Search Results for author:
Found 16 papers, 8 papers with code
Scaling Law of Sim2Real Transfer Learning in Expanding Computational Materials Databases for Real-World Predictions
To address the challenge of limited experimental materials data, extensive physical property databases are being developed based on high-throughput computational experiments, such as molecular dynamics simulations.
Advancing Extrapolative Predictions of Material Properties through Learning to Learn
Recent advancements in machine learning have showcased its potential to significantly accelerate the discovery of new materials.
Tree-Planted Transformers: Unidirectional Transformer Language Models with Implicit Syntactic Supervision
Syntactic Language Models (SLMs) can be trained efficiently to reach relatively high performance; however, they have trouble with inference efficiency due to the explicit generation of syntactic structures.
Emergent Word Order Universals from Cognitively-Motivated Language Models
The world's languages exhibit certain so-called typological or implicational universals; for example, Subject-Object-Verb (SOV) languages typically use postpositions.
Shotgun crystal structure prediction using machine-learned formation energies
Stable or metastable crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface within a broad space of atomic configurations.
Composition, Attention, or Both?
In this paper, we propose a novel architecture called Composition Attention Grammars (CAGs) that recursively compose subtrees into a single vector representation with a composition function, and selectively attend to previous structural information with a self-attention mechanism.
Transfer learning with affine model transformation
Supervised transfer learning has received considerable attention due to its potential to boost the predictive power of machine learning in scenarios where data are scarce.
Bayesian Sequential Stacking Algorithm for Concurrently Designing Molecules and Synthetic Reaction Networks
The design variables consist of a set of reactants in a reaction network and its network topology.
Crystal structure prediction with machine learning-based element substitution
The prediction of energetically stable crystal structures formed by a given chemical composition is a central problem in solid-state physics.
Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammars
In computational linguistics, it has been shown that hierarchical structures make language models (LMs) more human-like.
Lower Perplexity is Not Always Human-Like
Overall, our results suggest that a cross-lingual evaluation will be necessary to construct human-like computational models.
A General Class of Transfer Learning Regression without Implementation Cost
We propose a novel framework that unifies and extends existing methods of transfer learning (TL) for regression.
A Bayesian algorithm for retrosynthesis
The identification of synthetic routes that end with a desired product has been an inherently time-consuming process that is largely dependent on expert knowledge regarding a limited fraction of the entire reaction space.
Recreation of the Periodic Table with an Unsupervised Machine Learning Algorithm
In 1869, the first draft of the periodic table was published by Russian chemist Dmitri Mendeleev.
SPF-CellTracker: Tracking multiple cells with strongly-correlated moves using a spatial particle filter
Data types to which the method is applicable are characterized as follows: (i) cells are imaged as globular-like objects, (ii) it is difficult to distinguish cells based only on shape and size, (iii) the number of imaged cells ranges in several hundreds, (iv) moves of nearly-located cells are strongly correlated and (v) cells do not divide.
