171 citations · 694 across the 32 of their papers we have counts for
12 papers · 1 filter
Towards Understanding Knowledge Distillation
Mary Phuong, Christoph H. Lampert
Knowledge distillation, i.e., one classifier being trained on the outputs of another classifier, is an empirically very successful technique for knowledge transfer between classifi…
Fairness Through Regularization for Learning to Rank
Nikola Konstantinov, Christoph H. Lampert
Given the abundance of applications of ranking in recent years, addressing fairness concerns around automated ranking systems becomes necessary for increasing the trust among end-u…
Object-Centric Image Generation with Factored Depths, Locations, and Appearances
Titas Anciukevicius, Christoph H. Lampert, Paul Henderson
We present a generative model of images that explicitly reasons over the set of objects they show. Our model learns a structured latent representation that separates objects from e…
On the Sample Complexity of Adversarial Multi-Source PAC Learning
Nikola Konstantinov, Elias Frantar, Dan Alistarh +1
We study the problem of learning from multiple untrusted data sources, a scenario of increasing practical relevance given the recent emergence of crowdsourcing and collaborative le…
Robust Learning from Untrusted Sources
Nikola Konstantinov, Christoph Lampert
Modern machine learning methods often require more data for training than a single expert can provide. Therefore, it has become a standard procedure to collect data from external s…
Back to square one: probabilistic trajectory forecasting without bells and whistles
Ehsan Pajouheshgar, Christoph H. Lampert
We introduce a spatio-temporal convolutional neural network model for trajectory forecasting from visual sources. Applied in an auto-regressive way it provides an explicit probabil…
Learning Equations for Extrapolation and Control
Subham S. Sahoo, Christoph H. Lampert, Georg Martius
We present an approach to identify concise equations from data using a shallow neural network approach. In contrast to ordinary black-box regression, this approach allows understan…
Data-Dependent Stability of Stochastic Gradient Descent
Ilja Kuzborskij, Christoph H. Lampert
We establish a data-dependent notion of algorithmic stability for Stochastic Gradient Descent (SGD), and employ it to develop novel generalization bounds. This is in contrast to pr…
Extrapolation and learning equations
Georg Martius, Christoph H. Lampert
In classical machine learning, regression is treated as a black box process of identifying a suitable function from a hypothesis set without attempting to gain insight into the mec…
A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm for Training Structural SVMs with a Costly max-Oracle
Neel Shah, Vladimir Kolmogorov, Christoph H. Lampert
Structural support vector machines (SSVMs) are amongst the best performing models for structured computer vision tasks, such as semantic image segmentation or human pose estimation…
Closed-Form Training of Conditional Random Fields for Large Scale Image Segmentation
Alexander Kolesnikov, Matthieu Guillaumin, Vittorio Ferrari +1
We present LS-CRF, a new method for very efficient large-scale training of Conditional Random Fields (CRFs). It is inspired by existing closed-form expressions for the maximum like…
The Most Persistent Soft-Clique in a Set of Sampled Graphs
Novi Quadrianto, Chao Chen, Christoph Lampert
When searching for characteristic subpatterns in potentially noisy graph data, it appears self-evident that having multiple observations would be better than having just one. Howev…