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06/08/2023
PeFLL: A Lifelong Learning Approach to Personalized Federated Learning
Personalized federated learning (pFL) has emerged as a popular approach ...
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03/14/2023
Geolocation Predicting of Tweets Using BERT-Based Models
This research is aimed to solve the tweet/user geolocation prediction ta...
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10/12/2022
FedProp: Cross-client Label Propagation for Federated Semi-supervised Learning
Federated learning (FL) allows multiple clients to jointly train a machi...
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08/29/2022
Generalization In Multi-Objective Machine Learning
Modern machine learning tasks often require considering not just one but...
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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...
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07/28/2022
CrAM: A Compression-Aware Minimizer
We examine the question of whether SGD-based optimization of deep neural...
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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...
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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 ...
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06/22/2021
FLEA: Provably Fair Multisource Learning from Unreliable Training Data
Fairness-aware learning aims at constructing classifiers that not only m...
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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...
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05/27/2021
Towards Understanding Knowledge Distillation
Knowledge distillation, i.e., one classifier being trained on the output...
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02/11/2021
Fairness-Aware Learning from Corrupted Data
Addressing fairness concerns about machine learning models is a crucial ...
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02/11/2021
Fairness Through Regularization for Learning to Rank
Given the abundance of applications of ranking in recent years, addressi...
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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-...
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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...
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04/08/2020
Leveraging 2D Data to Learn Textured 3D Mesh Generation
Numerous methods have been proposed for probabilistic generative modelli...
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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...
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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...
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06/19/2019
Strategy Representation by Decision Trees with Linear Classifiers
Graph games and Markov decision processes (MDPs) are standard models in ...
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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...
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07/05/2018
Detecting Visual Relationships Using Box Attention
In this paper we propose a new model for detecting visual relationships....
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06/19/2018
Learning Equations for Extrapolation and Control
We present an approach to identify concise equations from data using a s...
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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...
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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...
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05/11/2017
Probabilistic Image Colorization
We develop a probabilistic technique for colorizing grayscale natural im...
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12/24/2016
PixelCNN Models with Auxiliary Variables for Natural Image Modeling
We study probabilistic models of natural images and extend the autoregre...
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11/23/2016
iCaRL: Incremental Classifier and Representation Learning
A major open problem on the road to artificial intelligence is the devel...
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10/10/2016
Extrapolation and learning equations
In classical machine learning, regression is treated as a black box proc...
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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...
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02/21/2016
Multi-Task Learning with Labeled and Unlabeled Tasks
In multi-task learning, a learner is given a collection of prediction ta...
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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...
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04/28/2015
Identifying Reliable Annotations for Large Scale Image Segmentation
Challenging computer vision tasks, in particular semantic image segmenta...
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12/03/2014
Curriculum Learning of Multiple Tasks
Sharing information between multiple tasks enables algorithms to achieve...
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10/01/2014
Learning to Transfer Privileged Information
We introduce a learning framework called learning using privileged infor...
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07/01/2014
Mind the Nuisance: Gaussian Process Classification using Privileged Noise
The learning with privileged information setting has recently attracted ...
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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...
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11/12/2013