A Compressor Off-Line Washing Schedule Optimization Method With a LSTM Deep Learning Model Predicting the Fouling Trend

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Structured interviews often include past-behavior questions inviting applicants to recount a past work experience. While optimal responses to these questions should take the form of a story, applicants struggle to produce them extemporaneously. Asynchronou ...

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The current generation of large language models (LLMs) has limited chemical knowledge. Recently, it has been shown that these LLMs can learn and predict chemical properties through fine-tuning. Using natural language to train machine learning models opens ...

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Collecting data, extracting value, and combining insights from relational and context-rich sources of many modalities in data processing pipelines presents a challenge for traditional relational DBMS. While relational operators enable declarative and optim ...

A Compressor Off-Line Washing Schedule Optimization Method With a LSTM Deep Learning Model Predicting the Fouling Trend

Chattez avec Graph Search

Machine learning enhanced atom probe tomography analysis

Hadamard product in deep learning: Introduction, Advances and Challenges

Robustly Learning Regions of Attraction From Fixed Data

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Neural networks trained with SGD learn distributions of increasing complexity

Identifying storytelling in job interviews using deep learning

Representing and machine learning complex electronic-structure properties of materials

Songdo Vision: Vehicle Annotations from High-Altitude BeV Drone Imagery in a Smart City

A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs

synMARSS—An End-To-End Platform for the Parametric Generation of Synthetic In Vivo Magnetic Resonance Spectra

LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields

Positive-Unlabeled Constraint Learning for Inferring Nonlinear Continuous Constraints Functions From Expert Demonstrations

Higher-order knowledge-enhanced recommendation with heterogeneous hypergraph multi-attention

Unsupervised quality monitoring of metal additive manufacturing using Bayesian adaptive resonance

Truly No-Regret Learning in Constrained MDPs

Assessment of fine-tuned large language models for real-world chemistry and material science applications

Analysis of Monitoring Strategies for Photovoltaic Water Pumping Systems in Low-Income Remote Areas

Toward Self-Adjusting k-Ary Search Tree Networks

Rigorous Dynamical Mean-Field Theory for Stochastic Gradient Descent Methods

Optimizing Context-Enhanced Relational Joins