Zobrazeno 1 - 10
of 11 036
pro vyhledávání: '"AnandKumar"'
Autor:
Kumarappan, Adarsh, Tiwari, Mo, Song, Peiyang, George, Robert Joseph, Xiao, Chaowei, Anandkumar, Anima
Large Language Models (LLMs) have been successful in mathematical reasoning tasks such as formal theorem proving when integrated with interactive proof assistants like Lean. Existing approaches involve training or fine-tuning an LLM on a specific dat
Externí odkaz:
http://arxiv.org/abs/2410.06209
Autor:
Jatyani, Armeet Singh, Wang, Jiayun, Wu, Zihui, Liu-Schiaffini, Miguel, Tolooshams, Bahareh, Anandkumar, Anima
Compressed Sensing MRI (CS-MRI) reconstructs images of the body's internal anatomy from undersampled and compressed measurements, thereby reducing scan times and minimizing the duration patients need to remain still. Recently, deep neural networks ha
Externí odkaz:
http://arxiv.org/abs/2410.16290
Recent advancements in diffusion models have been effective in learning data priors for solving inverse problems. They leverage diffusion sampling steps for inducing a data prior while using a measurement guidance gradient at each step to impose data
Externí odkaz:
http://arxiv.org/abs/2410.03463
Autor:
Ding, Mucong, Deng, Chenghao, Choo, Jocelyn, Wu, Zichu, Agrawal, Aakriti, Schwarzschild, Avi, Zhou, Tianyi, Goldstein, Tom, Langford, John, Anandkumar, Anima, Huang, Furong
While generalization over tasks from easy to hard is crucial to profile language models (LLMs), the datasets with fine-grained difficulty annotations for each problem across a broad range of complexity are still blank. Aiming to address this limitati
Externí odkaz:
http://arxiv.org/abs/2409.18433
Autor:
Liu, Shengchao, Yan, Divin, Du, Weitao, Liu, Weiyang, Li, Zhuoxinran, Guo, Hongyu, Borgs, Christian, Chayes, Jennifer, Anandkumar, Anima
Artificial intelligence models have shown great potential in structure-based drug design, generating ligands with high binding affinities. However, existing models have often overlooked a crucial physical constraint: atoms must maintain a minimum pai
Externí odkaz:
http://arxiv.org/abs/2409.10584
Autor:
Shah, Freya, Patti, Taylor L., Berner, Julius, Tolooshams, Bahareh, Kossaifi, Jean, Anandkumar, Anima
Fourier Neural Operators (FNOs) excel on tasks using functional data, such as those originating from partial differential equations. Such characteristics render them an effective approach for simulating the time evolution of quantum wavefunctions, wh
Externí odkaz:
http://arxiv.org/abs/2409.03302
Autor:
Wang, Iria W., Brown, Robin, Patti, Taylor L., Anandkumar, Anima, Pavone, Marco, Yelin, Susanne F.
Quantum computation shows promise for addressing numerous classically intractable problems, such as optimization tasks. Many optimization problems are NP-hard, meaning that they scale exponentially with problem size and thus cannot be addressed at sc
Externí odkaz:
http://arxiv.org/abs/2408.07774
Autor:
Wang, Chuwei, Berner, Julius, Li, Zongyi, Zhou, Di, Wang, Jiayun, Bae, Jane, Anandkumar, Anima
Accurately predicting the long-term behavior of chaotic systems is crucial for various applications such as climate modeling. However, achieving such predictions typically requires iterative computations over a dense spatiotemporal grid to account fo
Externí odkaz:
http://arxiv.org/abs/2408.05177
We introduce Mini-Sequence Transformer (MsT), a simple and effective methodology for highly efficient and accurate LLM training with extremely long sequences. MsT partitions input sequences and iteratively processes mini-sequences to reduce intermedi
Externí odkaz:
http://arxiv.org/abs/2407.15892
Autor:
Sun, Jingtong, Berner, Julius, Richter, Lorenz, Zeinhofer, Marius, Müller, Johannes, Azizzadenesheli, Kamyar, Anandkumar, Anima
The task of sampling from a probability density can be approached as transporting a tractable density function to the target, known as dynamical measure transport. In this work, we tackle it through a principled unified framework using deterministic
Externí odkaz:
http://arxiv.org/abs/2407.07873