In recent years, concept-based approaches have emerged as some of the most promising explainability methods to help us interpret the decisions of Artificial Neural Networks (ANNs). These methods seek to discover intelligible visual "concepts" buried within the complex patterns of ANN activations in two key steps: (1) concept extraction followed by (2) importance estimation. While these two steps are shared across methods, they all differ in their specific implementations. Here, we introduce a unifying theoretical framework that recast the first step - concept extraction problem - as a special case of dictionary learning, and we formalize the second step - concept importance estimation - as a more general form of attribution method. This framework offers several advantages as it allows us: (i) to propose new evaluation metrics for comparing different concept extraction approaches; (ii) to leverage modern attribution methods and evaluation metrics to extend and systematically evaluate state-of-the-art concept-based approaches and importance estimation techniques; (iii) to derive theoretical guarantees regarding the optimality of such methods. We further leverage our framework to try to tackle a crucial question in explainability: how to efficiently identify clusters of data points that are classified based on a similar shared strategy. To illustrate these findings and to highlight the main strategies of a model, we introduce a visual representation called the strategic cluster graph. Finally, we present Lens, a dedicated website that offers a complete compilation of these visualizations for all classes of the ImageNet dataset.

Research in machine learning has polarized into two general approaches for regression tasks: Transductive methods construct estimates directly from available data but are usually problem unspecific. Inductive methods can be much more specific but generally require compute-intensive solution searches. In this work, we propose a hybrid approach and show that transductive regression principles can be meta-learned through gradient descent to form efficient in-context neural approximators by leveraging the theory of vector-valued Reproducing Kernel Banach Spaces (RKBS). We apply this approach to function spaces defined over finite and infinite-dimensional spaces (function-valued operators) and show that once trained, the Transducer can almost instantaneously capture an infinity of functional relationships given a few pairs of input and output examples and return new image estimates. We demonstrate the benefit of our meta-learned transductive approach to model complex physical systems influenced by varying external factors with little data at a fraction of the usual deep learning training computational cost for partial differential equations and climate modeling applications.

Recent meta-reinforcement learning work has emphasized the importance of mnemonic control for agents to quickly assimilate relevant experience in new contexts and suitably adapt their policy. However, what computational mechanisms support flexible behavioral adaptation from past experience remains an open question. Inspired by neuroscience, we propose MetODS (for Meta-Optimized Dynamical Synapses), a broadly applicable model of meta-reinforcement learning which leverages fast synaptic dynamics influenced by action-reward feedback. We develop a theoretical interpretation of MetODS as a model learning powerful control rules in the policy space and demonstrate empirically that robust reinforcement learning programs emerge spontaneously from them. We further propose a formalism which efficiently optimizes the meta-parameters governing MetODS synaptic processes. In multiple experiments and domains, MetODS outperforms or compares favorably with previous meta-reinforcement learning approaches. Our agents can perform one-shot learning, approaches optimal exploration/exploitation strategies, generalize navigation principles to unseen environments and demonstrate a strong ability to learn adaptive motor policies.

We describe a novel attribution method which is grounded in Sensitivity Analysis and uses Sobol indices. Beyond modeling the individual contributions of image regions, Sobol indices provide an efficient way to capture higher-order interactions between image regions and their contributions to a neural network's prediction through the lens of variance. We describe an approach that makes the computation of these indices efficient for high-dimensional problems by using perturbation masks coupled with efficient estimators to handle the high dimensionality of images. Importantly, we show that the proposed method leads to favorable scores on standard benchmarks for vision (and language models) while drastically reducing the computing time compared to other black-box methods - even surpassing the accuracy of state-of-the-art white-box methods which require access to internal representations. Our code is freely available: github.com/fel-thomas/

Despite their elegant formulation and lightweight memory cost, neural ordinary differential equations (NODEs) suffer from known representational limitations. In particular, the single flow learned by NODEs cannot express all homeomorphisms from a given data space to itself, and their static weight parametrization restricts the type of functions they can learn compared to discrete architectures with layer-dependent weights. Here, we describe a new module called neurally-controlled ODE (N-CODE) designed to improve the expressivity of NODEs. The parameters of N-CODE modules are dynamic variables governed by a trainable map from initial or current activation state, resulting in forms of open-loop and closed-loop control, respectively. A single module is sufficient for learning a distribution on non-autonomous flows that adaptively drive neural representations. We provide theoretical and empirical evidence that N-CODE circumvents limitations of previous models and show how increased model expressivity manifests in several domains. In supervised learning, we demonstrate that our framework achieves better performance than NODEs as measured by both training speed and testing accuracy. In unsupervised learning, we apply this control perspective to an image Autoencoder endowed with a latent transformation flow, greatly improving representational power over a vanilla model and leading to state-of-the-art image reconstruction on CIFAR-10.