6 citations · 21 across the 21 of their papers we have counts for
5 papers · 1 filter
Voxtral TTS
Mistral-AI, :, Alexander H. Liu +186
We introduce Voxtral TTS, an expressive multilingual text-to-speech model that generates natural speech from as little as 3 seconds of reference audio. Voxtral TTS adopts a hybrid…
Voxtral Realtime
Mistral-AI, :, Alexander H. Liu +166
We introduce Voxtral Realtime, a natively streaming automatic speech recognition model that matches offline transcription quality at sub-second latency. Unlike approaches that adap…
Memory-Enhanced Neural Solvers for Routing Problems
Felix Chalumeau, Refiloe Shabe, Noah De Nicola +3
Routing Problems are central to many real-world applications, yet remain challenging due to their (NP-)hard nature. Amongst existing approaches, heuristics often offer the best tra…
Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization
Nathan Grinsztajn, Daniel Furelos-Blanco, Shikha Surana +2
Applying reinforcement learning (RL) to combinatorial optimization problems is attractive as it removes the need for expert knowledge or pre-solved instances. However, it is unreal…
Geometric Deep Reinforcement Learning for Dynamic DAG Scheduling
Nathan Grinsztajn, Olivier Beaumont, Emmanuel Jeannot +1
In practice, it is quite common to face combinatorial optimization problems which contain uncertainty along with non-determinism and dynamicity. These three properties call for app…