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Found 10 papers, 4 papers with code
Global Transformer Architecture for Indoor Room Temperature Forecasting
This work presents a global Transformer architecture for indoor temperature forecasting in multi-room buildings, aiming at optimizing energy consumption and reducing greenhouse gas emissions associated with HVAC systems.
Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Series
The field of time series anomaly detection is constantly advancing, with several methods available, making it a challenge to determine the most appropriate method for a specific domain.
Persistence Initialization: A novel adaptation of the Transformer architecture for Time Series Forecasting
We also compare against existing recently proposed Transformer models for time series forecasting, showing superior performance on the M4 dataset.
Positional Encoding Augmented GAN for the Assessment of Wind Flow for Pedestrian Comfort in Urban Areas
Approximating wind flows using computational fluid dynamics (CFD) methods can be time-consuming.
Augmented Memory Networks for Streaming-Based Active One-Shot Learning
In stream-based AL, observations are continuously made available to the learner that have to decide whether to request a label or to make a prediction.
Dual Active Sampling on Batch-Incremental Active Learning
Recently, Convolutional Neural Networks (CNNs) have shown unprecedented success in the field of computer vision, especially on challenging image classification tasks by relying on a universal approach, i. e., training a deep model on a massive dataset of supervised examples.
Time is of the Essence: a Joint Hierarchical RNN and Point Process Model for Time and Item Predictions
In this work we combine these two extensions in a joint model for the tasks of recommendation and return-time prediction.
Context-Aware Sequence-to-Sequence Models for Conversational Systems
This work proposes a novel approach based on sequence-to-sequence (seq2seq) models for context-aware conversational systems.
A Deep Network Model for Paraphrase Detection in Short Text Messages
Our experimental results show that the proposed approach outperforms existing state-of-the-art approaches on user-generated noisy social media data, such as Twitter texts, and achieves highly competitive performance on a cleaner corpus.
Inter-Session Modeling for Session-Based Recommendation
In recent years, research has been done on applying Recurrent Neural Networks (RNNs) as recommender systems.
