Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Palowitch, John"'
Autor:
Balog, Krisztian, Palowitch, John, Ikica, Barbara, Radlinski, Filip, Alvari, Hamidreza, Manshadi, Mehdi
The emergence of synthetic data represents a pivotal shift in modern machine learning, offering a solution to satisfy the need for large volumes of data in domains where real data is scarce, highly private, or difficult to obtain. We investigate the
Externí odkaz:
http://arxiv.org/abs/2408.08379
Web authors frequently embed social media to support and enrich their content, creating the potential to derive web-based, cross-platform social media representations that can enable more effective social media retrieval systems and richer scientific
Externí odkaz:
http://arxiv.org/abs/2407.16007
Publikováno v:
ACL 2024 Findings
Similar to vision-and-language navigation (VLN) tasks that focus on bridging the gap between vision and language for embodied navigation, the new Rendezvous (RVS) task requires reasoning over allocentric spatial relationships (independent of the obse
Externí odkaz:
http://arxiv.org/abs/2406.19967
Autor:
Fatemi, Bahare, Kazemi, Mehran, Tsitsulin, Anton, Malkan, Karishma, Yim, Jinyeong, Palowitch, John, Seo, Sungyong, Halcrow, Jonathan, Perozzi, Bryan
Large language models (LLMs) have showcased remarkable reasoning capabilities, yet they remain susceptible to errors, particularly in temporal reasoning tasks involving complex temporal logic. Existing research has explored LLM performance on tempora
Externí odkaz:
http://arxiv.org/abs/2406.09170
Publikováno v:
EACL 2024
When communicating routes in natural language, the concept of acquired spatial knowledge is crucial for geographic information retrieval (GIR) and in spatial cognitive research. However, NLP navigation studies often overlook the impact of such acquir
Externí odkaz:
http://arxiv.org/abs/2402.16364
Despite a surge in interest in GNN development, homogeneity in benchmarking datasets still presents a fundamental issue to GNN research. GraphWorld is a recent solution which uses the Stochastic Block Model (SBM) to generate diverse populations of sy
Externí odkaz:
http://arxiv.org/abs/2307.08881
As the field of Graph Neural Networks (GNN) continues to grow, it experiences a corresponding increase in the need for large, real-world datasets to train and test new GNN models on challenging, realistic problems. Unfortunately, such graph datasets
Externí odkaz:
http://arxiv.org/abs/2207.04396
Autor:
Ferludin, Oleksandr, Eigenwillig, Arno, Blais, Martin, Zelle, Dustin, Pfeifer, Jan, Sanchez-Gonzalez, Alvaro, Li, Wai Lok Sibon, Abu-El-Haija, Sami, Battaglia, Peter, Bulut, Neslihan, Halcrow, Jonathan, de Almeida, Filipe Miguel Gonçalves, Gonnet, Pedro, Jiang, Liangze, Kothari, Parth, Lattanzi, Silvio, Linhares, André, Mayer, Brandon, Mirrokni, Vahab, Palowitch, John, Paradkar, Mihir, She, Jennifer, Tsitsulin, Anton, Villela, Kevin, Wang, Lisa, Wong, David, Perozzi, Bryan
TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow. It is designed from the bottom up to support the kinds of rich heterogeneous graph data that occurs in today's information ecosystems. In addition to enabling mach
Externí odkaz:
http://arxiv.org/abs/2207.03522
Graph learning algorithms have attained state-of-the-art performance on many graph analysis tasks such as node classification, link prediction, and clustering. It has, however, become hard to track the field's burgeoning progress. One reason is due t
Externí odkaz:
http://arxiv.org/abs/2204.01376
Autor:
Yoon, Minji, Palowitch, John, Zelle, Dustin, Hu, Ziniu, Salakhutdinov, Ruslan, Perozzi, Bryan
Data continuously emitted from industrial ecosystems such as social or e-commerce platforms are commonly represented as heterogeneous graphs (HG) composed of multiple node/edge types. State-of-the-art graph learning methods for HGs known as heterogen
Externí odkaz:
http://arxiv.org/abs/2203.02018