176 research outputs found
Your story, your life, your learning: Autobiography Reveals Basis for Supporting Personalized, Holistic Pedagogy
Each person ongoingly experiences the world uniquely through vital processes shaping their subjectivity, personhood and sense of self. Learning, an innate characteristic or modality of each human life, of living, likewise arises subjectively or idiosyncratically. In this paper, a phenomenological lens is applied to auto/biographical excerpts concerned with various learning experiences to help reveal essential, subjective characteristics of emergent learning. The insights help establish a basis for challenging the primacy of objectivist learning evaluations. The insights also confirm the importance of personalizing learning as a pedagogical gesture nurturing and enfranchising student learning in significant ways beyond conventional educational approaches that generally ignore subjectivity. Personalized, holistic learning is also proposed here as a solution to address many challenges and issues emerging from the Covid-19 pandemic. This paper is based on the author’s recent PhD research
MoleCLUEs: Optimizing Molecular Conformers by Minimization of Differentiable Uncertainty
Structure-based models in the molecular sciences can be highly sensitive to
input geometries and give predictions with large variance under subtle
coordinate perturbations. We present an approach to mitigate this failure mode
by generating conformations that explicitly minimize uncertainty in a
predictive model. To achieve this, we compute differentiable estimates of
aleatoric \textit{and} epistemic uncertainties directly from learned
embeddings. We then train an optimizer that iteratively samples embeddings to
reduce these uncertainties according to their gradients. As our predictive
model is constructed as a variational autoencoder, the new embeddings can be
decoded to their corresponding inputs, which we call \textit{MoleCLUEs}, or
(molecular) counterfactual latent uncertainty explanations
\citep{antoran2020getting}. We provide results of our algorithm for the task of
predicting drug properties with maximum confidence as well as analysis of the
differentiable structure simulations.Comment: Submitted to the Differentiable Almost Everything Workshop, ICML 202
SeO₂-Mediated Oxidative Transposition of Pauson–Khand Products
Oxidative transpositions of bicyclic cyclopentenones mediated by selenium dioxide (SeO₂) are disclosed. Treatment of Pauson–Khand reaction (PKR) products with SeO₂ in the presence or absence of water furnishes di- and trioxidized cyclopentenones, respectively. Mechanistic investigations reveal multiple competing oxidation pathways that depend on substrate identity and water concentration. Functionalization of the oxidized products via cross-coupling methods demonstrates their synthetic utility. These transformations allow rapid access to oxidatively transposed cyclopentenones from simple PKR products
BOtied: Multi-objective Bayesian optimization with tied multivariate ranks
Many scientific and industrial applications require joint optimization of
multiple, potentially competing objectives. Multi-objective Bayesian
optimization (MOBO) is a sample-efficient framework for identifying
Pareto-optimal solutions. We show a natural connection between non-dominated
solutions and the highest multivariate rank, which coincides with the outermost
level line of the joint cumulative distribution function (CDF). We propose the
CDF indicator, a Pareto-compliant metric for evaluating the quality of
approximate Pareto sets that complements the popular hypervolume indicator. At
the heart of MOBO is the acquisition function, which determines the next
candidate to evaluate by navigating the best compromises among the objectives.
Multi-objective acquisition functions that rely on box decomposition of the
objective space, such as the expected hypervolume improvement (EHVI) and
entropy search, scale poorly to a large number of objectives. We propose an
acquisition function, called BOtied, based on the CDF indicator. BOtied can be
implemented efficiently with copulas, a statistical tool for modeling complex,
high-dimensional distributions. We benchmark BOtied against common acquisition
functions, including EHVI and random scalarization (ParEGO), in a series of
synthetic and real-data experiments. BOtied performs on par with the baselines
across datasets and metrics while being computationally efficient.Comment: 10 pages (+5 appendix), 9 figures. Submitted to NeurIP
3D Computer Vision Models Predict DFT-Level HOMO-LUMO Gap Energies from Force-Field-Optimized Geometries
We investigate 3D deep learning methods for predicting quantum mechanical energies at high-theory-level accuracy from inexpensive, rapidly computed molecular geometries. Using space-filled volumetric representations (voxels), we explore the effects of radial decay from atom centers and rotational data augmentation on learnability. We test several published computer vision models for 3D shape learning, and construct our own architecture based on 3D inception networks with physically meaningful kernels. We provide a framework for further studies and propose a modeling challenge for the computer vision and molecular machine learning communities
Multilabel Classification Models for the Prediction of Cross-Coupling Reaction Conditions
Machine-learned ranking models have been developed for the prediction of substrate-specific cross-coupling reaction conditions. Data sets of published reactions were curated for Suzuki, Negishi, and C–N couplings, as well as Pauson–Khand reactions. String, descriptor, and graph encodings were tested as input representations, and models were trained to predict the set of conditions used in a reaction as a binary vector. Unique reagent dictionaries categorized by expert-crafted reaction roles were constructed for each data set, leading to context-aware predictions. We find that relational graph convolutional networks and gradient-boosting machines are very effective for this learning task, and we disclose a novel reaction-level graph attention operation in the top-performing model
Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions
We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reactions from the organic chemistry literature. We evaluate seven different GNN architectures for classification tasks pertaining to the identification of experimental reagents and conditions. We find that models are able to identify specific graph features that affect reaction conditions and lead to accurate predictions. The results herein show great promise in advancing molecular machine learning
Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions
We present a systematic investigation using graph neural networks (GNNs) to
model organic chemical reactions. To do so, we prepared a dataset collection of
four ubiquitous reactions from the organic chemistry literature. We evaluate
seven different GNN architectures for classification tasks pertaining to the
identification of experimental reagents and conditions. We find that models are
able to identify specific graph features that affect reaction conditions and
lead to accurate predictions. The results herein show great promise in
advancing molecular machine learning.Comment: 23 pages, 10 tables, 13 figures, to appear in the ICML 2020 Workshop
on Graph Representation Learning and Beyond (GRLB
Multilabel Classification Models for the Prediction of Cross-Coupling Reaction Conditions
Machine-learned ranking models have been developed for the prediction of substrate-specific cross-coupling reaction conditions. Data sets of published reactions were curated for Suzuki, Negishi, and C–N couplings, as well as Pauson–Khand reactions. String, descriptor, and graph encodings were tested as input representations, and models were trained to predict the set of conditions used in a reaction as a binary vector. Unique reagent dictionaries categorized by expert-crafted reaction roles were constructed for each data set, leading to context-aware predictions. We find that relational graph convolutional networks and gradient-boosting machines are very effective for this learning task, and we disclose a novel reaction-level graph attention operation in the top-performing model
SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers
We investigate Siamese networks for learning related embeddings for augmented
samples of molecular conformers. We find that a non-contrastive (positive-pair
only) auxiliary task aids in supervised training of Euclidean neural networks
(E3NNs) and increases manifold smoothness (MS) around point-cloud geometries.
We demonstrate this property for multiple drug-activity prediction tasks while
maintaining relevant performance metrics, and propose an extension of MS to
probabilistic and regression settings. We provide an analysis of representation
collapse, finding substantial effects of task-weighting, latent dimension, and
regularization. We expect the presented protocol to aid in the development of
reliable E3NNs from molecular conformers, even for small-data drug discovery
programs.Comment: Submitted to the MLDD workshop, ICLR 202
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