Anirudh Goyal

Recent promising results have generated a surge of interest in continuous optimization methods for causal discovery from observational data.… (voir plus) However, there are theoretical limitations on the identifiability of underlying structures obtained solely from observational data. Interventional data, on the other hand, provides richer information about the underlying data-generating process. Nevertheless, extending and applying methods designed for observational data to include interventions is a challenging problem. To address this issue, we propose a general framework based on neural networks to develop models that incorporate both observational and interventional data. Notably, our method can handle the challenging and realistic scenario where the identity of the intervened upon variable is unknown. We evaluate our proposed approach in the context of graph recovery, both de novo and from a partially-known edge set. Our method achieves strong benchmark results on various structure learning tasks, including structure recovery of synthetic graphs as well as standard graphs from the Bayesian Network Repository.

2023-09-10

TMLR (accepté)

Discrete Key-Value Bottleneck

Frederik Träuble

Nasim Rahaman

Michael Curtis Mozer

Kenji Kawaguchi

Bernhard Schölkopf

2023-07-03

Proceedings of the 40th International Conference on Machine Learning (publié)

Cycle Consistency Driven Object Discovery

Aniket Rajiv Didolkar

Developing deep learning models that effectively learn object-centric representations, akin to human cognition, remains a challenging task. … (voir plus)Existing approaches facilitate object discovery by representing objects as fixed-size vectors, called ``slots'' or ``object files''. While these approaches have shown promise in certain scenarios, they still exhibit certain limitations. First, they rely on architectural priors which can be unreliable and usually require meticulous engineering to identify the correct objects. Second, there has been a notable gap in investigating the practical utility of these representations in downstream tasks. To address the first limitation, we introduce a method that explicitly optimizes the constraint that each object in a scene should be associated with a distinct slot. We formalize this constraint by introducing consistency objectives which are cyclic in nature. By integrating these consistency objectives into various existing slot-based object-centric methods, we showcase substantial improvements in object-discovery performance. These enhancements consistently hold true across both synthetic and real-world scenes, underscoring the effectiveness and adaptability of the proposed approach. To tackle the second limitation, we apply the learned object-centric representations from the proposed method to two downstream reinforcement learning tasks, demonstrating considerable performance enhancements compared to conventional slot-based and monolithic representation learning methods. Our results suggest that the proposed approach not only improves object discovery, but also provides richer features for downstream tasks.

2023-06-03

ArXiv (prépublication)

Cycle Consistency Driven Object Discovery

Aniket Rajiv Didolkar

Developing deep learning models that effectively learn object-centric representations, akin to human cognition, remains a challenging task. … (voir plus)Existing approaches facilitate object discovery by representing objects as fixed-size vectors, called ``slots'' or ``object files''. While these approaches have shown promise in certain scenarios, they still exhibit certain limitations. First, they rely on architectural priors which can be unreliable and usually require meticulous engineering to identify the correct objects. Second, there has been a notable gap in investigating the practical utility of these representations in downstream tasks. To address the first limitation, we introduce a method that explicitly optimizes the constraint that each object in a scene should be associated with a distinct slot. We formalize this constraint by introducing consistency objectives which are cyclic in nature. By integrating these consistency objectives into various existing slot-based object-centric methods, we showcase substantial improvements in object-discovery performance. These enhancements consistently hold true across both synthetic and real-world scenes, underscoring the effectiveness and adaptability of the proposed approach. To tackle the second limitation, we apply the learned object-centric representations from the proposed method to two downstream reinforcement learning tasks, demonstrating considerable performance enhancements compared to conventional slot-based and monolithic representation learning methods. Our results suggest that the proposed approach not only improves object discovery, but also provides richer features for downstream tasks.

2023-06-03

ArXiv (prépublication)

Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior

Ayush K Chakravarthy

Trang M. Nguyen

Michael Curtis Mozer

The aim of object-centric vision is to construct an explicit representation of the objects in a scene. This representation is obtained via a… (voir plus) set of interchangeable modules called \emph{slots} or \emph{object files} that compete for local patches of an image. The competition has a weak inductive bias to preserve spatial continuity; consequently, one slot may claim patches scattered diffusely throughout the image. In contrast, the inductive bias of human vision is strong, to the degree that attention has classically been described with a spotlight metaphor. We incorporate a spatial-locality prior into state-of-the-art object-centric vision models and obtain significant improvements in segmenting objects in both synthetic and real-world datasets. Similar to human visual attention, the combination of image content and spatial constraints yield robust unsupervised object-centric learning, including less sensitivity to model hyperparameters.

2023-05-31

ArXiv (prépublication)

Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior

Ayush K Chakravarthy

Trang M. Nguyen

Michael Curtis Mozer

2023-05-31

ArXiv (prépublication)

Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning

Dianbo Liu

Tianmin Shu

Michael Curtis Mozer

Nicolas Heess

2023-02-01

ICLR.cc/2023/Conference (poster)

Leveraging the Third Dimension in Contrastive Learning

Sumukh K Aithal

Alex Lamb

Michael Curtis Mozer

Self-Supervised Learning (SSL) methods operate on unlabeled data to learn robust representations useful for downstream tasks. Most SSL metho… (voir plus)ds rely on augmentations obtained by transforming the 2D image pixel map. These augmentations ignore the fact that biological vision takes place in an immersive three-dimensional, temporally contiguous environment, and that low-level biological vision relies heavily on depth cues. Using a signal provided by a pretrained state-of-the-art monocular RGB-to-depth model (the \emph{Depth Prediction Transformer}, Ranftl et al., 2021), we explore two distinct approaches to incorporating depth signals into the SSL framework. First, we evaluate contrastive learning using an RGB+depth input representation. Second, we use the depth signal to generate novel views from slightly different camera positions, thereby producing a 3D augmentation for contrastive learning. We evaluate these two approaches on three different SSL methods -- BYOL, SimSiam, and SwAV -- using ImageNette (10 class subset of ImageNet), ImageNet-100 and ImageNet-1k datasets. We find that both approaches to incorporating depth signals improve the robustness and generalization of the baseline SSL methods, though the first approach (with depth-channel concatenation) is superior. For instance, BYOL with the additional depth channel leads to an increase in downstream classification accuracy from 85.3\% to 88.0\% on ImageNette and 84.1\% to 87.0\% on ImageNet-C.

2023-01-27

ArXiv (prépublication)

GFlowOut: Dropout with Generative Flow Networks

Dianbo Liu

Moksh J. Jain

Bonaventure F. P. Dossou

Qianli Shen

2023-01-01

ICML (publié)

GFlowOut: Dropout with Generative Flow Networks

Dianbo Liu

Moksh J. Jain

Bonaventure F. P. Dossou

Qianli Shen

2023-01-01

ICML (publié)

Neural Causal Structure Discovery from Interventions

Nan Rosemary Ke

Bernhard Schölkopf

Michael Curtis Mozer

Chris Pal

Recent promising results have generated a surge of interest in continuous optimization methods for causal discovery from observational data.… (voir plus) However, there are theoretical limitations on the identiﬁability of underlying structures obtained solely from observational data. Interventional data, on the other hand, provides richer information about the underlying data-generating process. Nevertheless, extending and applying methods designed for observational data to include interventions is a challenging problem. To address this issue, we propose a general framework based on neural networks to develop models that incorporate both observational and interventional data. Notably, our method can handle the challenging and realistic scenario where the identity of the intervened upon variable is unknown. We evaluate our proposed approach in the context of graph recovery, both de novo and from a partially-known edge set. Our method achieves strong benchmark results on various structure learning tasks, including structure recovery of synthetic graphs as well as standard graphs from the Bayesian Network Repository

2023-01-01

Trans. Mach. Learn. Res. (publié)

dblp.uni-trier.de

Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning

Hongyu Zang

Xin Li

Romain Laroche