8 papers
Towards Scalable Modeling of Compressed Videos for Efficient Action Recognition
Shristi Das Biswas, Efstathia Soufleri, Arani Roy +1
Training robust deep video representations has proven to be computationally challenging due to substantial decoding overheads, the enormous size of raw video streams, and their inh…
Finding the Muses: Identifying Coresets through Loss Trajectories
Manish Nagaraj, Deepak Ravikumar, Efstathia Soufleri +1
Deep learning models achieve state-of-the-art performance across domains but face scalability challenges in real-time or resource-constrained scenarios. To address this, we propose…
Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance
Xueqing Peng, Triantafillos Papadopoulos, Efstathia Soufleri +7
Despite Greece's pivotal role in the global economy, large language models (LLMs) remain underexplored for Greek financial context due to the linguistic complexity of Greek and the…
Curvature Clues: Decoding Deep Learning Privacy with Input Loss Curvature
Deepak Ravikumar, Efstathia Soufleri, Kaushik Roy
In this paper, we explore the properties of loss curvature with respect to input data in deep neural networks. Curvature of loss with respect to input (termed input loss curvature)…
Advancing Compressed Video Action Recognition through Progressive Knowledge Distillation
Efstathia Soufleri, Deepak Ravikumar, Kaushik Roy
Compressed video action recognition classifies video samples by leveraging the different modalities in compressed videos, namely motion vectors, residuals, and intra-frames. For th…
Unveiling Privacy, Memorization, and Input Curvature Links
Deepak Ravikumar, Efstathia Soufleri, Abolfazl Hashemi +1
Deep Neural Nets (DNNs) have become a pervasive tool for solving many emerging problems. However, they tend to overfit to and memorize the training set. Memorization is of keen int…
Synthetic Dataset Generation for Privacy-Preserving Machine Learning
Efstathia Soufleri, Gobinda Saha, Kaushik Roy
Machine Learning (ML) has achieved enormous success in solving a variety of problems in computer vision, speech recognition, object detection, to name a few. The principal reason f…
Evaluating the Stability of Recurrent Neural Models during Training with Eigenvalue Spectra Analysis
Priyadarshini Panda, Efstathia Soufleri, Kaushik Roy
We analyze the stability of recurrent networks, specifically, reservoir computing models during training by evaluating the eigenvalue spectra of the reservoir dynamics. To circumve…