Predictive coding is biologically plausible. It operates locally. There are no separate prediction and training phases which must be synchronized. Most importantly, it lets you train a neural network without sending axon potentials backwards. Predictive coding is easier to implement in hardware. It is...
This was by far the most significant iteration of the ongoing exercise where I challenge an audience to produce a keyword search that works better than technology-assisted review (also known as predictive coding or supervised machine learning). There were far more participants than previous rounds,...
This article describes a deep predictive coding network (PredNet)-based approach for unsupervised pedestrian pose prediction from 2D camera imagery and provides experimental results of two real-world autonomous vehicle data sets. The article also discusses topics for future work in unsupervised and semi...
Representation Learning with Contrastive Predictive CodingAaron van den Oord DeepMind avdnoord@google.com &Yazhe Li DeepMind yazhe@google.com &Oriol Vinyals DeepMind vinyals@google.com Abstract While supervised learning has enabled great progress in many applications, unsupervised learning has not seen ...
This was by far the most significant iteration of the ongoing exercise where I challenge an audience to produce a keyword search that works better than technology-assisted review (also known as predictive coding or supervised machine learning). There were far more participants than previous rounds,...
The PFF algorithm integrates the idea of local hypothesis generation from predictive coding (PC) into the inference process by leveraging the representations acquired within the recurrent representation circuit’s iterative processing window. Specifically, each layer of the representation circuit, at each ...
Convolutional DNNs trained through supervised learning use depth to progressively separate representations2. To understand whether networks trained with LPL similarly leverage depth, we measured the linear readout accuracy of the internal representations at every layer in the network. Crucially, we found ...
Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review. BMC Med Res Methodol. 2022;22(1):12. doi:10.1186/s12874-021-01469-6 PubMedGoogle ScholarCrossref 14. Daneshjou R, Smith MP, Sun MD, Rotemberg V, ...
It severely limits the network depth due to learning stagnation. Here, we prove why this bottleneck occurs. We then propose a new forward-inference strategy based on accelerated proximal gradients. This strategy has faster theoretical convergence guarantees than the one used for DPCNs. It overcomes...
network solves a simpler task of predicting the next symbols, but not their exact timing, while the encoding network is trained to produce piece-wise constant latent codes. We evaluate the model on a speech coding task and demonstrate that the proposed Aligned Contrastive Predictive Coding (ACPC)...