Stephen Blystone @BlyNotes
Father of twin girls. BSEE and MSCS (Intelligent Systems focus). Interests: #MachineLearning, #AI, #DeepReinforcementLearning, #DataScience, #SDN, #NFV github.com/blynotes Dallas, TX Joined July 2017-
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Happy to see LLM security taken seriously by OWASP owasp.org/www-project-to… Here's my (offhand) thoughts on the list: 1/
Still use ⛓️Chain-of-Thought (CoT) for all your prompting? May be underutilizing LLM capabilities🤠 Introducing 🌲Tree-of-Thought (ToT), a framework to unleash complex & general problem solving with LLMs, through a deliberate ‘System 2’ tree search. arxiv.org/abs/2305.10601
JUST. LOOK. AT. ALL. THESE. *FREE*. ARTICLES!! Highly readable, written by experts, looking at the last 50 years & ahead to what comes next for research on #consciousness, #memory, #hearing, #neuroprosthetics, #antipsychotics, emotions, #stemcells buff.ly/2T2ZOQG
@ylecun @hardmaru I believe there are strong reasons to not only use gradient descent even when it is applicable. Namely that gradient descent performs inductive learning, stochastic methods can do something like hypothetico-deductivism, which is fundamentally more capable. togelius.blogspot.com/2018/05/empiri…
This is how little code it takes to implement a siamese net using @fastdotai and @PyTorch. I share this because I continue to be amazed.
@hardmaru While one should strive to use gradient-based optim whenever possible, there are situations where the only option is gradient-free. Gradient-based is way more efficient, but not always applicable. PS: my 1st paper on perturbative learning was at NIPS 1988 papers.nips.cc/paper/122-gemi…
This library from @ylecun’s lab implemented benchmarked versions of evolutionary computation algorithms such as: Differential Evolution, Fast Genetic Algorithm, CMA-ES, Particle Swarm Optimization. 🦎🐞🐜
This library from @ylecun’s lab implemented benchmarked versions of evolutionary computation algorithms such as: Differential Evolution, Fast Genetic Algorithm, CMA-ES, Particle Swarm Optimization. 🦎🐞🐜
For those who missed the "Neural Ordinary Differential Equations" (arxiv.org/abs/1806.07366) presentation by David Duvenaud on NIPS, here is the video session: facebook.com/nipsfoundation…, it starts at 24:00.
✨🧠Friendly periodic reminder that @TensorFlow supports traditional models like random forest, linear and logistic regression, k-means clustering, and gradient-boosted trees! The syntax is a bit more verbose than scikit-learn; but you get the added bonus of GPU acceleration. 👍
10 Exciting Ideas of 2018 in NLP: A collection of 10 ideas that I found exciting and impactful this year—and that we'll likely see more of in the future. ruder.io/10-exciting-id…
The code of the final project I worked on within the @OpenAI scholarship program is on Github. In the GridWorld environment an agent navigates according to commands to hit/avoid target cells. github.com/SophiaAr/OpenA… #MachineLearning #ArtificialIntelligence #AI #technology
A lot of what's considered "elective" in CS education should actually be required. HCI is a lot more relevant to most software engineers these days than something like compiler design.
A lot of what's considered "elective" in CS education should actually be required. HCI is a lot more relevant to most software engineers these days than something like compiler design.
When a statisticians hear, "Successful people start their day at 4 a.m.", they think: 1. Waking early makes you successful? 2. Something about success makes it hard to sleep at night? 3. Success is lethal; Only early risers survive? 4. You did your survey at 4am. #epitwitter
Still the best thing that's ever been made for me. :) ♥, @letstechno!
#Data #generation for #DataScience and #MachineLearning a must-have #skill for #DataScientist towardsdatascience.com/synthetic-data…
#PyMC3 3.6 RC1 is released: github.com/pymc-devs/pymc… Please help us test it and report issues on our tracker, this is not production-ready yet. 3.6 will be the last release to support Python 2.
"Continual Match Based Training in Pommerman: Technical Report," Peng and Pang et al.: arxiv.org/abs/1812.07297 Winner of Pommerman competition
"Malthusian Reinforcement Learning," Leibo et al.: arxiv.org/abs/1812.07019
Really cool design of a spiking NN that appears to actually outperform LSTM and GRU! Deep Networks Incorporating Spiking Neural Dynamics arxiv.org/abs/1812.07040

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Microsoft News and St... @MSFTnews
820K Followers 2K Following News and stories directly from Microsoft.
Ai x Summit @aixsummit
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Deep Learning Weekly @dl_weekly
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