Search Results for author:
Found 11 papers, 1 papers with code
Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector
We revisit the likelihood ratio between a pretrained large language model (LLM) and its finetuned variant as a criterion for out-of-distribution (OOD) detection.
Constructing Semantics-Aware Adversarial Examples with Probabilistic Perspective
We propose a probabilistic perspective on adversarial examples.
SR-OOD: Out-of-Distribution Detection via Sample Repairing
Specifically, our framework consists of two components: a sample repairing module and a detection module.
Generative Adversarial Network
Out-of-Distribution Detection
+1
Falsehoods that ML researchers believe about OOD detection
An intuitive way to detect out-of-distribution (OOD) data is via the density function of a fitted probabilistic generative model: points with low density may be classed as OOD.
Out-of-Distribution Detection with Class Ratio Estimation
In this work, we propose to unify density ratio based methods under a novel framework that builds energy-based models and employs differing base distributions.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
A Dual-fusion Semantic Segmentation Framework With GAN For SAR Images
With the better representation capability of optical images, we propose to enrich SAR images with generated optical images via the generative adversative network (GAN) trained by numerous SAR and optical images.
On the Out-of-distribution Generalization of Probabilistic Image Modelling
Out-of-distribution (OOD) detection and lossless compression constitute two problems that can be solved by the training of probabilistic models on a first dataset with subsequent likelihood evaluation on a second dataset, where data distributions differ.
Out-of-Distribution Generalization
Out of Distribution (OOD) Detection
Measuring Uncertainty in Signal Fingerprinting with Gaussian Processes Going Deep
In indoor positioning, signal fluctuation is highly location-dependent.
Solipsistic Reinforcement Learning
We introduce a new model-based reinforcement learning framework that aims to tackle environments with high dimensional state spaces.
Model-based Reinforcement Learning
reinforcement-learning
+1
Memory-augmented Neural Machine Translation
Neural machine translation (NMT) has achieved notable success in recent times, however it is also widely recognized that this approach has limitations with handling infrequent words and word pairs.
Flexible and Creative Chinese Poetry Generation Using Neural Memory
It has been shown that Chinese poems can be successfully generated by sequence-to-sequence neural models, particularly with the attention mechanism.
