4 citations · 12 across the 28 of their papers we have counts for
8 papers · 1 filter
Learning Functional Subspaces for Neural Network Compression
Massimo Bini, Anders Christensen, Stephan Alaniz +5
Modern transformers pair impressive capabilities with substantial memory and compute demands. Low-rank weight factorization reduces both while keeping the matrices dense, and thus…
CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models
Stefano Riva, Carolina Introini, Antonio Cammi +13
The demand for clean energy is ever increasing, with new nuclear technologies presenting a complementary solution to renewable energies. However, designing and operating these syst…
The Seismic Wavefield Common Task Framework
Alexey Yermakov, Yue Zhao, Marine Denolle +13
Seismology faces fundamental challenges in state forecasting and reconstruction (e.g., earthquake early warning and ground motion prediction) and managing the parametric variabilit…
Exploring Human-AI Conceptual Alignment through the Prism of Chess
Semyon Lomasov, Judah Goldfeder, Mehmet Hamza Erol +5
Do AI systems truly understand human concepts or merely mimic surface patterns? We investigate this through chess, where human creativity meets precise strategic concepts. Analyzin…
Generating Auxiliary Tasks with Reinforcement Learning
Judah Goldfeder, Matthew So, Hod Lipson
Auxiliary Learning (AL) is a form of multi-task learning in which a model trains on auxiliary tasks to boost performance on a primary objective. While AL has improved generalizatio…
Antislop: A Comprehensive Framework for Identifying and Eliminating Repetitive Patterns in Language Models
Samuel Paech, Allen Roush, Judah Goldfeder +1
Widespread LLM adoption has introduced characteristic repetitive phraseology, termed "slop," which degrades output quality and makes AI-generated text immediately recognizable. We…
NdLinear: Preserving Multi-Dimensional Structure for Parameter-Efficient Neural Networks
Alex Reneau, Jerry Yao-Chieh Hu, Zhongfang Zhuang +6
In deep learning, processing multidimensional inputs (e.g., images, medical scans, and time series) is an important task that often requires flattening the inputs. We introduce $\m…
Sequencing the Neurome: Towards Scalable Exact Parameter Reconstruction of Black-Box Neural Networks
Judah Goldfeder, Quinten Roets, Gabe Guo +2
Inferring the exact parameters of a neural network with only query access is an NP-Hard problem, with few practical existing algorithms. Solutions would have major implications for…