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07/23/2023
Tackling the Curse of Dimensionality with Physics-Informed Neural Networks
The curse-of-dimensionality (CoD) taxes computational resources heavily ...
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05/23/2023
Automatic Model Selection with Large Language Models for Reasoning
Chain-of-Thought and Program-Aided Language Models represent two distinc...
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05/01/2023
Decomposition Enhances Reasoning via Self-Evaluation Guided Decoding
We endow Large Language Models (LLMs) with fine-grained self-evaluation ...
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04/08/2023
Last-Layer Fairness Fine-tuning is Simple and Effective for Neural Networks
As machine learning has been deployed ubiquitously across applications i...
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11/16/2022
Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology
In this paper, we propose the augmented physics-informed neural network ...
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07/22/2022
Discrete Key-Value Bottleneck
Deep neural networks perform well on prediction and classification tasks...
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02/02/2022
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization
Vector Quantization (VQ) is a method for discretizing latent representat...
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01/14/2022
Training Free Graph Neural Networks for Graph Matching
We present TFGM (Training Free Graph Matching), a framework to boost the...
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05/10/2021
Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth
Graph Neural Networks (GNNs) have been studied through the lens of expre...
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04/12/2021
A Recipe for Global Convergence Guarantee in Deep Neural Networks
Existing global convergence guarantees of (stochastic) gradient descent ...
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02/23/2021
CAC: A Clustering Based Framework for Classification
In data containing heterogeneous subpopulations, classification performa...
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01/02/2019
Elimination of All Bad Local Minima in Deep Learning
In this paper, we theoretically prove that we can eliminate all suboptim...
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02/21/2018
