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Found 8 papers, 5 papers with code
Amplifying human performance in combinatorial competitive programming
Recent years have seen a significant surge in complex AI systems for competitive programming, capable of performing at admirable levels against human competitors.
Transformers meet Neural Algorithmic Reasoners
Transformers have revolutionized machine learning with their simple yet effective architecture.
The CLRS-Text Algorithmic Reasoning Language Benchmark
Three years ago, a similar issue was identified and rectified in the field of neural algorithmic reasoning, with the advent of the CLRS benchmark.
Estimating Gibbs free energies via isobaric-isothermal flows
We present a machine-learning model based on normalizing flows that is trained to sample from the isobaric-isothermal ensemble.
Neural Algorithmic Reasoning with Causal Regularisation
We evaluate our method on the CLRS algorithmic reasoning benchmark, where we show up to 3$\times$ improvements on the OOD test data.
A Generalist Neural Algorithmic Learner
The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks, especially in a way that generalises out of distribution.
Normalizing flows for atomic solids
We present a machine-learning approach, based on normalizing flows, for modelling atomic solids.
Reward learning from human preferences and demonstrations in Atari
To solve complex real-world problems with reinforcement learning, we cannot rely on manually specified reward functions.
