GHGbench supplies a harmonized dataset and multi-task benchmark for company and building carbon emission prediction, with baselines showing large OOD gaps and benefits from multimodal embeddings.
Donti, Lynn H
6 Pith papers cite this work. Polarity classification is still indexing.
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81-92% of chemically valid and metastable crystals from generative models are training duplicates or substitution-derived, with low-symmetry cases showing interpolation and high-symmetry cases showing memorization.
M-CaStLe generalizes local stencil-based causal discovery to the multivariate case and decomposes resulting graphs into reaction and spatial components for interpretation in space-time gridded data.
Foundation models outperform dataset-specific machine learning in energy time series forecasting across 54 datasets in 9 categories.
GFlowState introduces interactive visualizations such as trajectory node-link diagrams and transition heatmaps to make GFlowNet training dynamics observable for debugging and quality assessment.
The paper frames 'AI against sustainability' as a missing category in current debates and calls for a three-pronged response of strengthened regulation, proactive industry self-commitment, and constructive stakeholder dialogue.
citing papers explorer
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GHGbench: A Unified Multi-Entity, Multi-Task Benchmark for Carbon Emission Prediction
GHGbench supplies a harmonized dataset and multi-task benchmark for company and building carbon emission prediction, with baselines showing large OOD gaps and benefits from multimodal embeddings.
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Substitution-Based Analysis of Structural Novelty for Generative Models of Materials
81-92% of chemically valid and metastable crystals from generative models are training duplicates or substitution-derived, with low-symmetry cases showing interpolation and high-symmetry cases showing memorization.
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M-CaStLe: Uncovering Local Causal Structures in Multivariate Space-Time Gridded Data
M-CaStLe generalizes local stencil-based causal discovery to the multivariate case and decomposes resulting graphs into reaction and spatial components for interpretation in space-time gridded data.
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FETS Benchmark: Foundation Models Outperform Dataset-specific Machine Learning in Energy Time Series Forecasting
Foundation models outperform dataset-specific machine learning in energy time series forecasting across 54 datasets in 9 categories.
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Tackling "AI against sustainability"
The paper frames 'AI against sustainability' as a missing category in current debates and calls for a three-pronged response of strengthened regulation, proactive industry self-commitment, and constructive stakeholder dialogue.