no code implementations • 14 Nov 2023 • Xidong Wu, Wan-Yi Lin, Devin Willmott, Filipe Condessa, Yufei Huang, Zhenzhen Li, Madan Ravi Ganesh
Federated Learning (FL) is a distributed training paradigm that enables clients scattered across the world to cooperatively learn a global model without divulging confidential data.
no code implementations • 9 Oct 2023 • Chen Qiu, Xingyu Li, Chaithanya Kumar Mummadi, Madan Ravi Ganesh, Zhenzhen Li, Lu Peng, Wan-Yi Lin
Prompt learning for vision-language models, e. g., CoOp, has shown great success in adapting CLIP to different downstream tasks, making it a promising solution for federated learning due to computational reasons.
no code implementations • 14 Apr 2022 • Madan Ravi Ganesh, Salimeh Yasaei Sekeh, Jason J. Corso
Raw deep neural network (DNN) performance is not enough; in real-world settings, computational load, training efficiency and adversarial security are just as or even more important.
no code implementations • 22 Jun 2020 • Madan Ravi Ganesh, Dawsin Blanchard, Jason J. Corso, Salimeh Yasaei Sekeh
Finally, we define a novel sensitivity criterion for filters that measures the strength of their contributions to the succeeding layer and highlights critical filters that need to be completely protected from pruning.
no code implementations • 18 Mar 2020 • Madan Ravi Ganesh, Jason J. Corso, Salimeh Yasaei Sekeh
Most approaches to deep neural network compression via pruning either evaluate a filter's importance using its weights or optimize an alternative objective function with sparsity constraints.
no code implementations • 13 Jan 2020 • Madan Ravi Ganesh, Jason J. Corso
In this work, we propose Learning with Incremental Labels and Adaptive Compensation (LILAC), a two-phase method that incrementally increases the number of unique output labels rather than the difficulty of samples while consistently using the entire dataset throughout training.
1 code implementation • 7 Oct 2019 • Madan Ravi Ganesh, Eric Hofesmann, Nathan Louis, Jason Corso
This work presents the Video Platform for PyTorch (ViP), a deep learning-based framework designed to handle and extend to any problem domain based on videos.
no code implementations • 2 Oct 2019 • Salimeh Yasaei Sekeh, Madan Ravi Ganesh, Shurjo Banerjee, Jason J. Corso, Alfred O. Hero
In this work, firstly, we assert that OSFS's main assumption of having data from all the samples available at runtime is unrealistic and introduce a new setting where features and samples are streamed concurrently called OSFS with Streaming Samples (OSFS-SS).
1 code implementation • 16 Apr 2018 • Eric Hofesmann, Madan Ravi Ganesh, Jason J. Corso
We present M-PACT to overcome existing issues by removing the need to develop boilerplate code which allows users to quickly prototype action classification models while leveraging existing state-of-the-art (SOTA) models available in the platform.
no code implementations • 21 Mar 2018 • Madan Ravi Ganesh, Eric Hofesmann, Byungsu Min, Nadha Gafoor, Jason J. Corso
We explore the erratic behavior caused by this phenomena on state-of-the-art deep network-based methods for action recognition in terms of maximum performance and stability in recognition accuracy across a range of input video speeds.
no code implementations • 12 Apr 2016 • Suren Kumar, Vikas Dhiman, Madan Ravi Ganesh, Jason J. Corso
We propose an online spatiotemporal articulation model estimation framework that estimates both articulated structure as well as a temporal prediction model solely using passive observations.