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Found 29 papers, 19 papers with code
Inter-domain Deep Gaussian Processes with RKHS Fourier Features
We propose Inter-domain Deep Gaussian Processes with RKHS Fourier Features, an extension of shallow inter-domain GPs that combines the advantages of inter-domain and deep Gaussian processes (DGPs) and demonstrate how to leverage existing approximate inference approaches to perform simple and scalable approximate inference on Inter-domain Deep Gaussian Processes.
Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors
Machine learning models often perform poorly under subpopulation shifts in the data distribution.
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets.
Non-Vacuous Generalization Bounds for Large Language Models
Modern language models can contain billions of parameters, raising the question of whether they can generalize beyond the training data or simply regurgitate their training corpora.
Continual Learning via Sequential Function-Space Variational Inference
Sequential Bayesian inference over predictive functions is a natural framework for continual learning from streams of data.
Tractable Function-Space Variational Inference in Bayesian Neural Networks
Recognizing that the primary object of interest in most settings is the distribution over functions induced by the posterior distribution over neural network parameters, we frame Bayesian inference in neural networks explicitly as inferring a posterior distribution over functions and propose a scalable function-space variational inference method that allows incorporating prior information and results in reliable predictive uncertainty estimates.
Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution
Vision-language pretrained models have seen remarkable success, but their application to safety-critical settings is limited by their lack of interpretability.
Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning?
To answer this question, we consider a set of tailored offline reinforcement learning datasets that exhibit causal ambiguity and assess the ability of active sampling techniques to reduce causal confusion at evaluation.
Function-Space Regularization in Neural Networks: A Probabilistic Perspective
In this work, we approach regularization in neural networks from a probabilistic perspective and show that by viewing parameter-space regularization as specifying an empirical prior distribution over the model parameters, we can derive a probabilistically well-motivated regularization technique that allows explicitly encoding information about desired predictive functions into neural network training.
Should We Learn Most Likely Functions or Parameters?
Moreover, the most likely parameters under the parameter posterior do not generally correspond to the most likely function induced by the parameter posterior.
Informative Priors Improve the Reliability of Multimodal Clinical Data Classification
We use simple and scalable Gaussian mean-field variational inference to train a Bayesian neural network using the M2D2 prior.
Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions
Accelerating the discovery of novel and more effective therapeutics is an important pharmaceutical problem in which deep learning is playing an increasingly significant role.
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization
Bayesian optimization is a highly efficient approach to optimizing objective functions which are expensive to query.
Protein Design with Guided Discrete Diffusion
A popular approach to protein design is to combine a generative model with a discriminative model for conditional sampling.
An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization
In this paper, we provide an information-theoretic perspective on Variance-Invariance-Covariance Regularization (VICReg) for self-supervised learning.
On Sequential Bayesian Inference for Continual Learning
Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks.
On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations
KL-regularized reinforcement learning from expert demonstrations has proved successful in improving the sample efficiency of deep reinforcement learning algorithms, allowing them to be applied to challenging physical real-world tasks.
Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks
We use these tasks to benchmark well-established and state-of-the-art Bayesian deep learning methods on task-specific evaluation metrics.
Plex: Towards Reliability using Pretrained Large Model Extensions
A recent trend in artificial intelligence is the use of pretrained models for language and vision tasks, which have achieved extraordinary performance but also puzzling failures.
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations
Using this suite of benchmarking tasks, we show that simple modifications to two popular vision-based online reinforcement learning algorithms, DreamerV2 and DrQ-v2, suffice to outperform existing offline RL methods and establish competitive baselines for continuous control in the visual domain.
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
In this paper we introduce Uncertainty Baselines: high-quality implementations of standard and state-of-the-art deep learning methods on a variety of tasks.
Outcome-Driven Reinforcement Learning via Variational Inference
While reinforcement learning algorithms provide automated acquisition of optimal policies, practical application of such methods requires a number of design decisions, such as manually designing reward functions that not only define the task, but also provide sufficient shaping to accomplish it.
Rethinking Function-Space Variational Inference in Bayesian Neural Networks
Bayesian neural networks (BNNs) define distributions over functions induced by distributions over parameters.
Inter-domain Deep Gaussian Processes
We propose Inter-domain Deep Gaussian Processes, an extension of inter-domain shallow GPs that combines the advantages of inter-domain and deep Gaussian processes (DGPs), and demonstrate how to leverage existing approximate inference methods to perform simple and scalable approximate inference using inter-domain features in DGPs.
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes
We show that the gradient estimates used in training Deep Gaussian Processes (DGPs) with importance-weighted variational inference are susceptible to signal-to-noise ratio (SNR) issues.
A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks
From our comparison we conclude that some current techniques which solve benchmarks such as UCI `overfit' their uncertainty to the dataset---when evaluated on our benchmark these underperform in comparison to simpler baselines.
The StarCraft Multi-Agent Challenge
In this paper, we propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap.
Ranked #6 on
SMAC
on SMAC 6h_vs_8z
Multi$^{\mathbf{3}}$Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery
We propose a novel approach for rapid segmentation of flooded buildings by fusing multiresolution, multisensor, and multitemporal satellite imagery in a convolutional neural network.
VIREL: A Variational Inference Framework for Reinforcement Learning
This gives VIREL a mode-seeking form of KL divergence, the ability to learn deterministic optimal polices naturally from inference and the ability to optimise value functions and policies in separate, iterative steps.
