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03/20/2023
Model Stitching: Looking For Functional Similarity Between Representations
Model stitching (Lenc Vedaldi 2015) is a compelling methodology to c...
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03/04/2023
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries
Deep ensembles (DE) have been successful in improving model performance ...
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02/23/2023
Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows
Studying the dynamics of open quantum systems holds the potential to ena...
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02/06/2023
Geometry of contact: contact planning for multi-legged robots via spin models duality
Contact planning is crucial in locomoting systems.Specifically, appropri...
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11/02/2022
Koopman Operator learning for Accelerating Quantum Optimization and Machine Learning
Finding efficient optimization methods plays an important role for quant...
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10/11/2022
Learning to Optimize Quasi-Newton Methods
We introduce a novel machine learning optimizer called LODO, which onlin...
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10/10/2022
On the Importance of Calibration in Semi-supervised Learning
State-of-the-art (SOTA) semi-supervised learning (SSL) methods have been...
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10/02/2022
AI-Assisted Discovery of Quantitative and Formal Models in Social Science
In social science, formal and quantitative models, such as ones describi...
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08/31/2022
Discovering Conservation Laws using Optimal Transport and Manifold Learning
Conservation laws are key theoretical and practical tools for understand...
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07/01/2022
Deep Learning and Symbolic Regression for Discovering Parametric Equations
Symbolic regression is a machine learning technique that can learn the g...
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12/22/2021
Meta-Learning and Self-Supervised Pretraining for Real World Image Translation
Recent advances in deep learning, in particular enabled by hardware adva...
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10/15/2021
Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science
Deep learning techniques have been increasingly applied to the natural s...
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07/22/2021
Discovering Sparse Interpretable Dynamics from Partial Observations
Identifying the governing equations of a nonlinear dynamical system is k...
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04/23/2021
Scalable and Flexible Deep Bayesian Optimization with Auxiliary Information for Scientific Problems
Bayesian optimization (BO) is a popular paradigm for global optimization...
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07/18/2020
On a Novel Application of Wasserstein-Procrustes for Unsupervised Cross-Lingual Learning
The emergence of unsupervised word embeddings, pre-trained on very large...
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07/16/2020
Interpretable Neuroevolutionary Models for Learning Non-Differentiable Functions and Programs
A key factor in the modern success of deep learning is the astonishing e...
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01/27/2020
Deep learning with coherent nanophotonic circuits
Artificial neural networks are computational network models inspired by ...
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12/10/2019
Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery
Symbolic regression is a powerful technique that can discover analytical...
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07/13/2019
Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning
Experimental data is often affected by uncontrolled variables that make ...
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11/28/2018
WaveletNet: Logarithmic Scale Efficient Convolutional Neural Networks for Edge Devices
We present a logarithmic-scale efficient convolutional neural network ar...
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08/27/2018
Migrating Knowledge between Physical Scenarios based on Artificial Neural Networks
Deep learning is known to be data-hungry, which hinders its application ...
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10/26/2017
Rotational Unit of Memory
The concepts of unitary evolution matrices and associative memory have b...
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06/08/2017
Gated Orthogonal Recurrent Units: On Learning to Forget
We present a novel recurrent neural network (RNN) based model that combi...
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12/15/2016
Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs
Using unitary (instead of general) matrices in artificial neural network...
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08/29/2014