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Found 7 papers, 4 papers with code
Unified Scaling Laws for Routed Language Models
The performance of a language model has been shown to be effectively modeled as a power-law in its parameter count.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
Ranked #1 on
Analogical Similarity
on BIG-bench
Improving language models by retrieving from trillions of tokens
We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with preceding tokens.
Ranked #4 on
Question Answering
on Natural Questions (short)
Reverb: A Framework For Experience Replay
A central component of training in Reinforcement Learning (RL) is Experience: the data used for training.
Acme: A Research Framework for Distributed Reinforcement Learning
Ultimately, we show that the design decisions behind Acme lead to agents that can be scaled both up and down and that, for the most part, greater levels of parallelization result in agents with equivalent performance, just faster.
Transformation-based Adversarial Video Prediction on Large-Scale Data
Recent breakthroughs in adversarial generative modeling have led to models capable of producing video samples of high quality, even on large and complex datasets of real-world video.
Ranked #1 on
Video Prediction
on Kinetics-600 12 frames, 64x64
Randomized Prior Functions for Deep Reinforcement Learning
Dealing with uncertainty is essential for efficient reinforcement learning.
