This post describes four projects that share a common theme of enhancing or using generative models, a branch of unsupervised learning techniques in machine learning. In addition to describing our work, this pos
generative models have been successfully applied to inverse design various properties43,44. Despite the success in efficiently generating samples, the usage of generative ML models is very limited, mainly due to the difference in the architecture of ...
In this work, we characterize motor control as an outcome of a learnt hierarchical generative model; in particular, generative models that include the consequences of action. This proposal inherits from hierarchical functional organization of human motor control and ensuing planning as inference21,23,24...
"The most recent breakthroughs in AI models have come from pre-training models on large amounts of data and using self-supervised learning to train models without explicit labels," said Adnan Masood, chief AI architect at UST, a digital transformation consultancy. For example, OpenAI's Gene...
When training machine learning models, the focus is on achieving high accuracy in prediction or classification tasks. This involves optimizing the model’s ability to make correct predictions or accurately classify data based on the input it receives. For example, a machine learning model might be ...
Let’s not forget that this enhanced engagement through AI is particularly effective in maintaining student interest, which is vital for remote or hybrid learning models where keeping students focused can be challenging. This shift towards a more engaging learning environment showcases AI’s potential...
In particular, it reduces the effect of resolution and lighting on domain shifts. Solution 3, Siamese networks is effective for learning invariant representations. Cons: label is a must condition here. Unclear how to change for unsupervised models. Solution 4, Multimodal deep learning. It can ...
Research, private industry, and open-source efforts have created impactful models that innovate at higher levels of neural network architecture and application. For example, there have been crucial innovations in the training process, in how feedback from training is incorporated to improve the model...
Train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with IBM watsonx.ai, a next-generation enterprise studio for AI builders. Build AI applications in a fraction of the time with a fraction of the data. ...
Most of the Machine Learning and Deep Learning problems that you solve are conceptualized from the Generative and Discriminative Models. In Machine Learning, one can clearly distinguish between the two modelling types: Classifying an image as a dog or a