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Found 13 papers, 8 papers with code
Intelligent Learning Rate Distribution to reduce Catastrophic Forgetting in Transformers
We combine the learning rate distributions thus found and show that they generalize to better performance with respect to the problem of catastrophic forgetting.
Debiasing Sentence Embedders through Contrastive Word Pairs
It is problematic that most debiasing approaches are directly transferred from word embeddings, therefore these approaches fail to take into account the nonlinear nature of sentence embedders and the embeddings they produce.
Targeted Visualization of the Backbone of Encoder LLMs
Attention based Large Language Models (LLMs) are the state-of-the-art in natural language processing (NLP).
Semantic Properties of cosine based bias scores for word embeddings
Furthermore, we formally analyze cosine based scores from the literature with regard to these requirements.
"Why Here and Not There?" -- Diverse Contrasting Explanations of Dimensionality Reduction
Dimensionality reduction is a popular preprocessing and a widely used tool in data mining.
The SAME score: Improved cosine based bias score for word embeddings
Furthermore, we thoroughly investigate the existing cosine-based scores and their limitations in order to show why these scores fail to report biases in some situations.
BERT WEAVER: Using WEight AVERaging to enable lifelong learning for transformer-based models in biomedical semantic search engines
Recent developments in transfer learning have boosted the advancements in natural language processing tasks.
Evaluating Metrics for Bias in Word Embeddings
However, lately some works have raised doubts about these metrics showing that even though such metrics report low biases, other tests still show biases.
Reservoir Stack Machines
In this paper, we introduce the reservoir stack machine, a model which can provably recognize all deterministic context-free languages and circumvents the training problem by training only the output layer of a recurrent net and employing auxiliary information during training about the desired interaction with a stack.
Reservoir Memory Machines as Neural Computers
Differentiable neural computers extend artificial neural networks with an explicit memory without interference, thus enabling the model to perform classic computation tasks such as graph traversal.
Reservoir memory machines
In recent years, Neural Turing Machines have gathered attention by joining the flexibility of neural networks with the computational capabilities of Turing machines.
DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction
So far, most methods in the literature investigate the decision of the model for a single given input datum.
Expectation maximization transfer learning and its application for bionic hand prostheses
Machine learning models in practical settings are typically confronted with changes to the distribution of the incoming data.
