930 research outputs found
Reinforcement Learning for Generative AI: A Survey
Deep Generative AI has been a long-standing essential topic in the machine
learning community, which can impact a number of application areas like text
generation and computer vision. The major paradigm to train a generative model
is maximum likelihood estimation, which pushes the learner to capture and
approximate the target data distribution by decreasing the divergence between
the model distribution and the target distribution. This formulation
successfully establishes the objective of generative tasks, while it is
incapable of satisfying all the requirements that a user might expect from a
generative model. Reinforcement learning, serving as a competitive option to
inject new training signals by creating new objectives that exploit novel
signals, has demonstrated its power and flexibility to incorporate human
inductive bias from multiple angles, such as adversarial learning,
hand-designed rules and learned reward model to build a performant model.
Thereby, reinforcement learning has become a trending research field and has
stretched the limits of generative AI in both model design and application. It
is reasonable to summarize and conclude advances in recent years with a
comprehensive review. Although there are surveys in different application areas
recently, this survey aims to shed light on a high-level review that spans a
range of application areas. We provide a rigorous taxonomy in this area and
make sufficient coverage on various models and applications. Notably, we also
surveyed the fast-developing large language model area. We conclude this survey
by showing the potential directions that might tackle the limit of current
models and expand the frontiers for generative AI
SuperNet in Neural Architecture Search: A Taxonomic Survey
Deep Neural Networks (DNN) have made significant progress in a wide range of
visual recognition tasks such as image classification, object detection, and
semantic segmentation. The evolution of convolutional architectures has led to
better performance by incurring expensive computational costs. In addition,
network design has become a difficult task, which is labor-intensive and
requires a high level of domain knowledge. To mitigate such issues, there have
been studies for a variety of neural architecture search methods that
automatically search for optimal architectures, achieving models with
impressive performance that outperform human-designed counterparts. This survey
aims to provide an overview of existing works in this field of research and
specifically focus on the supernet optimization that builds a neural network
that assembles all the architectures as its sub models by using weight sharing.
We aim to accomplish that by categorizing supernet optimization by proposing
them as solutions to the common challenges found in the literature: data-side
optimization, poor rank correlation alleviation, and transferable NAS for a
number of deployment scenarios
Automatic machine learning:methods, systems, challenges
This open access book presents the first comprehensive overview of general methods in Automatic Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first international challenge of AutoML systems. The book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. Many of the recent machine learning successes crucially rely on human experts, who select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters; however the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself
Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration
Future AI applications require performance, reliability and privacy that the
existing, cloud-dependant system architectures cannot provide. In this article,
we study orchestration in the device-edge-cloud continuum, and focus on AI for
edge, that is, the AI methods used in resource orchestration. We claim that to
support the constantly growing requirements of intelligent applications in the
device-edge-cloud computing continuum, resource orchestration needs to embrace
edge AI and emphasize local autonomy and intelligence. To justify the claim, we
provide a general definition for continuum orchestration, and look at how
current and emerging orchestration paradigms are suitable for the computing
continuum. We describe certain major emerging research themes that may affect
future orchestration, and provide an early vision of an orchestration paradigm
that embraces those research themes. Finally, we survey current key edge AI
methods and look at how they may contribute into fulfilling the vision of
future continuum orchestration.Comment: 50 pages, 8 figures (Revised content in all sections, added figures
and new section
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