Details of: Connectionist networks can possess all important elements of human thinking or understanding
Neural TTS latency benchmarks for voice agents: TTFB goals, end-to-end budgets, where delays come from, and how streaming and deployment choices cut lag
Written for the course `Random Graphs' at Eindhoven University of Technology, and class room tested for over ten years, the book `Random Graphs and Complex Networks' has now finally appeared in print at Cambridge University Press
I’ve done several experiments with GPT-2, a neural net that OpenAI trained on millions of pages from the internet. OpenAI has been releasing the neural net in stages, with the second-biggest model, 774M, just recently released. I decided to put it through its paces. Last week’s experiment, where I generated new
Xander Steenbrugge shared his latest work on LinkedIn yesterday, and I was completely stunned! Xander had been working on, what he called, a "fun side-project", but which was in my eyes, absolutely awesome. He had used two generative adversarial networks (GANs) to teach one another how to respond visually to changing audio cues. This resulted
# __formless ## Introduction In a previous post , I talked about neural network Gaussian processes (NNGPs), and how they let us do exact Bayesian inference for neural networks at initialization, in the limit of large layer width, via straightforward matrix computations. This is great, but there are two big open questions: - Can we use a similar technique to describe the network during training, - rather than just at initialization? - Can we use this formalism to describe networks with finite layer widths
Oh, I wrote a masters thesis! I’m hoping to post the thesis later, but today I wanted to post about a cool system I built for training and organize a bunch of neu...
Covers dropout regularization: inverted dropout scaling, MC dropout uncertainty, spatial dropout for sequences, and dropout in transformers.
The text discusses the challenges of using deep neural networks in market forecasting, emphasizing that such models are more suited for image, video, and NLP tasks due to their hierarchical feature extraction. It highlights the importance of focusing on the target variable and its predictors, rather than the model itself, for successful trading. The author also touches on data preparation, model optimization, and the limitations of current optimization techniques, suggesting that future programming language
We know that in a neural network, weights are initialized usually randomly and that kind of initialization takes fair / significant amount of repetitions to converge to the least loss and reach to the ideal weight matrix. The problem is, this kind of initialization is prone to vanishing or exploding gradient problems