Named Entity Recognition (NER) state-ofthe-art methods requires high-quality labeled datasets. Is... more Named Entity Recognition (NER) state-ofthe-art methods requires high-quality labeled datasets. Issues such as scarcity of labeled data, under-representation of entities, and privacy concerns with using sensitive data for training, can be significant barriers. Generating synthetic data to train models is a promising solution to mitigate these problems. We propose ECG-QALM, a contextual question and answering approach using pre-trained language models to synthetically generate entitycontrolled text. Generated text is then used to augment small labeled datasets for downstream NER tasks. We evaluate our method on two publicly available datasets. We find ECG-QALM is capable of producing full text samples with desired entities appearing in a controllable way, while retaining sentence coherence closest to the real world data. Evaluations on NER tasks show significant improvements (75%-140%) in low-labeled data regimes.
2022 International Joint Conference on Neural Networks (IJCNN), Jul 18, 2022
Eigenvalue problems are critical to several fields of science and engineering. We expand on the m... more Eigenvalue problems are critical to several fields of science and engineering. We expand on the method of using unsupervised neural networks for discovering eigenfunctions and eigenvalues for differential eigenvalue problems. The obtained solutions are given in an analytical and differentiable form that identically satisfies the desired boundary conditions. The network optimization is data-free and depends solely on the predictions of the neural network. We introduce two physics-informed loss functions. The first, called ortho-loss, motivates the network to discover pair-wise orthogonal eigenfunctions. The second loss term, called norm-loss, requests the discovery of normalized eigenfunctions and is used to avoid trivial solutions. We find that embedding even or odd symmetries to the neural network architecture further improves the convergence for relevant problems. Lastly, a patience condition can be used to automatically recognize eigenfunction solutions. This proposed unsupervised learning method is used to solve the finite well, multiple finite wells, and hydrogen atom eigenvalue quantum problems.
Eigenvalue problems are critical to several fields of science and engineering. We present a novel... more Eigenvalue problems are critical to several fields of science and engineering. We present a novel unsupervised neural network for discovering eigenfunctions and eigenvalues for differential eigenvalue problems with solutions that identically satisfy the boundary conditions. A scanning mechanism is embedded allowing the method to find an arbitrary number of solutions. The network optimization is data-free and depends solely on the predictions. The unsupervised method is used to solve the quantum infinite well and quantum oscillator eigenvalue problems. Preprint. Under review.
Findings of the Association for Computational Linguistics: ACL 2023
Named Entity Recognition (NER) state-ofthe-art methods requires high-quality labeled datasets. Is... more Named Entity Recognition (NER) state-ofthe-art methods requires high-quality labeled datasets. Issues such as scarcity of labeled data, under-representation of entities, and privacy concerns with using sensitive data for training, can be significant barriers. Generating synthetic data to train models is a promising solution to mitigate these problems. We propose ECG-QALM, a contextual question and answering approach using pre-trained language models to synthetically generate entitycontrolled text. Generated text is then used to augment small labeled datasets for downstream NER tasks. We evaluate our method on two publicly available datasets. We find ECG-QALM is capable of producing full text samples with desired entities appearing in a controllable way, while retaining sentence coherence closest to the real world data. Evaluations on NER tasks show significant improvements (75%-140%) in low-labeled data regimes.
2022 International Joint Conference on Neural Networks (IJCNN)
Eigenvalue problems are critical to several fields of science and engineering. We expand on the m... more Eigenvalue problems are critical to several fields of science and engineering. We expand on the method of using unsupervised neural networks for discovering eigenfunctions and eigenvalues for differential eigenvalue problems. The obtained solutions are given in an analytical and differentiable form that identically satisfies the desired boundary conditions. The network optimization is data-free and depends solely on the predictions of the neural network. We introduce two physics-informed loss functions. The first, called ortho-loss, motivates the network to discover pair-wise orthogonal eigenfunctions. The second loss term, called norm-loss, requests the discovery of normalized eigenfunctions and is used to avoid trivial solutions. We find that embedding even or odd symmetries to the neural network architecture further improves the convergence for relevant problems. Lastly, a patience condition can be used to automatically recognize eigenfunction solutions. This proposed unsupervised learning method is used to solve the finite well, multiple finite wells, and hydrogen atom eigenvalue quantum problems.
Machine learning applications such as finance and medicine demand accurate and justifiable predic... more Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use. In response, previous work combines decision trees with deep learning, yielding models that (1) sacrifice interpretability to maintain accuracy or (2) underperform modern neural networks to maintain interpretability. We forgo this dilemma by proposing Neural-Backed Decision Trees (NBDTs), modified hierarchical classifiers that use trees constructed in weight-space. Our NBDTs achieve (1) interpretability and (2) neural network accuracy: We preserve interpretable properties -- e.g., leaf purity and a non-ensembled model -- and demonstrate interpretability of model predictions both qualitatively and quantitatively. Furthermore, NBDTs match state-of-the-art neural networks on CIFAR10, CIFAR100, TinyImageNet, and ImageNet to within 1-2%. This yields state-of-the-art interpretable models on ImageNet, with NBDTs besting all decision-tree-based...
Eigenvalue problems are critical to several fields of science and engineering. We present a novel... more Eigenvalue problems are critical to several fields of science and engineering. We present a novel unsupervised neural network for discovering eigenfunctions and eigenvalues for differential eigenvalue problems with solutions that identically satisfy the boundary conditions. A scanning mechanism is embedded allowing the method to find an arbitrary number of solutions. The network optimization is data-free and depends solely on the predictions. The unsupervised method is used to solve the quantum infinite well and quantum oscillator eigenvalue problems.
Named Entity Recognition (NER) state-ofthe-art methods requires high-quality labeled datasets. Is... more Named Entity Recognition (NER) state-ofthe-art methods requires high-quality labeled datasets. Issues such as scarcity of labeled data, under-representation of entities, and privacy concerns with using sensitive data for training, can be significant barriers. Generating synthetic data to train models is a promising solution to mitigate these problems. We propose ECG-QALM, a contextual question and answering approach using pre-trained language models to synthetically generate entitycontrolled text. Generated text is then used to augment small labeled datasets for downstream NER tasks. We evaluate our method on two publicly available datasets. We find ECG-QALM is capable of producing full text samples with desired entities appearing in a controllable way, while retaining sentence coherence closest to the real world data. Evaluations on NER tasks show significant improvements (75%-140%) in low-labeled data regimes.
2022 International Joint Conference on Neural Networks (IJCNN), Jul 18, 2022
Eigenvalue problems are critical to several fields of science and engineering. We expand on the m... more Eigenvalue problems are critical to several fields of science and engineering. We expand on the method of using unsupervised neural networks for discovering eigenfunctions and eigenvalues for differential eigenvalue problems. The obtained solutions are given in an analytical and differentiable form that identically satisfies the desired boundary conditions. The network optimization is data-free and depends solely on the predictions of the neural network. We introduce two physics-informed loss functions. The first, called ortho-loss, motivates the network to discover pair-wise orthogonal eigenfunctions. The second loss term, called norm-loss, requests the discovery of normalized eigenfunctions and is used to avoid trivial solutions. We find that embedding even or odd symmetries to the neural network architecture further improves the convergence for relevant problems. Lastly, a patience condition can be used to automatically recognize eigenfunction solutions. This proposed unsupervised learning method is used to solve the finite well, multiple finite wells, and hydrogen atom eigenvalue quantum problems.
Eigenvalue problems are critical to several fields of science and engineering. We present a novel... more Eigenvalue problems are critical to several fields of science and engineering. We present a novel unsupervised neural network for discovering eigenfunctions and eigenvalues for differential eigenvalue problems with solutions that identically satisfy the boundary conditions. A scanning mechanism is embedded allowing the method to find an arbitrary number of solutions. The network optimization is data-free and depends solely on the predictions. The unsupervised method is used to solve the quantum infinite well and quantum oscillator eigenvalue problems. Preprint. Under review.
Findings of the Association for Computational Linguistics: ACL 2023
Named Entity Recognition (NER) state-ofthe-art methods requires high-quality labeled datasets. Is... more Named Entity Recognition (NER) state-ofthe-art methods requires high-quality labeled datasets. Issues such as scarcity of labeled data, under-representation of entities, and privacy concerns with using sensitive data for training, can be significant barriers. Generating synthetic data to train models is a promising solution to mitigate these problems. We propose ECG-QALM, a contextual question and answering approach using pre-trained language models to synthetically generate entitycontrolled text. Generated text is then used to augment small labeled datasets for downstream NER tasks. We evaluate our method on two publicly available datasets. We find ECG-QALM is capable of producing full text samples with desired entities appearing in a controllable way, while retaining sentence coherence closest to the real world data. Evaluations on NER tasks show significant improvements (75%-140%) in low-labeled data regimes.
2022 International Joint Conference on Neural Networks (IJCNN)
Eigenvalue problems are critical to several fields of science and engineering. We expand on the m... more Eigenvalue problems are critical to several fields of science and engineering. We expand on the method of using unsupervised neural networks for discovering eigenfunctions and eigenvalues for differential eigenvalue problems. The obtained solutions are given in an analytical and differentiable form that identically satisfies the desired boundary conditions. The network optimization is data-free and depends solely on the predictions of the neural network. We introduce two physics-informed loss functions. The first, called ortho-loss, motivates the network to discover pair-wise orthogonal eigenfunctions. The second loss term, called norm-loss, requests the discovery of normalized eigenfunctions and is used to avoid trivial solutions. We find that embedding even or odd symmetries to the neural network architecture further improves the convergence for relevant problems. Lastly, a patience condition can be used to automatically recognize eigenfunction solutions. This proposed unsupervised learning method is used to solve the finite well, multiple finite wells, and hydrogen atom eigenvalue quantum problems.
Machine learning applications such as finance and medicine demand accurate and justifiable predic... more Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use. In response, previous work combines decision trees with deep learning, yielding models that (1) sacrifice interpretability to maintain accuracy or (2) underperform modern neural networks to maintain interpretability. We forgo this dilemma by proposing Neural-Backed Decision Trees (NBDTs), modified hierarchical classifiers that use trees constructed in weight-space. Our NBDTs achieve (1) interpretability and (2) neural network accuracy: We preserve interpretable properties -- e.g., leaf purity and a non-ensembled model -- and demonstrate interpretability of model predictions both qualitatively and quantitatively. Furthermore, NBDTs match state-of-the-art neural networks on CIFAR10, CIFAR100, TinyImageNet, and ImageNet to within 1-2%. This yields state-of-the-art interpretable models on ImageNet, with NBDTs besting all decision-tree-based...
Eigenvalue problems are critical to several fields of science and engineering. We present a novel... more Eigenvalue problems are critical to several fields of science and engineering. We present a novel unsupervised neural network for discovering eigenfunctions and eigenvalues for differential eigenvalue problems with solutions that identically satisfy the boundary conditions. A scanning mechanism is embedded allowing the method to find an arbitrary number of solutions. The network optimization is data-free and depends solely on the predictions. The unsupervised method is used to solve the quantum infinite well and quantum oscillator eigenvalue problems.
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