9 papers
On The Relationship Between Continual Learning and Long-Tailed Recognition
Mahdiyar Molahasani, Michael Greenspan, Ali Etemad
Real-world datasets often exhibit long-tailed distributions, where a few dominant "Head" classes have abundant samples while most "Tail" classes are severely underrepresented, lead…
Learning Time-Series Representations by Hierarchical Uniformity-Tolerance Latent Balancing
Amin Jalali, Milad Soltany, Michael Greenspan +1
We propose TimeHUT, a novel method for learning time-series representations by hierarchical uniformity-tolerance balancing of contrastive representations. Our method uses two disti…
PRISM: Reducing Spurious Implicit Biases in Vision-Language Models with LLM-Guided Embedding Projection
Mahdiyar Molahasani, Azadeh Motamedi, Michael Greenspan +2
We introduce Projection-based Reduction of Implicit Spurious bias in vision-language Models (PRISM), a new data-free and task-agnostic solution for bias mitigation in VLMs like CLI…
Partial Label Learning for Emotion Recognition from EEG
Guangyi Zhang, Ali Etemad
Fully supervised learning has recently achieved promising performance in various electroencephalography (EEG) learning tasks by training on large datasets with ground truth labels.…
Diffusion Models with Deterministic Normalizing Flow Priors
Mohsen Zand, Ali Etemad, Michael Greenspan
For faster sampling and higher sample quality, we propose DiNof (ffusion with rmalizing low priors), a technique that makes use of normalizing…
Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training
Milad Soltany, Farhad Pourpanah, Mahdiyar Molahasani +2
In this paper, we propose a novel approach, Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training (FedSB), to address the challenges of data hete…