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Found 11 papers, 7 papers with code
Advantage Alignment Algorithms
The growing presence of artificially intelligent agents in everyday decision-making, from LLM assistants to autonomous vehicles, hints at a future in which conflicts may arise from each agent optimizing individual interests.
LOQA: Learning with Opponent Q-Learning Awareness
In various real-world scenarios, interactions among agents often resemble the dynamics of general-sum games, where each agent strives to optimize its own utility.
Best Response Shaping
We investigate the challenge of multi-agent deep reinforcement learning in partially competitive environments, where traditional methods struggle to foster reciprocity-based cooperation.
Meta-Value Learning: a General Framework for Learning with Learning Awareness
Gradient-based learning in multi-agent systems is difficult because the gradient derives from a first-order model which does not account for the interaction between agents' learning processes.
MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling
Musical expression requires control of both what notes are played, and how they are performed.
Counterpoint by Convolution
Machine learning models of music typically break up the task of composition into a chronological process, composing a piece of music in a single pass from beginning to end.
Ranked #4 on
Music Modeling
on JSB Chorales
On the Variance of Unbiased Online Recurrent Optimization
The recently proposed Unbiased Online Recurrent Optimization algorithm (UORO, arXiv:1702. 05043) uses an unbiased approximation of RTRL to achieve fully online gradient-based learning in RNNs.
Harmonic Recomposition using Conditional Autoregressive Modeling
We demonstrate a conditional autoregressive pipeline for efficient music recomposition, based on methods presented in van den Oord et al.(2017).
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
Recurrent Batch Normalization
We propose a reparameterization of LSTM that brings the benefits of batch normalization to recurrent neural networks.
Ranked #20 on
Language Modelling
on Text8
Dynamic Capacity Networks
The low-capacity sub-networks are applied across most of the input, but also provide a guide to select a few portions of the input on which to apply the high-capacity sub-networks.
