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Found 10 papers, 3 papers with code
Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series
Consequently, there has been a recent surge in utilizing pre-trained large language models (LLMs) with token adaptations for TS forecasting.
AutoMixer for Improved Multivariate Time-Series Forecasting on Business and IT Observability Data
Business and IT Observability (BizITObs) data fuses both Biz-KPIs and IT event channels together as multivariate time series data.
ST-MLP: A Cascaded Spatio-Temporal Linear Framework with Channel-Independence Strategy for Traffic Forecasting
The criticality of prompt and precise traffic forecasting in optimizing traffic flow management in Intelligent Transportation Systems (ITS) has drawn substantial scholarly focus.
A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
Our channel-independent patch time series Transformer (PatchTST) can improve the long-term forecasting accuracy significantly when compared with that of SOTA Transformer-based models.
Ranked #1 on
Time Series Forecasting
on Electricity (192)
Multivariate Time Series Forecasting
Representation Learning
+1
A Strong Baseline for Vehicle Re-Identification
Vehicle Re-Identification (Re-ID) aims to identify the same vehicle across different cameras, hence plays an important role in modern traffic management systems.
Ranked #1 on
Vehicle Re-Identification
on CityFlow
Experimental evaluation of quantum Bayesian networks on IBM QX hardware
Bayesian Networks (BN) are probabilistic graphical models that are widely used for uncertainty modeling, stochastic prediction and probabilistic inference.
Quantum circuit representation of Bayesian networks
We develop a systematic method for designing a quantum circuit to represent a generic discrete Bayesian network with nodes that may have two or more states, where nodes with more than two states are mapped to multiple qubits.
Quantum Physics Computational Engineering, Finance, and Science
A Scale Invariant Flatness Measure for Deep Network Minima
It has been empirically observed that the flatness of minima obtained from training deep networks seems to correlate with better generalization.
When Does Stochastic Gradient Algorithm Work Well?
In this paper, we consider a general stochastic optimization problem which is often at the core of supervised learning, such as deep learning and linear classification.
Collaborative Multi-sensor Classification via Sparsity-based Representation
In this paper, we propose a general collaborative sparse representation framework for multi-sensor classification, which takes into account the correlations as well as complementary information between heterogeneous sensors simultaneously while considering joint sparsity within each sensor's observations.
