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In this work we present a method to improve the pruning step of the current state-of-the-art methodology to compress neural networks. The novelty of the propose
In this work we present a method to improve the pruning step of the current state-of-the-art methodology to compress neural networks. The novelty of the propose
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Agent Trading Arena probes numerical understanding of LLM-based trading agents in a zero-sum market simulation
InvestorBench evaluates LLM-based agents on financial decision-making tasks across multiple asset classes and markets
FinMTEB is a finance-specific massive text embedding benchmark with 64 datasets across seven financial NLP task types
