2 citations · 3 across the 7 of their papers we have counts for
7 papers
From Tokens to Watt-hours: Analytical Energy Estimation for LLM Inference on Modern GPUs
Tina Vartziotis, Rodopi Kosteli, Elli Vartziotis +5
The operational energy consumption of large language model (LLM) inference is becoming an increasingly important component of the environmental footprint of deployed AI systems. Ho…
STRAND: Sequence-Conditioned Transport for Single-Cell Perturbations
Boyang Fu, George Dasoulas, Sameer Gabbita +5
Predicting how genetic perturbations change cellular state is a core problem for building controllable models of gene regulation. Perturbations targeting the same gene can produce…
Carbon Footprint Evaluation of Code Generation through LLM as a Service
Tina Vartziotis, Maximilian Schmidt, George Dasoulas +7
Due to increased computing use, data centers consume and emit a lot of energy and carbon. These contributions are expected to rise as big data analytics, digitization, and large AI…
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain
Guillermo Bernárdez, Lev Telyatnikov, Marco Montagna +70
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learnin…
Geometric Self-Supervised Pretraining on 3D Protein Structures using Subgraphs
Michail Chatzianastasis, Yang Zhang, George Dasoulas +1
Protein representation learning aims to learn informative protein embeddings capable of addressing crucial biological questions, such as protein function prediction. Although seque…
TopoBench: A Framework for Benchmarking Topological Deep Learning
Lev Telyatnikov, Guillermo Bernardez, Marco Montagna +34
This work introduces TopoBench, an open-source library designed to standardize benchmarking and accelerate research in topological deep learning (TDL). TopoBench decomposes TDL int…
E(n) Equivariant Topological Neural Networks
Claudio Battiloro, Ege Karaismailoğlu, Mauricio Tec +3
Graph neural networks excel at modeling pairwise interactions, but they cannot flexibly accommodate higher-order interactions and features. Topological deep learning (TDL) has emer…