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Quantum Machine Learning. We now know that quantum computers have the potential to boost the performance of machine learning systems, and may eventually power efforts in fields from drug discovery to fraud detection. We're doing foundational research in quantum ML to power tomorrow’s smart quantum algorithms.
Snap Machine Learning (Snap ML in short) is a library for training and scoring traditional machine learning models. Such traditional models power most of today's machine learning applications in business and are very popular among practitioners as well (see the 2019 Kaggle survey for details). Snap ML has been designed to address some of the ...
Machine Learning. Machine learning uses data to teach AI systems to imitate the way that humans learn. They can find the signal in the noise of big data, helping businesses improve their operations. We've been in the field since since the beginning: IBMer Arthur Samuel even coined the term “Machine Learning” back in 1959.
In vertical federated learning, the data are complementary; movie and book reviews, for example, are combined to predict someone’s music preferences. Finally, in federated transfer learning, a pre-trained foundation model designed to perform one task, like detecting cars, is trained on another dataset to do something else, like identify cats.
International Conference on Machine Learning. ... ICLR 2024. 38. International Conference on Learning ...
Part of the Linux Foundation, PyTorch is a machine-learning framework that ties together software and hardware to let users run AI workloads in the hybrid cloud. One of PyTorch’s key advantages is that it can run AI models on any hardware backend: GPUs, TPUs, IBM AIUs, and traditional CPUs.
Since then, machine learning for structured data has become one of the major research areas in data mining and machine learning. Proud of our successes, we are actively tackling the frontiers in machine learning and data mining, and applying the results to the real world, taking full advantage of our merit of proximity to advanced companies and ...
The Machine Learning for Drug Development and Causal Inference group is developing machine learning models for innovative drug discovery technologies and bringing them to fruition for IBM clients. Our researchers believe that drug discovery can benefit from technologies that learn from the rich clinical, omics, and molecular data being ...
Neuro-symbolic AI. We see Neuro-symbolic AI as a pathway to achieve artificial general intelligence. By augmenting and combining the strengths of statistical AI, like machine learning, with the capabilities of human-like symbolic knowledge and reasoning, we're aiming to create a revolution in AI, rather than an evolution.
These quantum improvements either try to improve over classical algorithms or consider quantum learning when the data itself could be presented in a quantum manner. In this talk, I will look at a few specific problems/computational models for which quantum algorithms provide a speedup to machine learning tasks in a rigorous manner.