Christoph H. Lampert

research

∙ 06/08/2023

PeFLL: A Lifelong Learning Approach to Personalized Federated Learning

Personalized federated learning (pFL) has emerged as a popular approach ...

0 Jonathan Scott, et al. ∙

research

∙ 03/14/2023

Geolocation Predicting of Tweets Using BERT-Based Models

This research is aimed to solve the tweet/user geolocation prediction ta...

0 Kateryna Lutsai, et al. ∙

research

∙ 10/12/2022

FedProp: Cross-client Label Propagation for Federated Semi-supervised Learning

Federated learning (FL) allows multiple clients to jointly train a machi...

0 Jonathan Scott, et al. ∙

research

∙ 08/29/2022

Generalization In Multi-Objective Machine Learning

Modern machine learning tasks often require considering not just one but...

0 Peter Súkeník, et al. ∙

research

∙ 08/05/2022

Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks

It is a highly desirable property for deep networks to be robust against...

0 Bernd Prach, et al. ∙

research

∙ 07/28/2022

CrAM: A Compression-Aware Minimizer

We examine the question of whether SGD-based optimization of deep neural...

0 Alexandra Peşte, et al. ∙

research

∙ 06/10/2022

Lightweight Conditional Model Extrapolation for Streaming Data under Class-Prior Shift

We introduce LIMES, a new method for learning with non-stationary stream...

0 Paulina Tomaszewska, et al. ∙

research

∙ 07/08/2021

SSSE: Efficiently Erasing Samples from Trained Machine Learning Models

The availability of large amounts of user-provided data has been key to ...

0 Alexandra Peşte, et al. ∙

research

∙ 06/22/2021

FLEA: Provably Fair Multisource Learning from Unreliable Training Data

Fairness-aware learning aims at constructing classifiers that not only m...

0 Eugenia Iofinova, et al. ∙

research

∙ 06/16/2021

Unsupervised Video Prediction from a Single Frame by Estimating 3D Dynamic Scene Structure

Our goal in this work is to generate realistic videos given just one ini...

0 Paul Henderson, et al. ∙

research

∙ 05/27/2021

Towards Understanding Knowledge Distillation

Knowledge distillation, i.e., one classifier being trained on the output...

0 Mary Phuong, et al. ∙

research

∙ 02/11/2021

Fairness-Aware Learning from Corrupted Data

Addressing fairness concerns about machine learning models is a crucial ...

0 Nikola Konstantinov, et al. ∙

research

∙ 02/11/2021

Fairness Through Regularization for Learning to Rank

Given the abundance of applications of ranking in recent years, addressi...

0 Nikola Konstantinov, et al. ∙

research

∙ 08/27/2020

A Flexible Selection Scheme for Minimum-Effort Transfer Learning

Fine-tuning is a popular way of exploiting knowledge contained in a pre-...

0 Amélie Royer, et al. ∙

research

∙ 04/27/2020

Localizing Grouped Instances for Efficient Detection in Low-Resource Scenarios

State-of-the-art detection systems are generally evaluated on their abil...

0 Amélie Royer, et al. ∙

research

∙ 04/08/2020

Leveraging 2D Data to Learn Textured 3D Mesh Generation

Numerous methods have been proposed for probabilistic generative modelli...

0 Paul Henderson, et al. ∙

research

∙ 04/01/2020

Object-Centric Image Generation with Factored Depths, Locations, and Appearances

We present a generative model of images that explicitly reasons over the...

0 Titas Anciukevicius, et al. ∙

research

∙ 02/24/2020

On the Sample Complexity of Adversarial Multi-Source PAC Learning

We study the problem of learning from multiple untrusted data sources, a...

0 Nikola Konstantinov, et al. ∙

research

∙ 06/19/2019

Strategy Representation by Decision Trees with Linear Classifiers

Graph games and Markov decision processes (MDPs) are standard models in ...

0 Pranav Ashok, et al. ∙

research

∙ 12/07/2018

Back to square one: probabilistic trajectory forecasting without bells and whistles

We introduce a spatio-temporal convolutional neural network model for tr...

0 Ehsan Pajouheshgar, et al. ∙

research

∙ 07/05/2018

Detecting Visual Relationships Using Box Attention

In this paper we propose a new model for detecting visual relationships....

0 Alexander Kolesnikov, et al. ∙

research

∙ 06/19/2018

Learning Equations for Extrapolation and Control

We present an approach to identify concise equations from data using a s...

0 Subham S. Sahoo, et al. ∙

research

∙ 04/11/2018

KS(conf ): A Light-Weight Test if a ConvNet Operates Outside of Its Specifications

Computer vision systems for automatic image categorization have become a...

2 Rémy Sun, et al. ∙

research

∙ 07/03/2017

Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly

Due to the importance of zero-shot learning, i.e. classifying images whe...

0 Yongqin Xian, et al. ∙

research

∙ 05/11/2017

Probabilistic Image Colorization

We develop a probabilistic technique for colorizing grayscale natural im...

0 Amélie Royer, et al. ∙

research

∙ 12/24/2016

PixelCNN Models with Auxiliary Variables for Natural Image Modeling

We study probabilistic models of natural images and extend the autoregre...

0 Alexander Kolesnikov, et al. ∙

research

∙ 11/23/2016

iCaRL: Incremental Classifier and Representation Learning

A major open problem on the road to artificial intelligence is the devel...

0 Sylvestre-Alvise Rebuffi, et al. ∙

research

∙ 10/10/2016

Extrapolation and learning equations

In classical machine learning, regression is treated as a black box proc...

0 Georg Martius, et al. ∙

research

∙ 03/19/2016

Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation

We introduce a new loss function for the weakly-supervised training of s...

0 Alexander Kolesnikov, et al. ∙

research

∙ 02/21/2016

Multi-Task Learning with Labeled and Unlabeled Tasks

In multi-task learning, a learner is given a collection of prediction ta...

0 Anastasia Pentina, et al. ∙

research

∙ 10/09/2015

Conditional Risk Minimization for Stochastic Processes

We study the task of learning from non-i.i.d. data. In particular, we ai...

0 Alexander Zimin, et al. ∙

research

∙ 04/28/2015

Identifying Reliable Annotations for Large Scale Image Segmentation

Challenging computer vision tasks, in particular semantic image segmenta...

0 Alexander Kolesnikov, et al. ∙

research

∙ 12/03/2014

Curriculum Learning of Multiple Tasks

Sharing information between multiple tasks enables algorithms to achieve...

0 Anastasia Pentina, et al. ∙

research

∙ 10/01/2014

Learning to Transfer Privileged Information

We introduce a learning framework called learning using privileged infor...

0 Viktoriia Sharmanska, et al. ∙

research

∙ 07/01/2014

Mind the Nuisance: Gaussian Process Classification using Privileged Noise

The learning with privileged information setting has recently attracted ...

0 Daniel Hernández-Lobato, et al. ∙

research

∙ 06/20/2014

Predicting the Future Behavior of a Time-Varying Probability Distribution

We study the problem of predicting the future, though only in the probab...

0 Christoph H. Lampert, et al. ∙

research

∙ 11/12/2013

A PAC-Bayesian bound for Lifelong Learning

Transfer learning has received a lot of attention in the machine learnin...

0 Anastasia Pentina, et al. ∙

Christoph H. Lampert

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