ML climate emulators degrade under seasonal distribution shifts that proxy long-term climate change, but physically motivated compositional decompositions improve out-of-distribution performance with modest in-distribution trade-offs.
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3 Pith papers cite this work. Polarity classification is still indexing.
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HPC-LLM fine-tunes Llama 3.1 8B via QLoRA on 9k-24k HPC examples and adds dense retrieval to deliver practical support for job scheduling, MPI, and GPU workflows, approaching the performance of larger general models at lower memory and latency cost.
Four new Reddit-derived datasets for mental health detection tasks are presented with inter-annotator agreement above 0.8 and reported model F1 scores of 93-99%.
citing papers explorer
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No Epoch Like the Present: Robust Climate Emulation Requires Out-of-Distribution Generalisation
ML climate emulators degrade under seasonal distribution shifts that proxy long-term climate change, but physically motivated compositional decompositions improve out-of-distribution performance with modest in-distribution trade-offs.
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HPC-LLM: Practical Domain Adaptation and Retrieval-Augmented Generation for HPC Support
HPC-LLM fine-tunes Llama 3.1 8B via QLoRA on 9k-24k HPC examples and adds dense retrieval to deliver practical support for job scheduling, MPI, and GPU workflows, approaching the performance of larger general models at lower memory and latency cost.
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A Benchmark Suite of Reddit-Derived Datasets for Mental Health Detection
Four new Reddit-derived datasets for mental health detection tasks are presented with inter-annotator agreement above 0.8 and reported model F1 scores of 93-99%.