Stephen Blystone

Father of twin girls. BSEE and MSCS (Intelligent Systems focus). Interests: #MachineLearning, #AI, #DeepReinforcementLearning, #DataScience, #SDN, #NFV

[Shunyu Yao @ShunyuYao12]

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.

https://t.co/V6hjbUNjbt

[British Neuroscience Association @BritishNeuro]

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 https://t.co/LWL6KHVo6w

[Julian Togelius @togelius]

@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.
https://t.co/krQACUK5IO

[Radek Osmulski @radekosmulski]

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.

[Yann LeCun @ylecun]

@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 https://t.co/tIoHzrsIek

[hardmaru @hardmaru]

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. 🦎🐞🐜

[Christian S. Perone @tarantulae]

For those who missed the "Neural Ordinary Differential Equations" (https://t.co/upUhWNtpvC) presentation by David Duvenaud on NIPS, here is the video session: https://t.co/swdiFPr2Wu, it starts at 24:00.

[Shivon Zilis @shivon]

Cards Against Machine Learning

[👩‍💻 Paige Bailey @DynamicWebPaige]

✨🧠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. 👍

[Sebastian Ruder @seb_ruder]

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.
https://t.co/iv29bxYbq4

[Sophia @sopharicks]

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.
https://t.co/jj1m29bfio

#MachineLearning #ArtificialIntelligence #AI #technology

[julia ferraioli @juliaferraioli]

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.

[Dr Kareem Carr @kareem_carr]

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

[👩‍💻 Paige Bailey @DynamicWebPaige]

Still the best thing that's ever been made for me. :) ♥, @letstechno!

[Dr. Tirthajyoti Sarkar (data, AI, free thought) @tirthajyotiS]

#Data #generation for #DataScience and #MachineLearning a must-have #skill for #DataScientist
https://t.co/NaqO7RnyAI

[Thomas Wiecki @twiecki]

#PyMC3 3.6 RC1 is released: https://t.co/9UPBM0o86v 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.

[Miles Brundage @Miles_Brundage]

"Continual Match Based Training in Pommerman: Technical Report," Peng and Pang et al.: https://t.co/rxBcN4rx1Z

Winner of Pommerman competition

[Miles Brundage @Miles_Brundage]

"Malthusian Reinforcement Learning," Leibo et al.: https://t.co/M31aHsPMgQ

[evolvingstuff @evolvingstuff]

Really cool design of a spiking NN that appears to actually outperform LSTM and GRU!

Deep Networks Incorporating Spiking Neural Dynamics
https://t.co/VeJBnkgPcB