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Found 14 papers, 7 papers with code
"Define Your Terms" : Enhancing Efficient Offensive Speech Classification with Definition
The propagation of offensive content through social media channels has garnered attention of the research community.
Jointly Reparametrized Multi-Layer Adaptation for Efficient and Private Tuning
This can be a drawback for low-resource applications and training with differential-privacy constraints, where excessive noise may be introduced during finetuning.
Transferring Models Trained on Natural Images to 3D MRI via Position Encoded Slice Models
Transfer learning has remarkably improved computer vision.
Towards Sparsified Federated Neuroimaging Models via Weight Pruning
In our experiments in centralized and federated settings on the brain age prediction task (estimating a person's age from their brain MRI), we demonstrate that models can be pruned up to 95% sparsity without affecting performance even in challenging federated learning environments with highly heterogeneous data distributions.
Secure & Private Federated Neuroimaging
Each site trains the neural network over its private data for some time, then shares the neural network parameters (i. e., weights, gradients) with a Federation Controller, which in turn aggregates the local models, sends the resulting community model back to each site, and the process repeats.
Federated Progressive Sparsification (Purge, Merge, Tune)+
Second, the models are incrementally constrained to a smaller set of parameters, which facilitates alignment/merging of the local models and improved learning performance at high sparsification rates.
Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal
Language models excel at generating coherent text, and model compression techniques such as knowledge distillation have enabled their use in resource-constrained settings.
Attributing Fair Decisions with Attention Interventions
The widespread use of Artificial Intelligence (AI) in consequential domains, such as healthcare and parole decision-making systems, has drawn intense scrutiny on the fairness of these methods.
Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption
Federated learning (FL) enables distributed computation of machine learning models over various disparate, remote data sources, without requiring to transfer any individual data to a centralized location.
Membership Inference Attacks on Deep Regression Models for Neuroimaging
In particular, we show that it is possible to infer if a sample was used to train the model given only access to the model prediction (black-box) or access to the model itself (white-box) and some leaked samples from the training data distribution.
Improved Brain Age Estimation with Slice-based Set Networks
Deep Learning for neuroimaging data is a promising but challenging direction.
Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation
Controlling bias in training datasets is vital for ensuring equal treatment, or parity, between different groups in downstream applications.
Deep Generative Dual Memory Network for Continual Learning
This phenomenon called catastrophic forgetting is a fundamental challenge to overcome before neural networks can learn continually from incoming data.
A Sentiment-and-Semantics-Based Approach for Emotion Detection in Textual Conversations
In this paper, we propose a novel approach to detect emotions like happy, sad or angry in textual conversations using an LSTM based Deep Learning model.
