Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities...

include multi-task learning, along with more formal theoretical foundations. Influential publications on transfer learning include the book Learning to Learn...

applications in matrix completion, multivariate regression, and multi-task learning. Ideas of feature and group selection can also be extended to matrices...

Universal Sentence Encoder Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning Barkan, Oren; Razin, Noam;...

learning research. It is part of the list of datasets for machine-learning research. These datasets consist primarily of images or videos for tasks such...

data. The authors found that multi-task learning improved overall performance compared to models specialized to one task. They conjectured that the best...

paradox Multi-task learning – Solving multiple machine learning tasks at the same time Neural scaling law – Statistical law in machine learning Outline...

Self-supervised learning (SSL) is a paradigm in machine learning where a model is trained on a task using the data itself to generate supervisory signals...

models for learning and predicting across both known and unknown tasks. His research introduced directions in spatial multi-task learning, balancing the...

classification One-class classification Multi-label classification Multiclass perceptron Multi-task learning In multi-label classification, OvR is known as...

involved in learning and memory Merged transistor logic, a class of digital circuits Metric temporal logic, in computer science Multi-task learning MTL Harbor...

time, he co-authored the paper "One Model to Learn Them All" about multi-task learning by a single neural network. In 2019, Gomez left Google Brain to launch...

vector-valued functions as this extension is particularly important in multi-task learning and manifold regularization. The main difference is that the reproducing...

classification, Entity Linking and more Statistical models for 19 languages Multi-task learning with pretrained transformers like BERT Support for custom models...

model Multi-armed bandit Multi-task learning Multilinear subspace learning Multimodal learning Multiple instance learning Multiple-instance learning Never-Ending...

Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that...

"The road ahead for augmented reality". pwc. Pereira, Fernando. "Deep Learning-Based Extended Reality: Making Humans and Machines Speak the Same Visual...

in machine learning, including natural language processing tasks such as text summarization and conversational agents, computer vision tasks like text-to-image...

PMID 31390003. Zhou D, Miao L, He Y (May 2018). "Position-aware deep multi-task learning for drug-drug interaction extraction" (PDF). Artificial Intelligence...

and thus perform tasks without explicit instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed neural...

Imitation learning is a paradigm in reinforcement learning, where an agent learns to perform a task by supervised learning from expert demonstrations....

Active learning (machine learning) Apprenticeship learning Error-driven learning Model-free (reinforcement learning) Multi-agent reinforcement learning Optimal...

interaction Smart city (ubiquitous city) Ubiquitous commerce Ubiquitous learning Ubiquitous robot Wearable computer Nieuwdorp, E. (2007). "The pervasive...

have extended kernel methods to handle multiple outputs, as seen in multi-task learning. The mathematical framework for kernel methods typically involves...

theoretically better. Ensemble learning trains two or more machine learning algorithms on a specific classification or regression task. The algorithms within...

In deep learning, transformer is an architecture based on the multi-head attention mechanism, in which text is converted to numerical representations...

features and use them to perform a specific task. Feature learning is motivated by the fact that ML tasks such as classification often require input that...

In probability theory and machine learning, the multi-armed bandit problem (sometimes called the K- or N-armed bandit problem) is named from imagining...

video games Game Description Language Multi-task learning Outline of artificial intelligence Transfer learning Pell, Barney (1992). H. van den Herik;...

ensemble deep learning methods.,” J Am Med Inform Assoc, Aug. 2019. D. Zhou, L. Miao, and Y. He, “Position-aware deep multi-task learning for drug–drug...