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Found 12 papers, 6 papers with code
Fairness-Aware Structured Pruning in Transformers
The increasing size of large language models (LLMs) has introduced challenges in their training and inference.
Successor Features for Efficient Multisubject Controlled Text Generation
While large language models (LLMs) have achieved impressive performance in generating fluent and realistic text, controlling the generated text so that it exhibits properties such as safety, factuality, and non-toxicity remains challenging.
Should We Attend More or Less? Modulating Attention for Fairness
In this work, we investigate the role of attention, a widely-used technique in current state-of-the-art NLP models, in the propagation of social biases.
Systematic Rectification of Language Models via Dead-end Analysis
Other methods rely on rule-based or prompt-based token elimination, which are limited as they dismiss future tokens and the overall meaning of the complete discourse.
Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairness
The fairness achieved by our method surpasses that of data augmentation on three text classification datasets, using no more than half of the examples in the augmented dataset.
Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization
FIDO learns to limit data collection based on an interpretation of data minimization tied to system performance.
Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics
With the recent expanding attention of machine learning researchers and practitioners to fairness, there is a void of a common framework to analyze and compare the capabilities of proposed models in deep representation learning.
Convergence Properties of Deep Neural Networks on Separable Data
While a lot of progress has been made in recent years, the dynamics of learning in deep nonlinear neural networks remain to this day largely misunderstood.
On the Learning Dynamics of Deep Neural Networks
While a lot of progress has been made in recent years, the dynamics of learning in deep nonlinear neural networks remain to this day largely misunderstood.
Variational Bi-LSTMs
Bidirectional LSTMs (Bi-LSTMs) on the other hand model sequences along both forward and backward directions and are generally known to perform better at such tasks because they capture a richer representation of the data.
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
Bidirectional Helmholtz Machines
We present a lower-bound for the likelihood of this model and we show that optimizing this bound regularizes the model so that the Bhattacharyya distance between the bottom-up and top-down approximate distributions is minimized.
