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Found 9 papers, 2 papers with code
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm
Various pruning approaches have been proposed to reduce the footprint requirements of Transformer-based language models.
Data Augmentation with Adversarial Training for Cross-Lingual NLI
Due to recent pretrained multilingual representation models, it has become feasible to exploit labeled data from one language to train a cross-lingual model that can then be applied to multiple new languages.
Rethinking Network Pruning -- under the Pre-train and Fine-tune Paradigm
In particular, common wisdom in pruning CNN states that sparse pruning technique compresses a model more than that obtained by reducing number of channels and layers (Elsen et al., 2020; Zhu and Gupta, 2017), while existing works on sparse pruning of BERT yields inferior results than its small-dense counterparts such as TinyBERT (Jiao et al., 2020).
Parameterized Explainer for Graph Neural Network
The unique explanation interpreting each instance independently is not sufficient to provide a global understanding of the learned GNN model, leading to a lack of generalizability and hindering it from being used in the inductive setting.
Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification
The resulting model then serves as a teacher to induce labels for unlabeled target language samples that can be used during further adversarial training, allowing us to gradually adapt our model to the target language.
Longitudinal Deep Kernel Gaussian Process Regression
Specifically, L-DKGPR eliminates the need for ad hoc heuristics or trial and error using a novel adaptation of deep kernel learning that combines the expressive power of deep neural networks with the flexibility of non-parametric kernel methods.
How Do We Move: Modeling Human Movement with System Dynamics
To the best of our knowledge, we are the first to learn to model the state transition of moving agents with system dynamics.
LMLFM: Longitudinal Multi-Level Factorization Machine
However, the current state-of-the-art methods are unable to select the most predictive fixed effects and random effects from a large number of variables, while accounting for complex correlation structure in the data and non-linear interactions among the variables.
PIGMIL: Positive Instance Detection via Graph Updating for Multiple Instance Learning
To the end, we propose a positive instance detection via graph updating for multiple instance learning, called PIGMIL, to detect TPI accurately.