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Found 10 papers, 2 papers with code
Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies
We analyze a DP bootstrap procedure that releases multiple private bootstrap estimates to infer the sampling distribution and construct confidence intervals.
MICO: Selective Search with Mutual Information Co-training
In contrast to traditional exhaustive search, selective search first clusters documents into several groups before all the documents are searched exhaustively by a query, to limit the search executed within one group or only a few groups.
A Medical Semantic-Assisted Transformer for Radiographic Report Generation
Automated radiographic report generation is a challenging cross-domain task that aims to automatically generate accurate and semantic-coherence reports to describe medical images.
CLIP4Caption: CLIP for Video Caption
It is noted that our model is only trained on the MSR-VTT dataset.
CLIP4Caption ++: Multi-CLIP for Video Caption
We make the following improvements on the proposed CLIP4Caption++: We employ an advanced encoder-decoder model architecture X-Transformer as our main framework and make the following improvements: 1) we utilize three strong pre-trained CLIP models to extract the text-related appearance visual features.
A Self-Boosting Framework for Automated Radiographic Report Generation
On one hand, the image-text matching branch helps to learn highly text-correlated visual features for the report generation branch to output high quality reports.
Variance Reduction on General Adaptive Stochastic Mirror Descent
In this work, we investigate the idea of variance reduction by studying its properties with general adaptive mirror descent algorithms in nonsmooth nonconvex finite-sum optimization problems.
Online Regularization towards Always-Valid High-Dimensional Dynamic Pricing
In this paper, we propose a novel approach for designing dynamic pricing policy based regularized online statistical learning with theoretical guarantees.
Directional Pruning of Deep Neural Networks
In the light of the fact that the stochastic gradient descent (SGD) often finds a flat minimum valley in the training loss, we propose a novel directional pruning method which searches for a sparse minimizer in or close to that flat region.
The Sample Complexity of Meta Sparse Regression
A key difference between meta-learning and the classical multi-task learning, is that meta-learning focuses only on the recovery of the parameters of the novel task, while multi-task learning estimates the parameter of all tasks, which requires l to grow with T .
