3.3k citations · 3.9k across the 24 of their papers we have counts for
24 papers
Source-free Domain Adaptation Requires Penalized Diversity
Laya Rafiee Sevyeri, Ivaxi Sheth, Farhood Farahnak +4
While neural networks are capable of achieving human-like performance in many tasks such as image classification, the impressive performance of each model is limited to its own dat…
Pitfalls of Conditional Batch Normalization for Contextual Multi-Modal Learning
Ivaxi Sheth, Aamer Abdul Rahman, Mohammad Havaei +1
Humans have perfected the art of learning from multiple modalities through sensory organs. Despite their impressive predictive performance on a single modality, neural networks can…
FL Games: A Federated Learning Framework for Distribution Shifts
Sharut Gupta, Kartik Ahuja, Mohammad Havaei +2
Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. However, participating clients typically each h…
FHIST: A Benchmark for Few-shot Classification of Histological Images
Fereshteh Shakeri, Malik Boudiaf, Sina Mohammadi +4
Few-shot learning has recently attracted wide interest in image classification, but almost all the current public benchmarks are focused on natural images. The few-shot paradigm is…
FL Games: A federated learning framework for distribution shifts
Sharut Gupta, Kartik Ahuja, Mohammad Havaei +2
Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. However, participating clients typically each h…
AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation
Farshid Varno, Marzie Saghayi, Laya Rafiee Sevyeri +3
In Federated Learning (FL), a number of clients or devices collaborate to train a model without sharing their data. Models are optimized locally at each client and further communic…