Zobrazeno 1 - 10
of 960
pro vyhledávání: '"LESKOVEC, JURE"'
Autor:
Bunne, Charlotte, Roohani, Yusuf, Rosen, Yanay, Gupta, Ankit, Zhang, Xikun, Roed, Marcel, Alexandrov, Theo, AlQuraishi, Mohammed, Brennan, Patricia, Burkhardt, Daniel B., Califano, Andrea, Cool, Jonah, Dernburg, Abby F., Ewing, Kirsty, Fox, Emily B., Haury, Matthias, Herr, Amy E., Horvitz, Eric, Hsu, Patrick D., Jain, Viren, Johnson, Gregory R., Kalil, Thomas, Kelley, David R., Kelley, Shana O., Kreshuk, Anna, Mitchison, Tim, Otte, Stephani, Shendure, Jay, Sofroniew, Nicholas J., Theis, Fabian, Theodoris, Christina V., Upadhyayula, Srigokul, Valer, Marc, Wang, Bo, Xing, Eric, Yeung-Levy, Serena, Zitnik, Marinka, Karaletsos, Theofanis, Regev, Aviv, Lundberg, Emma, Leskovec, Jure, Quake, Stephen R.
The cell is arguably the smallest unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (
Externí odkaz:
http://arxiv.org/abs/2409.11654
Autor:
Chang, Serina, Chaszczewicz, Alicja, Wang, Emma, Josifovska, Maya, Pierson, Emma, Leskovec, Jure
Generating social networks is essential for many applications, such as epidemic modeling and social simulations. Prior approaches either involve deep learning models, which require many observed networks for training, or stylized models, which are li
Externí odkaz:
http://arxiv.org/abs/2408.16629
Autor:
Robinson, Joshua, Ranjan, Rishabh, Hu, Weihua, Huang, Kexin, Han, Jiaqi, Dobles, Alejandro, Fey, Matthias, Lenssen, Jan E., Yuan, Yiwen, Zhang, Zecheng, He, Xinwei, Leskovec, Jure
We present RelBench, a public benchmark for solving predictive tasks over relational databases with graph neural networks. RelBench provides databases and tasks spanning diverse domains and scales, and is intended to be a foundational infrastructure
Externí odkaz:
http://arxiv.org/abs/2407.20060
Autor:
Chang, Serina, Lin, Zhiyin, Yan, Benjamin, Bembde, Swapnil, Xiu, Qi, Wong, Chi Heem, Qin, Yu, Kloster, Frank, Luo, Alex, Palleti, Raj, Leskovec, Jure
The global economy relies on the flow of goods over supply chain networks, with nodes as firms and edges as transactions between firms. While we may observe these external transactions, they are governed by unseen production functions, which determin
Externí odkaz:
http://arxiv.org/abs/2407.18772
Autor:
Gu, Siyi, Xu, Minkai, Powers, Alexander, Nie, Weili, Geffner, Tomas, Kreis, Karsten, Leskovec, Jure, Vahdat, Arash, Ermon, Stefano
Generating ligand molecules for specific protein targets, known as structure-based drug design, is a fundamental problem in therapeutics development and biological discovery. Recently, target-aware generative models, especially diffusion models, have
Externí odkaz:
http://arxiv.org/abs/2407.01648
Autor:
Wu, Shirley, Zhao, Shiyu, Huang, Qian, Huang, Kexin, Yasunaga, Michihiro, Cao, Kaidi, Ioannidis, Vassilis N., Subbian, Karthik, Leskovec, Jure, Zou, James
Large language model (LLM) agents have demonstrated impressive capability in utilizing external tools and knowledge to boost accuracy and reduce hallucinations. However, developing the prompting techniques that make LLM agents able to effectively use
Externí odkaz:
http://arxiv.org/abs/2406.11200
Autor:
Althoff, Tim, Ivanovic, Boris, Hicks, Jennifer L., Delp, Scott L., King, Abby C., Leskovec, Jure
While physical activity is critical to human health, most people do not meet recommended guidelines. More walkable built environments have the potential to increase activity across the population. However, previous studies on the built environment an
Externí odkaz:
http://arxiv.org/abs/2406.04557
Despite impressive advances in recent multimodal large language models (MLLMs), state-of-the-art models such as from the GPT-4 suite still struggle with knowledge-intensive tasks. To address this, we consider Reverse Image Retrieval (RIR) augmented g
Externí odkaz:
http://arxiv.org/abs/2405.18740
Autor:
Roohani, Yusuf, Vora, Jian, Huang, Qian, Steinhart, Zachary, Marson, Alexander, Liang, Percy, Leskovec, Jure
Agents based on large language models have shown great potential in accelerating scientific discovery by leveraging their rich background knowledge and reasoning capabilities. Here, we develop BioDiscoveryAgent, an agent that designs new experiments,
Externí odkaz:
http://arxiv.org/abs/2405.17631
Autor:
Wu, Shirley, Zhao, Shiyu, Yasunaga, Michihiro, Huang, Kexin, Cao, Kaidi, Huang, Qian, Ioannidis, Vassilis N., Subbian, Karthik, Zou, James, Leskovec, Jure
Answering real-world complex queries, such as complex product search, often requires accurate retrieval from semi-structured knowledge bases that involve blend of unstructured (e.g., textual descriptions of products) and structured (e.g., entity rela
Externí odkaz:
http://arxiv.org/abs/2404.13207