346 citations · 450 across the 13 of their papers we have counts for
7 papers · 1 filter
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…
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,…
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…
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…
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…