Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Ashok, Arjun"'
The growing power of data science can play a crucial role in addressing social discrimination, necessitating nuanced understanding and effective mitigation strategies of potential biases. Data Science Looks At Discrimination (dsld) is an R and Python
Externí odkaz:
http://arxiv.org/abs/2411.04228
Autor:
Williams, Andrew Robert, Ashok, Arjun, Marcotte, Étienne, Zantedeschi, Valentina, Subramanian, Jithendaraa, Riachi, Roland, Requeima, James, Lacoste, Alexandre, Rish, Irina, Chapados, Nicolas, Drouin, Alexandre
Forecasting is a critical task in decision making across various domains. While numerical data provides a foundation, it often lacks crucial context necessary for accurate predictions. Human forecasters frequently rely on additional information, such
Externí odkaz:
http://arxiv.org/abs/2410.18959
Autor:
Rasul, Kashif, Ashok, Arjun, Williams, Andrew Robert, Ghonia, Hena, Bhagwatkar, Rishika, Khorasani, Arian, Bayazi, Mohammad Javad Darvishi, Adamopoulos, George, Riachi, Roland, Hassen, Nadhir, Biloš, Marin, Garg, Sahil, Schneider, Anderson, Chapados, Nicolas, Drouin, Alexandre, Zantedeschi, Valentina, Nevmyvaka, Yuriy, Rish, Irina
Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-shot and few-shot generalization. However, despite the success of foundation models in modalities such as natural
Externí odkaz:
http://arxiv.org/abs/2310.08278
Autor:
Ashok, Arjun, Marcotte, Étienne, Zantedeschi, Valentina, Chapados, Nicolas, Drouin, Alexandre
We introduce a new model for multivariate probabilistic time series prediction, designed to flexibly address a range of tasks including forecasting, interpolation, and their combinations. Building on copula theory, we propose a simplified objective f
Externí odkaz:
http://arxiv.org/abs/2310.01327
The separation between training and deployment of machine learning models implies that not all scenarios encountered in deployment can be anticipated during training, and therefore relying solely on advancements in training has its limits. Out-of-dis
Externí odkaz:
http://arxiv.org/abs/2209.09858
In class-incremental learning, the model is expected to learn new classes continually while maintaining knowledge on previous classes. The challenge here lies in preserving the model's ability to effectively represent prior classes in the feature spa
Externí odkaz:
http://arxiv.org/abs/2208.03767
Out-of-distribution (O.O.D.) generalization remains to be a key challenge for real-world machine learning systems. We describe a method for O.O.D. generalization that, through training, encourages models to only preserve features in the network that
Externí odkaz:
http://arxiv.org/abs/2208.03753
Autor:
Wang, Yizhong, Mishra, Swaroop, Alipoormolabashi, Pegah, Kordi, Yeganeh, Mirzaei, Amirreza, Arunkumar, Anjana, Ashok, Arjun, Dhanasekaran, Arut Selvan, Naik, Atharva, Stap, David, Pathak, Eshaan, Karamanolakis, Giannis, Lai, Haizhi Gary, Purohit, Ishan, Mondal, Ishani, Anderson, Jacob, Kuznia, Kirby, Doshi, Krima, Patel, Maitreya, Pal, Kuntal Kumar, Moradshahi, Mehrad, Parmar, Mihir, Purohit, Mirali, Varshney, Neeraj, Kaza, Phani Rohitha, Verma, Pulkit, Puri, Ravsehaj Singh, Karia, Rushang, Sampat, Shailaja Keyur, Doshi, Savan, Mishra, Siddhartha, Reddy, Sujan, Patro, Sumanta, Dixit, Tanay, Shen, Xudong, Baral, Chitta, Choi, Yejin, Smith, Noah A., Hajishirzi, Hannaneh, Khashabi, Daniel
How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions
Externí odkaz:
http://arxiv.org/abs/2204.07705
Autor:
Teichner, Eric M., Subtirelu, Robert C., Patil, Shiv, Parikh, Chitra, Ashok, Arjun B., Talasila, Sahithi, Anderson, Victoria A., Khan, Talha, Su, Yvonne, Werner, Thomas, Alavi, Abass, Revheim, Mona-Elisabeth
Publikováno v:
In Clinical Neurology and Neurosurgery November 2024 246
Autor:
Stephens, Caroline Q., Ashok, Arjun, Gee, Arvin, Jafri, Mubeen, Hamilton, Nicholas A., Lehrfeld, David, Newgard, Craig, Krishnaswami, Sanjay
Publikováno v:
In Journal of Surgical Research August 2023 288:178-187