Deep Learning, NLP, NMT, AI, ML
Welcome to Research Focus, a series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft. EVENT Register now for Research Forum on September 3 Discover what’s next in the world of AI at Microsoft Research Forum
Read more[TEASER ENDS] GRETCHEN HUIZINGA: You’re listening to Collaborators, a Microsoft Research Podcast showcasing the range of expertise that goes into transforming mind-blowing ideas into world-changing technologies. I’m Dr. Gretchen Huizinga. [MUSIC FADES] On today’s episode, I’m talking to Dr. Brendan Lucier, a senior principal researcher in the economics and computation group at Microsoft Research, and Dr. Mert Demirer, an assistant professor of applied economics at the MIT Sloan School of Management. Brendan and Mert are exploring the economic impact of […]
Read moreThe games industry has long been a frontier of innovation for AI. In the early 2000s, programmers hand-coded neural networks to breathe life into virtual worlds (opens in new tab), creating engaging AI characters (opens in new tab) that interact with players. Fast forward two decades, neural networks have grown from their humble beginnings to colossal architectures with billions of parameters, powering real-world applications like
Read moreWelcome to Research Focus, a series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft. NEW RESEARCH Appropriate reliance on Generative AI: Research synthesis Appropriate reliance on AI happens when people accept correct AI outputs and reject incorrect ones. It requires users of AI systems to know when to trust the AI and when to trust themselves. But fostering appropriate reliance comes with new complexities when
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About GIA Greenstocks Investment Advisor (GIA) is an online application powered by machine learning algorithms, that facilitates reliable stock market investments in companies that implement a high Environmental, Social and Governance (ESG) policy. What is ESG Investing Investors interested in ESG are increasingly applying these non-financial factors as part of their analysis process to identify material risks and growth opportunities. ESG metrics are not commonly part of mandatory financial reporting, though companies are increasingly making disclosures in their annual report […]
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PICO is an algorithm for exploiting Reinforcement Learning (RL) on Multi-agent Path Finding tasks.It is developed by the Multi-Agent Artificial Intelligence Lab (MAIL) in East China Normal University and the AI Research Institute in Geekplus Technology Co., Ltd.PICO is constructed based on the framework of PRIMAL:Pathfinding via Reinforcement and Imitation Multi-Agent Learning and focuses more on the collision avoidance rather than manual post-processing when collision occurs.Exploiting the design of decentralized communication and implicit priority in these secenarios benifits better path […]
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“# bpe_algorithm_can_finetune_tokenizer” this is an implyment for https://github.com/huggingface/transformers/issues/15153 I just add tens of lines of code into the py_bpe algorithm.function finetune_tokenizer is main function added. Details can be see in example.py , actuctally it is very simple.the official python library tokenizer is written is rust. I am learning hoping to give a rust version of this code. ps:the_factor_of_new_added_token_divided_unk_number is the only param you should set.hoping can find a auto algorithm to set it. GitHub View Github
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© Zulkarnine, 2022. Algorithm to solve Wordle 100% of the time within 6 attempts. You can go ahead and run main.py to run it for all 2315 Wordle words and it solves 100% of them correctly within 6 attempts.Example output: Ran: 2315 games. Solved: 2315/2315 = 100.00% You can also run solver.py to get a sense of how it’s guessing and what is the Wordle game simulation returning. (I.e. the colored blocks) Example output:
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This is the implementation of our paper “Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks“. Dataset To successfully test performance, we created TPIC Dataset, a temporal popularity image collection dataset. Overview Our DTCN contains three main components, from embedding, learning to predicting. With a joint embedding network, we obtain a unified deep representation of multi-modal user-post data in a common embedding space. Then, based on the embedded data sequence over time, temporal context learning attempts to recurrently […]
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