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07/19/2023
Forecasting Early with Meta Learning
In the early observation period of a time series, there might be only a ...
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05/22/2023
Forecasting Irregularly Sampled Time Series using Graphs
Forecasting irregularly sampled time series with missing values is a cru...
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04/14/2023
Phantom Embeddings: Using Embedding Space for Model Regularization in Deep Neural Networks
The strength of machine learning models stems from their ability to lear...
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04/14/2023
Directly Optimizing IoU for Bounding Box Localization
Object detection has seen remarkable progress in recent years with the i...
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02/10/2023
Neural Capacitated Clustering
Recent work on deep clustering has found new promising methods also for ...
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12/22/2022
Few-shot human motion prediction for heterogeneous sensors
Human motion prediction is a complex task as it involves forecasting var...
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12/15/2022
Put Attention to Temporal Saliency Patterns of Multi-Horizon Time Series
Time series, sets of sequences in chronological order, are essential dat...
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12/05/2022
Auxiliary Quantile Forecasting with Linear Networks
We propose a novel multi-task method for quantile forecasting with share...
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10/18/2022
Deep Multi-Representation Model for Click-Through Rate Prediction
Click-Through Rate prediction (CTR) is a crucial task in recommender sys...
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10/05/2022
Tripletformer for Probabilistic Interpolation of Asynchronous Time Series
Asynchronous time series are often observed in several applications such...
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10/01/2022
Offline Handwritten Amharic Character Recognition Using Few-shot Learning
Few-shot learning is an important, but challenging problem of machine le...
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09/02/2022
When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development
Machine learning (ML) has significantly contributed to the development o...
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08/24/2022
DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time Series
Asynchronous Time Series is a multivariate time series where all the cha...
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07/14/2022
Attention, Filling in The Gaps for Generalization in Routing Problems
Machine Learning (ML) methods have become a useful tool for tackling veh...
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07/04/2022
Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement Learning
This work presents solutions to the Traveling Salesperson Problem with p...
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06/27/2022
Learning to Control Local Search for Combinatorial Optimization
Combinatorial optimization problems are encountered in many practical co...
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06/16/2022
Zero-Shot AutoML with Pretrained Models
Given a new dataset D and a low compute budget, how should we choose a p...
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05/05/2022
End-to-End Image-Based Fashion Recommendation
In fashion-based recommendation settings, incorporating the item image f...
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05/02/2022
Large Neighborhood Search based on Neural Construction Heuristics
We propose a Large Neighborhood Search (LNS) approach utilizing a learne...
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04/07/2022
Few-Shot Forecasting of Time-Series with Heterogeneous Channels
Learning complex time series forecasting models usually requires a large...
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04/04/2022
CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-Attention
In sparse recommender settings, users' context and item attributes play ...
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02/25/2022
Improving Amharic Handwritten Word Recognition Using Auxiliary Task
Amharic is one of the official languages of the Federal Democratic Repub...
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02/11/2022
Positive-Unlabeled Domain Adaptation
Domain Adaptation methodologies have shown to effectively generalize fro...
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02/09/2022
A.I. and Data-Driven Mobility at Volkswagen Financial Services AG
Machine learning is being widely adapted in industrial applications owin...
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01/05/2022
Supervised Permutation Invariant Networks for Solving the CVRP with Bounded Fleet Size
Learning to solve combinatorial optimization problems, such as the vehic...
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10/15/2021
Improving Hyperparameter Optimization by Planning Ahead
Hyperparameter optimization (HPO) is generally treated as a bi-level opt...
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09/03/2021
Deep Metric Learning for Ground Images
Ground texture based localization methods are potential prospects for lo...
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08/05/2021
Multimodal Meta-Learning for Time Series Regression
Recent work has shown the efficiency of deep learning models such as Ful...
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06/15/2021
Multi-script Handwritten Digit Recognition Using Multi-task Learning
Handwritten digit recognition is one of the extensively studied area in ...
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04/25/2021
RP-DQN: An application of Q-Learning to Vehicle Routing Problems
In this paper we present a new approach to tackle complex routing proble...
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02/07/2021
Hyperparameter Optimization with Differentiable Metafeatures
Metafeatures, or dataset characteristics, have been shown to improve the...
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01/06/2021
Do We Really Need Deep Learning Models for Time Series Forecasting?
Time series forecasting is a crucial task in machine learning, as it has...
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06/16/2020
Learning to Solve Vehicle Routing Problems with Time Windows through Joint Attention
Many real-world vehicle routing problems involve rich sets of constraint...
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10/28/2019
HIDRA: Head Initialization across Dynamic targets for Robust Architectures
The performance of gradient-based optimization strategies depends heavil...
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09/30/2019
Chameleon: Learning Model Initializations Across Tasks With Different Schemas
Parametric models, and particularly neural networks, require weight init...
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09/23/2019
Handwritten Amharic Character Recognition Using a Convolutional Neural Network
Amharic is the official language of the Federal Democratic Republic of E...
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06/27/2019
Hyp-RL : Hyperparameter Optimization by Reinforcement Learning
Hyperparameter tuning is an omnipresent problem in machine learning as i...
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06/24/2019
In Hindsight: A Smooth Reward for Steady Exploration
In classical Q-learning, the objective is to maximize the sum of discoun...
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05/27/2019
Dataset2Vec: Learning Dataset Meta-Features
Machine learning tasks such as optimizing the hyper-parameters of a mode...
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05/24/2019
Learning Surrogate Losses
The minimization of loss functions is the heart and soul of Machine Lear...
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02/25/2019
Multi-Label Network Classification via Weighted Personalized Factorizations
Multi-label network classification is a well-known task that is being us...
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02/09/2019
Data-Driven Vehicle Trajectory Forecasting
An active area of research is to increase the safety of self-driving veh...
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12/20/2018
NeuralWarp: Time-Series Similarity with Warping Networks
Research on time-series similarity measures has emphasized the need for ...
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10/13/2016
Bank Card Usage Prediction Exploiting Geolocation Information
We describe the solution of team ISMLL for the ECML-PKDD 2016 Discovery ...
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04/03/2016
Multi-Relational Learning at Scale with ADMM
Learning from multiple-relational data which contains noise, ambiguities...
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05/03/2015
Optimal Time-Series Motifs
Motifs are the most repetitive/frequent patterns of a time-series. The d...
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07/24/2013
Time-Series Classification Through Histograms of Symbolic Polynomials
Time-series classification has attracted considerable research attention...
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05/09/2012
