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Found 8 papers, 1 papers with code
Data-driven Model Reduction for Soft Robots via Lagrangian Operator Inference
Data-driven model reduction methods provide a nonintrusive way of constructing computationally efficient surrogates of high-fidelity models for real-time control of soft robots.
Dataflow-Aware PIM-Enabled Manycore Architecture for Deep Learning Workloads
However, as the complexity of Deep convolutional neural networks (DNNs) grows, we need to design a manycore architecture with multiple ReRAM-based processing elements (PEs) on a single chip.
Bayesian identification of nonseparable Hamiltonians with multiplicative noise using deep learning and reduced-order modeling
We compare the Bayesian method to a state-of-the-art machine learning method on a canonical nonseparable Hamiltonian model and a chaotic double pendulum model with small, noisy training datasets.
Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds
This work presents two novel approaches for the symplectic model reduction of high-dimensional Hamiltonian systems using data-driven quadratic manifolds.
Bayesian Identification of Nonseparable Hamiltonian Systems Using Stochastic Dynamic Models
This paper proposes a probabilistic Bayesian formulation for system identification (ID) and estimation of nonseparable Hamiltonian systems using stochastic dynamic models.
Combating high variance in Data-Scarce Implicit Hate Speech Classification
Hate speech classification has been a long-standing problem in natural language processing.
Data Agnostic RoBERTa-based Natural Language to SQL Query Generation
Although we have not achieved state of the art results, we have eliminated the need for the table data, right from the training of the model, and have achieved a test set execution accuracy of 76. 7%.
Cyclegen: Cyclic consistency based product review generator from attributes
We combine this with a sentiment analysis system which performs the complimentary task of assigning ratings to reviews based purely on the textual content of the review.
