Microsoft Research Lab – Asia
뉴스 & 기능
NeurIPS 2024: The co-evolution of AI and systems with Lidong Zhou
| Lidong Zhou 그리고 Eliza Strickland
Just after his NeurIPS 2024 keynote on the co-evolution of systems and AI, Microsoft CVP Lidong Zhou joins the podcast to discuss how rapidly advancing AI impacts the systems supporting it and the opportunities to use AI to enhance systems…
Abstracts: NeurIPS 2024 with Jindong Wang and Steven Euijong Whang
| Gretchen Huizinga, Jindong Wang, 그리고 Steven Euijong Whang
Researcher Jindong Wang and Associate Professor Steven Euijong Whang explore the NeurIPS 2024 work ERBench. ERBench leverages relational databases to create LLM benchmarks that can verify model rationale via keywords in addition to checking answer correctness.
We’re excited to be a part of #NeurIPS2024! Explore the future of AI with over 100 groundbreaking papers, including oral and spotlight sessions, on reinforcement learning, advanced language model training, and multilingual, culturally inclusive benchmarks.
Abstracts: NeurIPS 2024 with Weizhu Chen
| Amber Tingle 그리고 Weizhu Chen
Next-token prediction trains a language model on all tokens in a sequence. VP Weizhu Chen discusses his team’s 2024 NeurIPS paper on how distinguishing between useful and “noisy” tokens in pretraining can improve token efficiency and model performance.
Since the Industrial Revolution, the burning of fossil fuels and changes in land use, especially deforestation, have driven the rise in atmospheric carbon dioxide (CO2). While terrestrial vegetation and oceans serve as natural carbon sinks, absorbing some of this CO2,…
Although large language models (LLMs) excel in language-focused tasks like news writing, document summarization, customer service, and virtual assistants, they face challenges when it comes to learning and inference on numeric and structured industry data, such as tabular data and…
Research Focus: Week of December 2, 2024
Can a new SOS-RMT protocol enable more efficient CL-MPC?; A fair-by-design, cloud-based algorithmic trading platform; LLM2CLIP unlocks richer visual representation; New technique enhances Low-Rank Adaptation’s expressiveness, generalization capabilities.
MarS: A unified financial market simulation engine in the era of generative foundation models
| Weiqing Liu, Junjie Li, Yang Liu, Chang Xu, Shikai Fang, Lewen Wang, 그리고 Jiang Bian
Microsoft Research presents a new large market model and Financial Market Simulation Engine (MarS) to help empower financial researchers with enhanced efficiency and more accurate insights for downstream tasks in financial markets.
