Portrait de Amin Mansouri

Amin Mansouri

amin.mansouri@mila.quebec
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e
Irina Rish
Sujets de recherche
Apprentissage de représentations
Apprentissage profond
Causalité
Raisonnement
Traitement du langage naturel
Site web
Google Scholar
GitHub

Publications

Object centric architectures enable efficient causal representation learning
Amin Mansouri
Jason Hartford
Yang Zhang
Yoshua Bengio
Causal representation learning has showed a variety of settings in which we can disentangle latent variables with identifiability guarantees… (voir plus) (up to some reasonable equivalence class). Common to all of these approaches is the assumption that (1) the latent variables are represented as
2024-01-16
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Object-centric architectures enable efficient causal representation learning
Amin Mansouri
Jason Hartford
Yan Zhang
Yoshua Bengio
Causal representation learning has showed a variety of settings in which we can disentangle latent variables with identifiability guarantees… (voir plus) (up to some reasonable equivalence class). Common to all of these approaches is the assumption that (1) the latent variables are represented as
2023-10-29
ArXiv (prépublication)
doi.org
arxiv.org
Reusable Slotwise Mechanisms
Trang Nguyen
Amin Mansouri
Kanika Madan
Khuong N. Nguyen
Nguyen Duy Khuong
Kartik Ahuja
Dianbo Liu
Yoshua Bengio
Agents with the ability to comprehend and reason about the dynamics of objects would be expected to exhibit improved robustness and generali… (voir plus)zation in novel scenarios. However, achieving this capability necessitates not only an effective scene representation but also an understanding of the mechanisms governing interactions among object subsets. Recent studies have made significant progress in representing scenes using object slots. In this work, we introduce Reusable Slotwise Mechanisms, or RSM, a framework that models object dynamics by leveraging communication among slots along with a modular architecture capable of dynamically selecting reusable mechanisms for predicting the future states of each object slot. Crucially, RSM leverages the Central Contextual Information (CCI), enabling selected mechanisms to access the remaining slots through a bottleneck, effectively allowing for modeling of higher order and complex interactions that might require a sparse subset of objects. Experimental results demonstrate the superior performance of RSM compared to state-of-the-art methods across various future prediction and related downstream tasks, including Visual Question Answering and action planning. Furthermore, we showcase RSM's Out-of-Distribution generalization ability to handle scenes in intricate scenarios.
openreview.net
Object-centric causal representation learning
Amin Mansouri
Jason Hartford
Kartik Ahuja
Yoshua Bengio
2022-11-07
NeurIPS.cc/2022/Workshop/NeurReps (poster)
openreview.net
openreview.net
Extended Abstract Track
Amin Mansouri
Jason Hartford
Kartik Ahuja
Yoshua Bengio
Christian Shewmake
Simone Azeglio
Arianna Di Bernardo
Nina Miolane
Extended Abstract Track
Amin Mansouri
Jason Hartford
Yoshua Bengio
Sophia Sanborn
Christian Shewmake
Simone Azeglio
Arianna Di Bernardo
Nina Miolane
Extended Abstract Track
Amin Mansouri
Jason Hartford
Kartik Ahuja
Yoshua Bengio
Christian Shewmake
Simone Azeglio
Arianna Di Bernardo
Nina Miolane
Extended Abstract Track
Amin Mansouri
Jason Hartford
Kartik Ahuja
Yoshua Bengio
Christian Shewmake
Simone Azeglio
Arianna Di Bernardo
Nina Miolane
Extended Abstract Track
Amin Mansouri
Jason Hartford
Kartik Ahuja
Yoshua Bengio
Christian Shewmake
Simone Azeglio
Arianna Di Bernardo
Nina Miolane
There has been significant recent progress in causal representation learning that has showed a variety of settings in which we can disentang… (voir plus)le latent variables with identifiability guarantees (up to some reasonable equivalence class). Common to all of these approaches is the assumption that (1) the latent variables are d − dimensional vectors, and (2) that the observations are the output of some injective observation function of these latent variables. While these assumptions appear benign—they amount to assuming that any changes in the latent space are reflected in the observation space, and that we can use standard encoders to infer the latent variables—we show that when the observations are of multiple objects, the observation function is no longer injective, and disentanglement fails in practice. We can address this failure by combining recent developments in object-centric learning and causal representation learning. By modifying the Slot Attention architecture (Locatello et al., 2020b), we develop an object-centric architecture that leverages weak supervision from sparse perturbations to disentangle each object’s properties. We argue that this approach is more data-efficient in the sense that it requires significantly fewer perturbations than a comparable approach that encodes to a Euclidean space and, we show that this approach successfully disentangles the properties of a set of objects in a series of simple image-based disentanglement experiments.
2022-01-01
(publié)
www.semanticscholar.org