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With big tech still fighting in the big race for AI supremacy, an AGI race is slowly gaining momentum. Who will succeed? And, how?
With big tech still fighting in the big race for AI supremacy, an AGI race is slowly gaining momentum. Who will succeed? And, how?
Yann LeCun said that though RL is inevitable in machine learning, the purpose behind incorporating it in algorithms should be to eventually minimise its use.
LeCun clearly is at odds with reinforcement learning and believes that for AI with common sense, it is not the way forward
Just like reinforcement learning, self-supervised or unsupervised learning also witnessed various new models in 2022. Here are a few of them
While there are various practical applications of reinforcement learning, the concept as a whole poses some limitations when used in developing autonomous machine intelligence
Giving the ability to machines to learn, reason, and plan without being trained or rewarded for failing or succeeding at a task is the ideal future of AI
The Graph Contrastive Learning aims to learn the graph representation with the help of contrastive learning.
As per Gartner, supervised learning will continue to be the most popular type of machine learning in 2022.
Meta is coming up with their first conference detailing their progress of the metaverse after the major rebranding from Facebook. The virtual event ‘Inside the lab: Building for the metaverse
graph structure has much additional information with them like node attributes, and label information of nodes. Using this source of information, we can have unprecedented opportunities to design advanced level
in the self supervised learning process we are mainly focused about making the data workable to the downstream algorithms. but when using the self-supervised learning we make the data specifically
For instance, Facebook AI Research (FAIR) has been championing self-supervised learning (SSL) for quite some time.
Anomaly detection is used to detect fraud and identify intrusions, and figure ecosystem disturbances.
VICReg combines the variance term with a decorrelation mechanism based on redundancy reduction and covariance regularisation.
Article is about Pitch Recognition, aka Pitch Estimation.
Over time, scientists have introduced several techniques that offer the best of both.
Did you know that almost 90% of the world’s data has been created in the last two years alone, and nearly 2.5 quintillion bytes of data are produced by humans
Barlow twins is a novel architecture inspired by the redundancy reduction principle in the work of neuroscientist H. Barlow.
SelfTime is the state-of-the-art time series framework by finding inter-sample and intra-temporal relations
Self-supervised learning (SSL) has become a handy technique in computer vision tasks. Significant advances in SSL means its methods can now learn representations even if the input samples are distorted.
Just like the human brain, deep learning uses a neural network for object detection, speech recognition, translation, decision-making, and more. However, for deep learning — a subset of machine learning
VISSL is a computer VIsion library for state-of-the-art Self-Supervised Learning research. This framework is based on PyTorch. The key idea of this library is to speed up the self-supervised learning
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