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Found 8 papers, 7 papers with code
UPS: Efficiently Building Foundation Models for PDE Solving via Cross-Modal Adaptation
We present Unified PDE Solvers (UPS), a data- and compute-efficient approach to developing unified neural operators for diverse families of spatiotemporal PDEs from various domains, dimensions, and resolutions.
Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains
Large Language Models (LLMs) have demonstrated remarkable proficiency in understanding and generating natural language.
Cross-Modal Fine-Tuning: Align then Refine
Fine-tuning large-scale pretrained models has led to tremendous progress in well-studied modalities such as vision and NLP.
Efficient Architecture Search for Diverse Tasks
While neural architecture search (NAS) has enabled automated machine learning (AutoML) for well-researched areas, its application to tasks beyond computer vision is still under-explored.
NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks
This makes the performance of NAS approaches in more diverse areas poorly understood.
Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation
To mitigate these issues, we propose a theoretically principled nearest neighbor (NN) function approximator that can improve the value networks in deep RL methods.
Iterative Teacher-Aware Learning
Recently, the benefits of integrating this cooperative pedagogy into machine concept learning in discrete spaces have been proved by multiple works.
Emergence of Pragmatics from Referential Game between Theory of Mind Agents
Pragmatics studies how context can contribute to language meanings.
