12 citations · 14 across the 7 of their papers we have counts for
6 papers · 1 filter
When Embedding Models Meet: Procrustes Bounds and Applications
Lucas Maystre, Alvaro Ortega Gonzalez, Charles Park +4
Embedding models trained separately on similar data often produce representations that encode stable information but are not directly interchangeable. This lack of interoperability…
Toward Autonomous UI Exploration: The UIExplorer Benchmark
Andrei Cristian Nica, Akshaya Vishnu Kudlu Shanbhogue, Harshil Shah +4
Autonomous agents must know how to explore user interfaces (UIs) for reliable task solving, yet systematic evaluation of this crucial phase is lacking. We introduce UIExplore-Bench…
Incremental Sequence Classification with Temporal Consistency
Lucas Maystre, Gabriel Barello, Tudor Berariu +5
We address the problem of incremental sequence classification, where predictions are updated as new elements in the sequence are revealed. Drawing on temporal-difference learning f…
When Does Re-initialization Work?
Sheheryar Zaidi, Tudor Berariu, Hyunjik Kim +4
Re-initializing a neural network during training has been observed to improve generalization in recent works. Yet it is neither widely adopted in deep learning practice nor is it o…
A study on the plasticity of neural networks
Tudor Berariu, Wojciech Czarnecki, Soham De +4
One aim shared by multiple settings, such as continual learning or transfer learning, is to leverage previously acquired knowledge to converge faster on the current task. Usually t…
Spectral Normalisation for Deep Reinforcement Learning: an Optimisation Perspective
Florin Gogianu, Tudor Berariu, Mihaela Rosca +3
Most of the recent deep reinforcement learning advances take an RL-centric perspective and focus on refinements of the training objective. We diverge from this view and show we can…