Publications

GEO-Bench: Toward Foundation Models for Earth Monitoring

Alexandre Lacoste

Nils Lehmann

Pau Rodríguez

Evan David Sherwin

Hannah Kerner

Björn Lütjens

Jeremy Irvin

David Dao

Hamed Alemohammad

Mehmet Gunturkun

Dava Newman

Stefano Ermon

Xiao Xiang Zhu

Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to subst… (voir plus)antial increases in generalization to downstream tasks. Such models, recently coined foundation models, have been transformational to the field of natural language processing. Variants have also been proposed for image data, but their applicability to remote sensing tasks is limited. To stimulate the development of foundation models for Earth monitoring, we propose a benchmark comprised of six classification and six segmentation tasks, which were carefully curated and adapted to be both relevant to the field and well-suited for model evaluation. We accompany this benchmark with a robust methodology for evaluating models and reporting aggregated results to enable a reliable assessment of progress. Finally, we report results for 20 baselines to gain information about the performance of existing models. We believe that this benchmark will be a driver of progress across a variety of Earth monitoring tasks.

2023-12-11

Conference on Neural Information Processing Systems (Accept (Poster))

doi.org

openreview.net

Group Robust Classification Without Any Group Information

Christos Tsirigotis

Joao Monteiro

Pau Rodríguez

David Vázquez

Aaron Courville

Empirical risk minimization (ERM) is sensitive to spurious correlations in the training data, which poses a significant risk when deploying … (voir plus)systems trained under this paradigm in high-stake applications. While the existing literature focuses on maximizing group-balanced or worst-group accuracy, estimating these accuracies is hindered by costly bias annotations. This study contends that current bias-unsupervised approaches to group robustness continue to rely on group information to achieve optimal performance. Firstly, these methods implicitly assume that all group combinations are represented during training. To illustrate this, we introduce a systematic generalization task on the MPI3D dataset and discover that current algorithms fail to improve the ERM baseline when combinations of observed attribute values are missing. Secondly, bias labels are still crucial for effective model selection, restricting the practicality of these methods in real-world scenarios. To address these limitations, we propose a revised methodology for training and validating debiased models in an entirely bias-unsupervised manner. We achieve this by employing pretrained self-supervised models to reliably extract bias information, which enables the integration of a logit adjustment training loss with our validation criterion. Our empirical analysis on synthetic and real-world tasks provides evidence that our approach overcomes the identified challenges and consistently enhances robust accuracy, attaining performance which is competitive with or outperforms that of state-of-the-art methods, which, conversely, rely on bias labels for validation.

2023-12-11

Conference on Neural Information Processing Systems (Accept (poster))

doi.org

openreview.net

Guiding The Last Layer in Federated Learning with Pre-Trained Models

Lucas Caccia

Federated Learning (FL) is an emerging paradigm that allows a model to be trained across a number of participants without sharing data. Rece… (voir plus)nt works have begun to consider the effects of using pre-trained models as an initialization point for existing FL algorithms; however, these approaches ignore the vast body of efficient transfer learning literature from the centralized learning setting. Here we revisit the problem of FL from a pre-trained model considered in prior work and expand it to a set of computer vision transfer learning problems. We first observe that simply fitting a linear classification head can be efficient and effective in many cases. We then show that in the FL setting, fitting a classifier using the Nearest Class Means (NCM) can be done exactly and orders of magnitude more efficiently than existing proposals, while obtaining strong performance. Finally, we demonstrate that using a two-phase approach of obtaining the classifier and then fine-tuning the model can yield rapid convergence and improved generalization in the federated setting. We demonstrate the potential our method has to reduce communication and compute costs while achieving better model performance.

2023-12-11

Neural Information Processing Systems (Accept (poster))

doi.org

openreview.net

A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction

Guillaume Huguet

Alexander Tong

Edward De Brouwer

Yanlei Zhang

Guy Wolf

Ian Adelstein

Smita Krishnaswamy

Diffusion-based manifold learning methods have proven useful in representation learning and dimensionality reduction of modern high dimensio… (voir plus)nal, high throughput, noisy datasets. Such datasets are especially present in fields like biology and physics. While it is thought that these methods preserve underlying manifold structure of data by learning a proxy for geodesic distances, no specific theoretical links have been established. Here, we establish such a link via results in Riemannian geometry explicitly connecting heat diffusion to manifold distances. In this process, we also formulate a more general heat kernel based manifold embedding method that we call heat geodesic embeddings. This novel perspective makes clearer the choices available in manifold learning and denoising. Results show that our method outperforms existing state of the art in preserving ground truth manifold distances, and preserving cluster structure in toy datasets. We also showcase our method on single cell RNA-sequencing datasets with both continuum and cluster structure, where our method enables interpolation of withheld timepoints of data. Finally, we show that parameters of our more general method can be configured to give results similar to PHATE (a state-of-the-art diffusion based manifold learning method) as well as SNE (an attraction/repulsion neighborhood based method that forms the basis of t-SNE).

2023-12-11

Neural Information Processing Systems (Accept (poster))

doi.org

openreview.net

A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems

Alexandre AGM Duval

Simon V. Mathis

Chaitanya K. Joshi

Victor Schmidt

Santiago Miret

Fragkiskos D. Malliaros

Taco Cohen

Pietro Lio’

Yoshua Bengio

Michael M. Bronstein

2023-12-11

ArXiv (prépublication)

doi.org

arxiv.org

Improving Compositional Generalization using Iterated Learning and Simplicial Embeddings

Yi Ren

Samuel Lavoie

Mikhail Galkin

Danica J. Sutherland

Aaron Courville

Compositional generalization, the ability of an agent to generalize to unseen combinations of latent factors, is easy for humans but hard fo… (voir plus)r deep neural networks. A line of research in cognitive science has hypothesized a process, ``iterated learning,'' to help explain how human language developed this ability; the theory rests on simultaneous pressures towards compressibility (when an ignorant agent learns from an informed one) and expressivity (when it uses the representation for downstream tasks). Inspired by this process, we propose to improve the compositional generalization of deep networks by using iterated learning on models with simplicial embeddings, which can approximately discretize representations. This approach is further motivated by an analysis of compositionality based on Kolmogorov complexity. We show that this combination of changes improves compositional generalization over other approaches, demonstrating these improvements both on vision tasks with well-understood latent factors and on real molecular graph prediction tasks where the latent structure is unknown.

2023-12-11

Conference on Neural Information Processing Systems (Accept (poster))

doi.org

openreview.net

Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context

Ghait Boukachab

Solar power harbors immense potential in mitigating climate change by substantially reducing CO…

2023-12-11

Advances in Neural Information Processing Systems (Accept (poster))

doi.org

openreview.net

Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network

Tristan Deleu

Mizu Nishikawa-Toomey

Jithendaraa Subramanian

Nikolay Malkin

Laurent Charlin

Yoshua Bengio

Generative Flow Networks (GFlowNets), a class of generative models over discrete and structured sample spaces, have been previously applied … (voir plus)to the problem of inferring the marginal posterior distribution over the directed acyclic graph (DAG) of a Bayesian Network, given a dataset of observations. Based on recent advances extending this framework to non-discrete sample spaces, we propose in this paper to approximate the joint posterior over not only the structure of a Bayesian Network, but also the parameters of its conditional probability distributions. We use a single GFlowNet whose sampling policy follows a two-phase process: the DAG is first generated sequentially one edge at a time, and then the corresponding parameters are picked once the full structure is known. Since the parameters are included in the posterior distribution, this leaves more flexibility for the local probability models of the Bayesian Network, making our approach applicable even to non-linear models parametrized by neural networks. We show that our method, called JSP-GFN, offers an accurate approximation of the joint posterior, while comparing favorably against existing methods on both simulated and real data.

2023-12-11

Neural Information Processing Systems (Accept (poster))

doi.org

openreview.net

Joint Prompt Optimization of Stacked LLMs using Variational Inference

Alessandro Sordoni

Xingdi Yuan

Marc-Alexandre Côté

Matheus Pereira

Adam Trischler

Ziang Xiao

Arian Hosseini

Friederike Niedtner

Nicolas Le Roux

Large language models (LLMs) can be seen as atomic units of computation mapping sequences to a distribution over sequences. Thus, they can b… (voir plus)e seen as stochastic language layers in a language network, where the learnable parameters are the natural language prompts at each layer. By stacking two such layers and feeding the output of one layer to the next, we obtain a Deep Language Network (DLN). We first show how to effectively perform prompt optimization for a 1-Layer language network (DLN-1). Then, we present an extension that applies to 2-layer DLNs (DLN-2), where two prompts must be learned. The key idea is to consider the output of the first layer as a latent variable, which requires inference, and prompts to be learned as the parameters of the generative distribution. We first test the effectiveness of DLN-1 in multiple reasoning and natural language understanding tasks. Then, we show that DLN-2 can reach higher performance than a single layer, showing promise that we might reach comparable performance to GPT-4, even when each LLM in the network is smaller and less powerful.

2023-12-11

Conference on Neural Information Processing Systems (Accept (poster))

doi.org

openreview.net

Language Model Alignment with Elastic Reset

Michael Noukhovitch

Samuel Lavoie

Florian Strub

Aaron Courville

Finetuning language models with reinforcement learning (RL), e.g. from human feedback (HF), is a prominent method for alignment. But optimiz… (voir plus)ing against a reward model can improve on reward while degrading performance in other areas, a phenomenon known as reward hacking, alignment tax, or language drift. First, we argue that commonly-used test metrics are insufficient and instead measure how different algorithms tradeoff between reward and drift. The standard method modified the reward with a Kullback-Lieber (KL) penalty between the online and initial model. We propose Elastic Reset, a new algorithm that achieves higher reward with less drift without explicitly modifying the training objective. We periodically reset the online model to an exponentially moving average (EMA) of itself, then reset the EMA model to the initial model. Through the use of an EMA, our model recovers quickly after resets and achieves higher reward with less drift in the same number of steps. We demonstrate that fine-tuning language models with Elastic Reset leads to state-of-the-art performance on a small scale pivot-translation benchmark, outperforms all baselines in a medium-scale RLHF-like IMDB mock sentiment task and leads to a more performant and more aligned technical QA chatbot with LLaMA-7B. Code available at github.com/mnoukhov/elastic-reset.

2023-12-11

Neural Information Processing Systems (Accept (poster))

doi.org

openreview.net

Learning better with Dale's Law: A Spectral Perspective

Most recurrent neural networks (RNNs) do not include a fundamental constraint of real neural circuits: Dale’s Law, which implies that neur… (voir plus)ons must be excitatory (E) or inhibitory (I). Dale’s Law is generally absent from RNNs because simply partitioning a standard network’s units into E and I populations impairs learning. However, here we extend a recent feedforward bio-inspired EI network architecture, named Dale’s ANNs, to recurrent networks, and demonstrate that good performance is possible while respecting Dale’s Law. This begs the question: What makes some forms of EI network learn poorly and others learn well? And, why does the simple approach of incorporating Dale’s Law impair learning? Historically the answer was thought to be the sign constraints on EI network parameters, and this was a motivation behind Dale’s ANNs. However, here we show the spectral properties of the recurrent weight matrix at initialisation are more impactful on network performance than sign constraints. We find that simple EI partitioning results in a singular value distribution that is multimodal and dispersed, whereas standard RNNs have an unimodal, more clustered singular value distribution, as do recurrent Dale’s ANNs. We also show that the spectral properties and performance of partitioned EI networks are worse for small networks with fewer I units, and we present normalised SVD entropy as a measure of spectrum pathology that correlates with performance. Overall, this work sheds light on a long-standing mystery in neuroscience-inspired AI and computational neuroscience, paving the way for greater alignment between neural networks and biology.

2023-12-11

Conference on Neural Information Processing Systems (Accept (poster))

doi.org

openreview.net

Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets

Dinghuai Zhang

Hanjun Dai

Nikolay Malkin

Aaron Courville

Yoshua Bengio

Ling Pan

2023-12-11