Results 121 to 130 of about 1,338,397 (280)

Emergent universal long-range structure in random-organizing systems

open access: yesNature Communications
Self-organization through noisy interactions is ubiquitous across physics, mathematics, and machine learning, yet how long-range structure emerges from local noisy dynamics remains poorly understood.
Satyam Anand   +2 more
doaj   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

open access: yesAdvanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena   +3 more
wiley   +1 more source

Path‐Decoupled Cation‐Eutaxy III–V van der Waals Memristive Semiconductors for Mitigating the Neuromorphic Accuracy‐Energy Trade‐off

open access: yesAdvanced Materials, EarlyView.
Path‐decoupled III–V van der Waals memtransistors spatially separate ionic and electronic transport to overcome the conventional trade‐off between accuracy and energy in neuromorphic hardware. Mobile K+ ions in the vdW gaps set a wide conductance window, Gmax/Gmin, while gate‐tunable hole conduction lowers programming energy, enabling reliable ...
Jihong Bae   +13 more
wiley   +1 more source

Mixing of Stochastic Accelerated Gradient Descent

open access: yesCoRR, 2019
We study the mixing properties for stochastic accelerated gradient descent (SAGD) on least-squares regression. First, we show that stochastic gradient descent (SGD) and SAGD are simulating the same invariant distribution. Motivated by this, we then establish mixing rate for SAGD-iterates and compare it with those of SGD-iterates.
Peiyuan Zhang   +2 more
openaire   +3 more sources

“Smelltronics”—From Gas to Smell Sensing

open access: yesAdvanced Materials, EarlyView.
The emerging field of smelltronics, encompassing sensing technologies for complex volatile organic compounds, holds significant potential for extracting valuable chemical information. It facilitates the noninvasive, real‐time monitoring of humans, food, and the environment.
Takeshi Ono   +7 more
wiley   +1 more source

A note on diffusion limits for stochastic gradient descent

open access: yes, 2022
In the machine learning literature stochastic gradient descent has recently been widely discussed for its purported implicit regularization properties. Much of the theory, that attempts to clarify the role of noise in stochastic gradient algorithms, has ...
Lanconelli, Alberto   +1 more
core  

Flax Composites With Improved Interfacial Strength Through Microbially Induced Mineral Precipitation

open access: yesAdvanced Materials, EarlyView.
A bio‐inspired biomineralization strategy introduces an additional hierarchy to flax fiber composites. By controlling microbe‐mediated mineral particle deposition through tuned salt concentrations, stress transfer within the natural fiber composite is enhanced.
Deniz Sayinbas   +5 more
wiley   +1 more source

Asynchronous parallel stochastic gradient descent

open access: yes, 2022
The implementation of a vast majority of machine learning (ML) algorithms boils down to solving a numerical optimization problem. In this context, Stochastic Gradient Descent (SGD) methods have long proven to provide good results, both in terms of ...
Pfreundt, Franz-Josef, Keuper, Janis
core   +1 more source

Atomic‐Scale Detection of Néel Vector Switching in the Single‐Layer A‐Type Antiferromagnet Cr2S3‐2D

open access: yesAdvanced Materials, EarlyView.
Interfacial electron donation from graphene redistributes charge within single‐layer Cr2S3‐2D, preferentially accumulating at the lower Cr/S plane. The resulting minute magnetic imbalance lifts the equivalence of the two magnetic sublayers, enabling atomic‐resolution identification and field‐induced control of Néel‐vector states in a two‐dimensional A ...
Affan Safeer   +11 more
wiley   +1 more source

A note on diffusion limits for stochastic gradient descent [PDF]

open access: yes
In the machine learning literature stochastic gradient descent has recently been widely discussed for its purported implicit regularization properties. Much of the theory, that attempts to clarify the role of noise in stochastic gradient algorithms, has ...
Lanconelli, Alberto   +1 more
core   +1 more source

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