15 citations · 40 across the 9 of their papers we have counts for
10 papers
Fairness-Aware Structured Pruning in Transformers
Abdelrahman Zayed, Goncalo Mordido, Samira Shabanian +2
The increasing size of large language models (LLMs) has introduced challenges in their training and inference. Removing model components is perceived as a solution to tackle the la…
Successor Features for Efficient Multisubject Controlled Text Generation
Meng Cao, Mehdi Fatemi, Jackie Chi Kit Cheung +1
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…
Should We Attend More or Less? Modulating Attention for Fairness
Abdelrahman Zayed, Goncalo Mordido, Samira Shabanian +1
The advances in natural language processing (NLP) pose both opportunities and challenges. While recent progress enables the development of high-performing models for a variety of t…
Systematic Rectification of Language Models via Dead-end Analysis
Meng Cao, Mehdi Fatemi, Jackie Chi Kit Cheung +1
With adversarial or otherwise normal prompts, existing large language models (LLM) can be pushed to generate toxic discourses. One way to reduce the risk of LLMs generating undesir…
Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairness
Abdelrahman Zayed, Prasanna Parthasarathi, Goncalo Mordido +3
Data-driven predictive solutions predominant in commercial applications tend to suffer from biases and stereotypes, which raises equity concerns. Prediction models may discover, us…
Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization
Divya Shanmugam, Samira Shabanian, Fernando Diaz +2
Modern machine learning systems are increasingly characterized by extensive personal data collection, despite the diminishing returns and increasing societal costs of such practice…
On the Learning Dynamics of Deep Neural Networks
Remi Tachet, Mohammad Pezeshki, Samira Shabanian +2
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. In this work, we study t…
Variational Bi-LSTMs
Samira Shabanian, Devansh Arpit, Adam Trischler +1
Recurrent neural networks like long short-term memory (LSTM) are important architectures for sequential prediction tasks. LSTMs (and RNNs in general) model sequences along the forw…
Theano: A Python framework for fast computation of mathematical expressions
The Theano Development Team, Rami Al-Rfou, Guillaume Alain +110
Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Since its introduction, it has bee…
Bidirectional Helmholtz Machines
Jorg Bornschein, Samira Shabanian, Asja Fischer +1
Efficient unsupervised training and inference in deep generative models remains a challenging problem. One basic approach, called Helmholtz machine, involves training a top-down di…