Semanlink - Memory in deep learning

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Memory in deep learning

Σχετικές ετικέτες:

Knowledge-augmented Language Models

Απόγονοι

Allen Institute for AI sur Twitter : "MemPrompt, appearing at #EMNLP2022, is a new way to "fix" #GPT3 after deployment via user interaction"

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2022-12-11 About

Leshem Choshen sur Twitter : "Computational (Chomskian) hierarchies can predict OOD capabilities..."

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2022-07-11 About

[2203.08913] Memorizing Transformers

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2022-05-07 About

Guillaume Lample sur Twitter : "Last year, we showed that you can outperform a 24-layer transformer in language modeling with just...

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2020-10-10 About

[1911.05507] Compressive Transformers for Long-Range Sequence Modelling

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2020-02-11 About

A new model and dataset for long-range memory | DeepMind

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2020-02-11 About

[1909.04120] Span Selection Pre-training for Question Answering

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> a **new pre-training task inspired by reading
comprehension** and an **effort to avoid encoding general knowledge in the transformer network itself**

Current transformer architectures store general knowledge -> large models, long pre-training time. Better to offload the requirement of general knowledge to a sparsely activated network.

"Span selection" as an additional auxiliary task: the query is a sentence drawn from a corpus
with a term replaced with a special token: [BLANK]. The term replaced by the blank is the answer term. The passage is
relevant as determined by a BM25 search, and answer-bearing (containing the answer
term). Unlike BERT’s cloze task, where the answer must be drawn from the model itself, the answer is found in a passage
using language understanding.

> **We hope to progress to a model of general purpose language modeling that uses an indexed long
term memory to retrieve world knowledge, rather than holding it in the densely activated transformer encoder layers.**

2019-09-18 About

[1907.05242] Large Memory Layers with Product Keys

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> **a structured memory which can be easily integrated into a neural network.** The memory is very large by design and therefore significantly increases the capacity of the architecture, by up to a billion parameters with a negligible computational overhead. Its design and access pattern is based on **product keys**, which enable fast and exact nearest neighbor search. The ability to increase the number of parameters while keeping the same computational budget lets the overall system strike a better trade-off between prediction accuracy and computation efficiency both at training and test time.

> a key-value memory layer that can increase model capacity for a negligible computational cost. A 12-layer transformer with a memory outperforms a 24-layer transformer, and is 2x faster!

[Implementation](/doc/2019/08/product_key_memory_pkm_minima)

TODO: compare with [[2007.00849] Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge](doc:2020/07/2007_00849_facts_as_experts_)

2019-07-13 About

The Kanerva Machine: A Generative Distributed Memory | OpenReview (2018)

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2018-12-06 About

[1703.03129] Learning to Remember Rare Events

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2018-10-23 About

Ιδιότητες

sl:creationDate : 2018-12-02
sl:creationTime : 2018-12-02T10:00:08Z
rdf:type : sl:Tag
skos:prefLabel : Memory in deep learning