Portrait de Rishika Bhagwatkar

Rishika Bhagwatkar

Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e
Sujets de recherche
Apprentissage de représentations
Apprentissage en ligne
Apprentissage multimodal
Apprentissage profond
Traitement du langage naturel

Publications

Towards Adversarially Robust Vision-Language Models: Insights from Design Choices and Prompt Formatting Techniques
Vision-Language Models (VLMs) have witnessed a surge in both research and real-world applications. However, as they becoming increasingly pr… (voir plus)evalent, ensuring their robustness against adversarial attacks is paramount. This work systematically investigates the impact of model design choices on the adversarial robustness of VLMs against image-based attacks. Additionally, we introduce novel, cost-effective approaches to enhance robustness through prompt formatting. By rephrasing questions and suggesting potential adversarial perturbations, we demonstrate substantial improvements in model robustness against strong image-based attacks such as Auto-PGD. Our findings provide important guidelines for developing more robust VLMs, particularly for deployment in safety-critical environments.
Improving Adversarial Robustness in Vision-Language Models with Architecture and Prompt Design.
Lag-Llama: Towards Foundation Models for Time Series Forecasting
Kashif Rasul
Andrew Robert Williams
Marin Biloš
Hena Ghonia
Anderson Schneider
Sahil Garg
Yuriy Nevmyvaka
Aiming to build foundation models for time-series forecasting and study their scaling behavior, we present here our work-in-progress on Lag-… (voir plus)Llama, a general-purpose univariate probabilistic time-series forecasting model trained on a large collection of time-series data. The model shows good zero-shot prediction capabilities on unseen "out-of-distribution" time-series datasets, outperforming supervised baselines. We use smoothly broken power-laws to fit and predict model scaling behavior. The open source code is made available at https://github.com/kashif/pytorch-transformer-ts.
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
Kashif Rasul
Andrew Robert Williams
Marin Bilovs
Hena Ghonia
N. Hassen
Anderson Schneider
Sahil Garg
Yuriy Nevmyvaka
Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-sho… (voir plus)t and few-shot generalization. However, despite the success of foundation models in modalities such as natural language processing and computer vision, the development of foundation models for time series forecasting has lagged behind. We present Lag-Llama, a general-purpose foundation model for univariate probabilistic time series forecasting based on a decoder-only transformer architecture that uses lags as covariates. Lag-Llama is pretrained on a large corpus of diverse time series data from several domains, and demonstrates strong zero-shot generalization capabilities compared to a wide range of forecasting models on downstream datasets across domains. Moreover, when fine-tuned on relatively small fractions of such previously unseen datasets, Lag-Llama achieves state-of-the-art performance, outperforming prior deep learning approaches, emerging as the best general-purpose model on average. Lag-Llama serves as a strong contender to the current state-of-art in time series forecasting and paves the way for future advancements in foundation models tailored to time series data.
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
Kashif Rasul
Andrew Robert Williams
Marin Bilovs
Hena Ghonia
Anderson Schneider
Sahil Garg
Yuriy Nevmyvaka
Lag-Llama: Towards Foundation Models for Time Series Forecasting
Kashif Rasul
Andrew Robert Williams
Marin Biloš
Hena Ghonia
N. Hassen
Anderson Schneider
Sahil Garg
Yuriy Nevmyvaka
Aiming to build foundation models for time-series forecasting and study their scaling behavior, we present here our work-in-progress on Lag-… (voir plus)Llama , a general-purpose univariate probabilistic time-series forecasting model trained on a large collection of time-series data. The model shows good zero-shot prediction capabilities on unseen “out-of-distribution” time-series datasets, outperforming supervised baselines. We use smoothly broken power-laws [7] to fit and predict model scaling behavior. The open source code is made available at https://github