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Found 20 papers, 5 papers with code
Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions
We note that existing continual learning methods do not take into account variability in input dimensions arising due to different subsets of sensors being available across tasks, and struggle to adapt to such variable input dimensions (VID) tasks.
Learning to Liquidate Forex: Optimal Stopping via Adaptive Top-K Regression
We consider learning a trading agent acting on behalf of the treasury of a firm earning revenue in a foreign currency (FC) and incurring expenses in the home currency (HC).
Electricity Consumption Forecasting for Out-of-distribution Time-of-Use Tariffs
One of the objectives of such demand response management is to incentivize the consumers to adjust their consumption so that the overall electricity procurement in the wholesale markets is minimized, e. g. it is desirable that consumers consume less during peak hours when cost of procurement for brokers from wholesale markets are high.
Systematic Generalization in Neural Networks-based Multivariate Time Series Forecasting Models
Systematic generalization aims to evaluate reasoning about novel combinations from known components, an intrinsic property of human cognition.
Batch-Constrained Distributional Reinforcement Learning for Session-based Recommendation
Most of the existing deep reinforcement learning (RL) approaches for session-based recommendations either rely on costly online interactions with real users, or rely on potentially biased rule-based or data-driven user-behavior models for learning.
Handling Variable-Dimensional Time Series with Graph Neural Networks
Such a combinatorial generalization is achieved by conditioning the layers of a core neural network-based time series model with a "conditioning vector" that carries information of the available combination of sensors for each time series.
Graph Neural Networks for Leveraging Industrial Equipment Structure: An application to Remaining Useful Life Estimation
Deep learning models such as those based on recurrent neural networks (RNNs) or convolutional neural networks (CNNs) fail to explicitly leverage this potentially rich source of domain-knowledge into the learning procedure.
Meta-Learning for Few-Shot Time Series Classification
We overcome this limitation in order to train a common agent across domains with each domain having different number of target classes, we utilize a triplet-loss based learning procedure that does not require any constraints to be enforced on the number of classes for the few-shot TSC tasks.
NISER: Normalized Item and Session Representations to Handle Popularity Bias
The models using normalized item and session-graph representations perform significantly better: i. for the less popular long-tail items in the offline setting, and ii.
Ranked #2 on
Session-Based Recommendations
on Last.FM
Meta-Learning for Black-box Optimization
Recently, neural networks trained as optimizers under the "learning to learn" or meta-learning framework have been shown to be effective for a broad range of optimization tasks including derivative-free black-box function optimization.
ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification
We also provide qualitative insights into the working of CTN by: i) analyzing the activations and filters of first convolution layer suggesting the filters in CTN are generically useful, ii) analyzing the impact of the design decision to incorporate multiple length decisions, and iii) finding regions of time series that affect the final classification decision via occlusion sensitivity analysis.
Transfer Learning for Clinical Time Series Analysis using Deep Neural Networks
We, therefore, conclude that pre-trained deep models like TimeNet and HealthNet allow leveraging the advantages of deep learning for clinical time series analysis tasks, while also minimize dependence on hand-crafted features, deal robustly with scarce labeled training data scenarios without overfitting, as well as reduce dependence on expertise and resources required to train deep networks from scratch.
Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models
We propose a novel deep learning based approach for Prognostics with Uncertainty Quantification that is useful in scenarios where: (i) access to labeled failure data is scarce due to rarity of failures (ii) future operational conditions are unobserved and (iii) inherent noise is present in the sensor readings.
Transfer Learning for Clinical Time Series Analysis using Recurrent Neural Networks
We consider transferring the knowledge captured in an RNN trained on several source tasks simultaneously using a large labeled dataset to build the model for a target task with limited labeled data.
Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks
We propose Embed-RUL: a novel approach for RUL estimation from sensor data that does not rely on any degradation-trend assumptions, is robust to noise, and handles missing values.
TimeNet: Pre-trained deep recurrent neural network for time series classification
Inspired by the tremendous success of deep Convolutional Neural Networks as generic feature extractors for images, we propose TimeNet: a deep recurrent neural network (RNN) trained on diverse time series in an unsupervised manner using sequence to sequence (seq2seq) models to extract features from time series.
Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder
Many approaches for estimation of Remaining Useful Life (RUL) of a machine, using its operational sensor data, make assumptions about how a system degrades or a fault evolves, e. g., exponential degradation.
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine.
ODE - Augmented Training Improves Anomaly Detection in Sensor Data from Machines
The available data is then augmented with data generated from the ODE, and the anomaly detector is retrained on this augmented dataset.
Long Short Term Memory Networks for Anomaly Detection in Time Series
Long Short Term Memory (LSTM) networks have been demonstrated to be particularly useful for learning sequences containing longer term patterns of unknown length, due to their ability to maintain long term memory.
