5 citations · 14 across the 8 of their papers we have counts for
8 papers
M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models
Junxiong Wang, Wen-Ding Li, Daniele Paliotta +3
Effective reasoning is crucial to solving complex mathematical problems. Recent large language models (LLMs) have boosted performance by scaling test-time computation through long…
Thinking Slow, Fast: Scaling Inference Compute with Distilled Reasoners
Daniele Paliotta, Junxiong Wang, Matteo Pagliardini +6
Recent advancements have demonstrated that the performance of large language models (LLMs) can be significantly enhanced by scaling computational resources at test time. A common s…
Leveraging the true depth of LLMs
Ramón Calvo González, Daniele Paliotta, Matteo Pagliardini +2
The remarkable capabilities of Large Language Models (LLMs) are overshadowed by their immense computational cost. While recent work has shown that many LLM layers can be reordered…
The Mamba in the Llama: Distilling and Accelerating Hybrid Models
Junxiong Wang, Daniele Paliotta, Avner May +2
Linear RNN architectures, like Mamba, can be competitive with Transformer models in language modeling while having advantageous deployment characteristics. Given the focus on train…
Understanding and Minimising Outlier Features in Neural Network Training
Bobby He, Lorenzo Noci, Daniele Paliotta +2
Outlier Features (OFs) are neurons whose activation magnitudes significantly exceed the average over a neural network's (NN) width. They are well known to emerge during standard tr…
Faster Causal Attention Over Large Sequences Through Sparse Flash Attention
Matteo Pagliardini, Daniele Paliotta, Martin Jaggi +1
Transformer-based language models have found many diverse applications requiring them to process sequences of increasing length. For these applications, the causal self-attention -…
Graph Neural Networks Go Forward-Forward
Daniele Paliotta, Mathieu Alain, Bálint Máté +1
We present the Graph Forward-Forward (GFF) algorithm, an extension of the Forward-Forward procedure to graphs, able to handle features distributed over a graph's nodes. This allows…
SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics
Atul Kumar Sinha, Daniele Paliotta, Bálint Máté +4
Deep learning methods have gained popularity in high energy physics for fast modeling of particle showers in detectors. Detailed simulation frameworks such as the gold standard Gea…