Publications

Coordination Among Neural Modules Through a Shared Global Workspace

Anirudh Goyal

Aniket Rajiv Didolkar

Alex Lamb

Kartikeya Badola

Nan Rosemary Ke

Nasim Rahaman

Jonathan Binas

Charles Blundell

Michael Curtis Mozer

Deep learning has seen a movement away from representing examples with a monolithic hidden state towards a richly structured state. For exam… (see more)ple, Transformers segment by position, and object-centric architectures decompose images into entities. In all these architectures, interactions between different elements are modeled via pairwise interactions: Transformers make use of self-attention to incorporate information from other positions and object-centric architectures make use of graph neural networks to model interactions among entities. We consider how to improve on pairwise interactions in terms of global coordination and a coherent, integrated representation that can be used for downstream tasks. In cognitive science, a global workspace architecture has been proposed in which functionally specialized components share information through a common, bandwidth-limited communication channel. We explore the use of such a communication channel in the context of deep learning for modeling the structure of complex environments. The proposed method includes a shared workspace through which communication among different specialist modules takes place but due to limits on the communication bandwidth, specialist modules must compete for access. We show that capacity limitations have a rational basis in that (1) they encourage specialization and compositionality and (2) they facilitate the synchronization of otherwise independent specialists.

2022-01-28

ICLR.cc/2022/Conference (oral)

Fortuitous Forgetting in Connectionist Networks

Hattie Zhou

Ankit Vani

Hugo Larochelle

Aaron Courville

Forgetting is often seen as an unwanted characteristic in both human and machine learning. However, we propose that forgetting can in fact b… (see more)e favorable to learning. We introduce"forget-and-relearn"as a powerful paradigm for shaping the learning trajectories of artificial neural networks. In this process, the forgetting step selectively removes undesirable information from the model, and the relearning step reinforces features that are consistently useful under different conditions. The forget-and-relearn framework unifies many existing iterative training algorithms in the image classification and language emergence literature, and allows us to understand the success of these algorithms in terms of the disproportionate forgetting of undesirable information. We leverage this understanding to improve upon existing algorithms by designing more targeted forgetting operations. Insights from our analysis provide a coherent view on the dynamics of iterative training in neural networks and offer a clear path towards performance improvements.

2022-01-28

ICLR.cc/2022/Conference (poster)

Graph Neural Networks with Learnable Structural and Positional Representations

Vijay Prakash Dwivedi

Anh Tuan Luu

Thomas Laurent

Xavier Bresson

Graph neural networks (GNNs) have become the standard learning architectures for graphs. GNNs have been applied to numerous domains ranging … (see more)from quantum chemistry, recommender systems to knowledge graphs and natural language processing. A major issue with arbitrary graphs is the absence of canonical positional information of nodes, which decreases the representation power of GNNs to distinguish e.g. isomorphic nodes and other graph symmetries. An approach to tackle this issue is to introduce Positional Encoding (PE) of nodes, and inject it into the input layer, like in Transformers. Possible graph PE are Laplacian eigenvectors. In this work, we propose to decouple structural and positional representations to make easy for the network to learn these two essential properties. We introduce a novel generic architecture which we call LSPE (Learnable Structural and Positional Encodings). We investigate several sparse and fully-connected (Transformer-like) GNNs, and observe a performance increase for molecular datasets, from 1.79% up to 64.14% when considering learnable PE for both GNN classes.

2022-01-28

ICLR.cc/2022/Conference (poster)

Medical Doctors in Health Reforms

Jean-Louis Denis

Sabrina Germain

Catherine Régis

Gianluca Veronesi

Health and legal experts from England and Canada consider the influence of medical doctors on reforms in this comparative study. With reflec… (see more)tions on participation since the inception of publicly funded healthcare systems, they show how the status of doctors affects change.

2022-01-28

(published)

New Insights on Reducing Abrupt Representation Change in Online Continual Learning

Lucas Caccia

Rahaf Aljundi

Nader Asadi

Tinne Tuytelaars

Joelle Pineau

Eugene Belilovsky

In the online continual learning paradigm, agents must learn from a changing distribution while respecting memory and compute constraints. E… (see more)xperience Replay (ER), where a small subset of past data is stored and replayed alongside new data, has emerged as a simple and effective learning strategy. In this work, we focus on the change in representations of observed data that arises when previously unobserved classes appear in the incoming data stream, and new classes must be distinguished from previous ones. We shed new light on this question by showing that applying ER causes the newly added classes’ representations to overlap significantly with the previous classes, leading to highly disruptive parameter updates. Based on this empirical analysis, we propose a new method which mitigates this issue by shielding the learned representations from drastic adaptation to accommodate new classes. We show that using an asymmetric update rule pushes new classes to adapt to the older ones (rather than the reverse), which is more effective especially at task boundaries, where much of the forgetting typically occurs. Empirical results show significant gains over strong baselines on standard continual learning benchmarks.

2022-01-28

ICLR.cc/2022/Conference (poster)

Properties from mechanisms: an equivariance perspective on identifiable representation learning

Kartik Ahuja

Jason Hartford

A key goal of unsupervised representation learning is ``inverting'' a data generating process to recover its latent properties. Existing wo… (see more)rk that provably achieves this goal relies on strong assumptions on relationships between the latent variables (e.g., independence conditional on auxiliary information). In this paper, we take a very different perspective on the problem and ask, ``Can we instead identify latent properties by leveraging knowledge of the mechanisms that govern their evolution?'' We provide a complete characterization of the sources of non-identifiability as we vary knowledge about a set of possible mechanisms. In particular, we prove that if we know the exact mechanisms under which the latent properties evolve, then identification can be achieved up to any equivariances that are shared by the underlying mechanisms. We generalize this characterization to settings where we only know some hypothesis class over possible mechanisms, as well as settings where the mechanisms are stochastic. We demonstrate the power of this mechanism-based perspective by showing that we can leverage our results to generalize existing identifiable representation learning results. These results suggest that by exploiting inductive biases on mechanisms, it is possible to design a range of new identifiable representation learning approaches.

2022-01-28

ICLR.cc/2022/Conference (spotlight)

R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning

Shengyao Lu

Bang Liu

Keith G Mills

SHANGLING JUI

Di Niu

Systematicity, i.e., the ability to recombine known parts and rules to form new sequences while reasoning over relational data, is critical … (see more)to machine intelligence. A model with strong systematicity is able to train on small-scale tasks and generalize to large-scale tasks. In this paper, we propose R5, a relational reasoning framework based on reinforcement learning that reasons over relational graph data and explicitly mines underlying compositional logical rules from observations. R5 has strong systematicity and being robust to noisy data. It consists of a policy value network equipped with Monte Carlo Tree Search to perform recurrent relational prediction and a backtrack rewriting mechanism for rule mining. By alternately applying the two components, R5 progressively learns a set of explicit rules from data and performs explainable and generalizable relation prediction. We conduct extensive evaluations on multiple datasets. Experimental results show that R5 outperforms various embedding-based and rule induction baselines on relation prediction tasks while achieving a high recall rate in discovering ground truth rules.

2022-01-28

ICLR.cc/2022/Conference (spotlight)

Boosting Exploration in Multi-Task Reinforcement Learning using Adversarial Networks

Ramnath Kumar

Tristan Deleu

2022-01-27

ArXiv (preprint)

arxiv.org

Lacking social support is associated with structural divergences in hippocampus–default network co-variation patterns

Chris Zajner

Nathan Spreng

Danilo Bzdok

2022-01-27

Social Cognitive and Affective Neuroscience (published)

Multilevel development of cognitive abilities in an artificial neural network

Konstantin Volzhenin

Jean-Pierre Changeux

Guillaume Dumas

Several neuronal mechanisms have been proposed to account for the formation of cognitive abilities through postnatal interactions with the p… (see more)hysical and socio-cultural environment. Here, we introduce a three-level computational model of information processing and acquisition of cognitive abilities. We propose minimal architectural requirements to build these levels and how the parameters affect their performance and relationships. The first sensorimotor level handles local nonconscious processing, here during a visual classification task. The second level or cognitive level globally integrates the information from multiple local processors via long-ranged connections and synthesizes it in a global, but still nonconscious manner. The third and cognitively highest level handles the information globally and consciously. It is based on the Global Neuronal Workspace (GNW) theory and is referred to as conscious level. We use trace and delay conditioning tasks to, respectively, challenge the second and third levels. Results first highlight the necessity of epigenesis through selection and stabilization of synapses at both local and global scales to allow the network to solve the first two tasks. At the global scale, dopamine appears necessary to properly provide credit assignment despite the temporal delay between perception and reward. At the third level, the presence of interneurons becomes necessary to maintain a self-sustained representation within the GNW in the absence of sensory input. Finally, while balanced spontaneous intrinsic activity facilitates epigenesis at both local and global scales, the balanced excitatory-inhibitory ratio increases performance. Finally, we discuss the plausibility of the model in both neurodevelopmental and artificial intelligence terms.

2022-01-25

bioRxiv (preprint)

Multilevel development of cognitive abilities in an artificial neural network

Konstantin Volzhenin

J. Changeux

Guillaume Dumas

Several neuronal mechanisms have been proposed to account for the formation of cognitive abilities through postnatal interactions with the p… (see more)hysical and socio-cultural environment. Here, we introduce a three-level computational model of information processing and acquisition of cognitive abilities. We propose minimal architectural requirements to build these levels and how the parameters affect their performance and relationships. The first sensorimotor level handles local nonconscious processing, here during a visual classification task. The second level or cognitive level globally integrates the information from multiple local processors via long-ranged connections and synthesizes it in a global, but still nonconscious manner. The third and cognitively highest level handles the information globally and consciously. It is based on the Global Neuronal Workspace (GNW) theory and is referred to as conscious level. We use trace and delay conditioning tasks to, respectively, challenge the second and third levels. Results first highlight the necessity of epigenesis through selection and stabilization of synapses at both local and global scales to allow the network to solve the first two tasks. At the global scale, dopamine appears necessary to properly provide credit assignment despite the temporal delay between perception and reward. At the third level, the presence of interneurons becomes necessary to maintain a self-sustained representation within the GNW in the absence of sensory input. Finally, while balanced spontaneous intrinsic activity facilitates epigenesis at both local and global scales, the balanced excitatory-inhibitory ratio increases performance. Finally, we discuss the plausibility of the model in both neurodevelopmental and artificial intelligence terms.

2022-01-25

bioRxiv (preprint)