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Hot data science related posts every hour. Chat: https://telegram.me/r_channels Contacts: @lgyanf
Number of guns per 100 inhabitants in Europe
/r/MapPorn
https://redd.it/zhskx1
Academic How perceptions of the metaverse vary across cultures (All welcome)
Hello,
I'm conducting a survey to see how perceptions of the metaverse vary across different cultures and demographics.
Everyone is welcome to take the survey!
Compensation: Giving away two $50 gift card of your choice to 2 winners.
Survey Link: https://forms.gle/77qF3yKzhHYrrXfT9
Thanks so much!
/r/SampleSize
https://redd.it/zhozda
Bermuda is nearly 7% golf course. Golfing covers roughly 1.4 sq miles of the 20.5 sq mile island.
/r/MapPorn
https://redd.it/zh9fph
Boundary
https://arxiv.org/pdf/2009.11848.pdf
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[Ahmed Imtiaz Humayun \] MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining | ICLR 2022
https://www.youtube.com/watch?v=0Muk7nKzOW8
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[Randall Balestriero, Jerome Pesenti, Yann LeCun\] Learning in High Dimension Always Amounts to Extrapolation
https://arxiv.org/abs/2110.09485
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[Hattie Zhou\] Teaching Algorithmic Reasoning via In-context Learning
https://arxiv.org/abs/2211.09066
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[Ahmed Imtiaz\] Exact Visualization of Deep Neural Network Geometry and Decision Boundary
https://openreview.net/pdf?id=VSLbmsoZxai
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[Beatrice Bevilacqua\] Size-Invariant Graph Representations for Graph Classification Extrapolations
https://arxiv.org/pdf/2103.05045.pdf
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[Brendan Fong David I. Spivak\] Seven Sketches in Compositionality: An Invitation to Applied Category Theory
https://math.mit.edu/\~dspivak/teaching/sp18/7Sketches.pdf
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Tim’s examples of applied Category theory cited:
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[Lennox\] Robert Rosen and Relational System Theory: An Overview
https://academicworks.cuny.edu/cgi/viewcontent.cgi?article=5866&context=gc\_etds
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[Bob Coecke\] Introducing categories to the practicing physicist
https://www.cs.ox.ac.uk/bob.coecke/Cats.pdf
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[Bob Coecke\] Categorical Quantum Mechanics I: Causal Quantum Processes
https://www.researchgate.net/publication/283043644\_Categorical\_Quantum\_Mechanics\_I\_Causal\_Quantum\_Processes
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[Bob Coecke\] Quantum Natural Language Processing
https://www.cs.ox.ac.uk/people/bob.coecke/QNLP-ACT.pdf
/r/MachineLearning
https://redd.it/zgue6r
The US has never recorded this many positive flu tests in one week: And health care systems are getting absolutely crushed ... again.
https://www.vox.com/2022/12/6/23494948/flu-influenza-rsv-covid-vaccine-chart-tripledemic-tridemic
/r/dataisbeautiful
https://redd.it/zhdspu
[R] General-Purpose In-Context Learning by Meta-Learning Transformers
https://arxiv.org/abs/2212.04458
/r/MachineLearning
https://redd.it/zgn26z
Tracking tech layoff in 2022
/r/Infographics
https://redd.it/zgyb3t
Turbāre circulōs meōs | 29-05-17 | by Xponentialdesign
/r/mathpics
https://redd.it/zcw6sc
2022 Tech Company Layoffs
/r/visualization
https://redd.it/zgr0r0
Countries with completely free ($0 tuition fees) university education
/r/MapPorn
https://redd.it/zh0hvz
An emoji map of the United States
/r/MapPorn
https://redd.it/zgwza2
Ludacris’ Hoes Area Code Map
/r/MapPorn
https://redd.it/zgm0vr
[OC] What forms of development do Americans support nationally and locally?
https://redd.it/zfyqom
@datascientology
Land reclamation in the Netherlands
/r/MapPorn
https://redd.it/zgo2xf
[OC] Circular calendar showing how sunrise and sunset times change throughout the year
https://redd.it/zg83a5
@datascientology
Q Does anyone know of an IRT model that will accommodate both binary and ordinal data simultaneously?
I am interested in using an IRT model to develop a measure of a latent concept. I have fourteen indicators. Most of them are dichotomous, but there are a few ordinal ones as well. Does anyone know of an IRT model that will accommodate both dichotomous and ordinal responses? One option would be to collapse the ordinal indicators into binary ones, but that would mean losing out on a lot of information. Any thoughts would be appreciated!
/r/statistics
https://redd.it/zhi3vu
[OC] Portion of players not born in the country of their team. World Cup 2022
/r/dataisbeautiful
https://redd.it/zhpatm
[OC] "Also" Is The Most Common Word With No Rhymes
/r/dataisbeautiful
https://redd.it/zhdme4
D Dr. Petar Veličković (Deepmind) - Categories, Graphs, Reasoning and Graph Expander Propagation
Hey folks,
We interviewed Petar Veličković at NeurIPS last week here -- https://www.youtube.com/watch?v=1lkdWduuN14
Categories (Cats for AI) [00:00:00\]
Algorithmic Reasoning [00:14:44\]
Extrapolation [00:19:09\]
Ishan Misra Skit [00:27:50\]
Graphs (Expander Graph Propagation) [00:29:18\]
​
References
MLST#60 Geometric Deep Learning Blueprint (Special Edition)
https://www.youtube.com/watch?v=bIZB1hIJ4u8
​
Categories for AI
https://cats.for.ai/
Organised by:
Andrew Dudzik - DeepMind
Bruno Gavranović - University of Strathclyde
João Guilherme Araújo - Cohere / Universidade de São Paulo
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Petar Veličković - DeepMind / University of Cambridge
Pim de Haan - University of Amsterdam / Qualcomm AI Research
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[Petar Veličković\] Graph Attention Networks
https://arxiv.org/abs/1710.10903
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Learning to Configure Computer Networks with Neural Algorithmic Reasoning [NeurIPS 2022\] [Luca Beurer-Kellner, Martin Vechev, Laurent Vanbever, Petar Veličković\]
https://openreview.net/forum?id=AiY6XvomZV4
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Graph Neural Networks are Dynamic Programmers [Andrew Joseph Dudzik, Petar Veličković\]
https://arxiv.org/abs/2203.15544
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Expander Graph Propagation [Andreea Deac, Marc Lackenby, Petar Veličković\]
https://openreview.net/forum?id=IKevTLt3rT
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[Pim de Haan, Taco Cohen, Max Welling\] Natural Graph Networks
https://papers.nips.cc/paper/2020/file/2517756c5a9be6ac007fe9bb7fb92611-Paper.pdf
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[Uri Alon, Eran Yahav\] On the Bottleneck of Graph Neural Networks and its Practical Implications (they discovered oversquashing)
https://arxiv.org/abs/2006.05205
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[Topping,...,Bronstein\] Understanding over-squashing and bottlenecks on graphs via curvature
https://arxiv.org/abs/2111.14522
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[Andreea Deac, Petar Velickovic, Ognjen Milinkovic et al\] XLVIN: eXecuted Latent Value Iteration Nets
https://arxiv.org/abs/2010.13146
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[Petar Veličković et al\] Reasoning-Modulated Representations (RMR)
https://openreview.net/forum?id=cggphp7nPuI
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Dual Algorithmic Reasoning [PetarV, under review ICLR\]
https://openreview.net/pdf?id=hhvkdRdWt1F
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[Petar Veličković, Charles Blundell\] Neural Algorithmic Reasoning
https://arxiv.org/abs/2105.02761
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[Andreea Deac, Petar Veličković, ...\] Neural Algorithmic Reasoners are Implicit Planners (which got a NeurIPS spotlight in 2021!)
https://arxiv.org/abs/2110.05442
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A Generalist Neural Algorithmic Learner
https://arxiv.org/abs/2209.11142
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ETA Prediction with Graph Neural Networks in Google Maps
https://arxiv.org/abs/2108.11482
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[Randall Balestriero\] A Spline Theory of Deep Networks
https://proceedings.mlr.press/v80/balestriero18b/balestriero18b.pdf
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[Ahmed Imtiaz Humayun \] Exact Visualization of Deep Neural Network Geometry and Decision
[SURVEY] Your coffee and energy drinks consumption's effects (EVERYONE)
https://docs.google.com/forms/d/e/1FAIpQLSeTQL6QiGSOjCjPPAKe72lC2K054jgPVGZJFXvXsfQVADkLdg/viewform?usp=sf_link
/r/SampleSize
https://redd.it/zgxssl
R Illustrating Reinforcement Learning from Human Feedback (RLHF)
New HuggingFace blog post on RLHF: https://huggingface.co/blog/rlhf
Motivated by ChatGPT and the lack of conceptually focused resources on the topic.
/r/MachineLearning
https://redd.it/zh2u3k
Linguistic map of Spain
/r/MapPorn
https://redd.it/zh52g1
But where do folks actually attend church? [OC]
/r/dataisbeautiful
https://redd.it/zh4358
[OC] São Paulo cut its homicide rate by 90% and is now about as safe as Boston. Mexico City is currently safer than Dallas and Denver.
/r/dataisbeautiful
https://redd.it/zh2r3h
[OC] Largest mergers & acquisitions, inflation adjusted
/r/dataisbeautiful
https://redd.it/zgt8ye
[OC] How to spot misleading charts? I would like to hear your opinion on the subject, also any tips design-wise?
/r/dataisbeautiful
https://redd.it/zg7pck
R Large language models are not zero-shot communicators
Paper: Large language models are not zero-shot communicators (arXiv)
Abstract:
Despite widespread use of LLMs as conversational agents, evaluations of performance fail to capture a crucial aspect of communication: interpreting language in context. Humans interpret language using beliefs and prior knowledge about the world. For example, we intuitively understand the response "I wore gloves" to the question "Did you leave fingerprints?" as meaning "No". To investigate whether LLMs have the ability to make this type of inference, known as an implicature, we design a simple task and evaluate widely used state-of-the-art models. We find that, despite only evaluating on utterances that require a binary inference (yes or no), most perform close to random. Models adapted to be "aligned with human intent" perform much better, but still show a significant gap with human performance. We present our findings as the starting point for further research into evaluating how LLMs interpret language in context and to drive the development of more pragmatic and useful models of human discourse.
Authors: Laura Ruis, Akbir Khan, Stella Biderman, Sara Hooker, Tim Rocktäschel, Edward Grefenstette
/r/MachineLearning
https://redd.it/zgr7nr
R What the DAAM: Interpreting Stable Diffusion and Uncovering Generation Entanglement
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https://preview.redd.it/m2pg8yhahr4a1.png?width=2117&format=png&auto=webp&s=c6ef4cbef10f5d04045fb606e5123fb7a64f2ed5
Paper: What the DAAM: Interpreting Stable Diffusion Using Cross Attention (arXiv paper, codebase)
Abstract:
Large-scale diffusion neural networks represent a substantial milestone in text-to-image generation, but they remain poorly understood, lacking interpretability analyses. In this paper, we perform a text-image attribution analysis on Stable Diffusion, a recently open-sourced model. To produce pixel-level attribution maps, we upscale and aggregate cross-attention word-pixel scores in the denoising subnetwork, naming our method DAAM. We evaluate its correctness by testing its semantic segmentation ability on nouns, as well as its generalized attribution quality on all parts of speech, rated by humans. We then apply DAAM to study the role of syntax in the pixel space, characterizing head--dependent heat map interaction patterns for ten common dependency relations. Finally, we study several semantic phenomena using DAAM, with a focus on feature entanglement, where we find that cohyponyms worsen generation quality and descriptive adjectives attend too broadly. To our knowledge, we are the first to interpret large diffusion models from a visuolinguistic perspective, which enables future lines of research.
Authors: Raphael Tang, Linqing Liu, Akshat Pandey, Zhiying Jiang, Gefei Yang, Karun Kumar, Pontus Stenetorp, Jimmy Lin, Ferhan Ture
/r/MachineLearning
https://redd.it/zgg7y7
Please help me explain to a student, in the simplest terms possible, what is wrong here.
/r/dataisugly
https://redd.it/zgcgay
[OC] Length of Time to Watch Professional Sports
/r/dataisbeautiful
https://redd.it/zgggp6
