Zobrazeno 1 - 10
of 4 331
pro vyhledávání: '"CHEN, PETER"'
Autor:
Chen, Peter Yichen, Liu, Chao, Ma, Pingchuan, Eastman, John, Rus, Daniela, Randle, Dylan, Ivanov, Yuri, Matusik, Wojciech
Differentiable simulation has become a powerful tool for system identification. While prior work has focused on identifying robot properties using robot-specific data or object properties using object-specific data, our approach calibrates object pro
Externí odkaz:
http://arxiv.org/abs/2410.03920
Autor:
Chen, Peter Baile, Wenz, Fabian, Zhang, Yi, Kayali, Moe, Tatbul, Nesime, Cafarella, Michael, Demiralp, Çağatay, Stonebraker, Michael
Existing text-to-SQL benchmarks have largely been constructed using publicly available tables from the web with human-generated tests containing question and SQL statement pairs. They typically show very good results and lead people to think that LLM
Externí odkaz:
http://arxiv.org/abs/2409.02038
Autor:
Chang, Yue, Benchekroun, Otman, Chiaramonte, Maurizio M., Chen, Peter Yichen, Grinspun, Eitan
The eigenfunctions of the Laplace operator are essential in mathematical physics, engineering, and geometry processing. Typically, these are computed by discretizing the domain and performing eigendecomposition, tying the results to a specific mesh.
Externí odkaz:
http://arxiv.org/abs/2408.10099
Autor:
Demiralp, Çağatay, Wenz, Fabian, Chen, Peter Baile, Kayali, Moe, Tatbul, Nesime, Stonebraker, Michael
Large language models (LLMs) know little about enterprise database tables in the private data ecosystem, which substantially differ from web text in structure and content. As LLMs' performance is tied to their training data, a crucial question is how
Externí odkaz:
http://arxiv.org/abs/2407.20256
We propose accelerating the simulation of Lagrangian dynamics, such as fluid flows, granular flows, and elastoplasticity, with neural-operator-based reduced-order modeling. While full-order approaches simulate the physics of every particle within the
Externí odkaz:
http://arxiv.org/abs/2407.03925
The same real-life questions posed to different individuals may lead to different answers based on their unique situations. For instance, whether a student is eligible for a scholarship depends on eligibility conditions, such as major or degree requi
Externí odkaz:
http://arxiv.org/abs/2406.11784
Integrating multiple generative foundation models, especially those trained on different modalities, into something greater than the sum of its parts poses significant challenges. Two key hurdles are the availability of aligned data (concepts that co
Externí odkaz:
http://arxiv.org/abs/2405.18669
Autor:
Liu, Chunwei, Russo, Matthew, Cafarella, Michael, Cao, Lei, Chen, Peter Baille, Chen, Zui, Franklin, Michael, Kraska, Tim, Madden, Samuel, Vitagliano, Gerardo
A long-standing goal of data management systems has been to build systems which can compute quantitative insights over large corpora of unstructured data in a cost-effective manner. Until recently, it was difficult and expensive to extract facts from
Externí odkaz:
http://arxiv.org/abs/2405.14696
Retrieving relevant tables containing the necessary information to accurately answer a given question over tables is critical to open-domain question-answering (QA) systems. Previous methods assume the answer to such a question can be found either in
Externí odkaz:
http://arxiv.org/abs/2404.09889