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Found 9 papers, 6 papers with code
Augmenting Softmax Information for Selective Classification with Out-of-Distribution Data
However, the performance of detection methods is generally evaluated on the task in isolation, rather than also considering potential downstream tasks in tandem.
Window-Based Early-Exit Cascades for Uncertainty Estimation: When Deep Ensembles are More Efficient than Single Models
Experiments on ImageNet-scale data across a number of network architectures and uncertainty tasks show that the proposed window-based early-exit approach is able to achieve a superior uncertainty-computation trade-off compared to scaling single models.
Latent Diffusion Model for DNA Sequence Generation
On the other hand, Diffusion Models are a promising new class of generative models that are not burdened with these problems, enabling them to reach the state-of-the-art in domains such as image generation.
Score Normalization for a Faster Diffusion Exponential Integrator Sampler
As a simple fix, we propose to instead reparameterise the score (at inference) by dividing it by the average absolute value of previous score estimates at that time step collected from offline high NFE generations.
An Underexplored Dilemma between Confidence and Calibration in Quantized Neural Networks
We show that this robustness can be partially explained by the calibration behavior of modern CNNs, and may be improved with overconfidence.
On the Usefulness of Deep Ensemble Diversity for Out-of-Distribution Detection
As such we show that practically, even better OOD detection performance can be achieved for Deep Ensembles by averaging task-specific detection scores such as Energy over the ensemble.
Logit-Based Ensemble Distribution Distillation for Robust Autoregressive Sequence Uncertainties
Efficiently and reliably estimating uncertainty is an important objective in deep learning.
DiscDiff: Latent Diffusion Model for DNA Sequence Generation
This paper introduces a novel framework for DNA sequence generation, comprising two key components: DiscDiff, a Latent Diffusion Model (LDM) tailored for generating discrete DNA sequences, and Absorb-Escape, a post-training algorithm designed to refine these sequences.
Understanding Why Label Smoothing Degrades Selective Classification and How to Fix It
We first demonstrate empirically across a range of tasks and architectures that LS leads to a consistent degradation in SC.
