Gagandeep Singh

Goa Medical College

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i-10 index

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Co-Authors

Martin Vechev

ETH Zurich

Markus Püschel

ETH Zurich

Saša Misailović

University of Illinois Urbana-Champaign

Shubham Ugare

Urbana University

Mislav Balunović

ETH Zurich

Anil Kumar

Indian Institute of Information Technology Design and Manufacturing Jabalpur

Timon Gehr

ETH Zurich

Prateek Prasanna

Stony Brook Medicine

Juan Gómez-Luna

ETH Zurich

Isha Chaudhary

University of Illinois Urbana-Champaign

Cooperative Institutions

ETH Zurich

All India Institute of Medical Sciences

University of Illinois Urbana-Champaign

Chaudhary Charan Singh Haryana Agricultural University

Indian Institute of Technology Delhi

Carnegie Mellon University

Stony Brook University

Institute of Medical Sciences

Chandigarh University

All India Institute of Medical Sciences Raipur

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IMPROVED COLOR EDGE DETECTOR USING FUZZY SET THEORY

International journal of advanced research in computer science and electronics engineering (2014)

Simranjit Singh Walia Gagandeep Singh

To overcome the limitations of the earlier edge detection techniques a new fuzzy based color edge detector is proposed in this paper. Edge detection has found to be most important step in many critical vision applications. It actually results in the black and white (binary) image where each object is differentiate by lines (either black or white). Edges are basically the area in the image where sharp changes exist. It has been found that the most of the existing techniques has neglected the use of colours while detecting the edges but in many applications a region can be categorized based upon the color. This paper has shown the result of different color based edge detectors i.e. hue, PCA and integrated Hue and PCA based methods. To overcome the problem of the color based edge detector a fuzzy set theory based edge detector is proposed. The results of the proposed fuzzy based edge detectors are also taken for experimental purpose. The experiments have shown that the fuzzy based edge detector hasquite significant results over the integrated hue and PCA based edge detectors.

Hue

Canny edge detector

Deriche edge detector

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Citations (2)

Generating Medical Reports from Patient-Doctor Conversations Using Sequence-to-Sequence Models

Seppo Enarvi Marilisa Amoia Miguel Del-Agua Teba Brian Delaney Frank Diehl

Seppo Enarvi, Marilisa Amoia, Miguel Del-Agua Teba, Brian Delaney, Frank Diehl, Stefan Hahn, Kristina Harris, Liam McGrath, Yue Pan, Joel Pinto, Luca Rubini, Miguel Ruiz, Gagandeep Singh, Fabian Stemmer, Weiyi Sun, Paul Vozila, Thomas Lin, Ranjani Ramamurthy. Proceedings of the First Workshop on Natural Language Processing for Medical Conversations. 2020.

Sequence (biology)

10.18653/v1/2020.nlpmc-1.4

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Citations (47)

Outlier and Trend Detection Using Approximate Median and Median Absolute Deviation

Gagandeep Singh Suman Kundu

In this modern era of technologies of scale, vast amounts of data are generated both by users and machines every day. This data comes as streams that may contain outliers. Detecting those outliers can be helpful in many ways, such as machine failures due to overload. Similarly, trends in social media posts are also outliers, and detecting them at different levels has great benefits. The current paper proposes an algorithm to approximate median and median absolute deviation from a stream of numerical values. The algorithm takes a fixed number of memory spaces and linear to the size of the memory. The median and median absolute deviation are then used to detect outliers and multi-level trends without being prone to noise in the data. Experimental results with CPU usage benchmark data and Twitter post data show the effectiveness of the proposed algorithms.

Absolute deviation

Benchmark (surveying)

Least absolute deviations

10.1109/cine56307.2022.10037489

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Citations (2)

Robustness Certification with Refinement

Gagandeep Singh Timon Gehr Markus Püschel Martin Vechev

Robustness

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Citations (14)

Slipped capital femoral epiphysis

Radiopaedia.org (2009)

Mostafa El-Feky Gagandeep Singh

Epiphysis

Capital (architecture)

10.53347/rid-7228

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Agile Autotuning of a Transprecision Tensor Accelerator Overlay for TVM Compiler Stack

arXiv (Cornell University) (2020)

Dionysios Diamantopoulos Burkhard Ringlein Mitra Purandare Gagandeep Singh Christoph Hagleitner

Specialized accelerators for tensor-operations, such as blocked-matrix operations and multi-dimensional convolutions, have been emerged as powerful architecture choices for high-performance Deep-Learning computing. The rapid development of frameworks, models, and precision options challenges the adaptability of such tensor-accelerators since the adaptation to new requirements incurs significant engineering costs. Programmable tensor accelerators offer a promising alternative by allowing reconfiguration of a virtual architecture that overlays on top of the physical FPGA configurable fabric. We propose an overlay ({\tau}-VTA) and an optimization method guided by agile-inspired auto-tuning techniques. We achieve higher performance and faster convergence than state-of-art.

Control reconfiguration

Adaptability

Reconfigurability

10.48550/arxiv.2004.10854

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BLEND: a fast, memory-efficient and accurate mechanism to find fuzzy seed matches in genome analysis

Zenodo (CERN European Organization for Nuclear Research) (2023)

Can Fırtına Jisung Park Mohammed Alser Jeremie S. Kim Damla Senol Cali

10.5281/zenodo.8087049

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An efficient method for multilevel color image thresholding using cuckoo search algorithm based on minimum cross entropy

Applied Soft Computing (2017)

S. Pare Anil Kumar Varun Bajaj Gagandeep Singh

Cuckoo search

Otsu's method

Balanced histogram thresholding

10.1016/j.asoc.2017.08.039

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Citations (87)

Compact Planar Antennas for various wireless applications

Journal of Emerging Technologies and Innovative Research (2019)

Gurpreet Kumar Jaspreet Kaur Gagandeep Singh Aditi Thakur

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Probabilistic Trust Intervals for Out of Distribution Detection

arXiv (Cornell University) (2021)

Gagandeep Singh Deepak Mishra

Building neural network classifiers with an ability to distinguish between in and out-of distribution inputs is an important step towards faithful deep learning systems. Some of the successful approaches for this, resort to architectural novelties, such as ensembles, with increased complexities in terms of the number of parameters and training procedures. Whereas some other approaches make use of surrogate samples, which are easy to create and work as proxies for actual out-of-distribution (OOD) samples, to train the networks for OOD detection. In this paper, we propose a very simple approach for enhancing the ability of a pretrained network to detect OOD inputs without even altering the original parameter values. We define a probabilistic trust interval for each weight parameter of the network and optimize its size according to the in-distribution (ID) inputs. It allows the network to sample additional weight values along with the original values at the time of inference and use the observed disagreement among the corresponding outputs for OOD detection. In order to capture the disagreement effectively, we also propose a measure and establish its suitability using empirical evidence. Our approach outperforms the existing state-of-the-art methods on various OOD datasets by considerable margins without using any real or surrogate OOD samples. We also analyze the performance of our approach on adversarial and corrupted inputs such as CIFAR-10-C and demonstrate its ability to clearly distinguish such inputs as well. By using fundamental theorem of calculus on neural networks, we explain why our technique doesn't need to observe OOD samples during training to achieve results better than the previous works.

Sample (material)

10.48550/arxiv.2102.01336

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