1 code implementation • 30 Dec 2018 • Akash Kumar
In this work, we introduce an approach through which we can generate a report remotely that provides the amount of solar potential of a building using only its latitude and longitude.
2 code implementations • 9 Oct 2018 • Sourya Dipta Das, Akash Kumar
Bird species classification has received more and more attention in the field of computer vision, for its promising applications in biology and environmental studies.
1 code implementation • 19 Mar 2020 • Akash Kumar, Arnav Bhavsar
With the arrival of several face-swapping applications such as FaceApp, SnapChat, MixBooth, FaceBlender and many more, the authenticity of digital media content is hanging on a very loose thread.
1 code implementation • CVPR 2022 • Akash Kumar, Yogesh Singh Rawat
In this work, we focus on semi-supervised learning for video action detection which utilizes both labeled as well as unlabeled data.
1 code implementation • 4 Dec 2020 • Shubham Rai, Walter Lau Neto, Yukio Miyasaka, Xinpei Zhang, Mingfei Yu, Qingyang Yi Masahiro Fujita, Guilherme B. Manske, Matheus F. Pontes, Leomar S. da Rosa Junior, Marilton S. de Aguiar, Paulo F. Butzen, Po-Chun Chien, Yu-Shan Huang, Hoa-Ren Wang, Jie-Hong R. Jiang, Jiaqi Gu, Zheng Zhao, Zixuan Jiang, David Z. Pan, Brunno A. de Abreu, Isac de Souza Campos, Augusto Berndt, Cristina Meinhardt, Jonata T. Carvalho, Mateus Grellert, Sergio Bampi, Aditya Lohana, Akash Kumar, Wei Zeng, Azadeh Davoodi, Rasit O. Topaloglu, Yuan Zhou, Jordan Dotzel, Yichi Zhang, Hanyu Wang, Zhiru Zhang, Valerio Tenace, Pierre-Emmanuel Gaillardon, Alan Mishchenko, Satrajit Chatterjee
If the function is incompletely-specified, the implementation has to be true only on the care set.
1 code implementation • 3 Feb 2020 • Akash Kumar, Arnav Bhavasar
In recent years, image manipulation is becoming increasingly more accessible, yielding more natural-looking images, owing to the modern tools in image processing and computer vision techniques.
1 code implementation • 30 Nov 2018 • Akash Kumar, Sagnik Bhowmick, N. Jayanthi, S. Indu
Image Landmark Recognition has been one of the most sought-after classification challenges in the field of vision and perception.
1 code implementation • 15 Aug 2022 • Elias Trommer, Bernd Waschneck, Akash Kumar
We further demonstrate that our error model can predict the parameters of an approximate multiplier in the context of the commonly used additive Gaussian noise (AGN) model with high accuracy.
no code implementations • 1 Sep 2016 • Clément L. Canonne, Elena Grigorescu, Siyao Guo, Akash Kumar, Karl Wimmer
Our results include the following: - We demonstrate a separation between testing $k$-monotonicity and testing monotonicity, on the hypercube domain $\{0, 1\}^d$, for $k\geq 3$; - We demonstrate a separation between testing and learning on $\{0, 1\}^d$, for $k=\omega(\log d)$: testing $k$-monotonicity can be performed with $2^{O(\sqrt d \cdot \log d\cdot \log{1/\varepsilon})}$ queries, while learning $k$-monotone functions requires $2^{\Omega(k\cdot \sqrt d\cdot{1/\varepsilon})}$ queries (Blais et al. (RANDOM 2015)).
no code implementations • 14 Nov 2019 • Akash Kumar, Arnav Bhavsar
In the current era, image manipulation is becoming increasingly easier, yielding more natural looking images, owing to the modern tools in image processing and computer vision techniques.
no code implementations • 25 Jun 2020 • Akash Kumar, Adish Singla, Yisong Yue, Yuxin Chen
We investigate the average teaching complexity of the task, i. e., the minimal number of samples (halfspace queries) required by a teacher to help a version-space learner in locating a randomly selected target.
no code implementations • 24 Oct 2020 • Suresh Nambi, Salim Ullah, Aditya Lohana, Siva Satyendra Sahoo, Farhad Merchant, Akash Kumar
Towards this end, we propose a novel Posit to fixed-point converter for enabling high-performance and energy-efficient hardware implementations for ANNs with minimal drop in the output accuracy.
no code implementations • 27 Oct 2020 • Akash Kumar, Hanqi Zhang, Adish Singla, Yuxin Chen
As a warm-up, we show that the teaching complexity is $\Theta(d)$ for the exact teaching of linear perceptrons in $\mathbb{R}^d$, and $\Theta(d^k)$ for kernel perceptron with a polynomial kernel of order $k$.
no code implementations • 2 Nov 2020 • Zahra Ebrahimi, Salim Ullah, Akash Kumar
The ever-increasing quest for data-level parallelism and variable precision in ubiquitous multimedia and Deep Neural Network (DNN) applications has motivated the use of Single Instruction, Multiple Data (SIMD) architectures.
no code implementations • 8 Mar 2021 • Max Sponner, Bernd Waschneck, Akash Kumar
As the usage of deep learning becomes increasingly popular in mobile and embedded solutions, it is necessary to convert the framework-specific network representations into executable code for these embedded platforms.
no code implementations • NeurIPS 2021 • Akash Kumar, Yuxin Chen, Adish Singla
This learning paradigm has been extensively studied when the learner receives worst-case or random counterexamples; in this paper, we consider the optimal teacher who picks best-case counterexamples to teach the target hypothesis within a hypothesis class.
no code implementations • 17 Apr 2022 • Rajat Modi, Aayush Jung Rana, Akash Kumar, Praveen Tirupattur, Shruti Vyas, Yogesh Singh Rawat, Mubarak Shah
Beyond possessing large enough size to feed data hungry machines (eg, transformers), what attributes measure the quality of a dataset?
no code implementations • 2 Oct 2022 • Robi Bhattacharjee, Max Hopkins, Akash Kumar, Hantao Yu, Kamalika Chaudhuri
Developing simple, sample-efficient learning algorithms for robust classification is a pressing issue in today's tech-dominated world, and current theoretical techniques requiring exponential sample complexity and complicated improper learning rules fall far from answering the need.
no code implementations • 9 Jun 2023 • Akash Kumar, Ashlesha Kumar, Vibhav Vineet, Yogesh Singh Rawat
In this work, we first provide a benchmark that enables a comparison of existing approaches on the same ground.
Ranked #3 on Self-Supervised Action Recognition on UCF101
no code implementations • 11 Sep 2023 • Max Sponner, Julius Ott, Lorenzo Servadei, Bernd Waschneck, Robert Wille, Akash Kumar
Radar sensors offer power-efficient solutions for always-on smart devices, but processing the data streams on resource-constrained embedded platforms remains challenging.
no code implementations • 22 Sep 2023 • Siva Satyendra Sahoo, Salim Ullah, Soumyo Bhattacharjee, Akash Kumar
The rising usage of AI and ML-based processing across application domains has exacerbated the need for low-cost ML implementation, specifically for resource-constrained embedded systems.
no code implementations • 23 Sep 2023 • Siva Satyendra Sahoo, Salim Ullah, Akash Kumar
Compared to traditional evolutionary algorithms-based optimization, we report up to 21% improvement in the hypervolume, for joint optimization of PPA and BEHAV, in the design of signed 8-bit multipliers.
no code implementations • 12 Dec 2023 • Ayush Singh, Aayush J Rana, Akash Kumar, Shruti Vyas, Yogesh Singh Rawat
First, we demonstrate its effectiveness on video action detection where the proposed approach outperforms prior works in semi-supervised and weakly-supervised learning along with several baseline approaches in both UCF101-24 and JHMDB-21.
no code implementations • 12 Mar 2024 • Max Sponner, Lorenzo Servadei, Bernd Waschneck, Robert Wille, Akash Kumar
These findings highlight the importance of considering temporal correlation in sensor data to improve the termination decision.
no code implementations • 12 Mar 2024 • Max Sponner, Lorenzo Servadei, Bernd Waschneck, Robert Wille, Akash Kumar
For an ECG classification task, it was able to terminate all samples early, reducing the mean inference energy by 74. 9% and computations by 78. 3%.