2 citations · 3 across the 7 of their papers we have counts for
8 papers
From Characters to Tokens: Dynamic Grouping with Hierarchical BPE
Rares Dolga, Lucas Maystre, Tudor Berariu +1
Subword tokenization methods like Byte Pair Encoding (BPE) are widely used in large language models due to their balance of vocabulary compactness and representational power. Howev…
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…
Code Revert Prediction with Graph Neural Networks: A Case Study at J.P. Morgan Chase
Yulong Pei, Salwa Alamir, Rares Dolga +1
Code revert prediction, a specialized form of software defect detection, aims to forecast or predict the likelihood of code changes being reverted or rolled back in software develo…
Log Summarisation for Defect Evolution Analysis
Rares Dolga, Ran Zmigrod, Rui Silva +2
Log analysis and monitoring are essential aspects in software maintenance and identifying defects. In particular, the temporal nature and vast size of log data leads to an interest…
Unifying Linear-Time Attention via Latent Probabilistic Modelling
Rares Dolga, Lucas Maystre, Marius Cobzarenco +1
Transformers have achieved state-of-the-art results across a range of domains, but their quadratic attention mechanism poses significant challenges for long-sequence modelling. Rec…