8 citations · 9 across the 8 of their papers we have counts for
8 papers
Flash PD-SSM: Memory-Optimized Structured Sparse State-Space Models
Aleksandar Terzić, Francesco Carzaniga, Nicolas Menet +4
State-space models (SSMs) face a fundamental trade-off between efficiency and expressivity that is mainly dictated by the structure of the model's transition matrix. Unstructured t…
POETS: Uncertainty-Aware LLM Optimization via Compute-Efficient Policy Ensembles
Nicolas Menet, Andreas Krause, Abbas Rahimi
Balancing exploration with exploitation is a core challenge in black-box optimization and reinforcement learning. We introduce POETS (licy nsembles for $\t…
Locally Coherent Parallel Decoding in Diffusion Language Models
Michael Hersche, Nicolas Menet, Ronan Tanios +1
Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive (AR) models, offering sub-linear generation latency and bidirectional capabilities that a…
A Theoretical Analysis of Test-Driven Code Generation
Nicolas Menet, Michael Hersche, Andreas Krause +1
Code assistants are increasingly utilized in test-driven software development, yet the theoretical mechanisms behind their environment-interaction strategies remain underexplored.…
Thompson Sampling via Fine-Tuning of LLMs
Nicolas Menet, Aleksandar Terzić, Michael Hersche +2
Bayesian optimization in large unstructured discrete spaces is often hindered by the computational cost of maximizing acquisition functions due to the absence of gradients. We prop…
Structured Sparse Transition Matrices to Enable State Tracking in State-Space Models
Aleksandar Terzić, Nicolas Menet, Michael Hersche +2
Modern state-space models (SSMs) often utilize transition matrices which enable efficient computation but pose restrictions on the model's expressivity, as measured in terms of the…
LITE: Efficiently Estimating Gaussian Probability of Maximality
Nicolas Menet, Jonas Hübotter, Parnian Kassraie +1
We consider the problem of computing the probability of maximality (PoM) of a Gaussian random vector, i.e., the probability for each dimension to be maximal. This is a key challeng…
MIMONets: Multiple-Input-Multiple-Output Neural Networks Exploiting Computation in Superposition
Nicolas Menet, Michael Hersche, Geethan Karunaratne +3
With the advent of deep learning, progressively larger neural networks have been designed to solve complex tasks. We take advantage of these capacity-rich models to lower the cost…