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AI + Knowledge Bases - Arxiv Doc - Attention mechanism - Conversational AI - Deep Learning - Distributional semantics - Document embeddings - Domain adaptation - Domain adaptation (NLP) - Embeddings - Entities - Entity embeddings - Entity Retrieval - GitHub - GitHub project - Graphs+Machine Learning - Hugging Face - Information extraction - Information retrieval - Knowledge-augmented Language Models - Knowledge Graphs - Knowledge Graphs and NLP - Language - Language Model - LLM - Machine learning - Neural networks - Neural Search - NLP: pretraining - NLP and Search - NLP Teams - Not Encoding Factual Knowledge in Language Model - Pre-Trained Language Models - Question Answering - Representation learning - Retrieval-based NLP - Self-Attention - Sentence Embeddings - Structured knowledge - Text Classification - Transfer learning - Transfer learning in NLP - Transformers - Tutorial - Tweet - Unsupervised machine learning - Word embeddings -

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17 Documents (Long List)

Sergey Levine sur Twitter : "What if we train a language model on images & robot data? That's the idea behind PaLM-E: a huge LLM (562B params) that is trained on language and "multimodal sentences" that include images and language"

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2023-03-08 About

Rohan Anil sur Twitter : "Next big jump with Neural Network performance is going to happen when community embraces non-uniformity

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2022-12-18 About

google/tapas-base-finetuned-wtq · Hugging Face

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2020-12-17 About

[2010.00402] From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering

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2020-10-03 About

[1804.03235] Large scale distributed neural network training through online distillation

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2020-06-06 About

Thomas Kipf's PhD thesis: "Deep Learning with Graph-Structured Representations"

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2020-05-05 About

[1503.03832] FaceNet: A Unified Embedding for Face Recognition and Clustering

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2020-01-25 About

Artificial Human Intelligence: The Programmer’s Apprentice - Tom Dean and Rishabh Singh - Google Research

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2019-11-16 About

[1807.00082] Amanuensis: The Programmer's Apprentice

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**The use of natural language to facilitate communication
between the expert programmer and apprentice AI system.**

> an overview of the material covered in a course taught at Stanford in the spring quarter of 2018. The course draws upon **insight from cognitive and systems neuroscience to implement hybrid connectionist and symbolic reasoning systems** that leverage and extend the state of the art in machine learning **by integrating human and machine intelligence**. As a concrete example we focus on digital assistants that learn from continuous dialog with an expert software engineer while providing initial value as powerful analytical, computational and mathematical savants.

> [#Dehaene](/tag/stanislas_dehaene)'s work extends the [#Global Workspace Theory](/tag/global_workspace_theory) of Bernard Baars. Dehaene’s version of the theory combined with Yoshua Bengio’s concept of a [#consciousness prior](/tag/consciousness_prior.html) and deep reinforcement learning suggest a model for constructing and maintaining the cognitive states that arise and persist during complex problem solving.

2019-11-12 About

[1808.02590] A Tutorial on Network Embeddings

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2019-08-25 About

Watch Your Step: Learning Node Embeddings via Graph Attention

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2019-08-23 About

Google AI Blog: Harnessing Organizational Knowledge for Machine Learning (2019)

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2019-06-28 About

Hamiltonian Neural Networks

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2019-06-11 About

Google explores AI's mysterious polytope | ZDNet

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2019-02-09 About

Simple guide to Neural Arithmetic Logic Units (NALU): Explanation, Intuition and Code

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AI@Google

2018-08-21 About

[1806.01261] Relational inductive biases, deep learning, and graph networks

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2018-06-13 About

[1712.01208] The Case for Learned Index Structures

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2017-12-11 About

Properties

sl:creationDate : 2018-08-21
sl:creationTime : 2018-08-21T17:29:10Z
rdf:type : sl:Tag
skos:prefLabel : AI@Google@en