Results 51 to 60 of about 38,613 (267)

Automatic Curriculum Design for Zero-Shot Human-AI Coordination

open access: yesIEEE Access
Zero-shot human-AI coordination is the training of an ego-agent to coordinate with humans without human data. Most studies on zero-shot human-AI coordination have focused on enhancing the ego-agent’s coordination ability in a given environment ...
Won-Sang You   +3 more
doaj   +1 more source

Zero-shot program representation learning

open access: yesProceedings of the 30th IEEE/ACM International Conference on Program Comprehension, 2022
In 30th International Conference on Program Comprehension (ICPC 22), May 16 to 17, 2022, Virtual Event ...
Nan Cui   +3 more
openaire   +2 more sources

Pharmacological chromatin remodeling enhances response to estrogen therapy in ER+ breast cancer

open access: yesMolecular Oncology, EarlyView.
Estrogen therapy elicits clinical benefit in ~ 30% of patients with endocrine‐resistant estrogen receptor (ER)‐positive breast cancer. Based on findings that ER transcriptional activation underlies response to estrogen therapy, we tested the effects of epigenetic dysregulation via pharmacological inhibition of histone deacetylases (HDACi).
Anneka L. Johnson Thomas   +16 more
wiley   +1 more source

A review on NLP zero-shot and few-shot learning: methods and applications

open access: yesDiscover Applied Sciences
Zero-shot and few-shot learning techniques in natural language processing (NLP), this comprehensive review traces their evolution from traditional methods to cutting-edge approaches like transfer learning and pre-trained language models, semantic ...
G. Ramesh   +6 more
doaj   +1 more source

Polarization‐resolved femtosecond Vis/IR spectroscopy tailored for resolving weak signals in biological samples using minimal sample volume

open access: yesFEBS Open Bio, EarlyView.
Unique biological samples, such as site‐specific mutant proteins, are available only in limited quantities. Here, we present a polarization‐resolved transient infrared spectroscopy setup with referencing to improve signal‐to‐noise tailored towards tracing small signals. We provide an overview of characterizing the excitation conditions for polarization‐
Clark Zahn, Karsten Heyne
wiley   +1 more source

On Zero-Shot Recognition of Generic Objects [PDF]

open access: yes2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Many recent advances in computer vision are the result of a healthy competition among researchers on high quality, task-specific, benchmarks. After a decade of active research, zero-shot learning (ZSL) models accuracy on the Imagenet benchmark remains far too low to be considered for practical object recognition applications.
Tristan Hascoet   +2 more
openaire   +2 more sources

Directed evolution of enzymes at the crossroads of tradition and innovation

open access: yesFEBS Open Bio, EarlyView.
An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova   +2 more
wiley   +1 more source

Zero-shot evaluation reveals limitations of single-cell foundation models

open access: yesGenome Biology
Foundation models such as scGPT and Geneformer have not been rigorously evaluated in a setting where they are used without any further training (i.e., zero-shot).
Kasia Z. Kedzierska   +3 more
doaj   +1 more source

Optimizing photoexcitation conditions for time‐resolved X‐ray solution scattering experiments

open access: yesFEBS Open Bio, EarlyView.
Time‐resolved X‐ray solution scattering (TR‐XSS) is a powerful technique to visualize how proteins change their structure in real time after light activation. Selecting the right laser photoexcitation conditions—fluence, excitation geometry, and sample refresh rate—is critical to maximize the experimental signal while avoiding unwanted side effects ...
Matteo Levantino
wiley   +1 more source

Zero-Shot Task Transfer

open access: yes2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019
In this work, we present a novel meta-learning algorithm, i.e. TTNet, that regresses model parameters for novel tasks for which no ground truth is available (zero-shot tasks). In order to adapt to novel zero-shot tasks, our meta-learner learns from the model parameters of known tasks (with ground truth) and the correlation of known tasks to zero-shot ...
Arghya Pal, Vineeth N. Balasubramanian
openaire   +3 more sources

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