Search Results for author:
Found 8 papers, 4 papers with code
DiscoGen: Learning to Discover Gene Regulatory Networks
To accurately identify GRNs, perturbational data is required.
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.
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 #1 on
Language Modelling
on WikiText-103
(using extra training data)
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
College Mathematics
on BIG-bench
(using extra training data)
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
These implementations serve both as a validation of our design decisions as well as an important contribution to reproducibility in RL research.
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 #6 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.
