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pro vyhledávání: '"Lee,Andrew"'
We outline a new tool that can promote coherence within and across higher education mathematics courses by focusing on problem-solving: the Mathematical Problem-Solving Pipeline or MPSP. The MPSP can be used for teaching mathematics and mathematical
Externí odkaz:
http://arxiv.org/abs/2412.00009
Imitation learning has demonstrated significant potential in performing high-precision manipulation tasks using visual feedback from cameras. However, it is common practice in imitation learning for cameras to be fixed in place, resulting in issues l
Externí odkaz:
http://arxiv.org/abs/2409.17435
Bimanual manipulation presents unique challenges compared to unimanual tasks due to the complexity of coordinating two robotic arms. In this paper, we introduce InterACT: Inter-dependency aware Action Chunking with Hierarchical Attention Transformers
Externí odkaz:
http://arxiv.org/abs/2409.07914
Modern generative models demonstrate impressive capabilities, likely stemming from an ability to identify and manipulate abstract concepts underlying their training data. However, fundamental questions remain: what determines the concepts a model lea
Externí odkaz:
http://arxiv.org/abs/2406.19370
Autor:
Gregory, Wilson G., Tonelli-Cueto, Josué, Marshall, Nicholas F., Lee, Andrew S., Villar, Soledad
This work characterizes equivariant polynomial functions from tuples of tensor inputs to tensor outputs. Loosely motivated by physics, we focus on equivariant functions with respect to the diagonal action of the orthogonal group on tensors. We show h
Externí odkaz:
http://arxiv.org/abs/2406.01552
Autor:
Lee, Andrew H., Semnani, Sina J., Castillo-López, Galo, de Chalendar, Gäel, Choudhury, Monojit, Dua, Ashna, Kavitha, Kapil Rajesh, Kim, Sungkyun, Kodali, Prashant, Kumaraguru, Ponnurangam, Lombard, Alexis, Moradshahi, Mehrad, Park, Gihyun, Semmar, Nasredine, Seo, Jiwon, Shen, Tianhao, Shrivastava, Manish, Xiong, Deyi, Lam, Monica S.
Creating multilingual task-oriented dialogue (TOD) agents is challenging due to the high cost of training data acquisition. Following the research trend of improving training data efficiency, we show for the first time, that in-context learning is su
Externí odkaz:
http://arxiv.org/abs/2405.17840
Autor:
Roohani, Yusuf, Lee, Andrew, Huang, Qian, Vora, Jian, Steinhart, Zachary, Huang, Kexin, 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. In this paper, we introduce BioDiscoveryAgent, an agent that designs new e
Externí odkaz:
http://arxiv.org/abs/2405.17631
Synthetic data generation has the potential to impact applications and domains with scarce data. However, before such data is used for sensitive tasks such as mental health, we need an understanding of how different demographics are represented in it
Externí odkaz:
http://arxiv.org/abs/2403.16909
Autor:
Lee, Andrew, Colbert, Cory H.
We consider the embedding function $c_b(a)$ describing the problem of symplectically embedding an ellipsoid $E(1,a)$ into the smallest possible scaling by $\lambda>1$ of the polydisc $P(1,b)$. In particular, we calculate rigid-flexible values, i.e. t
Externí odkaz:
http://arxiv.org/abs/2402.16223
One of the grand challenges of Mathematics instruction is to provide students with problems that are both accessible and have a reasonably elegant solution. Instructors commonly resort to resources like course textbooks, online-learning platforms, or
Externí odkaz:
http://arxiv.org/abs/2402.06648