Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Jain, Arnav"'
Autor:
Jain, Arnav, Sanjotra, Jasmer Singh, Choudhary, Harshvardhan, Agrawal, Krish, Shah, Rupal, Jha, Rohan, Sajid, M., Hussain, Amir, Tanveer, M.
Publikováno v:
INTERSPEECH 2024
In this paper, we propose long short term memory speech enhancement network (LSTMSE-Net), an audio-visual speech enhancement (AVSE) method. This innovative method leverages the complementary nature of visual and audio information to boost the quality
Externí odkaz:
http://arxiv.org/abs/2409.02266
Contrastive Language-Image Pre-training (CLIP) on large-scale image-caption datasets learns representations that can achieve remarkable zero-shot generalization. However, such models require a massive amount of pre-training data. Improving the qualit
Externí odkaz:
http://arxiv.org/abs/2403.12267
In this paper, we consider the extent to which the transformer-based Dense Passage Retrieval (DPR) algorithm, developed by (Karpukhin et. al. 2020), can be optimized without further pre-training. Our method involves two particular insights: we apply
Externí odkaz:
http://arxiv.org/abs/2306.15917
Animals have a developed ability to explore that aids them in important tasks such as locating food, exploring for shelter, and finding misplaced items. These exploration skills necessarily track where they have been so that they can plan for finding
Externí odkaz:
http://arxiv.org/abs/2306.14808
Time series classification is an important data mining task that has received a lot of interest in the past two decades. Due to the label scarcity in practice, semi-supervised time series classification with only a few labeled samples has become popu
Externí odkaz:
http://arxiv.org/abs/2301.04838
Autor:
Jain, Arnav Kumar, Sujit, Shivakanth, Joshi, Shruti, Michalski, Vincent, Hafner, Danijar, Ebrahimi-Kahou, Samira
Learning world models from their sensory inputs enables agents to plan for actions by imagining their future outcomes. World models have previously been shown to improve sample-efficiency in simulated environments with few objects, but have not yet b
Externí odkaz:
http://arxiv.org/abs/2210.11698
Autor:
Cherukuri Aswani Kumar, Sannuthi Shria, Elagandula Neha, Gadamsetty Rishita, Singh Neha, Jain Arnav, Sumaiya Thaseen I., Priya V., Jonnalagadda Annapurna, Kamalov Firuz
Publikováno v:
Cybernetics and Information Technologies, Vol 23, Iss 3, Pp 126-144 (2023)
In this work, we build upon an implementation of a peer-to-peer image encryption algorithm: “Rubik’s cube algorithm”. The algorithm utilizes pixel-level scrambling and XOR-based diffusion, facilitated through the symmetric key. Empirical analys
Externí odkaz:
https://doaj.org/article/f5b25ec28d3a4f6895bf88521ffc0d77
Autor:
Samil, Hadia Mohmmed Osman Ahmed, Martin, Annabelle, Jain, Arnav Kumar, Amin, Susan, Kahou, Samira Ebrahimi
Locust infestation of some regions in the world, including Africa, Asia and Middle East has become a concerning issue that can affect the health and the lives of millions of people. In this respect, there have been attempts to resolve or reduce the s
Externí odkaz:
http://arxiv.org/abs/2011.14371
Autor:
Jain, Arnav
Testing is an important activity when developing a system. Testing requires resources in terms of time, labour and money. By correctly automating the tests, the development time may either be shortened or there will be a possibility to run more tests
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-356157
Contemporary deep learning based inpainting algorithms are mainly based on a hybrid dual stage training policy of supervised reconstruction loss followed by an unsupervised adversarial critic loss. However, there is a dearth of literature for a fully
Externí odkaz:
http://arxiv.org/abs/1908.05861