2 citations · 3 across the 7 of their papers we have counts for
4 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…
wd1: Weighted Policy Optimization for Reasoning in Diffusion Language Models
Xiaohang Tang, Rares Dolga, Sangwoong Yoon +1
Improving the reasoning capabilities of diffusion-based large language models (dLLMs) through reinforcement learning (RL) remains an open problem. The intractability of dLLMs likel…
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…
RotRNN: Modelling Long Sequences with Rotations
Kai Biegun, Rares Dolga, Jake Cunningham +1
Linear recurrent neural networks, such as State Space Models (SSMs) and Linear Recurrent Units (LRUs), have recently shown state-of-the-art performance on long sequence modelling b…