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pro vyhledávání: '"Ramalingam, P"'
Autor:
Fahrbach, Matthew, Ramalingam, Srikumar, Zadimoghaddam, Morteza, Ahmadian, Sara, Citovsky, Gui, DeSalvo, Giulia
We propose a novel subset selection task called min-distance diverse data summarization ($\textsf{MDDS}$), which has a wide variety of applications in machine learning, e.g., data sampling and feature selection. Given a set of points in a metric spac
Externí odkaz:
http://arxiv.org/abs/2405.18754
Machine learning has become an effective tool for automatically annotating unstructured data (e.g., images) with structured labels (e.g., object detections). As a result, a new programming paradigm called neurosymbolic programming has emerged where u
Externí odkaz:
http://arxiv.org/abs/2405.15912
Publikováno v:
IEEE Transactions on Information Forensics and Security, vol.15, no.1, pp. 487 to 499 (2019)
In automatic signature verification, questioned specimens are usually compared with reference signatures. In writer-dependent schemes, a number of reference signatures are required to build up the individual signer model while a writer-independent sy
Externí odkaz:
http://arxiv.org/abs/2405.14409
The study aims to highlight the growth and development of Indo-German collaborative research over the past three decades. Moreover, this study encompasses an in-depth examination of funding acknowledgements to gain valuable insights into the financia
Externí odkaz:
http://arxiv.org/abs/2404.17171
Autor:
Böther, Maximilian, Sebastian, Abraham, Awasthi, Pranjal, Klimovic, Ana, Ramalingam, Srikumar
Many learning problems hinge on the fundamental problem of subset selection, i.e., identifying a subset of important and representative points. For example, selecting the most significant samples in ML training cannot only reduce training costs but a
Externí odkaz:
http://arxiv.org/abs/2402.16442
In this work, we introduce a novel paradigm called Simulated Overparametrization (SOP). SOP merges the computational efficiency of compact models with the advanced learning proficiencies of overparameterized models. SOP proposes a unique approach to
Externí odkaz:
http://arxiv.org/abs/2402.05033
Autor:
Yenduri, Gokul, M, Ramalingam, Maddikunta, Praveen Kumar Reddy, Gadekallu, Thippa Reddy, Jhaveri, Rutvij H, Bandi, Ajay, Chen, Junxin, Wang, Wei, Shirawalmath, Adarsh Arunkumar, Ravishankar, Raghav, Wang, Weizheng
Spatial computing is a technological advancement that facilitates the seamless integration of devices into the physical environment, resulting in a more natural and intuitive digital world user experience. Spatial computing has the potential to becom
Externí odkaz:
http://arxiv.org/abs/2402.07912
The success of deep learning hinges on enormous data and large models, which require labor-intensive annotations and heavy computation costs. Subset selection is a fundamental problem that can play a key role in identifying smaller portions of the tr
Externí odkaz:
http://arxiv.org/abs/2312.10602
Autor:
Jayasumana, Sadeep, Ramalingam, Srikumar, Veit, Andreas, Glasner, Daniel, Chakrabarti, Ayan, Kumar, Sanjiv
As with many machine learning problems, the progress of image generation methods hinges on good evaluation metrics. One of the most popular is the Frechet Inception Distance (FID). FID estimates the distance between a distribution of Inception-v3 fea
Externí odkaz:
http://arxiv.org/abs/2401.09603
We study the problem of making predictions of an adversarially chosen high-dimensional state that are unbiased subject to an arbitrary collection of conditioning events, with the goal of tailoring these events to downstream decision makers. We give e
Externí odkaz:
http://arxiv.org/abs/2310.17651