Speakers: Jonas Andrulis (CEO) and Robert Baldock (senior researcher) @ aleph-alpha.de

Title:  Aleph Alpha meets Mila

Abstract: An overview of ongoing projects at aleph-alpha.com, including recent work on MAGMA, a large-scale multimodal transformer.

Video: here  (Mila internal)

Speakers: Edward Hu PhD Student @ mila.quebec

Title:  Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer

Abstract: You can’t train GPT-3 on a single GPU, much less tune its hyperparameters (HPs)… or so it seems. I’m here to tell you this is not true: you can tune its HPs on a single GPU even if you can’t train it that way! In this talk, I’ll describe how, in the so-called maximal update parametrization (abbreviated µP), narrow and wide neural networks share the same set of optimal HPs. This lets us tune any large model by just tuning a small version of it — we call this µTransfer. In particular, this allowed us to tune the 6.7 billion parameter version of GPT-3 using only 7% of its pretraining compute budget, and, with some asterisks, we get a performance comparable to the original GPT-3 model with twice the parameter count. This talk is based on https://arxiv.org/abs/2203.03466.

Video: here

Speakers: Julien Launay industrial PhD student @ lighton.ai

Title: What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?

Abstract: Large pretrained Transformer language models have been shown to exhibit zero-shot generalization, i.e. they can perform a wide variety of tasks that they were not explicitly trained on. However, the architectures and pretraining objectives used across state-of-the-art models differ significantly, and there has been limited systematic comparison of these factors. In this work, we present a large-scale evaluation of modeling choices and their impact on zero-shot generalization. In particular, we focus on text-to-text models and experiment with three model architectures (causal/non-causal decoder-only and encoder-decoder), trained with two different pretraining objectives (autoregressive and masked language modeling), and evaluated with and without multitask prompted finetuning. We train models with over 5 billion parameters for more than 170 billion tokens, thereby increasing the likelihood that our conclusions will transfer to even larger scales. Our experiments show that causal decoder-only models trained on an autoregressive language modeling objective exhibit the strongest zero-shot generalization after purely unsupervised pretraining. However, models with non-causal visibility on their input trained with a masked language modeling objective followed by multitask finetuning perform the best among our experiments. We therefore consider the adaptation of pretrained models across architectures and objectives. We find that pretrained non-causal decoder models can be adapted into performant generative causal decoder models, using autoregressive language modeling as a downstream task. Furthermore, we find that pretrained causal decoder models can be efficiently adapted into non-causal decoder models, ultimately achieving competitive performance after multitask finetuning. Code and checkpoints are available at this https URL. 

This talk is based on https://arxiv.org/abs/2204.05832.

Meeting link: https://umontreal.zoom.us/j/3046597454?pwd=REtaVW1NaXdqK3pEMTNrNnB6dnJDdz09

Video: here

Speakers: David Krueger University Lecturer @ University of Cambridge

Video: TBD 

Speakers: Guillermo Cecchi Principal Research Staff Member @ research.ibm.com

Video: TBD 

Speakers: Jenia Jitsev and Christoph Schuhman Scientific Lead and Organizational Lead @ laion.ai

Video: TBD 

Speakers: Thomas Wolf  Science Team Lead @ huggingface.co

Video: TBD 

Speakers:

Video: TBD