A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan von Arık, Sercan Ö., Shor, Joel, Sinha, Rajarishi, Yoon, Jinsung, Ledsam, Joseph R., Le, Long T., Dusenberry, Michael W., Yoder, Nathanael C., Popendorf, Kris, Epshteyn, Arkady, Euphrosine, Johan, Kanal, Elli, Jones, Isaac, Li, Chun-Liang, Luan, Beth, Mckenna, Joe, Menon, Vikas, Singh, Shashank, Sun, Mimi, Ravi, Ashwin Sura, Zhang, Leyou, Sava, Dario, Cunningham, Kane, Kayama, Hiroki, Tsai, Thomas, Yoneoka, Daisuke, Nomura, Shuhei, Miyata, Hiroaki, Pfister, Tomas

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Retrieving people: Identifying potential answerers in Community Question‐Answering von Le, Long T., Shah, Chirag

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Assessing the quality of answers autonomously in community question–answering von Le, Long T., Shah, Chirag, Choi, Erik

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Retrieving Rising Stars in Focused Community Question-Answering von Le, Long T., Shah, Chirag

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Synthesis and characterization of side-chain liquid crystalline polymers bearing cholesterol mesogen von Ahn, Suk-Kyun, Nguyen Le, Long T, Kasi, Rajeswari M

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Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States von Cramer, Estee Y, Ray, Evan L, Lopez, Velma K, Bracher, Johannes, Brennen, Andrea, Castro Rivadeneira, Alvaro J, Gerding, Aaron, Gneiting, Tilmann, House, Katie H, Huang, Yuxin, Jayawardena, Dasuni, Kanji, Abdul H, Khandelwal, Ayush, Le, Khoa, Mühlemann, Anja, Niemi, Jarad, Shah, Apurv, Stark, Ariane, Wang, Yijin, Wattanachit, Nutcha, Zorn, Martha W, Gu, Youyang, Jain, Sansiddh, Bannur, Nayana, Deva, Ayush, Kulkarni, Mihir, Merugu, Srujana, Raval, Alpan, Shingi, Siddhant, Tiwari, Avtansh, White, Jerome, Abernethy, Neil F, Woody, Spencer, Dahan, Maytal, Fox, Spencer, Gaither, Kelly, Lachmann, Michael, Meyers, Lauren Ancel, Scott, James G, Tec, Mauricio, Srivastava, Ajitesh, George, Glover E, Cegan, Jeffrey C, Dettwiller, Ian D, England, William P, Farthing, Matthew W, Hunter, Robert H, Lafferty, Brandon, Linkov, Igor, Mayo, Michael L, Parno, Matthew D, Rowland, Michael A, Trump, Benjamin D, Zhang-James, Yanli, Chen, Samuel, Faraone, Stephen V, Hess, Jonathan, Morley, Christopher P, Salekin, Asif, Wang, Dongliang, Corsetti, Sabrina M, Baer, Thomas M, Eisenberg, Marisa C, Falb, Karl, Huang, Yitao, Martin, Emily T, McCauley, Ella, Myers, Robert L, Schwarz, Tom, Sheldon, Daniel, Gibson, Graham Casey, Yu, Rose, Gao, Liyao, Ma, Yian, Wu, Dongxia, Yan, Xifeng, Jin, Xiaoyong, Wang, Yu-Xiang, Chen, YangQuan, Guo, Lihong, Zhao, Yanting, Gu, Quanquan, Chen, Jinghui, Wang, Lingxiao, Xu, Pan, Zhang, Weitong, Zou, Difan, Biegel, Hannah, Lega, Joceline, McConnell, Steve, Nagraj, V P, Guertin, Stephanie L, Hulme-Lowe, Christopher, Turner, Stephen D, Shi, Yunfeng, Ban, Xuegang, Walraven, Robert, Hong, Qi-Jun, Kong, Stanley, van de Walle, Axel

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The United States COVID-19 Forecast Hub dataset von Cramer, Estee Y., Huang, Yuxin, Wang, Yijin, Ray, Evan L., Cornell, Matthew, Bracher, Johannes, Brennen, Andrea, Rivadeneira, Alvaro J. Castro, Gerding, Aaron, House, Katie, Jayawardena, Dasuni, Kanji, Abdul Hannan, Khandelwal, Ayush, Le, Khoa, Mody, Vidhi, Mody, Vrushti, Niemi, Jarad, Stark, Ariane, Shah, Apurv, Wattanchit, Nutcha, Zorn, Martha W., Reich, Nicholas G.

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Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States von Cramer, Estee Y, Lopez, Velma K, Castro Rivadeneira, Alvaro J, Gerding, Aaron, Jayawardena, Dasuni, Kanji, Abdul H, Shah, Apurv, Bannur, Nayana, Merugu, Srujana, White, Jerome, Dahan, Maytal, Meyers, Lauren Ancel, George, Glover E, Dettwiller, Ian D, Lafferty, Brandon, Mayo, Michael L, Corsetti, Sabrina M, Baer, Thomas M, Sheldon, Daniel, Yan, Xifeng, Chen, YangQuan, Guo, Lihong, Ban, Xuegang, Hong, Qi-Jun, Ben-Nun, Michal, DesRoches, David, Kyriakides, Christina, Morgan, James, Piwonka, Noah, Siegel, Daniel, Stiefeling, Chris, Wong, Alexander, Kraus, Andrea, Cao, Wei, Lavista Ferres, Juan, Liu, Tie-Yan, Xie, Xing, Davis, Jessica T, Xiong, Xinyue, Zheng, Andrew, Levi, Retsef, Sinha, Deeksha, Wilde, Joshua, Perakis, Georgia, Bennouna, Mohammed Amine, Singhvi, Divya, Spantidakis, Ioannis, Tsiourvas, Asterios, Sundar, Saketh, Wolfinger, Russ, Fairchild, Geoffrey, Michaud, Isaac, Mullany, Luke C, Tallaksen, Katharine, Wilson, Shelby, Dent, Juan, Hill, Alison L, Lessler, Justin, Perez-Saez, Javier, Wills, Josh, Gardner, Lauren, Nixon, Kristen, Burant, John C, Gu, Zhiling, Reiner, Robert C, Gurung, Heidi L, Prakash, B Aditya, Adhikari, Bijaya, Cui, Jiaming, Rodríguez, Alexander, Tabassum, Anika, Keskinocak, Pinar, Baxter, Arden, Arik, Sercan O, Dusenberry, Mike, Li, Chun-Liang, Yoon, Jinsung, Zhang, Leyou, Hellewell, Joel, Sherratt, Katharine, Zhou, Mingyuan, Yamana, Teresa K, Pei, Sen, Shaman, Jeffrey, Soni, Saksham, Chhatwal, Jagpreet, Dalgic, Ozden O, Mueller, Peter, Xiao, Jade, Wang, Yuanjia, Green, Alden, Brooks, Logan, McDonald, Daniel, Rumack, Aaron, Tibshirani, Rob, O'Dea, Eamon B, Drake, John M, Ho, Lam Si Tung, Huynh, Huong, Biggerstaff, Matthew

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CaLM: Contrasting Large and Small Language Models to Verify Grounded Generation von Hsu, I-Hung, Wang, Zifeng, Le, Long T, Miculicich, Lesly, Peng, Nanyun, Lee, Chen-Yu, Pfister, Tomas

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CodecLM: Aligning Language Models with Tailored Synthetic Data von Wang, Zifeng, Li, Chun-Liang, Perot, Vincent, Le, Long T, Miao, Jin, Zhang, Zizhao, Lee, Chen-Yu, Pfister, Tomas

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Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling von Xu, Wenda, Han, Rujun, Wang, Zifeng, Le, Long T, Madeka, Dhruv, Li, Lei, Wang, William Yang, Agarwal, Rishabh, Lee, Chen-Yu, Pfister, Tomas

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Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization von Hsieh, Cheng-Yu, Chuang, Yung-Sung, Li, Chun-Liang, Wang, Zifeng, Le, Long T, Kumar, Abhishek, Glass, James, Ratner, Alexander, Lee, Chen-Yu, Krishna, Ranjay, Pfister, Tomas

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CaLM: Contrasting Large and Small Language Models to Verify Grounded Generation von I-Hung, Hsu, Wang, Zifeng, Le, Long T, Miculicich, Lesly, Peng, Nanyun, Chen-Yu, Lee, Pfister, Tomas

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CodecLM: Aligning Language Models with Tailored Synthetic Data von Wang, Zifeng, Chun-Liang, Li, Perot, Vincent, Le, Long T, Miao, Jin, Zhang, Zizhao, Chen-Yu, Lee, Pfister, Tomas

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Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling von Xu, Wenda, Han, Rujun, Wang, Zifeng, Le, Long T, Madeka, Dhruv, Li, Lei, William Yang Wang, Agarwal, Rishabh, Chen-Yu, Lee, Pfister, Tomas

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Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization von Cheng-Yu, Hsieh, Yung-Sung Chuang, Chun-Liang, Li, Wang, Zifeng, Le, Long T, Kumar, Abhishek, Glass, James, Ratner, Alexander, Chen-Yu, Lee, Krishna, Ranjay, Pfister, Tomas

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Interpretable Sequence Learning for COVID-19 Forecasting von Arik, Sercan O, Li, Chun-Liang, Yoon, Jinsung, Sinha, Rajarishi, Epshteyn, Arkady, Le, Long T, Menon, Vikas, Singh, Shashank, Zhang, Leyou, Yoder, Nate, Nikoltchev, Martin, Sonthalia, Yash, Nakhost, Hootan, Kanal, Elli, Pfister, Tomas

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Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States von Cramer, Estee Y, Lopez, Velma K, Castro Rivadeneira, Alvaro J, Gerding, Aaron, Jayawardena, Dasuni, Kanji, Abdul H, Shah, Apurv, Bannur, Nayana, Merugu, Srujana, White, Jerome, Dahan, Maytal, Meyers, Lauren Ancel, George, Glover E, Dettwiller, Ian D, Lafferty, Brandon, Mayo, Michael L, Corsetti, Sabrina M, Baer, Thomas M, Sheldon, Daniel, Yan, Xifeng, Chen, YangQuan, Guo, Lihong, Ban, Xuegang, Hong, Qi-Jun, Ben-Nun, Michal, DesRoches, David, Kyriakides, Christina, Morgan, James, Piwonka, Noah, Siegel, Daniel, Stiefeling, Chris, Wong, Alexander, Kraus, Andrea, Cao, Wei, Lavista Ferres, Juan, Liu, Tie-Yan, Xie, Xing, Davis, Jessica T, Xiong, Xinyue, Zheng, Andrew, Levi, Retsef, Sinha, Deeksha, Wilde, Joshua, Perakis, Georgia, Bennouna, Mohammed Amine, Singhvi, Divya, Spantidakis, Ioannis, Tsiourvas, Asterios, Sundar, Saketh, Wolfinger, Russ, Fairchild, Geoffrey, Michaud, Isaac, Mullany, Luke C, Tallaksen, Katharine, Wilson, Shelby, Dent, Juan, Hill, Alison L, Lessler, Justin, Perez-Saez, Javier, Wills, Josh, Gardner, Lauren, Nixon, Kristen, Burant, John C, Gu, Zhiling, Reiner, Robert C, Gurung, Heidi L, Prakash, B Aditya, Adhikari, Bijaya, Cui, Jiaming, Rodríguez, Alexander, Tabassum, Anika, Keskinocak, Pinar, Baxter, Arden, Arik, Sercan O, Dusenberry, Mike, Li, Chun-Liang, Yoon, Jinsung, Zhang, Leyou, Hellewell, Joel, Sherratt, Katharine, Zhou, Mingyuan, Yamana, Teresa K, Pei, Sen, Shaman, Jeffrey, Soni, Saksham, Chhatwal, Jagpreet, Dalgic, Ozden O, Mueller, Peter, Xiao, Jade, Wang, Yuanjia, Green, Alden, Brooks, Logan, McDonald, Daniel, Rumack, Aaron, Tibshirani, Rob, O'Dea, Eamon B, Drake, John M, Ho, Lam Si Tung, Huynh, Huong, Biggerstaff, Matthew

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Interpretable Sequence Learning for COVID-19 Forecasting von Arik, Sercan O, Chun-Liang, Li, Yoon, Jinsung, Sinha, Rajarishi, Epshteyn, Arkady, Le, Long T, Menon, Vikas, Singh, Shashank, Zhang, Leyou, Yoder, Nate, Nikoltchev, Martin, Sonthalia, Yash, Nakhost, Hootan, Kanal, Elli, Pfister, Tomas

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Comparison in outcomes at two-years of age of very preterm infants born in 2000, 2005 and 2010 von Abily-Donval, Lénaïg, Pinto-Cardoso, Gaëlle, Chadie, Alexandra, Guerrot, Anne-Marie, Torre, Stéphanie, Rondeau, Stéphane, Marret, Stéphane

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