Semanlink - Self-Supervised Learning

Parents:

Machine learning

Self-Supervised Learning

Related Tags:

Natural Language Supervision

Descendants

12 Documents (Short List)

[2302.04761] Toolformer: Language Models Can Teach Themselves to Use Tools

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> Toolformer, **a model
trained to decide which APIs to call, when to
call them, what arguments to pass, and how to
best incorporate the results into future token
prediction**.

> fulfills the
following desiderata:
> - The use of tools should be learned in a
self-supervised way without requiring large
amounts of human annotations
>- The LM should be able to decide for itself when
and how to use which tool.

> Approach based
on the recent idea of using large LMs with incontext
learning (Brown et al., 2020) to generate
entire datasets from scratch.
>
> Given just a handful of human-written examples
of how an API can be used, 
> - we let a LM annotate
a huge language modeling dataset with potential
API calls. 
> - We then use a self-supervised loss to
determine which of these API calls actually help
the model in predicting future tokens. 
>- Finally, we
finetune the LM itself on the API calls that it considers
useful.

[Jay Hack @mathemagic1an sur twitter](https://twitter.com/mathemagic1an/status/1624870248221663232):

> from a small seed set of human inputs (essentially demonstrating usage of APIs), the training set for this behavior is generated by the LLM itself.
> 
> So what does this mean? We've found a promising way to tightly integrate arbitrary APIs with our best-performing models.

2023-02-13 About

Jay Hack sur Twitter : "My thoughts on Toolformer IMO the most important paper in the past few weeks..."

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2023-02-13 About

Timo Schick sur Twitter : "Introducing the Toolformer, a language model that teaches itself to use various tools in a self-supervised way..."

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2023-02-13 About

Yann LeCun sur Twitter : "Barlow Twins: a new super-simple self-supervised method to train joint-embedding architectures (aka Siamese nets) non contrastively. "

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2021-05-09 About

Contrastive Self-Supervised Learning | Ankesh Anand (2020)

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2020-02-15 About

Self-Supervised Representation Learning

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2020-02-15 About

Self-supervised learning and computer vision · fast.ai

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2020-01-21 About

Deep InfoMax: Learning good representations through mutual information maximization - Microsoft Research

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2019-08-09 About

Contrastive Unsupervised Learning of Semantic Representations: A Theoretical Framework – Off the convex path (2019-03)

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2019-03-20 About

Self-Supervised Learning, Yann LeCun, Facebook AI Research | Dartmouth News

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2018-10-27 About

Time-Contrastive Networks: Self-Supervised Learning from Video (2017)

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Self-supervised approach for learning representations and robotic behaviors entirely from unlabeled videos recorded from multiple viewpoints, and study how this representation can be used in two robotic imitation settings: imitating object interactions from videos of humans, and imitating human poses.

> We train our representations using a metric learning loss, where multiple simultaneous viewpoints of the same observation are attracted in the embedding space, while being repelled from temporal neighbors which are often visually similar but functionally different. In other words, the model simultaneously learns to recognize what is common between different-looking images, and what is different between similar-looking images.
> This signal causes our model to discover attributes that do not change across viewpoint, but do change across time, while ignoring nuisance variables such as occlusions, motion blur, lighting and background. We demonstrate that this representation can be used by a robot to directly mimic human poses without an explicit correspondence, and that it can be used as a reward function within a reinforcement learning algorithm.

2018-10-27 About

[1807.03748] Representation Learning with Contrastive Predictive Coding

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2018-07-21 About

Properties

sl:creationDate : 2018-10-27
sl:creationTime : 2018-10-27T14:41:10Z
rdf:type : sl:Tag
skos:prefLabel : Self-Supervised Learning