Studying and improving reasoning in humans and machines von Yax, Nicolas, Anlló, Hernán, Palminteri, Stefano

Volltext

Assessing Contamination in Large Language Models: Introducing the LogProber method von Yax, Nicolas, Oudeyer, Pierre-Yves, Palminteri, Stefano

Volltext bestellen

Large Language Models are Biased Reinforcement Learners von Hayes, William M, Yax, Nicolas, Palminteri, Stefano

Volltext bestellen

PhyloLM : Inferring the Phylogeny of Large Language Models and Predicting their Performances in Benchmarks von Yax, Nicolas, Oudeyer, Pierre-Yves, Palminteri, Stefano

Volltext bestellen

Relative Value Biases in Large Language Models von Hayes, William M, Yax, Nicolas, Palminteri, Stefano

Volltext bestellen

Studying and improving reasoning in humans and machines von Yax, Nicolas, Anlló, Hernan, Palminteri, Stefano

Volltext bestellen

Assessing Contamination in Large Language Models: Introducing the LogProber method von Yax, Nicolas, Pierre-Yves Oudeyer, Palminteri, Stefano

Volltext

PhyloLM : Inferring the Phylogeny of Large Language Models and Predicting their Performances in Benchmarks von Yax, Nicolas, Pierre-Yves Oudeyer, Palminteri, Stefano

Volltext

Large Language Models are Biased Reinforcement Learners von Hayes, William M, Yax, Nicolas, Palminteri, Stefano

Volltext

Relative Value Biases in Large Language Models von Hayes, William M, Yax, Nicolas, Palminteri, Stefano

Volltext

Studying and improving reasoning in humans and machines von Yax, Nicolas, Anlló, Hernan, Palminteri, Stefano

Volltext

MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information von Heyndrickx, Wouter, Mervin, Lewis, Morawietz, Tobias, Sturm, Noé, Friedrich, Lukas, Zalewski, Adam, Pentina, Anastasia, Humbeck, Lina, Oldenhof, Martijn, Niwayama, Ritsuya, Schmidtke, Peter, Fechner, Nikolas, Simm, Jaak, Arany, Adam, Drizard, Nicolas, Jabal, Rama, Afanasyeva, Arina, Loeb, Regis, Verma, Shlok, Harnqvist, Simon, Holmes, Matthew, Pejo, Balazs, Telenczuk, Maria, Holway, Nicholas, Dieckmann, Arne, Rieke, Nicola, Zumsande, Friederike, Clevert, Djork-Arné, Krug, Michael, Luscombe, Christopher, Green, Darren, Ertl, Peter, Antal, Peter, Marcus, David, Do Huu, Nicolas, Fuji, Hideyoshi, Pickett, Stephen, Acs, Gergely, Boniface, Eric, Beck, Bernd, Sun, Yax, Gohier, Arnaud, Rippmann, Friedrich, Engkvist, Ola, Göller, Andreas H., Moreau, Yves, Galtier, Mathieu N., Schuffenhauer, Ansgar, Ceulemans, Hugo

Volltext

MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information von Heyndrickx, Wouter, Mervin, Lewis, Morawietz, Tobias, Sturm, Noe, Friedrich, Lukas, Zalewski, Adam, Pentina, Anastasia, Humbeck, Lina, Oldenhof, Martijn, Niwayama, Ritsuya, Schmidtke, Peter, Fechner, Nikolas, Simm, Jaak, Arany, Adam, Drizard, Nicolas, Jabal, Rama, Afanasyeva, Arina, Loeb, Regis, Verma, Shlok, Harnqvist, Simon, Holmes, Matthew, Pejo, Balazs, Telenczuk, Maria, Holway, Nicholas, Dieckmann, Arne, Rieke, Nicola, Zumsande, Friederike, Clevert, Djork-Arne, Krug, Michael, Luscombe, Christopher, Green, Darren, Ertl, Peter, Antal, Peter, Marcus, David, Do Huu, Nicolas, Fuji, Hideyoshi, Pickett, Stephen, Acs, Gergely, Boniface, Eric, Beck, Bernd, Sun, Yax, Gohier, Arnaud, Rippmann, Friedrich, Engkvist, Ola, Goeller, Andreas H, Moreau, Yves, Galtier, Mathieu N, Schuffenhauer, Ansgar, Ceulemans, Hugo

Volltext

Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies von Mock, Marissa, Jacobitz, Alex W., Langmead, Christopher James, Sudom, Athena, Yoo, Daniel, Humphreys, Sara C., Alday, Mai, Alekseychyk, Larysa, Angell, Nicolas, Bi, Vivian, Catterall, Hannah, Chen, Chen-Chun, Chou, Hui-Ting, Conner, Kip P., Cook, Kevin D., Correia, Ana R., Dykstra, Andrew, Ghimire-Rijal, Sudipa, Graham, Kevin, Grandsard, Peter, Huh, Joon, Hui, John O., Jain, Mani, Jann, Victoria, Jia, Lei, Johnstone, Sheree, Khanal, Neelam, Kolvenbach, Carl, Narhi, Linda, Padaki, Rupa, Pelegri-O’Day, Emma M., Qi, Wei, Razinkov, Vladimir, Rice, Austin J., Smith, Richard, Spahr, Christopher, Stevens, Jennitte, Sun, Yax, Thomas, Veena A., van Driesche, Sarah, Vernon, Robert, Wagner, Victoria, Walker, Kenneth W., Wei, Yangjie, Winters, Dwight, Yang, Melissa, Campuzano, Iain D. G.

Volltext

Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies von Mock, Marissa, Jacobitz, Alex W., Langmead, Christopher James, Sudom, Athena, Yoo, Daniel, Humphreys, Sara C., Alday, Mai, Alekseychyk, Larysa, Angell, Nicolas, Bi, Vivian, Catterall, Hannah, Chen, Chen-Chun, Chou, Hui-Ting, Conner, Kip P., Cook, Kevin D., Correia, Ana R., Dykstra, Andrew, Ghimire-Rijal, Sudipa, Graham, Kevin, Grandsard, Peter, Huh, Joon, Hui, John O., Jain, Mani, Jann, Victoria, Jia, Lei, Johnstone, Sheree, Khanal, Neelam, Kolvenbach, Carl, Narhi, Linda, Padaki, Rupa, Pelegri-O'Day, Emma M., Qi, Wei, Razinkov, Vladimir, Rice, Austin J., Smith, Richard, Spahr, Christopher, Stevens, Jennitte, Sun, Yax, Thomas, Veena A., van Driesche, Sarah, Vernon, Robert, Wagner, Victoria, Walker, Kenneth W., Wei, Yangjie, Winters, Dwight, Yang, Melissa, Campuzano, Iain D. G.

Volltext