Computationally Budgeted Continual Learning | Ameya Prabhu PhD@Torr Vision Group, UniversityOfOxford

Paper Abstract:
This talk offers a fresh perspective on continual learning, presenting an innovative approach where computational constraints take precedence over memory limitations. By attending this event, you'll gain insights into a unique setup that challenges conventional paradigms and opens doors to cost-effective knowledge accumulation.
Agenda:
- Why Get Excited: Discover the significance of this unconventional approach and its potential impact on the world of continual learning.
- Progress Report: Explore the latest advancements made in the realm of computationally budgeted continual learning.
- Lessons Learned: Ameya Prabhu will share personal insights from his journey, shedding light on discoveries, obstacles overcome, and breakthroughs achieved.
- Missing Pieces: Dive into the unexplored aspects that still need attention to complete the puzzle in this evolving field.
Key Takeaways:
- 🎯 Optimized Knowledge Accumulation: Learn how to accumulate knowledge efficiently while being mindful of computational costs.
- 🛠️ Effective Strategies: Discover techniques that excel in this unique setup and understand those that might fall short.
- 📊 Real-World Challenges: Gain awareness of challenges posed by real-world data streams, such as near-duplicates and correlated datastreams, and their impact on algorithms.
- 🔄 Adaptive Deep Models: Explore the journey towards creating continual data structures that augment distributionally robust deep models.
About the Speaker:
Ameya Prabhu is a PhD Candidate at the Torr Vision Group, University of Oxford. He has dedicated his academic journey to optimizing neural networks for efficiency and has been an active contributor to the field of continual learning since 2020.
References from the video :
Kilian Weinberger talk : • On the Importance of D...
RDumb paper : arxiv.org/abs/2306.05401