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Pouya Bashivan

Membre académique associé
Professeur adjoint, McGill University, Département de physiologie
Sujets de recherche
Neurosciences computationnelles

Biographie

Pouya Bashivan est professeur adjoint au Département de physiologie et membre du programme intégré en neurosciences de l'Université McGill, ainsi que membre associé de Mila – Institut québécois d'intelligence artificielle. Avant de se joindre à l'Université McGill, il a été chercheur postdoctoral à Mila, travaillant avec Irina Rish et Blake Richards. Auparavant, il a été chercheur postdoctoral au Département des sciences du cerveau et de la cognition et à l'Institut McGovern pour la recherche sur le cerveau du Massachusetts Institute of Technology (MIT), où il a travaillé avec le professeur James DiCarlo. Il a obtenu un doctorat en génie informatique de l'Université de Memphis en 2016, après avoir obtenu une licence et une maîtrise en ingénierie électrique et de contrôle de l'Université KNT (Téhéran, Iran).

L'objectif de la recherche menée à son laboratoire est de développer des modèles de réseaux neuronaux qui exploitent la mémoire pour résoudre des tâches complexes. Alors que nous nous appuyons souvent sur des mesures de performance des tâches pour trouver des modèles de réseaux neuronaux et des algorithmes d'apprentissage améliorés, nous utilisons également des mesures neuronales et comportementales provenant de cerveaux d’humains et d'autres animaux pour évaluer la similitude de ces modèles avec des cerveaux biologiquement évolués. Nous pensons que ces contraintes supplémentaires pourraient accélérer les progrès vers l'ingénierie d'un agent artificiellement intelligent de niveau humain.

Étudiants actuels

Maxime Daigle
Maîtrise recherche - McGill
Ozhan Dehghani
Maîtrise recherche - McGill
Github
Google Scholar
Lucas Gomez
Doctorat - McGill
Site web
Herbie he
Maîtrise recherche - McGill
Site web
Github
Xiaoxuan Lei
Doctorat - McGill
Motahareh Pourrahimi
Doctorat - McGill
Co-superviseur⋅e :
Irina Rish
Zihan Weng Weng
Doctorat - McGill
Andrew Zeng
McGill
Github
Google Scholar

Publications

Towards Adversarially Robust Vision-Language Models: Insights from Design Choices and Prompt Formatting Techniques
Rishika Bhagwatkar
Shravan Nayak
Reza Bayat
Alexis Roger
Daniel Z Kaplan
Pouya Bashivan
Irina Rish
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.
2024-06-27
ICML.cc/2024/Workshop/NextGenAISafety (poster)
doi.org
openreview.net
Local lateral connectivity is sufficient for replicating cortex-like topographical organization in deep neural networks
Xinyu Qian
Amir Ozhan Dehghani
Asa Borzabadi Farahani
Pouya Bashivan
Across the primate cortex, neurons that perform similar functions tend to be spatially grouped together. This biological principle extends t… (voir plus)o many other species as well, reflecting a common way of organizing sensory processing across diverse forms of life. In the visual cortex, this biological principle manifests itself as a modular organization of neuronal clusters, each tuned to a specific visual property. The tendency toward short connections is widely believed to explain the existence of such an organization in the brains of many animals. However, the neural mechanisms underlying this phenomenon remain unclear. Here, we use artificial deep neural network models to demonstrate that a topographical organization akin to that in the primary, intermediate, and high-level human visual cortex emerges when units in these models are locally laterally connected and their weight parameters are tuned by top-down credit assignment. The emergence of modular organization without explicit topography-inducing learning rules or objective functions challenges their necessity and suggests that local lateral connectivity alone may suffice for the formation of topographic organization across the cortex. Furthermore, the incorporation of lateral connections in deep convolutional networks enhances their robustness to subtle alterations in visual inputs, such as those designed to deceive the model (i.e. adversarial examples), indicating an additional role for these connections in learning robust representations.
2024-06-15
ICML.cc/2024/Workshop/AI4Science (poster)
doi.org
openreview.net
A Hybrid CNN-Transformer Approach for Continuous Fine Finger Motion Decoding from sEMG Signals
Zihan Weng
Xiabing Zhang
Yufeng Mou
Chanlin Yi
Fali Li
Pouya Bashivan
Peng Xu
This work presents a novel approach that synergistically integrates convolutional neural networks (CNNs) and Transformer models for decoding… (voir plus) continuous fine finger motions from surface electromyography (sEMG) signals. This integration capitalizes on CNNs’ proficiency in extracting rich temporal and spatial features from multichannel sEMG data and the Transformer’s superior capability in recognizing complex patterns and long-range dependencies. A significant advancement in this field is the use of a custom-developed Epidermal Electrode Array Sleeve (EEAS) for capturing high-fidelity sEMG signals, enabling more accurate and reliable signal acquisition than traditional methods. The decoded joint angles could be used in seamless and intuitive human-machine interaction in various applications, such as virtual reality, augmented reality, robotic control, and prosthetic control. Evaluations demonstrate the superior performance of the proposed CNN-Transformer hybrid architecture in decoding continuous fine finger motions, outperforming individual CNN and Transformer models. The synergistic integration of CNNs and Transformers presents a powerful framework for sEMG decoding, offering exciting opportunities for naturalistic and intuitive human-machine interaction applications. Its robustness and efficiency make it an ideal choice for real-world applications, promising to enhance the interface between humans and machines significantly. The implications of this research extend to advancing the understanding of human neuromuscular signals and their application in computing interfaces.
2024-06-13
2024 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) (publié)
doi.org
How well do models of visual cortex generalize to out of distribution samples?
Yifei Ren
Pouya Bashivan
2024-05-30
PLOS Computational Biology (publié)
doi.org
Improving Adversarial Robustness in Vision-Language Models with Architecture and Prompt Design.
Rishika Bhagwatkar
Shravan Nayak
Pouya Bashivan
Irina Rish
2023-12-31
EMNLP (Findings) (publié)
doi.org
iWISDM: Assessing instruction following in multimodal models at scale
Xiaoxuan Lei
Lucas Gomez
Hao Yuan Bai
Pouya Bashivan
The ability to perform complex tasks from detailed instructions is a key to the remarkable achievements of our species. As humans, we are no… (voir plus)t only capable of performing a wide variety of tasks but also very complex ones that may entail hundreds or thousands of steps to complete. Large language models and their more recent multimodal counterparts that integrate textual and visual inputs have achieved unprecedented success in performing complex tasks. Yet, most existing benchmarks are largely confined to single-modality inputs — either text or vision — and thus, narrowing the scope of multimodal integration assessments, particularly for instruction-following in multimodal contexts. To bridge this gap, we introduce the instructed-Virtual VISual Decision Making (iWISDM) environment engineered to generate a limitless array of vision-language tasks of varying complexity. Using iWISDM, we compiled three distinct benchmarks of instruction following visual tasks across varying complexity levels and evaluated several newly developed multimodal models on these benchmarks. Our findings establish iWISDM as a robust benchmark for assessing the instructional adherence of both existing and emergent multimodal models and highlight a large gap in these models’ ability to precisely follow instructions.
2023-12-31
CoLLAs (publié)
doi.org
proceedings.mlr.press
RGP: Achieving Memory-Efficient Model Fine-tuning Via Randomized Gradient Projection
Ali Saheb Pasand
Pouya Bashivan
2023-12-31
ENLSP (publié)
proceedings.mlr.press
proceedings.mlr.press
The feature landscape of visual cortex
Rudi Tong
Ronan da Silva
Dongyan Lin
Arna Ghosh
James Wilsenach
Erica Cianfarano
Pouya Bashivan
Blake Richards
Stuart Trenholm
Understanding computations in the visual system requires a characterization of the distinct feature preferences of neurons in different visu… (voir plus)al cortical areas. However, we know little about how feature preferences of neurons within a given area relate to that area’s role within the global organization of visual cortex. To address this, we recorded from thousands of neurons across six visual cortical areas in mouse and leveraged generative AI methods combined with closed-loop neuronal recordings to identify each neuron’s visual feature preference. First, we discovered that the mouse’s visual system is globally organized to encode features in a manner invariant to the types of image transformations induced by self-motion. Second, we found differences in the visual feature preferences of each area and that these differences generalized across animals. Finally, we observed that a given area’s collection of preferred stimuli (‘own-stimuli’) drive neurons from the same area more effectively through their dynamic range compared to preferred stimuli from other areas (‘other-stimuli’). As a result, feature preferences of neurons within an area are organized to maximally encode differences among own-stimuli while remaining insensitive to differences among other-stimuli. These results reveal how visual areas work together to efficiently encode information about the external world.
2023-11-04
bioRxiv (prépublication)
doi.org
Using modular connectome-based predictive modeling to reveal brain-behavior relationships of individual differences in working memory
Huayi Yang
Junjun Zhang
Zhenlan Jin
Pouya Bashivan
Ling Li
2023-06-21
Brain Structure and Function (publié)
doi.org
Towards Out-of-Distribution Adversarial Robustness
Adam Ibrahim
Charles Guille-escuret
Ioannis Mitliagkas
Irina Rish
David Krueger
Pouya Bashivan
Adversarial robustness continues to be a major challenge for deep learning. A core issue is that robustness to one type of attack often fail… (voir plus)s to transfer to other attacks. While prior work establishes a theoretical trade-off in robustness against different
2023-06-19
ICML.cc/2023/Workshop/AdvML-Frontiers (publié)
doi.org
openreview.net
Learning Robust Kernel Ensembles with Kernel Average Pooling
Pouya Bashivan
Adam Ibrahim
Amirozhan Dehghani
Yifei Ren
2022-09-29
ArXiv (prépublication)
doi.org
openreview.net
Adversarial Feature Desensitization
Pouya Bashivan
Reza Bayat
Adam Ibrahim
Kartik Ahuja
Mojtaba Faramarzi
Touraj Laleh
Blake Richards
Irina Rish
Neural networks are known to be vulnerable to adversarial attacks -- slight but carefully constructed perturbations of the inputs which can … (voir plus)drastically impair the network's performance. Many defense methods have been proposed for improving robustness of deep networks by training them on adversarially perturbed inputs. However, these models often remain vulnerable to new types of attacks not seen during training, and even to slightly stronger versions of previously seen attacks. In this work, we propose a novel approach to adversarial robustness, which builds upon the insights from the domain adaptation field. Our method, called Adversarial Feature Desensitization (AFD), aims at learning features that are invariant towards adversarial perturbations of the inputs. This is achieved through a game where we learn features that are both predictive and robust (insensitive to adversarial attacks), i.e. cannot be used to discriminate between natural and adversarial data. Empirical results on several benchmarks demonstrate the effectiveness of the proposed approach against a wide range of attack types and attack strengths. Our code is available at https://github.com/BashivanLab/afd.
2020-12-31
Advances in Neural Information Processing Systems 34 (NeurIPS 2021) (publié)
doi.org
openreview.net