Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Hamid Palangi"'
Publikováno v:
International Journal of Information and Communication Technology Research, Vol 2, Iss 2, Pp 61-70 (2010)
In this paper we show how the classical and modern adaptive filter algorithms can be introduced in a unified way. The Max normalized least mean squares (MAX-NLMS), N-Max NLMS, the family of SPU-NLMS, SPU transform domain adaptive filter (SPU-TDAF), a
Externí odkaz:
https://doaj.org/article/0fa999251a4d47bca3e6450f12c2a81d
Publikováno v:
AAAI
This paper presents a unified Vision-Language Pre-training (VLP) model. The model is unified in that (1) it can be fine-tuned for either vision-language generation (e.g., image captioning) or understanding (e.g., visual question answering) tasks, and
Autor:
Jacob, Russin, Roland, Fernandez, Hamid, Palangi, Eric, Rosen, Nebojsa, Jojic, Paul, Smolensky, Jianfeng, Gao
Publikováno v:
Cogsci
A longstanding question in cognitive science concerns the learning mechanisms underlying compositionality in human cognition. Humans can infer the structured relationships (e.g., grammatical rules) implicit in their sensory observations (e.g., audito
Publikováno v:
Clinical Neurophysiology. 130:25-37
Objective Automatic detection of epileptic seizures based on deep learning methods received much attention last year. However, the potential of deep neural networks in seizure detection has not been fully exploited in terms of the optimal design of t
Autor:
Jianfeng Gao, Paul Smolensky, Asli Celikyilmaz, Yichen Jiang, Caitlin Smith, Mohit Bansal, Sudha Rao, Paul Soulos, Hamid Palangi, Roland Fernandez
Publikováno v:
NAACL-HLT
ive summarization, the task of generating a concise summary of input documents, requires: (1) reasoning over the source document to determine the salient pieces of information scattered across the long document, and (2) composing a cohesive text by r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::02c0746e192f82c6fb2c4cc75ffaccbc
Autor:
Kezhen Chen, Qiuyuan Huang, Daniel McDuff, Xiang Gao, Hamid Palangi, Jianfeng Wang, Kenneth Forbus, Jianfeng Gao
Publikováno v:
Findings of the Association for Computational Linguistics: EMNLP 2021.
Publikováno v:
Signal Processing: Image Communication. 68:68-75
Image hashing has attracted increasing popularity in recent years. Some off-the-shelf image hashing methods are able to generate more compact and robust hashes for fast indexing and content-based similarity retrieval. However, the ability to infer or
Publikováno v:
Signal Processing. 131:181-189
This paper addresses the reconstruction of sparse vectors in the Multiple Measurement Vectors (MMV) problem in compressive sensing, where the sparse vectors are correlated. This problem has so far been studied using model based and Bayesian methods.
Autor:
Li Deng, Hamid Palangi, Rabab K. Ward, Xinying Song, Yelong Shen, Xiaodong He, Jianshu Chen, Jianfeng Gao
Publikováno v:
IEEE/ACM Transactions on Audio, Speech, and Language Processing. 24:694-707
This paper develops a model that addresses sentence embedding, a hot topic in current natural language processing research, using recurrent neural networks with Long Short-Term Memory (LSTM) cells. Due to its ability to capture long term memory, the
Publikováno v:
ICASSP
Robust detection of epileptic seizures in the presence of inevitable artifacts in Electroencephalogram (EEG) signals is addressed. The EEG dataset considered contains 300 signals recorded from 15 volunteers. Current seizure detection systems achieve