Our last few posts on the Closed-form continuous-time (CfC) Liquid Neural Networks as described by [1], [2] and [3], have focused on learning to detect the last value in the input sequence. In this post, we focus on detecting multiple values where the previous 82 values of a curve are used to detect the next …
Continue reading “Detecting Multiple Values with a CfC Liquid Neural Network”
In this blog post, we compare the Liquid closed-form continuous-time neural network (CfC), as described by [1], [2] and [3], to the Long-Short Term Memory (LSTM) neural network and a simple Linear neural network to see how each model learns to track a simple Sine curve over time. To keep things simple, each test uses …
Continue reading “Comparing Other Models to a CfC Liquid Neural Network”
In this post we explore the impact of using different activations in the Closed-form Continuous-time (CfC) Liquid Neural Network as described by [1], [2] and [3]. Currently the CfC Unit Layer supports the following five different activations: RELU, SILU, GELU, TANH and LECUN. As shown below, each of these activations have different profiles. The visualization …
Continue reading “Comparing Activation Functions in a CfC Liquid Neural Network”
In our latest release of the MyCaffe AI Platform, version 1.12.2.41, we now support Liquid Neural Networks as described in [1], [2], [3] and [4]. Liquid neural networks, first introduced by [1], are dynamic networks constructed “of linear first-order dynamical systems modulated via nonlinear interlinked gates,” resulting in models that “represent dynamical systems with varying …
Continue reading “MyCaffe now supports Liquid Neural Networks!”
Liquid neural networks, first introduced by [1], are networks constructed “of linear first-order dynamical systems modulated via nonlinear interlinked gates,” resulting in models that “represent dynamical systems with varying (i.e., liquid) time-constants coupled to their hidden state, with outputs being computed by numerical differential equation solvers.” The Closed-form Continuous-time Models (CfC) ‘are powerful sequential liquid …
Continue reading “Closed-form Continuous-time Liquid Neural Net Models – A Programmer’s Perspective”
In our latest release of the MyCaffe AI Platform, version 1.12.1.82, we now support Temporal Fusion Transformer (TFT) Models as described in [1] and [2]. These powerful models provide multi-horizon time-series predictions while outperforming DeepAR from Amazon, Deep State Space Models, MQRNN, TRMF, and traditional models such as ARIMA and ETS according to [1]. The …
Continue reading “MyCaffe now supports Temporal Fusion Transformer Models!”
In our last post, we looked at the organization of the data used by Temporal Fusion Transformer models as described in the Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting article by Lim et al. [1]. In this post we take a deeper dive into the architecture of the Temporal Fusion Transformer model and how …
Continue reading “Temporal Fusion Transformers – Model Data Flow”
Temporal Fusion Transformers, as described in the Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting article by Lim et al. [1], use a complex mix of inputs to provide multi-horizon forecasting for timeseries data. The first step in understanding these models is to understand the data inputs fed into the model and the predicted outputs …
Continue reading “Temporal Fusion Transformers – Data Organization”
In our latest release, version 1.12.0.60, we now support ChatGPT type architectures with Encoder/Decoder Transformer Models based on the open-source transformer-translator-pytorch GitHub project by Song [1]. ChatGPT uses encoder/decoder transformer models to learn the context of the input query, the context of the likely responses and a mapping between the two via attention layers. The …
With GPT and ChatGPT, transformer models have been proven to be very powerful AI models. However, how do they work on the inside? With this post, we use the SignalPop AI Designer to visually walk through the forward pass of a transformer model used for language translation. Before showing a visual walk-through we wanted to …
Continue reading “Visually Walking Through a Transformer Model”
