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Found 8 papers, 2 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.
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.
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 #2 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.
