166 citations · 507 across the 29 of their papers we have counts for
9 papers · 1 filter
Extreme Classification via Adversarial Softmax Approximation
Robert Bamler, Stephan Mandt
Training a classifier over a large number of classes, known as 'extreme classification', has become a topic of major interest with applications in technology, science, and e-commer…
Tightening Bounds for Variational Inference by Revisiting Perturbation Theory
Robert Bamler, Cheng Zhang, Manfred Opper +1
Variational inference has become one of the most widely used methods in latent variable modeling. In its basic form, variational inference employs a fully factorized variational di…
A Quantum Field Theory of Representation Learning
Robert Bamler, Stephan Mandt
Continuous symmetries and their breaking play a prominent role in contemporary physics. Effective low-energy field theories around symmetry breaking states explain diverse phenomen…
Augmenting and Tuning Knowledge Graph Embeddings
Robert Bamler, Farnood Salehi, Stephan Mandt
Knowledge graph embeddings rank among the most successful methods for link prediction in knowledge graphs, i.e., the task of completing an incomplete collection of relational facts…
Active Mini-Batch Sampling using Repulsive Point Processes
Cheng Zhang, Cengiz Öztireli, Stephan Mandt +1
The convergence speed of stochastic gradient descent (SGD) can be improved by actively selecting mini-batches. We explore sampling schemes where similar data points are less likely…
Improving Optimization for Models With Continuous Symmetry Breaking
Robert Bamler, Stephan Mandt
Many loss functions in representation learning are invariant under a continuous symmetry transformation. For example, the loss function of word embeddings (Mikolov et al., 2013) re…