Hafez Ghaemi
@hafezghm.bsky.social
Ph.D. Student @mila-quebec.bsky.social and @umontreal.ca, AI Researcher
Reposted by Hafez Ghaemi
Thrilled to see this work accepted at NeurIPS!
Kudos to @hafezghm.bsky.social for the heroic effort in demonstrating the efficacy of seq-JEPA in representation learning from multiple angles.
#MLSky 🧠🤖
Kudos to @hafezghm.bsky.social for the heroic effort in demonstrating the efficacy of seq-JEPA in representation learning from multiple angles.
#MLSky 🧠🤖
Excited to share that seq-JEPA has been accepted to NeurIPS 2025!
Preprint Alert 🚀
Can we simultaneously learn transformation-invariant and transformation-equivariant representations with self-supervised learning?
TL;DR Yes! This is possible via simple predictive learning & architectural inductive biases – without extra loss terms and predictors!
🧵 (1/10)
Can we simultaneously learn transformation-invariant and transformation-equivariant representations with self-supervised learning?
TL;DR Yes! This is possible via simple predictive learning & architectural inductive biases – without extra loss terms and predictors!
🧵 (1/10)
September 19, 2025 at 6:46 PM
Thrilled to see this work accepted at NeurIPS!
Kudos to @hafezghm.bsky.social for the heroic effort in demonstrating the efficacy of seq-JEPA in representation learning from multiple angles.
#MLSky 🧠🤖
Kudos to @hafezghm.bsky.social for the heroic effort in demonstrating the efficacy of seq-JEPA in representation learning from multiple angles.
#MLSky 🧠🤖
Excited to share that seq-JEPA has been accepted to NeurIPS 2025!
Preprint Alert 🚀
Can we simultaneously learn transformation-invariant and transformation-equivariant representations with self-supervised learning?
TL;DR Yes! This is possible via simple predictive learning & architectural inductive biases – without extra loss terms and predictors!
🧵 (1/10)
Can we simultaneously learn transformation-invariant and transformation-equivariant representations with self-supervised learning?
TL;DR Yes! This is possible via simple predictive learning & architectural inductive biases – without extra loss terms and predictors!
🧵 (1/10)
September 19, 2025 at 6:02 PM
Excited to share that seq-JEPA has been accepted to NeurIPS 2025!
Preprint Alert 🚀
Can we simultaneously learn transformation-invariant and transformation-equivariant representations with self-supervised learning?
TL;DR Yes! This is possible via simple predictive learning & architectural inductive biases – without extra loss terms and predictors!
🧵 (1/10)
Can we simultaneously learn transformation-invariant and transformation-equivariant representations with self-supervised learning?
TL;DR Yes! This is possible via simple predictive learning & architectural inductive biases – without extra loss terms and predictors!
🧵 (1/10)
May 14, 2025 at 12:53 PM
Preprint Alert 🚀
Can we simultaneously learn transformation-invariant and transformation-equivariant representations with self-supervised learning?
TL;DR Yes! This is possible via simple predictive learning & architectural inductive biases – without extra loss terms and predictors!
🧵 (1/10)
Can we simultaneously learn transformation-invariant and transformation-equivariant representations with self-supervised learning?
TL;DR Yes! This is possible via simple predictive learning & architectural inductive biases – without extra loss terms and predictors!
🧵 (1/10)