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Found 35 papers, 19 papers with code
Choreographer: Learning and Adapting Skills in Imagination
Unsupervised skill learning aims to learn a rich repertoire of behaviors without external supervision, providing artificial agents with the ability to control and influence the environment.
A General Purpose Neural Architecture for Geospatial Systems
Geospatial Information Systems are used by researchers and Humanitarian Assistance and Disaster Response (HADR) practitioners to support a wide variety of important applications.
Unsupervised Model-based Pre-training for Data-efficient Control from Pixels
Controlling artificial agents from visual sensory data is an arduous task.
Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark
Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to impressive increases in generalisation for downstream tasks.
Typing assumptions improve identification in causal discovery
Causal discovery from observational data is a challenging task that can only be solved up to a set of equivalent solutions, called an equivalence class.
Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA
This work introduces a novel principle we call disentanglement via mechanism sparsity regularization, which can be applied when the latent factors of interest depend sparsely on past latent factors and/or observed auxiliary variables.
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
However, a crucial aspect to acting intelligently upon the knowledge about causal structure which has been inferred from finite data demands reasoning about its uncertainty.
Can Active Learning Preemptively Mitigate Fairness Issues?
We also explore the interaction of algorithmic fairness methods such as gradient reversal (GRAD) and BALD.
Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data
Transfer learning approaches can reduce the data requirements of deep learning algorithms.
Ranked #3 on
Change Detection
on OSCD - 13ch
(using extra training data)
Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations
Explainability for machine learning models has gained considerable attention within the research community given the importance of deploying more reliable machine-learning systems.
Beyond Trivial Counterfactual Generations with Diverse Valuable Explanations
In computer vision applications, most methods explain models by displaying the regions in the input image that they focus on for their prediction, but it is difficult to improve models based on these explanations since they do not indicate why the model fail.
Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning
The main challenge is that the agent must not forget previous tasks and also adapt to novel tasks in the stream.
Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery
Cattle farming is responsible for 8. 8\% of greenhouse gas emissions worldwide.
Synbols: Probing Learning Algorithms with Synthetic Datasets
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms.
Differentiable Causal Discovery from Interventional Data
This work constitutes a new step in this direction by proposing a theoretically-grounded method based on neural networks that can leverage interventional data.
Bayesian active learning for production, a systematic study and a reusable library
Active learning is able to reduce the amount of labelling effort by using a machine learning model to query the user for specific inputs.
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning
We propose Continual-MAML, an online extension of the popular MAML algorithm as a strong baseline for this scenario.
Embedding Propagation: Smoother Manifold for Few-Shot Classification
Furthermore, we show that embedding propagation consistently improves the accuracy of the models in multiple semi-supervised learning scenarios by up to 16\% points.
Quantifying the Carbon Emissions of Machine Learning
From an environmental standpoint, there are a few crucial aspects of training a neural network that have a major impact on the quantity of carbon that it emits.
MODELLING BIOLOGICAL ASSAYS WITH ADAPTIVE DEEP KERNEL LEARNING
Due to the significant costs of data generation, many prediction tasks within drug discovery are by nature few-shot regression (FSR) problems, including accurate modelling of biological assays.
Stochastic Neural Network with Kronecker Flow
Recent advances in variational inference enable the modelling of highly structured joint distributions, but are limited in their capacity to scale to the high-dimensional setting of stochastic neural networks.
Tackling Climate Change with Machine Learning
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help.
Adaptive Deep Kernel Learning
Deep kernel learning provides an elegant and principled framework for combining the structural properties of deep learning algorithms with the flexibility of kernel methods.
Hierarchical Importance Weighted Autoencoders
We believe a joint proposal has the potential of reducing the number of redundant samples, and introduce a hierarchical structure to induce correlation.
On Difficulties of Probability Distillation
Probability distillation has recently been of interest to deep learning practitioners as it presents a practical solution for sampling from autoregressive models for deployment in real-time applications.
Improving Explorability in Variational Inference with Annealed Variational Objectives
Despite the advances in the representational capacity of approximate distributions for variational inference, the optimization process can still limit the density that is ultimately learned.
Uncertainty in Multitask Transfer Learning
The result is a rich and meaningful prior capable of few-shot learning on new tasks.
TADAM: Task dependent adaptive metric for improved few-shot learning
We further propose a simple and effective way of conditioning a learner on the task sample set, resulting in learning a task-dependent metric space.
Neural Autoregressive Flows
Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, via Masked Autoregressive Flows (MAF), and to accelerate state-of-the-art WaveNet-based speech synthesis to 20x faster than real-time, via Inverse Autoregressive Flows (IAF).
Deep Prior
The recent literature on deep learning offers new tools to learn a rich probability distribution over high dimensional data such as images or sounds.
Bayesian Hypernetworks
We study Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks.
Hierarchical Question Answering for Long Documents
We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-of-the-art models.
WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia
The task contains a rich variety of challenging classification and extraction sub-tasks, making it well-suited for end-to-end models such as deep neural networks (DNNs).
PAC-Bayesian Theory Meets Bayesian Inference
That is, for the negative log-likelihood loss function, we show that the minimization of PAC-Bayesian generalization risk bounds maximizes the Bayesian marginal likelihood.
Sequential Model-Based Ensemble Optimization
One of the most tedious tasks in the application of machine learning is model selection, i. e. hyperparameter selection.
