How can Transformers and other nets implement general-purpose learning algorithms in-context? Training on 2^14 tasks leads to strong generalization. Diff. to scaling laws, the memory size matters more than parameter count!
Meta-Learn Workshop #NeurIPS2022
https://t.co/P5a19Qodcz
After 2 years of work by 442 contributors across 132 institutions, I am thrilled to announce that the https://t.co/wezEGzDEHt paper is now live: https://t.co/4Yg36EB9Ru. BIG-bench consists of 204 diverse tasks to measure and extrapolate the capabilities of large language models.
Job opportunity! Come work with me and @b_kellenb at @EPFL_en in beautiful Sion and study how to map species and their interactions at scale with #DeepLearning !
Two phd positions available, info here https://t.co/CpqcHr6lP5
@snf_ch@epflENAC
Interested in #graphs and #ML? I have an open #postdoc position on a collaborative project with skilled industrial partners. Email me or apply at the link below.
https://t.co/fl3ksb4WlW
I am super grateful and relieved that this is finally out - it's been a long process.🐌
In early 2019, we saw for the first time that our recurrent spiking neural network model could generate novelty responses solely through inhibitory plasticity.
A lot happened since then👇
before Lisbon, I gave a @mackelab teatime talk about surfing as a beginner to prepare folks for the frustration
but it was actually about the PhD: in SD, I realized surfing as a beginner is a lot like starting a PhD, when you, invariably, also suck
this was my guide for both:
Excited to share our preprint 'Meta Learning Backpropagation And Improving It'
VS-ML meta learns general purpose learning algos that start to rival backprop in their performance. This is achieved purely by weight sharing LSTMs - no grads required. https://t.co/IKSG0TVAsO
👇1/7
@sleepinyourhat Section 4 of https://t.co/7BOnIOmyoy is the interesting part. No commercial use and redistribution without authorization from the website.
New pre-print out! https://t.co/9cdY5kxO0v - we improve a state-of-the-art unrolled compressed sensing algorithm by employing a NN which adapts sparse reconstruction to the unknown target vector! 🔥 With @fidlojg and Peter Jung
Stay tuned for tuned with the news of our new #NeurIPS2020 paper on how to *greatly* speed up complex matrix operations (e.g., matrix inversion) via a parallel, GPU-friendly, algorithm for SVD re-parameterisation!