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Found 12 papers, 9 papers with code
Policy composition in reinforcement learning via multi-objective policy optimization
In two domains with continuous observation and action spaces, our agents successfully compose teacher policies in sequence and in parallel, and are also able to further extend the policies of the teachers in order to solve the task.
Training Compute-Optimal Large Language Models
We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget.
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
Language Modelling
on StackExchange
A Systematic Investigation of Commonsense Knowledge in Large Language Models
Language models (LMs) trained on large amounts of data have shown impressive performance on many NLP tasks under the zero-shot and few-shot setup.
AlgebraNets
Neural networks have historically been built layerwise from the set of functions in ${f: \mathbb{R}^n \to \mathbb{R}^m }$, i. e. with activations and weights/parameters represented by real numbers, $\mathbb{R}$.
Recurrent Independent Mechanisms
Learning modular structures which reflect the dynamics of the environment can lead to better generalization and robustness to changes which only affect a few of the underlying causes.
Ranked #10 on
Atari Games
on Atari 2600 Up and Down
Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures
We present a method to encode and decode the position of atoms in 3-D molecules from a dataset of nearly 50, 000 stable crystal unit cells that vary from containing 1 to over 100 atoms.
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
There are also some recent methods based on language models (e. g. graph2vec) but they tend to only consider certain substructures (e. g. subtrees) as graph representatives.
Ranked #25 on
Graph Classification
on IMDb-M
vGraph: A Generative Model for Joint Community Detection and Node Representation Learning
Experimental results on multiple real-world graphs show that vGraph is very effective in both community detection and node representation learning, outperforming many competitive baselines in both tasks.
Machine Learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets
Machine learning has gained widespread attention as a powerful tool to identify structure in complex, high-dimensional data.
Soft Condensed Matter
