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09/12/2023
Narrowing the Gap between Supervised and Unsupervised Sentence Representation Learning with Large Language Model
Sentence Representation Learning (SRL) is a fundamental task in Natural ...
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05/23/2023
Adversarial Defenses via Vector Quantization
Building upon Randomized Discretization, we develop two novel adversaria...
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05/16/2023
AdversarialWord Dilution as Text Data Augmentation in Low-Resource Regime
Data augmentation is widely used in text classification, especially in t...
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05/16/2023
ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification
Few-shot text classification has recently been promoted by the meta-lear...
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03/02/2023
Over-training with Mixup May Hurt Generalization
Mixup, which creates synthetic training instances by linearly interpolat...
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02/28/2023
Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction
Opinion target extraction (OTE) or aspect extraction (AE) is a fundament...
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02/05/2023
Tighter Information-Theoretic Generalization Bounds from Supersamples
We present a variety of novel information-theoretic generalization bound...
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11/19/2022
Two Facets of SDE Under an Information-Theoretic Lens: Generalization of SGD via Training Trajectories and via Terminal States
Stochastic differential equations (SDEs) have been shown recently to wel...
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10/03/2022
Information-Theoretic Analysis of Unsupervised Domain Adaptation
This paper uses information-theoretic tools to analyze the generalizatio...
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02/11/2022
Cross Domain Few-Shot Learning via Meta Adversarial Training
Few-shot relation classification (RC) is one of the critical problems in...
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01/21/2022
Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation
Contrastive learning has achieved remarkable success in representation l...
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10/18/2021
Intrusion-Free Graph Mixup
We present a simple and yet effective interpolation-based regularization...
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10/07/2021
On the Generalization of Models Trained with SGD: Information-Theoretic Bounds and Implications
This paper follows up on a recent work of (Neu, 2021) and presents new a...
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05/28/2021
Robust Regularization with Adversarial Labelling of Perturbed Samples
Recent researches have suggested that the predictive accuracy of neural ...
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11/19/2020
On the Dynamics of Training Attention Models
The attention mechanism has been widely used in deep neural networks as ...
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10/10/2020
Regularizing Neural Networks via Adversarial Model Perturbation
Recent research has suggested that when training neural networks, flat l...
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09/16/2020
Parallel Interactive Networks for Multi-Domain Dialogue State Generation
The dependencies between system and user utterances in the same turn and...
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09/16/2020
Neural Dialogue State Tracking with Temporally Expressive Networks
Dialogue state tracking (DST) is an important part of a spoken dialogue ...
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09/02/2020
On SkipGram Word Embedding Models with Negative Sampling: Unified Framework and Impact of Noise Distributions
SkipGram word embedding models with negative sampling, or SGN in short, ...
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05/01/2020
Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations
Named entity recognition (NER) and Relation extraction (RE) are two fund...
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01/12/2020
Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers
We consider the problem of learning a neural network classifier. Under t...
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09/12/2019
Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework
Distant supervision for relation extraction enables one to effectively a...
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06/17/2019
MixUp as Directional Adversarial Training
In this work, we explain the working mechanism of MixUp in terms of adve...
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05/22/2019
Augmenting Data with Mixup for Sentence Classification: An Empirical Study
Mixup, a recent proposed data augmentation method through linearly inter...
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03/03/2019
Understanding Feature Selection and Feature Memorization in Recurrent Neural Networks
In this paper, we propose a test, called Flagged-1-Bit (F1B) test, to st...
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09/07/2018
MixUp as Locally Linear Out-Of-Manifold Regularization
MixUp, a data augmentation approach through mixing random samples, has b...
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07/26/2018
Aggregated Learning: A Vector Quantization Approach to Learning with Neural Networks
We establish an equivalence between information bottleneck (IB) learning...
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07/11/2012
