for those interested in probabilistic generative models.
See more details on Sid's website here:
homepages.inf.ed.ac.uk/snara…
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Nicholas A. Lord retweeted
Pleased to announce a new paper from a team at @_FiveAI (@fgirbal, Majd Hawasly, Stefano Albrecht @UoE_Agents and myself) on "A Two-Stage Optimization-based Motion Planner for Safe Urban Driving", to appear in @ieeeras Transactions on Robotics.
five.ai/a-two-stage-optimiza…
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Nicholas A. Lord retweeted
A great thread by @ericarbailey got met thinking about managing innovation in industry and academia. I tried bringing processes like scrum to research but it didn't work. Breakthrough ideas requires long periods of "wandering in the woods", with no apparent measurable progress.
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If you spend most of your day coding to lofi, there are worse places to be than this: lofi.cafe
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Nicholas A. Lord retweeted
Interested in adv. robustness beyond security, or how adv. examples can help us understand deep learning? 🤔
Check our review paper “Optimism in the face of adversity”, just published in the Proc. IEEE 🚀
💎Xplore: bit.ly/3qz3men 📄arXiv: bit.ly/3s5Vz8a
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We're super nice, also.
If you are looking for a (6- to 12-month) research internship in computer vision / machine learning, our Oxford team is hiring - the call will close at the end of the week
apply.workable.com/five-ai-i…
For a glimpse of our current research: five.ai/our-research
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Nicholas A. Lord retweeted
Tired of obsessing over state-of-the-art results? Looking for hypothesis-based science in AI?
Join our #NeurIPS2020 workshop: “The pre-registration experiment: an alternative publication model for ML research" - from 09:15am (EST) on Dec 11th.
Details at preregister.science
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