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07/08/2023
On Regularization and Inference with Label Constraints
Prior knowledge and symbolic rules in machine learning are often express...
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12/31/2022
Rethinking with Retrieval: Faithful Large Language Model Inference
Despite the success of large language models (LLMs) in various natural l...
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10/31/2022
A Law of Data Separation in Deep Learning
Multilayer neural networks have achieved superhuman performance in many ...
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05/28/2021
Weighted Training for Cross-Task Learning
In this paper, we introduce Target-Aware Weighted Training (TAWT), a wei...
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01/29/2021
Layer-Peeled Model: Toward Understanding Well-Trained Deep Neural Networks
In this paper, we introduce the Layer-Peeled Model, a nonconvex yet anal...
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10/27/2020
Toward Better Generalization Bounds with Locally Elastic Stability
Classical approaches in learning theory are often seen to yield very loo...
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10/22/2020
Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity
As a popular approach to modeling the dynamics of training overparametri...
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10/20/2020
Towards Understanding the Dynamics of the First-Order Adversaries
An acknowledged weakness of neural networks is their vulnerability to ad...
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07/19/2020
Understanding Spatial Relations through Multiple Modalities
Recognizing spatial relations and reasoning about them is essential in m...
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06/09/2020
Foreshadowing the Benefits of Incidental Supervision
Learning theory mostly addresses the standard learning paradigm, assumin...
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10/15/2019
The Local Elasticity of Neural Networks
This paper presents a phenomenon in neural networks that we refer to as ...
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09/01/2019
Incidental Supervision from Question-Answering Signals
Human annotations are costly for many natural language processing (NLP) ...
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06/12/2019
Partial Or Complete, That's The Question
For many structured learning tasks, the data annotation process is compl...
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11/14/2016