Results 61 to 70 of about 37,169,246 (294)
FEBS Open Bio, EarlyView.This pilot study investigates the potential of Generative AI to provide formative feedback to students. ChatGPT was prompted to provide feedback on Year 1 Bioscience essays, which were evaluated against established good feedback practices. GenAI‐authored feedback had useful elements, but was limited in scope. GenAI may have potential to provide instant,
Annabel Court +3 more
wiley +1 more source
Model-Based Offline Quantum Reinforcement Learning
2024 IEEE International Conference on Quantum Computing and Engineering (QCE)This paper presents the first algorithm for model-based offline quantum reinforcement learning and demonstrates its functionality on the cart-pole benchmark. The model and the policy to be optimized are each implemented as variational quantum circuits. The model is trained by gradient descent to fit a pre-recorded data set. The policy is optimized with
Simon Eisenmann +3 more
openaire +2 more sources
A nationwide student survey on biochemistry learning resources in Italy
FEBS Open Bio, EarlyView.We conducted a survey among Italian university students to investigate their preferred study materials for biochemistry. We then analyzed the collected data to identify these materials. This information may be valuable to the Biochemistry educational community. The graphical abstract was created with Claude (Opus 5.5; Anthropic, 2026).
Anna Marabotti +4 more
wiley +1 more source
Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages
Sensors, 2021 The use of neural networks and reinforcement learning has become increasingly popular in autonomous vehicle control. However, the opaqueness of the resulting control policies presents a significant barrier to deploying neural network-based control in ...
Sampo Kuutti +2 more
doaj +1 more source
Model-Based Reinforcement Learning With Isolated Imaginations
IEEE Transactions on Pattern Analysis and Machine IntelligenceWorld models learn the consequences of actions in vision-based interactive systems. However, in practical scenarios like autonomous driving, noncontrollable dynamics that are independent or sparsely dependent on action signals often exist, making it challenging to learn effective world models.
Minting Pan +4 more
openaire +5 more sources
A Survey on Offline Model-Based Reinforcement Learning
CoRR, 2023 Model-based approaches are becoming increasingly popular in the field of offline reinforcement learning, with high potential in real-world applications due to the model's capability of thoroughly utilizing the large historical datasets available with supervised learning techniques. This paper presents a literature review of recent work in offline model-
openaire +3 more sources
Objective Mismatch in Model-based Reinforcement Learning
CoRR, 2020 9 pages, 2 pages references, 5 pages ...
Nathan O. Lambert +3 more
openaire +4 more sources
Maximum Entropy Model-based Reinforcement Learning
CoRR, 2021 NeurIPS'2021 Deep Reinforcement Learning ...
Oleg Svidchenko, Aleksei Shpilman
openaire +2 more sources
De novo drug design using reinforcement learning with graph-based deep generative models [PDF]
, 2021 Machine learning methods have proven to be effective tools for molecular design, allowing for efficient exploration of the vast chemical space via deep molecular generative models.
Sara, Romeo Atance +4 more
core +1 more source
RNA Sequencing Resolves Cryptic Pathogenic Variants in Mitochondrial Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Mitochondrial diseases are the most common inherited metabolic disorders, characterized by pronounced clinical and genetic heterogeneity that complicates molecular diagnosis. Although DNA‐based sequencing approaches have become standard in genetic testing, up to half of patients remain without a definitive diagnosis.
Zhimei Liu +21 more
wiley +1 more source