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tags Machine learning, LLM, Transfer learning, Neural network training A way of self-training is to generate synthetic labels/data that are then used to retrain the model.
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tags Machine learning, NLP Dense vector representations of text for retrieval, similarity, and classification tasks.
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tags Machine learning LLM, Meta-learning In-context learning is the mechanism through which LLMs perform few-shot learning without gradient updates.
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tags Machine learning, LLM, The Scaling Hypothesis Scaling laws inform the training and scaling of the largest models.
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tags NLP, Retrieval augmented generation, Semantic similarity Contrastive learning is one of the dominant paradigms for Self-supervised learning.
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tags Agent, LLM, Model Context Protocol
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tags NLP, Evaluating NLP N-gram matching For two sequences \(x\) and \(\hat{x}\), we denote the sequence of $n$-grams with \(S_x^n\) and \(S^n_{\hat{x}}\).
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tags Machine learning Definition Self supervised learning (SSL) is a learning paradigm based on the idea of using information contained within the training data to build better representations of it.
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tags Geospatial AI, LLM, Evaluating NLP, In-context learning The ability to reason about spatial relationships, compose spatial information into coherent mental maps, and perform geometric reasoning.
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tags Open-ended evolution, Co-evolution, Quality diversity, Genetic algorithms, Coding agent, LLM source (Qu et al. 2026) Summary CORAL is a framework for autonomous multi-agent evolution applied to open-ended discovery problems.
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tags LLM, Coding agent, Co-evolution
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tags LLM
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tags Cellular automata, Neural cellular automata, Transfer learning, Kolmogorov complexity source (Lee et al. 2026) Summary This paper proposes using neural cellular automata (NCA) as a source of synthetic, non-linguistic data for pre-pre-training large l…
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tags NLP, LLM, Semantic similarity source (Lee et al. 2024) Summary Gecko is a compact text embedding model (1.2B parameters) from Google DeepMind that achieves strong retrieval performance by distilling knowledge from LLMs into a retriever.
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tags Machine learning, NLP, Language modeling A large language model, which is the name now given mostly to large Transformer based architecture with usually at least several billions of parameters Retrieval augmented generation
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tags Complexity To study the complexity of various systems, researchers have come up with various metrics.
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tags Machine learning, LLM RAG stands for “Retrieval augmented generation”, which is a type of LLM Harness for conditioning the generated text based on data accessed in a database using a query.
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tags Optimization, Machine learning, Natural language processing source (yanglargelanguagemodels2023?)
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tags Machine learning, Language modeling It is the scaffolding code (prompts, retrieval, memory, context management) that wraps an LLM and determines what information it sees.
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Answer The knowledge base does not contain notes specifically about geospatial analysis, GIS, remote sensing, satellite imagery, or related topics.
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If you’re like me, you have a deep distaste for the ads, trackers, and popups plaguing every website and app you use.
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Links Website About GitHub repo Description Parliament is a data platform that ingests legislative data from the Assemblée Nationale open data portal, transforms it, and serves it through a read-only API.
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Links Website About GitHub repo Description Bring Your Laptop is a progressive web app for finding work-friendly cafes.
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Links GitHub repo Description EasyRoute is a REST API service built in Rust that generates personalized walking and cycling routes enriched with points of interest.
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Links GitLab repo Description This project is a renderer that raymarches 3D signed distance field (SDF) scenes and outputs SVG files with hatching lines.
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This post was originally published on go-inicio.com.
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tags Economics
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This post was originally published on go-inicio.com.
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tags Economics, Artificial Intelligence Labor and automation resources Interview with Juan Sebastian Carbonell, Interview with Aaron Benanav
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tags Computer science, Neural networks The goal of quantization in neural network training is to make neural networks more efficient by simplifying their computations.
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tags Economics, Economic liberalism Definition Haagh defines UBI as the desire to ‘give all residents a modest regular income grant that is not dependent on means-tests or work-requirements’ (Haagh 2019).
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tags Privacy, Society CCTV Surveillance and crime prevention (Piza et al. 2019) Bibliography Eric L.
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tags Transformers, GPT, Chinchilla paper (Glaese et al. 2022) blog post Deepmind announcement blog post Architecture Starts from the Chinchilla 70B model but adds RLHF (Reinforcement Learning with Human Feedback).
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tags Privacy IP Addresses In (Mishra et al. 2020), the authors analyze a set of users’ internet traffic for more than 100 days.
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tags Transformers, GPT paper (Rae et al. 2022) Architecture This model is very similar to GPT-2 but uses RSNorm instead of LayerNorm and relative positional encoding rather than absolute positional encoding.
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tags Cryptography SSH random art algorithm It is described in this paper by Dirk Loss, Tobias Limmer, and Alexander von Gernler.
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tags Society
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tags Neural networks paper (Ba et al. 2016) Definition Layer Normalization is a technique used in deep learning to normalize the inputs to a layer in a neural network.
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tags Neural networks
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tags GPT, Transformers, NLP blog post OpenAI blog post Architecture ChatGPT takes a GPT3.5 (aka GPT3 Davinci-003) pretrained model and uses RLHF to fine-tune the model similarly to InstructGPT but with some differences in the data collection.