One obstacle to applying reinforcement learning algorithms to real-world problems is the
lack of suitable reward functions. Designing such reward functions is difficult in part because …

We present a meta-learning approach for adaptive text-to-speech (TTS) with few data.
During training, we learn a multi-speaker model using a shared conditional WaveNet core …

Backdoors on federated learning will be diluted by subsequent benign updates. This is
reflected in the significant reduction of attack success rate as iterations increase, ultimately …

The state-of-the art machine learning approach to training deep neural networks,
backpropagation, is implausible for real neural networks: neurons need to know their …

The dominant view in neuroscience is that changes in synaptic weights underlie learning. It
is unclear, however, how the brain is able to determine which synapses should change, and …

This paper presents a new family of backpropagation-free neural architectures, Gated Linear
Networks (GLNs). What distinguishes GLNs from contemporary neural networks is the …

Abstract Recently proposed Gated Linear Networks (GLNs) present a tractable nonlinear
network architecture, and exhibit interesting capabilities such as learning with local error …

Abstract We propose the Gaussian Gated Linear Network (G-GLN), an extension to the
recently proposed GLN family of deep neural networks. Instead of using backpropagation to …

This paper introduces Associative Compression Networks (ACNs), a new framework for
variational autoencoding with neural networks. The system differs from existing variational …

We propose a class of neural models for graphs that do not rely on backpropagation for
training, thus making learning more biologically plausible and amenable to parallel …