Zobrazeno 1 - 10
of 125
pro vyhledávání: '"Fung, Glenn"'
Transformers have emerged as a preferred model for many tasks in natural langugage processing and vision. Recent efforts on training and deploying Transformers more efficiently have identified many strategies to approximate the self-attention matrix,
Externí odkaz:
http://arxiv.org/abs/2207.10284
Autor:
Zeng, Zhanpeng, Xiong, Yunyang, Ravi, Sathya N., Acharya, Shailesh, Fung, Glenn, Singh, Vikas
Transformer-based models are widely used in natural language processing (NLP). Central to the transformer model is the self-attention mechanism, which captures the interactions of token pairs in the input sequences and depends quadratically on the se
Externí odkaz:
http://arxiv.org/abs/2111.09714
Few-shot learning aims to generalize to novel classes with only a few samples with class labels. Research in few-shot learning has borrowed techniques from transfer learning, metric learning, meta-learning, and Bayesian methods. These methods also ai
Externí odkaz:
http://arxiv.org/abs/2111.00007
Document image classification remains a popular research area because it can be commercialized in many enterprise applications across different industries. Recent advancements in large pre-trained computer vision and language models and graph neural
Externí odkaz:
http://arxiv.org/abs/2106.13802
Metric space magnitude, an active field of research in algebraic topology, is a scalar quantity that summarizes the effective number of distinct points that live in a general metric space. The {\em weighting vector} is a closely-related concept that
Externí odkaz:
http://arxiv.org/abs/2106.00827
Autor:
Xiong, Yunyang, Zeng, Zhanpeng, Chakraborty, Rudrasis, Tan, Mingxing, Fung, Glenn, Li, Yin, Singh, Vikas
Transformers have emerged as a powerful tool for a broad range of natural language processing tasks. A key component that drives the impressive performance of Transformers is the self-attention mechanism that encodes the influence or dependence of ot
Externí odkaz:
http://arxiv.org/abs/2102.03902
Autor:
Kanchinadam, Teja, You, Qian, Westpfahl, Keith, Kim, James, Gunda, Siva, Seith, Sebastian, Fung, Glenn
In this work, we propose the use of a fully managed machine learning service, which utilizes active learning to directly build models from unstructured data. With this tool, business users can quickly and easily build machine learning models and then
Externí odkaz:
http://arxiv.org/abs/2102.00426
Customer satisfaction is an important factor in creating and maintaining long-term relationships with customers. Near real-time identification of potentially dissatisfied customers following phone calls can provide organizations the opportunity to ta
Externí odkaz:
http://arxiv.org/abs/2102.00420
With the rise of big data analytics, multi-layer neural networks have surfaced as one of the most powerful machine learning methods. However, their theoretical mathematical properties are still not fully understood. Training a neural network requires
Externí odkaz:
http://arxiv.org/abs/2012.15036
Recently, neural network architectures have been developed to accommodate when the data has the structure of a graph or, more generally, a hypergraph. While useful, graph structures can be potentially limiting. Hypergraph structures in general do not
Externí odkaz:
http://arxiv.org/abs/2012.06010