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Found 12 papers, 3 papers with code
hBERT + BiasCorp - Fighting Racism on the Web
In this work, we present hBERT, where we modify certain layers of the pretrained BERT model with the new Hopfield Layer.
How Different AI Chatbots Behave? Benchmarking Large Language Models in Behavioral Economics Games
The deployment of large language models (LLMs) in diverse applications requires a thorough understanding of their decision-making strategies and behavioral patterns.
OccScene: Semantic Occupancy-based Cross-task Mutual Learning for 3D Scene Generation
Within OccScene, the perception module can be effectively improved with customized and diverse generated scenes, while the perception priors in return enhance the generation performance for mutual benefits.
NeuroGauss4D-PCI: 4D Neural Fields and Gaussian Deformation Fields for Point Cloud Interpolation
Point Cloud Interpolation confronts challenges from point sparsity, complex spatiotemporal dynamics, and the difficulty of deriving complete 3D point clouds from sparse temporal information.
3DSFLabelling: Boosting 3D Scene Flow Estimation by Pseudo Auto-labelling
We present a novel approach from the perspective of auto-labelling, aiming to generate a large number of 3D scene flow pseudo labels for real-world LiDAR point clouds.
Using Crested Porcupine Optimizer Algorithm and CNN-LSTM-Attention Model Combined with Deep Learning Methods to Enhance Short-Term Power Forecasting in PV Generation
This is the first time that the CPO algorithm has been introduced into the LSTM algorithm for parameter optimization.
GEDepth: Ground Embedding for Monocular Depth Estimation
Monocular depth estimation is an ill-posed problem as the same 2D image can be projected from infinite 3D scenes.
Ranked #3 on
Monocular Depth Estimation
on DDAD
HBert + BiasCorp -- Fighting Racism on the Web
In this work, we present hBERT, where we modify certain layers of the pretrained BERT model with the new Hopfield Layer.
Noise Contrastive Estimation and Negative Sampling for Conditional Models: Consistency and Statistical Efficiency
Noise Contrastive Estimation (NCE) is a powerful parameter estimation method for log-linear models, which avoids calculation of the partition function or its derivatives at each training step, a computationally demanding step in many cases.
Ranked #14 on
Question Answering
on WikiQA
Exploration of Large Networks with Covariates via Fast and Universal Latent Space Model Fitting
Latent space models are effective tools for statistical modeling and exploration of network data.
Subspace Perspective on Canonical Correlation Analysis: Dimension Reduction and Minimax Rates
Canonical correlation analysis (CCA) is a fundamental statistical tool for exploring the correlation structure between two sets of random variables.
Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis
In this paper, we tackle the problem of large scale CCA, where classical algorithms, usually requiring computing the product of two huge matrices and huge matrix decomposition, are computationally and storage expensive.
