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1. A quantitative uncertainty metric controls error in neural network-driven chemical discovery (Electronic supplementary information (ESI) available: Information about ensembles and mc-dropout procedure, information about DFT methods, information about training ligands and structures, information about CSD test cases, PCA decay plots and model performance information for inorganic dataset, comparison between single ANN and ensemble predictions and distribution of CSD errors, error distributions with different distance metrics and numbers of neighbors, correlation between errors and uncertainty metrics, maximum and average retained inorganic errors as function of uncertain metrics, CSD codes used to calibrate latent distance model, variation in uncertainty model calibration parameters for CSD data, type I error rates and with retained errors, architecture and hyperparameters used for QM9 prediction task, performance results for different architectures on QM9 prediction task, variation in QM9 performance with test/train split, performance and error distribution for QM9 for single ANN and ensembles, correlation between QM9 errors and uncertainty metrics, retained mean QM9 errors with different uncertainty metrics, variation in uncertainty model calibration parameters for QM9 data, distribution of predicted and actual errors for QM9 data, results of active learning experiment, hyperparameters for inorganic ANN (PDF). Summary DFT results and information for training and CSD data; model predictions, errors and uncertainties for all tests; DFT-optimized geometries of training and CSD data; model weights, architectures and scaling data for inorganic and QM9 ANNs (ZIP). See DOI: 10.1039/c9sc02298h)
Chemical Science, ISSN 2041-6520, 01/2019, Volume 10, Issue 34, pp. 7913 - 7922
Machine learning (ML) models, such as artificial neural networks, have emerged as a complement to high-throughput screening, enabling characterization of new... 
Data points | Active learning | Artificial neural networks | Calibration | Learning theory | Training | Organic chemistry | Errors | Neural networks | Parameter uncertainty | Machine learning | Predictions | Cutoffs | Mathematical models | Space exploration | Predictive control
Journal Article
Journal Article
Behavioural Brain Research, ISSN 0166-4328, 02/2015, Volume 278, pp. 375 - 384
Memories are not static imprints of past experience, but rather are dynamic entities which enable us to predict outcomes of future situations and inform... 
Reconsolidation | NMDAR | Dopamine | Memory | Prediction error | PROTEIN-KINASE-A | LONG-TERM-MEMORY | BETA-ADRENERGIC-RECEPTORS | COCAINE-SEEKING BEHAVIOR | CONDITIONED PLACE PREFERENCE | FEAR MEMORIES | NEUROSCIENCES | DOPAMINE NEURONS | NUCLEUS-ACCUMBENS CORE | APPETITIVE LEARNING-PARADIGM | BEHAVIORAL SCIENCES | REACTIVATION-DEPENDENT AMNESIA
Journal Article
Journal Article
Journal of Neuroscience, ISSN 0270-6474, 09/2009, Volume 29, Issue 39, pp. 12183 - 12186
For social beings like humans, detecting one's own and others' errors is essential for efficient goal-directed behavior. Although one's own errors are always... 
SYSTEM | CHOICE | ANTERIOR CINGULATE CORTEX | DECISION-MAKING | MEDIAL FRONTAL-CORTEX | NEUROSCIENCES | PREDICTION | Young Adult | Photic Stimulation - methods | Humans | Adult | Female | Male | Brain Mapping - methods | Reaction Time - physiology | Reward | Psychomotor Performance - physiology | Research Design | Brief Communications
Journal Article
Journal of Climate, ISSN 0894-8755, 02/2014, Volume 27, Issue 4, pp. 1781 - 1798
The present study examines the correspondence between short- and long-term systematic errors in five atmospheric models by comparing the 16 five-day hindcast... 
COMMUNITY-ATMOSPHERE-MODEL | WEATHER | Model errors | CONFIGURATION | Climate models | Model comparison | SOUTHEAST PACIFIC STRATOCUMULUS | PREDICTION | CLOUD BIASES | performance | SYSTEMATIC-ERRORS | Model evaluation | SIMULATIONS | CAM2 | METEOROLOGY & ATMOSPHERIC SCIENCES | Rain | Precipitation | Clouds | Bias | Standard deviation | Physics | Meteorology | GEOSCIENCES | ENVIRONMENTAL SCIENCES
Journal Article
Frontiers in Psychology, ISSN 1664-1078, 2012, Volume 3, p. 548
Prediction errors (PE) are a central notion in theoretical models of reinforcement learning, perceptual inference, decision-making and cognition, and... 
Decision-making | Learning | Perceptual inference | Prediction | Prediction error | Predictive coding | Expectation | PSYCHOLOGY, MULTIDISCIPLINARY | predictive coding | decision-making | perceptual inference | prediction | learning | prediction error | expectation | Perception | Motivation | Attention
Journal Article