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Artificial Intelligence

Top news of the week: 06.07.2022.

Hallucination
Agent
Das Model
Java
Data
Learning

@stanfordnlp shared
On Jun 29, 2022
At tomorrow's NLP Seminar, we are delighted to host Nouha Dziri (@nouhadziri), who will be talking about her work toward building faithful conversational models. Join us over zoom tomorrow at 11 am PT. Registration: https://t.co/55ZT7OU4sL; Abstract: https://t.co/GQrkmg5Ldd https://t.co/jERaefEyQp
Open
The Stanford Natural Language Processing Group

The Stanford Natural Language Processing Group

Towards Building Faithful Conversational Models Nouha Dziri, University of Alberta/Amii Venue: Zoom (link hidden) Abstract Conversational Models powered by large pre-trained …

@stanfordnlp shared
On Jul 1, 2022
RT @SuryaGanguli: Hey @percyliang I think you & our colleagues did put much careful thought into the name “foundation models” and I for one like it. I am doubling down and proposing also the notion of “foundation datasets” as motivated by our recent paper on neural scaling: https://t.co/Z98EJSxKlH https://t.co/Vyb8i0qPeB
Open
Beyond neural scaling laws: beating power law scaling via data pruning

Beyond neural scaling laws: beating power law scaling via data pruning

Widely observed neural scaling laws, in which error falls off as a power of the training set size, model size, or both, have driven substantial performance improvements in deep learning. ...

@hardmaru shared
On Jul 3, 2022
RT @MitchellAGordon: "RETRO is so fast and cheap, in fact, that I cannot fathom why anyone would choose to do language modeling without retrieval." New blog post benchmarking RETRO's database! https://t.co/t6iYFbeZy8
Open
RETRO Is Blazingly Fast

RETRO Is Blazingly Fast

When I first read Google’s RETRO paper, I was skeptical. Sure, RETRO models are 25x smaller than the competition, supposedly leading to HUGE savings in training and inference costs. But ...

@mxlearn shared
On Jul 1, 2022
[D] Industrial applications of causal representation learning https://t.co/RdgtXoeIFX
Open
[D] Industrial applications of causal representation learning

[D] Industrial applications of causal representation learning

3 votes and 0 comments so far on Reddit

@stanfordnlp shared
On Jul 3, 2022
RT @simongraysays: New release of https://t.co/3ij3cSZRWv (0.2.171): - uses latest @stanfordnlp CoreNLP - support for Tregex (grammatical constituency tree pattern matching) - support for TokensRegex (token-based pattern matching) - removes the contribution by @carsten_behring at his own request
Open
simongray/datalinguist

simongray/datalinguist

Stanford CoreNLP in idiomatic Clojure. Contribute to simongray/datalinguist development by creating an account on GitHub.

@peteskomoroch shared
On Jul 2, 2022
RT @julien_c: BTW.... dalle-mega from @borisdayma is now openly accessible on @huggingface ⚡️ To download it (10GB): git clone https://t.co/m8ivn0CPiH https://t.co/EEddY81Jz7
Open
DALL·E Mega Model Card

DALL·E Mega Model Card

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

@hardmaru shared
On Jul 2, 2022
RT @jzh_000: So happy to introduce our recent paper “Learning Controllable 3D Level Generators,” w/ @Smearle_RH, @Bumblebor, and @togelius.Since the game is the best simulation of real life, how about pushing our research about PCG to 3D! https://t.co/1zWBCKUVId https://t.co/hwiQVrrHux
Open
Learning Controllable 3D Level Generators

Learning Controllable 3D Level Generators

Procedural Content Generation via Reinforcement Learning (PCGRL) foregoes the need for large human-authored data-sets and allows agents to train explicitly on functional constraints, using ...

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