Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images...

Multimodal representation learning is a subfield of representation learning focused on integrating and interpreting information from different modalities...

processing, computer vision (vision transformers), reinforcement learning, audio, multimodal learning, robotics, and even playing chess. It has also led to the...

an approach to psychotherapy Multimodal learning, machine learning methods using multiple input modalities Multimodal transport, a contract for delivery...

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn...

reinforcement learning to match OpenAI o1 — at 95% less cost". VentureBeat. Retrieved 2025-01-26. Zia, Dr Tehseen (2024-01-08). "Unveiling of Large Multimodal Models:...

and Meta AI's LLaMA. Many subsequent GPT models have been trained to be multimodal (able to process or generate multiple types of data). For example, GPT-4o...

In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves...

Breakthrough SSM Architecture Exceeding Transformer Efficiency for Multimodal Deep Learning Applications". MarkTechPost. Retrieved 13 January 2024. Wang, Junxiong;...

Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs...

highest dot product is outputted. CLIP has been used as a component in multimodal learning. For example, during the training of Google DeepMind's Flamingo (2022)...

In machine learning, attention is a method that determines the importance of each component in a sequence relative to the other components in that sequence...

Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or...

Multimodal pedagogy is an approach to the teaching of writing that implements different modes of communication. Multimodality refers to the use of visual...

Multimodal interaction provides the user with multiple modes of interacting with a system. A multimodal interface provides several distinct tools for...

In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a computational model inspired by the structure...

In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms...

In deep learning, a multilayer perceptron (MLP) is a name for a modern feedforward neural network consisting of fully connected neurons with nonlinear...

whose middle layer contains recurrent connections that change by a Hebbian learning rule. Later, in Principles of Neurodynamics (1961), he described "closed-loop...

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from...

Q-learning is a reinforcement learning algorithm that trains an agent to assign values to its possible actions based on its current state, without requiring...

learns features via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different...

Curriculum learning is a technique in machine learning in which a model is trained on examples of increasing difficulty, where the definition of "difficulty"...

The International Conference on Learning Representations (ICLR) is a machine learning conference typically held in late April or early May each year....

Multimodality is the application of multiple literacies within one medium. Multiple literacies or "modes" contribute to an audience's understanding of...

primarily employed supervised learning from large amounts of manually labeled data. This reliance on supervised learning limited their use of datasets...

In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable...

breaks in downstream scaling laws. Unlike its predecessors, GPT-4 is a multimodal model: it can take images as well as text as input; this gives it the...

Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. A survey from May 2020...

Waluigi". AI alignment Hallucination Existential risk from AGI Reinforcement learning from human feedback (RLHF) Suffering risks Bereska, Leonard; Gavves, Efstratios...