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Found 69 papers, 31 papers with code
Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training
For EEG classification many models have been developed with layer types and architectures we typically do not see in time series classification.
Hyperparameter Tuning MLPs for Probabilistic Time Series Forecasting
Time series forecasting attempts to predict future events by analyzing past trends and patterns.
Hyperparameter Optimization
Probabilistic Time Series Forecasting
+1
ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing
Used car pricing is a critical aspect of the automotive industry, influenced by many economic factors and market dynamics.
Probabilistic Forecasting of Irregular Time Series via Conditional Flows
Probabilistic forecasting of irregularly sampled multivariate time series with missing values is an important problem in many fields, including health care, astronomy, and climate.
Moco: A Learnable Meta Optimizer for Combinatorial Optimization
Our approach, Moco, learns a graph neural network that updates the solution construction procedure based on features extracted from the current search state.
Context-Aware Sequential Model for Multi-Behaviour Recommendation
Sequential recommendation models are crucial for next-item recommendations in online platforms, capturing complex patterns in user interactions.
Towards Comparable Active Learning
Active Learning has received significant attention in the field of machine learning for its potential in selecting the most informative samples for labeling, thereby reducing data annotation costs.
Routing Arena: A Benchmark Suite for Neural Routing Solvers
Neural Combinatorial Optimization has been researched actively in the last eight years.
Too Big, so Fail? -- Enabling Neural Construction Methods to Solve Large-Scale Routing Problems
In recent years new deep learning approaches to solve combinatorial optimization problems, in particular NP-hard Vehicle Routing Problems (VRP), have been proposed.
Forecasting Early with Meta Learning
In the early observation period of a time series, there might be only a few historic observations available to learn a model.
DeepStay: Stay Region Extraction from Location Trajectories using Weak Supervision
Common approaches to SR extraction are evaluated either solely unsupervised or on a small-scale private dataset, as popular public datasets are unlabeled.
Forecasting Irregularly Sampled Time Series using Graphs
Forecasting irregularly sampled time series with missing values is a crucial task for numerous real-world applications such as healthcare, astronomy, and climate sciences.
Ranked #1 on
Multivariate Time Series Forecasting
on USHCN-Daily
Directly Optimizing IoU for Bounding Box Localization
In the scope of work seen currently in this domain, IoU is approximated by using the Huber loss as a proxy but this indirect method does not leverage the IoU information and treats the bounding box as four independent, unrelated terms of regression.
Phantom Embeddings: Using Embedding Space for Model Regularization in Deep Neural Networks
We create phantom embeddings from a subset of homogenous samples and use these phantom embeddings to decrease the inter-class similarity of instances in their latent embedding space.
Neural Capacitated Clustering
Recent work on deep clustering has found new promising methods also for constrained clustering problems.
Few-shot human motion prediction for heterogeneous sensors
Human motion prediction is a complex task as it involves forecasting variables over time on a graph of connected sensors.
Auxiliary Quantile Forecasting with Linear Networks
We show that following similar intuition from multi-task learning to exploit correlations among forecast horizons, we can model multiple quantile estimates as auxiliary tasks for each of the forecast horizon to improve forecast accuracy across the quantile estimates compared to modeling only a single quantile estimate.
Deep Multi-Representation Model for Click-Through Rate Prediction
Click-Through Rate prediction (CTR) is a crucial task in recommender systems, and it gained considerable attention in the past few years.
Tripletformer for Probabilistic Interpolation of Irregularly sampled Time Series
Results indicate an improvement in negative loglikelihood error by up to 32% on real-world datasets and 85% on synthetic datasets when using the Tripletformer compared to the next best model.
Offline Handwritten Amharic Character Recognition Using Few-shot Learning
Learning from a few examples is also an important attempt towards learning like humans.
When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development
ML can be seen as a set of tools that contribute to the automation of the whole experimental cycle, including model building and practical planning, thus allowing human experts to focus on the more demanding and overarching cognitive tasks.
DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time Series
Because of the asynchronous nature, they pose a significant challenge to deep learning architectures, which presume that the time series presented to them are regularly sampled, fully observed, and aligned with respect to time.
Attention, Filling in The Gaps for Generalization in Routing Problems
Machine Learning (ML) methods have become a useful tool for tackling vehicle routing problems, either in combination with popular heuristics or as standalone models.
Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement Learning
In this work, we generalize this concept of heterogeneous attentions to the TSPPC.
Learning to Control Local Search for Combinatorial Optimization
Combinatorial optimization problems are encountered in many practical contexts such as logistics and production, but exact solutions are particularly difficult to find and usually NP-hard for considerable problem sizes.
Zero-Shot AutoML with Pretrained Models
Given a new dataset D and a low compute budget, how should we choose a pre-trained model to fine-tune to D, and set the fine-tuning hyperparameters without risking overfitting, particularly if D is small?
End-to-End Image-Based Fashion Recommendation
In fashion-based recommendation settings, incorporating the item image features is considered a crucial factor, and it has shown significant improvements to many traditional models, including but not limited to matrix factorization, auto-encoders, and nearest neighbor models.
Large Neighborhood Search based on Neural Construction Heuristics
The neural repair operator is combined with a local search routine, heuristic destruction operators and a selection procedure applied to a small population to arrive at a sophisticated solution approach.
Few-Shot Forecasting of Time-Series with Heterogeneous Channels
Learning complex time series forecasting models usually requires a large amount of data, as each model is trained from scratch for each task/data set.
CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-Attention
This cross-attention allows CARCA to harness the correlation between old and recent items in the user profile and their influence on deciding which item to recommend next.
Ranked #1 on
Sequential Recommendation
on Amazon Men
(using extra training data)
Improving Amharic Handwritten Word Recognition Using Auxiliary Task
Amharic is one of the official languages of the Federal Democratic Republic of Ethiopia.
Handwritten Text Recognition
Optical Character Recognition
+1
Positive-Unlabeled Domain Adaptation
In this work we are the first to introduce the challenge of Positive-Unlabeled Domain Adaptation where we aim to generalise from a fully labeled source domain to a target domain where only positive and unlabeled data is available.
A.I. and Data-Driven Mobility at Volkswagen Financial Services AG
Machine learning is being widely adapted in industrial applications owing to the capabilities of commercially available hardware and rapidly advancing research.
Supervised Permutation Invariant Networks for Solving the CVRP with Bounded Fleet Size
Learning to solve combinatorial optimization problems, such as the vehicle routing problem, offers great computational advantages over classical operations research solvers and heuristics.
Improving Hyperparameter Optimization by Planning Ahead
Hyperparameter optimization (HPO) is generally treated as a bi-level optimization problem that involves fitting a (probabilistic) surrogate model to a set of observed hyperparameter responses, e. g. validation loss, and consequently maximizing an acquisition function using a surrogate model to identify good hyperparameter candidates for evaluation.
Hyperparameter Optimization
Model-based Reinforcement Learning
+4
Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting
Time series data is ubiquitous in research as well as in a wide variety of industrial applications.
Transfer Learning for Bayesian HPO with End-to-End Meta-Features
As a result, our novel DKLM can learn contextualized dataset-specific similarity representations for hyperparameter configurations.
Deep Metric Learning for Ground Images
Ground texture based localization methods are potential prospects for low-cost, high-accuracy self-localization solutions for robots.
Multimodal Meta-Learning for Time Series Regression
Recent work has shown the efficiency of deep learning models such as Fully Convolutional Networks (FCN) or Recurrent Neural Networks (RNN) to deal with Time Series Regression (TSR) problems.
Multi-script Handwritten Digit Recognition Using Multi-task Learning
Therefore, in this study multi-script handwritten digit recognition using multi-task learning will be investigated.
RP-DQN: An application of Q-Learning to Vehicle Routing Problems
In this paper we present a new approach to tackle complex routing problems with an improved state representation that utilizes the model complexity better than previous methods.
Hyperparameter Optimization with Differentiable Metafeatures
In contrast to existing models, DMFBS i) integrates a differentiable metafeature extractor and ii) is optimized using a novel multi-task loss, linking manifold regularization with a dataset similarity measure learned via an auxiliary dataset identification meta-task, effectively enforcing the response approximation for similar datasets to be similar.
Do We Really Need Deep Learning Models for Time Series Forecasting?
In this paper, we report the results of prominent deep learning models with respect to a well-known machine learning baseline, a Gradient Boosting Regression Tree (GBRT) model.
Zero-shot Transfer Learning for Gray-box Hyper-parameter Optimization
Zero-shot hyper-parameter optimization refers to the process of selecting hyper- parameter configurations that are expected to perform well for a given dataset upfront, without access to any observations of the losses of the target response.
Learning to Solve Vehicle Routing Problems with Time Windows through Joint Attention
Many real-world vehicle routing problems involve rich sets of constraints with respect to the capacities of the vehicles, time windows for customers etc.
HIDRA: Head Initialization across Dynamic targets for Robust Architectures
In this paper, we present HIDRA, a meta-learning approach that enables training and evaluating across tasks with any number of target variables.
Chameleon: Learning Model Initializations Across Tasks With Different Schemas
Parametric models, and particularly neural networks, require weight initialization as a starting point for gradient-based optimization.
Atomic Compression Networks
Compressed forms of deep neural networks are essential in deploying large-scale computational models on resource-constrained devices.
Handwritten Amharic Character Recognition Using a Convolutional Neural Network
The model was further enhanced using multi-task learning from the relationships of the characters.
Hyp-RL : Hyperparameter Optimization by Reinforcement Learning
More recently, methods have been introduced that build a so-called surrogate model that predicts the validation loss for a specific hyperparameter setting, model and dataset and then sequentially select the next hyperparameter to test, based on a heuristic function of the expected value and the uncertainty of the surrogate model called acquisition function (sequential model-based Bayesian optimization, SMBO).
In Hindsight: A Smooth Reward for Steady Exploration
In classical Q-learning, the objective is to maximize the sum of discounted rewards through iteratively using the Bellman equation as an update, in an attempt to estimate the action value function of the optimal policy.
Dataset2Vec: Learning Dataset Meta-Features
As a data-driven approach, meta-learning requires meta-features that represent the primary learning tasks or datasets, and are estimated traditonally as engineered dataset statistics that require expert domain knowledge tailored for every meta-task.
Learning Surrogate Losses
Ultimately, the surrogate losses are learned jointly with the prediction model via bilevel optimization.
Multi-Label Network Classification via Weighted Personalized Factorizations
It can be formalized as a multi-relational learning task for predicting nodes labels based on their relations within the network.
Data-Driven Vehicle Trajectory Forecasting
An active area of research is to increase the safety of self-driving vehicles.
NeuralWarp: Time-Series Similarity with Warping Networks
Research on time-series similarity measures has emphasized the need for elastic methods which align the indices of pairs of time series and a plethora of non-parametric have been proposed for the task.
Channel masking for multivariate time series shapelets
Time series shapelets are discriminative sub-sequences and their similarity to time series can be used for time series classification.
Automatic Frankensteining: Creating Complex Ensembles Autonomously
Automating machine learning by providing techniques that autonomously find the best algorithm, hyperparameter configuration and preprocessing is helpful for both researchers and practitioners.
Integrating Distributional and Lexical Information for Semantic Classification of Words using MRMF
Thus we show, that MRMF provides an interesting approach for building semantic classifiers that (1) gives better results than unsupervised approaches based on vector similarity, (2) gives similar results as other supervised methods and (3) can naturally be extended with other sources of information in order to improve the results.
Bank Card Usage Prediction Exploiting Geolocation Information
We describe the solution of team ISMLL for the ECML-PKDD 2016 Discovery Challenge on Bank Card Usage for both tasks.
Scalable Hyperparameter Optimization with Products of Gaussian Process Experts
In this paper, we use products of Gaussian process experts as surrogate models for hyperparameter optimization.
Learning Thesaurus Relations from Distributional Features
The similarity of words can be computed by comparing their feature vectors.
Multi-Relational Learning at Scale with ADMM
Learning from multiple-relational data which contains noise, ambiguities, or duplicate entities is essential to a wide range of applications such as statistical inference based on Web Linked Data, recommender systems, computational biology, and natural language processing.
Optimal Time-Series Motifs
We demonstrate that searching is non-optimal since the domain of motifs is restricted, and instead we propose a principled optimization approach able to find optimal motifs.
Ultra-Fast Shapelets for Time Series Classification
A method for using shapelets for multivariate time series is proposed and Ultra-Fast Shapelets is proven to be successful in comparison to state-of-the-art multivariate time series classifiers on 15 multivariate time series datasets from various domains.
Scalable Discovery of Time-Series Shapelets
Time-series classification is an important problem for the data mining community due to the wide range of application domains involving time-series data.
Invariant Factorization Of Time-Series
Time-series classification is an important domain of machine learning and a plethora of methods have been developed for the task.
Time-Series Classification Through Histograms of Symbolic Polynomials
The coefficients of the polynomial functions are converted to symbolic words via equivolume discretizations of the coefficients' distributions.
BPR: Bayesian Personalized Ranking from Implicit Feedback
In this paper we present a generic optimization criterion BPR-Opt for personalized ranking that is the maximum posterior estimator derived from a Bayesian analysis of the problem.
