933 citations · 6k across the 51 of their papers we have counts for
55 papers
Context Training with Active Information Seeking
Zeyu Huang, Adhiguna Kuncoro, Qixuan Feng +4
Most existing large language models (LLMs) are expensive to adapt after deployment, especially when a task requires newly produced information or niche domain knowledge. Recent wor…
Decoupled DiLoCo for Resilient Distributed Pre-training
Arthur Douillard, Keith Rush, Yani Donchev +14
Modern large-scale language model pre-training relies heavily on the single program multiple data (SPMD) paradigm, which requires tight coupling across accelerators. Due to this co…
Streaming DiLoCo with overlapping communication: Towards a Distributed Free Lunch
Arthur Douillard, Yanislav Donchev, Keith Rush +11
Training of large language models (LLMs) is typically distributed across a large number of accelerators to reduce training time. Since internal states and parameter gradients need…
DiPaCo: Distributed Path Composition
Arthur Douillard, Qixuan Feng, Andrei A. Rusu +7
Progress in machine learning (ML) has been fueled by scaling neural network models. This scaling has been enabled by ever more heroic feats of engineering, necessary for accommodat…
Asynchronous Local-SGD Training for Language Modeling
Bo Liu, Rachita Chhaparia, Arthur Douillard +5
Local stochastic gradient descent (Local-SGD), also referred to as federated averaging, is an approach to distributed optimization where each device performs more than one SGD upda…
DiLoCo: Distributed Low-Communication Training of Language Models
Arthur Douillard, Qixuan Feng, Andrei A. Rusu +6
Large language models (LLM) have become a critical component in many applications of machine learning. However, standard approaches to training LLM require a large number of tightl…
Towards Robust and Efficient Continual Language Learning
Adam Fisch, Amal Rannen-Triki, Razvan Pascanu +4
As the application space of language models continues to evolve, a natural question to ask is how we can quickly adapt models to new tasks. We approach this classic question from a…
Towards Compute-Optimal Transfer Learning
Massimo Caccia, Alexandre Galashov, Arthur Douillard +6
The field of transfer learning is undergoing a significant shift with the introduction of large pretrained models which have demonstrated strong adaptability to a variety of downst…
Multi-step Planning for Automated Hyperparameter Optimization with OptFormer
Lucio M. Dery, Abram L. Friesen, Nando De Freitas +2
As machine learning permeates more industries and models become more expensive and time consuming to train, the need for efficient automated hyperparameter optimization (HPO) has n…
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research
Jorg Bornschein, Alexandre Galashov, Ross Hemsley +17
A shared goal of several machine learning communities like continual learning, meta-learning and transfer learning, is to design algorithms and models that efficiently and robustly…
Towards Learning Universal Hyperparameter Optimizers with Transformers
Yutian Chen, Xingyou Song, Chansoo Lee +9
Meta-learning hyperparameter optimization (HPO) algorithms from prior experiments is a promising approach to improve optimization efficiency over objective functions from a similar…
The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation
Naman Goyal, Cynthia Gao, Vishrav Chaudhary +7
One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the lack of good evaluation benchmarks. Current evaluation benchmarks eithe…
On Anytime Learning at Macroscale
Lucas Caccia, Jing Xu, Myle Ott +2
In many practical applications of machine learning data arrives sequentially over time in large chunks. Practitioners have then to decide how to allocate their computational budget…
Few-shot Sequence Learning with Transformers
Lajanugen Logeswaran, Ann Lee, Myle Ott +3
Few-shot algorithms aim at learning new tasks provided only a handful of training examples. In this work we investigate few-shot learning in the setting where the data points are s…
Efficient Continual Learning with Modular Networks and Task-Driven Priors
Tom Veniat, Ludovic Denoyer, Marc'Aurelio Ranzato
Existing literature in Continual Learning (CL) has focused on overcoming catastrophic forgetting, the inability of the learner to recall how to perform tasks observed in the past.…
Multi-scale Transformer Language Models
Sandeep Subramanian, Ronan Collobert, Marc'Aurelio Ranzato +1
We investigate multi-scale transformer language models that learn representations of text at multiple scales, and present three different architectures that have an inductive bias…
Residual Energy-Based Models for Text
Anton Bakhtin, Yuntian Deng, Sam Gross +3
Current large-scale auto-regressive language models display impressive fluency and can generate convincing text. In this work we start by asking the question: Can the generations o…
Facebook AI's WAT19 Myanmar-English Translation Task Submission
Peng-Jen Chen, Jiajun Shen, Matt Le +5
This paper describes Facebook AI's submission to the WAT 2019 Myanmar-English translation task. Our baseline systems are BPE-based transformer models. We explore methods to leverag…
Revisiting Self-Training for Neural Sequence Generation
Junxian He, Jiatao Gu, Jiajun Shen +1
Self-training is one of the earliest and simplest semi-supervised methods. The key idea is to augment the original labeled dataset with unlabeled data paired with the model's predi…
The Source-Target Domain Mismatch Problem in Machine Translation
Jiajun Shen, Peng-Jen Chen, Matt Le +5
While we live in an increasingly interconnected world, different places still exhibit strikingly different cultures and many events we experience in our every day life pertain only…
On The Evaluation of Machine Translation Systems Trained With Back-Translation
Sergey Edunov, Myle Ott, Marc'Aurelio Ranzato +1
Back-translation is a widely used data augmentation technique which leverages target monolingual data. However, its effectiveness has been challenged since automatic metrics such a…
Large Memory Layers with Product Keys
Guillaume Lample, Alexandre Sablayrolles, Marc'Aurelio Ranzato +2
This paper introduces a structured memory which can be easily integrated into a neural network. The memory is very large by design and significantly increases the capacity of the a…
Real or Fake? Learning to Discriminate Machine from Human Generated Text
Anton Bakhtin, Sam Gross, Myle Ott +3
Energy-based models (EBMs), a.k.a. un-normalized models, have had recent successes in continuous spaces. However, they have not been successfully applied to model text sequences. W…
Task-Driven Modular Networks for Zero-Shot Compositional Learning
Senthil Purushwalkam, Maximilian Nickel, Abhinav Gupta +1
One of the hallmarks of human intelligence is the ability to compose learned knowledge into novel concepts which can be recognized without a single training example. In contrast, c…