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Found 13 papers, 0 papers with code
On the Trustworthiness of Tree Ensemble Explainability Methods
The recent increase in the deployment of machine learning models in critical domains such as healthcare, criminal justice, and finance has highlighted the need for trustworthy methods that can explain these models to stakeholders.
Shrinking Bigfoot: Reducing wav2vec 2.0 footprint
Using a teacher-student approach, we distilled the knowledge from the original wav2vec 2. 0 model into a student model, which is 2 times faster and 4. 8 times smaller than the original model.
A Hybrid Instance-based Transfer Learning Method
Transfer learning can help overcome this issue by transferring the knowledge from readily available datasets (source) to a new dataset (target).
Boosting Model Performance through Differentially Private Model Aggregation
In this work, two approaches for private model aggregation are proposed that enable the transfer of knowledge from existing models trained on other companies' datasets to a new company with limited labeled data while protecting each client company's underlying individual sensitive information.
A Dataset for Multi-Target Stance Detection
Current models for stance classification often treat each target independently, but in many applications, there exist natural dependencies among targets, e. g., stance towards two or more politicians in an election or towards several brands of the same product.
Stance and Sentiment in Tweets
However, a person may express the same stance towards a target by using negative or positive language.
A Dataset for Detecting Stance in Tweets
Apart from stance, the tweets are also annotated for whether the target of interest is the target of opinion in the tweet.
Long Short-Term Memory Over Tree Structures
The chain-structured long short-term memory (LSTM) has showed to be effective in a wide range of problems such as speech recognition and machine translation.
