346 citations · 450 across the 13 of their papers we have counts for
14 papers
Adversarial Distilled Retrieval-Augmented Guarding Model for Online Malicious Intent Detection
Yihao Guo, Haocheng Bian, Liutong Zhou +16
With the deployment of Large Language Models (LLMs) in interactive applications, online malicious intent detection has become increasingly critical. However, existing approaches fa…
Stochastic Sampling from Deterministic Flow Models
Saurabh Singh, Ian Fischer
Deterministic flow models, such as rectified flows, offer a general framework for learning a deterministic transport map between two distributions, realized as the vector field for…
Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck
Anirban Samaddar, Sandeep Madireddy, Prasanna Balaprakash +3
The information bottleneck framework provides a systematic approach to learning representations that compress nuisance information in the input and extract semantically meaningful…
VIB is Half Bayes
Alexander A Alemi, Warren R Morningstar, Ben Poole +2
In discriminative settings such as regression and classification there are two random variables at play, the inputs X and the targets Y. Here, we demonstrate that the Variational I…
Cycles in Causal Learning
Katie Everett, Ian Fischer
In the causal learning setting, we wish to learn cause-and-effect relationships between variables such that we can correctly infer the effect of an intervention. While the differen…
Predictive Information Accelerates Learning in RL
Kuang-Huei Lee, Ian Fischer, Anthony Liu +4
The Predictive Information is the mutual information between the past and the future, I(X_past; X_future). We hypothesize that capturing the predictive information is useful in RL,…
An Unsupervised Information-Theoretic Perceptual Quality Metric
Sangnie Bhardwaj, Ian Fischer, Johannes Ballé +1
Tractable models of human perception have proved to be challenging to build. Hand-designed models such as MS-SSIM remain popular predictors of human image quality judgements due to…
Learnability for the Information Bottleneck
Tailin Wu, Ian Fischer, Isaac L. Chuang +1
The Information Bottleneck (IB) method (\cite{tishby2000information}) provides an insightful and principled approach for balancing compression and prediction for representation lea…
Information-Bottleneck Approach to Salient Region Discovery
Andrey Zhmoginov, Ian Fischer, Mark Sandler
We propose a new method for learning image attention masks in a semi-supervised setting based on the Information Bottleneck principle. Provided with a set of labeled images, the ma…
Dueling Decoders: Regularizing Variational Autoencoder Latent Spaces
Bryan Seybold, Emily Fertig, Alex Alemi +1
Variational autoencoders learn unsupervised data representations, but these models frequently converge to minima that fail to preserve meaningful semantic information. For example,…
TherML: Thermodynamics of Machine Learning
Alexander A. Alemi, Ian Fischer
In this work we offer a framework for reasoning about a wide class of existing objectives in machine learning. We develop a formal correspondence between this work and thermodynami…
GILBO: One Metric to Measure Them All
Alexander A. Alemi, Ian Fischer
We propose a simple, tractable lower bound on the mutual information contained in the joint generative density of any latent variable generative model: the GILBO (Generative Inform…
Adversarial examples for generative models
Jernej Kos, Ian Fischer, Dawn Song
We explore methods of producing adversarial examples on deep generative models such as the variational autoencoder (VAE) and the VAE-GAN. Deep learning architectures are known to b…
Deep Variational Information Bottleneck
Alexander A. Alemi, Ian Fischer, Joshua V. Dillon +1
We present a variational approximation to the information bottleneck of Tishby et al. (1999). This variational approach allows us to parameterize the information bottleneck model u…