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Found 19 papers, 6 papers with code
LLM-Oriented Retrieval Tuner
Dense Retrieval (DR) is now considered as a promising tool to enhance the memorization capacity of Large Language Models (LLM) such as GPT3 and GPT-4 by incorporating external memories.
Rethinking Dense Retrieval's Few-Shot Ability
Moreover, the dataset is disjointed into base and novel classes, allowing DR models to be continuously trained on ample data from base classes and a few samples in novel classes.
Scientific Computing Algorithms to Learn Enhanced Scalable Surrogates for Mesh Physics
With this, we were able to train MGN on meshes with \textit{millions} of nodes to generate computational fluid dynamics (CFD) simulations.
Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives
In this paper, we investigate the instability in the standard dense retrieval training, which iterates between model training and hard negative selection using the being-trained model.
A Contraction-constrained Model Predictive Control for Nonlinear Processes using Disturbance Forecasts
Model predictive control (MPC) has become the most widely used advanced control method in process industry.
A Contraction-constrained Model Predictive Control for Multi-timescale Nonlinear Processes
Many chemical processes exhibit diverse timescale dynamics with a strong coupling between timescale sensitive variables.
Adaptive Contraction-based Control of Uncertain Nonlinear Processes using Neural Networks
This neural network is then embedded in an adaptive contraction-based control law which is updated by parameter estimates online.
Electrolyte Flow Rate Control for Vanadium Redox Flow Batteries using the Linear Parameter Varying Framework
In this article, an electrolyte flow rate control approach is developed for an all-vanadium redox flow battery (VRB) system based on the linear parameter varying (LPV) framework.
Latent Space Simulation for Carbon Capture Design Optimization
This raises the possibility of a fast, accurate replacement for a CFD simulator and therefore efficient approximation of the IAs required by CCS design optimization.
Contraction Analysis and Control Synthesis for Discrete-time Nonlinear Processes
Shifting away from the traditional mass production approach, the process industry is moving towards more agile, cost-effective and dynamic process operation (next-generation smart plants).
Crowd-sensing Enhanced Parking Patrol using Trajectories of Sharing Bikes
Illegal vehicle parking is a common urban problem faced by major cities in the world, as it incurs traffic jams, which lead to air pollution and traffic accidents.
POI Alias Discovery in Delivery Addresses using User Locations
People often refer to a place of interest (POI) by an alias.
Discrete-time Contraction-based Control of Nonlinear Systems with Parametric Uncertainties using Neural Networks
In this paper, a contraction theory-based control approach using neural networks is developed for nonlinear chemical processes to achieve time-varying reference tracking.
Control Contraction Metric Synthesis for Discrete-time Nonlinear Systems
Flexible manufacturing has been the trend in the area of the modern chemical process nowadays.
$\rm ^{83}Rb$/$\rm ^{83m}Kr$ production and cross-section measurement with 3.4 MeV and 20 MeV proton beams
$\rm ^{83m}Kr$, with a short lifetime, is an ideal calibration source for liquid xenon or liquid argon detectors.
Nuclear Experiment Instrumentation and Detectors
Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision
To democratize the benefits of Neu-IR, this paper presents MetaAdaptRank, a domain adaptive learning method that generalizes Neu-IR models from label-rich source domains to few-shot target domains.
Capturing Global Informativeness in Open Domain Keyphrase Extraction
Open-domain KeyPhrase Extraction (KPE) aims to extract keyphrases from documents without domain or quality restrictions, e. g., web pages with variant domains and qualities.
Reward Advancement: Transforming Policy under Maximum Causal Entropy Principle
In this paper, we define and investigate a general reward trans-formation problem (namely, reward advancement): Recovering the range of additional reward functions that transform the agent's policy from original policy to a predefined target policy under MCE principle.
Human activity recognition based on time series analysis using U-Net
Traditional human activity recognition (HAR) based on time series adopts sliding window analysis method.
