982 citations · 2.8k across the 131 of their papers we have counts for
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Modality-Agnostic Variational Compression of Implicit Neural Representations
Jonathan Richard Schwarz, Jihoon Tack, Yee Whye Teh +2
We introduce a modality-agnostic neural compression algorithm based on a functional view of data and parameterised as an Implicit Neural Representation (INR). Bridging the gap betw…
RenyiCL: Contrastive Representation Learning with Skew Renyi Divergence
Kyungmin Lee, Jinwoo Shin
Contrastive representation learning seeks to acquire useful representations by estimating the shared information between multiple views of data. Here, the choice of data augmentati…
Learning Bounds for Risk-sensitive Learning
Jaeho Lee, Sejun Park, Jinwoo Shin
In risk-sensitive learning, one aims to find a hypothesis that minimizes a risk-averse (or risk-seeking) measure of loss, instead of the standard expected loss. In this paper, we p…
Robust Inference via Generative Classifiers for Handling Noisy Labels
Kimin Lee, Sukmin Yun, Kibok Lee +3
Large-scale datasets may contain significant proportions of noisy (incorrect) class labels, and it is well-known that modern deep neural networks (DNNs) poorly generalize from such…
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
Kimin Lee, Kibok Lee, Honglak Lee +1
Detecting test samples drawn sufficiently far away from the training distribution statistically or adversarially is a fundamental requirement for deploying a good classifier in man…
Gauged Mini-Bucket Elimination for Approximate Inference
Sungsoo Ahn, Michael Chertkov, Jinwoo Shin +1
Computing the partition function of a discrete graphical model is a fundamental inference challenge. Since this is computationally intractable, variational approximations are o…