280 citations · 1.1k across the 43 of their papers we have counts for
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Training Discrete Energy-Based Models with Energy Discrepancy
Tobias Schröder, Zijing Ou, Yingzhen Li +1
Training energy-based models (EBMs) on discrete spaces is challenging because sampling over such spaces can be difficult. We propose to train discrete EBMs with energy discrepancy…
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models
Tobias Schröder, Zijing Ou, Jen Ning Lim +3
Energy-based models are a simple yet powerful class of probabilistic models, but their widespread adoption has been limited by the computational burden of training them. We propose…
On the Identifiability of Switching Dynamical Systems
Carles Balsells-Rodas, Yixin Wang, Yingzhen Li
The identifiability of latent variable models has received increasing attention due to its relevance in interpretability and out-of-distribution generalisation. In this work, we st…
ESD: Expected Squared Difference as a Tuning-Free Trainable Calibration Measure
Hee Suk Yoon, Joshua Tian Jin Tee, Eunseop Yoon +4
Studies have shown that modern neural networks tend to be poorly calibrated due to over-confident predictions. Traditionally, post-processing methods have been used to calibrate th…
Calibrating Transformers via Sparse Gaussian Processes
Wenlong Chen, Yingzhen Li
Transformer models have achieved profound success in prediction tasks in a wide range of applications in natural language processing, speech recognition and computer vision. Extend…
Scalable Infomin Learning
Yanzhi Chen, Weihao Sun, Yingzhen Li +1
The task of infomin learning aims to learn a representation with high utility while being uninformative about a specified target, with the latter achieved by minimising the mutual…