Search Results for author:
Found 24 papers, 15 papers with code
TENG: Time-Evolving Natural Gradient for Solving PDEs with Deep Neural Net
Partial differential equations (PDEs) are instrumental for modeling dynamical systems in science and engineering.
Multimodal Learning for Materials
Artificial intelligence is transforming computational materials science, improving the prediction of material properties, and accelerating the discovery of novel materials.
ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation
Quantum many-body physics simulation has important impacts on understanding fundamental science and has applications to quantum materials design and quantum technology.
Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows
Studying the dynamics of open quantum systems can enable breakthroughs both in fundamental physics and applications to quantum engineering and quantum computation.
Learning to Optimize Quasi-Newton Methods
This paper introduces a novel machine learning optimizer called LODO, which tries to online meta-learn the best preconditioner during optimization.
Discovering Conservation Laws using Optimal Transport and Manifold Learning
We test this new approach on a variety of physical systems and demonstrate that our method is able to both identify the number of conserved quantities and extract their values.
Deep Learning and Symbolic Regression for Discovering Parametric Equations
Symbolic regression is a machine learning technique that can learn the governing formulas of data and thus has the potential to transform scientific discovery.
DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings
We propose DiffCSE, an unsupervised contrastive learning framework for learning sentence embeddings.
Ranked #13 on
Semantic Textual Similarity
on STS16
Topogivity: A Machine-Learned Chemical Rule for Discovering Topological Materials
Topological materials present unconventional electronic properties that make them attractive for both basic science and next-generation technological applications.
End-to-End Optimization of Metasurfaces for Imaging with Compressed Sensing
We present a framework for the end-to-end optimization of metasurface imaging systems that reconstruct targets using compressed sensing, a technique for solving underdetermined imaging problems when the target object exhibits sparsity (i. e. the object can be described by a small number of non-zero values, but the positions of these values are unknown).
Meta-Learning and Self-Supervised Pretraining for Real World Image Translation
In recent years, emerging fields such as meta-learning or self-supervised learning have been closing the gap between proof-of-concept results and real-life applications of machine learning by extending deep-learning to the semi-supervised and few-shot domains.
Equivariant Contrastive Learning
In state-of-the-art self-supervised learning (SSL) pre-training produces semantically good representations by encouraging them to be invariant under meaningful transformations prescribed from human knowledge.
Discovering Sparse Interpretable Dynamics from Partial Observations
Identifying the governing equations of a nonlinear dynamical system is key to both understanding the physical features of the system and constructing an accurate model of the dynamics that generalizes well beyond the available data.
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure
Bayesian optimization (BO) is a popular paradigm for global optimization of expensive black-box functions, but there are many domains where the function is not completely a black-box.
Casimir light in dispersive nanophotonics
Despite interest for their potential applications as sources of quantum light, DVEs are generally very weak, providing many opportunities for enhancement through modern techniques in nanophotonics, such as using media which support excitations such as plasmon and phonon polaritons.
Optics
Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks
The attention mechanism is a key component of the neural revolution in Natural Language Processing (NLP).
On a Novel Application of Wasserstein-Procrustes for Unsupervised Cross-Lingual Learning
We believe that our rethinking of the Wasserstein-Procrustes problem could enable further research, thus helping to develop better algorithms for aligning word embeddings across languages.
Contextualizing Enhances Gradient Based Meta Learning
We propose the implementation of contextualizers, which are generalizable prototypes that adapt to given examples and play a larger role in classification for gradient-based models.
OccamNet: A Fast Neural Model for Symbolic Regression at Scale
Neural networks' expressiveness comes at the cost of complex, black-box models that often extrapolate poorly beyond the domain of the training dataset, conflicting with the goal of finding compact analytic expressions to describe scientific data.
Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery
We find that the EQL-based architecture can extrapolate quite well outside of the training data set compared to a standard neural network-based architecture, paving the way for deep learning to be applied in scientific exploration and discovery.
Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning
Our method for discovering interpretable latent parameters in spatiotemporal systems will allow us to better analyze and understand real-world phenomena and datasets, which often have unknown and uncontrolled variables that alter the system dynamics and cause varying behaviors that are difficult to disentangle.
Gated Orthogonal Recurrent Units: On Learning to Forget
We present a novel recurrent neural network (RNN) based model that combines the remembering ability of unitary RNNs with the ability of gated RNNs to effectively forget redundant/irrelevant information in its memory.
Ranked #7 on
Question Answering
on bAbi
(Accuracy (trained on 1k) metric)
Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs
Using unitary (instead of general) matrices in artificial neural networks (ANNs) is a promising way to solve the gradient explosion/vanishing problem, as well as to enable ANNs to learn long-term correlations in the data.
Binary matrices of optimal autocorrelations as alignment marks
We define a new class of binary matrices by maximizing the peak-sidelobe distances in the aperiodic autocorrelations.
