Conference Presentation

• Visual query tuning: Towards effective usage of intermediate representations for parameter and memory efficient transfer learning.

  Presentation at CVPR 2023, Vancouver Canada

• Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning

  Oral presentation at CVPR 2021 Workshop on Continual Learning

• Online Class-Incremental Continual Learning with Adversarial Shapley Value

  Oral presentation at AAAI 2021, Vancouver Canada (Virtual due to COVID-19)

• Batch-level Experience Replay with Review for Continual Learning

  Oral presentation at Workshop on Continual Learning in Computer Vision, CVPR 2020, Seattle, United States (Virtual due to COVID-19)

• Attentive Autoencoders for Multifaceted Preference Learning in One-class Collaborative Filtering

  Oral presentation at Workshop on Advanced Neural Algorithms and Theories for Recommender Systems, ICDM 2020, Sorrento Italy (Virtual due to COVID-19)

Reading group paper presentation

• Robustness in deep learning:
  • Adversarial robustness
  • Explicit Inductive Bias for Transfer Learning with Convolutional Networks, ICML 2018
  • Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution, ICML 2022
  • Robust fine-tuning of zero-shot models, CVPR2022
  • Finetune like you pretrain: Improved finetuning of zero-shot vision models, CVPR2023
  • Spurious Features Everywhere – Large-Scale Detection of Harmful Spurious Features in ImageNet, ICCV2023
  • Mitigating Spurious Correlations in Multi-modal Models during Fine-tuning, ICML 2023
  • Zero-Shot Robustification of Zero-Shot Models, ICLR2024
  • Contrastive Adapters for Foundation Model Group Robustness, NeurIPS2022
  • Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations, ICML2023
• Recent Meta papers:
  • Scalable Diffusion Models with Transformers, ICCV23
  • Deconstructing Denoising Diffusion Models for Self-Supervised Learning.
  • A Decade’s Battle on Dataset Bias: Are We There Yet?
• Mixture of Experts
  • Outrageously large neural networks: The sparsely-gated mixture-of-experts layer, ICLR2017
  • Scaling Vision with Sparse Mixture of Experts, NeurIPS2021
  • Learning to Route by Task for Efficient Inference, EMNLP2021
  • Mixture-of-Experts with Expert Choice Routing, NeurIPS2022
  • Multimodal Contrastive Learning with LIMoE: the Language Image Mixture of Experts, NeurIPS2022
  • Towards Understanding the Mixture-of-Experts Layer in Deep Learning, NeurIPS2022
  • Sparse Mixture-of-Experts are Domain Generalizable Learners, ICLR2023
  • M³ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design, NeurIPS2022
  • Task-customized Masked Autoencoder via Mixture of Cluster-conditional Experts, ICLR2023
  • The Power of External Memory in Increasing Predictive Model Capacity
  • Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints, ICLR2023
• Frustratingly Easy Transferability Estimation
  • Frustratingly Easy Transferability Estimation, ICML2022

Talks

• A Simple and Effective Approach to Continual Learning for Image Classification

  Vector Institute, Toronto Canada, 2020

• Tutorial of Seq2Seq & Attention

  Data-Driven Decision Making Lab (D3M), University of Toronto, 2020

Thesis

Online Continual Learning In Image Classification

Committee: Prof. Elias Khalil, Prof. Scott Sanner